𝑎 O Bourrion
email: olivier.bourrion@lpsc.in2p3.fr

C Hoarau

𝑎 J Bounmy

𝑎 D Tourres

𝑎 C Vescovi

J.-L Bouly

𝑎 N Ponchant

𝑎 A Beelen

𝑐 M Calvo

𝑏 A Catalano

𝑎 J Goupy

𝑐 𝑏 G Lagache

J.-F Macías-Pérez

𝑎 J Marpaud

𝑎 A Monfardini 𝑏

P JINST CONCERTO: Readout and control electronics

Keywords: Data acquisition concepts, Control systems, Imaging spectroscopy

The CONCERTO spectral-imaging instrument was installed at the Atacama Pathfinder EXperiment (APEX) 12-meter telescope in April 2021. It has been designed to look at radiation emitted by ionised carbon atoms, [CII], and use the "intensity Mapping" technique to set the first constraints on the power spectrum of dusty star-forming galaxies. The instrument features two arrays of 2152 pixels constituted of Lumped Element Kinetic Inductance Detectors (LEKID) operated at cryogenic temperatures, cold optics and a fast Fourier Transform Spectrometer (FTS). To readout and operate the instrument, a newly designed electronic system hosted in five microTCA crates and composed of twelve readout boards and two control boards was designed and commissioned. The architecture and the performances are presented in this paper.

Introduction

CONCERTO is a millimeter-wave low spectral resolution imaging-spectrometer with an instantaneous field-of-view of 18.6 arcmin diameter operating in the range 130-310 GHz [START_REF] Ade | A wide field-of-view low-resolution spectrometer at apex: Instrument design and scientific forecast[END_REF][START_REF] Monfardini | CONCERTO at APEX: Installation and Technical Commissioning[END_REF]. It is installed at the Atacama Pathfinder EXperiment (APEX) 12-meter telescope located at 5100 m above sea level on the Chajnantor plateau. The primary goal of CONCERTO is to observe the radiation emitted by ionised carbon atoms using the intensity Mapping technique to set the first constraints on the power spectrum of dusty star-forming galaxies. Furthermore, it will open a new window towards large mapping speed low resolution spectroscopy for the study of galaxy clusters via the Sunyaev-Zel'dovich (SZ) effect [START_REF] Sunyaev | Comments on Astro[END_REF].

The instrument is composed of a dilution cryostat operating at 60 mK, which features two arrays of 2152 pixels made of Lumped Element Kinetic Inductance Detectors (LEKID) [START_REF] Day | A broadband superconducting detector suitable for use in large arrays[END_REF][START_REF] Baselmans | Kinetic inductance detectors[END_REF] and cold optics. To minimize the number of cryostat feedthroughs from the sensors to the warm electronics, each pixel array is constituted of only six transmission lines coupled to the LEKIDs. This amounts for about 360 detectors per feed-line. In acknowledgment of the unavoidable fabrication dispersion, which can cause KID self resonant frequency overlapping, an average frequency separation of 2.5 MHz was set by design between each LEKID of a given feedline. Indeed, in the past few years we have put some efforts in investigating a post-processing (trimming) technique allowing to individually adjust the resonances position. Despite the good results [START_REF] Shu | Understanding and minimizing resonance frequency deviations on a 4-in. kilo-pixel kinetic inductance detector array[END_REF] we concluded that this approach is somewhat impractical for our Aluminium thin films, in particular for an instrument with planned lifetime of the order of several years. Consequently, this leads to the requirement of having a 1 GHz bandwidth readout electronics dedicated to each feedline.

The cryostat is coupled to a room-temperature Martin-Puplett Interferometer (MpI) [START_REF] Martin | Polarised interferometric spectrometry for the millimetre and submillimetre spectrum[END_REF] that is designed to obtain a spectral resolution up to 1.2 GHz. To avoid atmospheric drifts during a single interferogram, this fast Fourier Transform Spectrometer (FTS) is designed to achieve four full interferograms per second and per pixel. Thus, to properly sample the interferograms, the readout electronics shall sample the LEKID at a sampling rate of about 4 kHz and the MpI movements must be controlled synchronously with the acquisition system.

As the last system constraints, i.e. the available room in the APEX, the reduced heat dissipation at high altitude due to lower convection and the requirement to have an easy remote operation and maintenance. Indeed the cabin drastically limit the available space for the electronic, the space available was 600 × 200 × 300 mm 3 for the entire CONCERTO readout, housekeeping and control of the C-cabin elements. Therefore, most of the warm electronics are directly integrated in the instrument chassis. This paper describes the readout and control electronics used for CONCERTO.

Electronics system description

The setup required to instrument a single resonator line is composed of a dedicated readout board operated at room temperature, which injects an excitation frequency comb in the cryostat and measures the modified returning signal. The frequency comb has each of its tones tuned to the LEKID self-resonant frequencies and has to be generated in the 1.5-2.5 GHz range. In the cryostat, the excitation signal is fed down to the low temperature stages. In order to attenuate the thermal noise coming from the warmer stages, we have installed at 4 K an attenuator of -20 dB. A further (distributed) attenuation of around -6 dB is provided by the stainless steel (lossy) injection cables running from the 4 K down to base temperature. This ensures the injected thermal noise is lower than the cold amplifier input noise. NbTi superconducting coaxial cables transfer the LEKID output signal with virtually no losses to the 4 K stage. The signal is first amplified with a Low Noise Amplifier (LNA) installed in the same 4 K stage , and then again with a second LNA amplifier at the 50 K stage , for a total amount of roughly +60 dB. Stainless steel cables link the 50 K stage and the room temperature output. The overall electrical gain of each radio-frequency line to and from the room temperature electronics has been measured and is confirmed to be about +10 dB. Fixed attenuators at 300 K are used to finely adjust the overall power to the available dynamic range of the ADC. The core principle of the readout system is similar to the one used in the NIKA2 experiment [START_REF] Bourrion | NIKEL_AMC: readout electronics for the NIKA2 experiment[END_REF]. Indeed, the excitation frequency comb is generated at baseband in the electronics using coordinate rotation digital computer (CORDIC) and the returning frequency comb is downconverted and analyzed by channelized Digital Down Converters (DDC) that provide In-phase (I) and Quadrature (Q) components. These components are used to compute each tone amplitude and phase.

Arizona State University 0.5-3 GHz; Gain: 30 dB. Noise T=5 K. Input 1 dB Compression (minimum): -36 dBm. Typical Power diss. 10 mW.

MITEQ AFS3-02000400-08-CR-4 2-4 GHz. Gain: 30 dB. Noise T < 50 K. Output 1 dB Compression (minimum): +5 dBm. Typical power diss. 50 mW .

Due to the limited space available at APEX, a large part of the warm electronics is directly integrated in the instrument chassis (see [START_REF] Ade | A wide field-of-view low-resolution spectrometer at apex: Instrument design and scientific forecast[END_REF] for details on the instrumental setup). CONCERTO needs (i) twelve KID readout boards to instrument the low and high frequency arrays , each composed of six resonator lines, (ii) one board dedicated to Martin-Puplett Interferometer management named Motor Controller and Martin-Puplett Monitor (MCMPM) and (iii) one board devoted to the Cryostat Positioning System (CPS). These boards were designed in the Advanced Mezzanine Card (AMC) format and are hosted in five compact micro Telecom Computer Architecture (microTCA) crates (Vadatech VT899). Each crate has a dimension of 127 mm × 311 mm × 260 mm.

A1 (KID array)

EXC.

MEAS.

DAQ Computer

Martin-Pupplet Interferometer

GPS

FS740

RF Synthesizer HMS2001

RF Splitter As shown in figure 1, four crates are used to specifically host the KID readout electronics. They are organized in two crates of three boards per array. Furthermore one crate is used to host the MCMPM and CPS boards. Each crate features one central clocking and synchronization board (CCSB) mounted on a MicroTCA Carrier Hub (MCH) and one 600 W power supply module. The MCH, which is required by the microTCA specification, is in charge of managing the crate (slot activation for hot-plug, power supply monitoring, fan speed controlling, sensors monitoring). It hosts a Gigabit Ethernet (GbE) switch function for communicating with each slot and the CCSB. The CCSB collects and distributes the reference clock and the demodulated Inter-Range Instrumentation Group standard B (IRIG-B) provided by the clock and timing box (CTB) via the crate backplane.

The CTB is composed of two different Printed Circuit Boards (PCB). It fans-out the 10 MHz reference clock provided by a FS725 Rubidium frequency standard (Stanford Research Systems) and the demodulated IRIG-B signal. The latter is produced, before fan-out, by the CTB, which uses the modulated IRIG-B-AM signal supplied by the GPS (FS740 from Stanford Research Systems).

A single RF synthesizer (Holzworth HSM2001A) is used to provide the Local Oscillator (LO) to each KID_READOUT board, thanks to a commercial RF passive splitter. The RF synthesizer uses the Rubidium 10 MHz as a reference clock.

One modulation output from one of the KID_READOUT boards is connected to the synthesizer in order to control the LO frequency which then permits to tune and calibrate the LEKIDs detectors. The tuning is the process that allows to find the optimal excitation frequencies for each LEDKID [START_REF] Bounmy | CONCERTO : Digital processing for finding and tuning LEKIDs[END_REF], while the calibration is used to determine the LEKID response to a known frequency shift. All readout boards are operated with the same reference clock and started simultaneously using a Pulse Per Second (PPS) signal which is extracted from the IRIG-B frame received by the CCSB and then distributed to all AMC boards.

KID readout board 3.1 Hardware description

The KID_READOUT board is composed of two parts, the digital and carrier part and the radio-frequency front-end part. The module fits in a full size double width AMC format (see figure 2). The carrier main components are a large Field Programmable Gate Array (FPGA) (Xilinx XCKU060FFVA1156-2), a 12-bit Analog to Digital Converter (ADC) (Texas Instrument ADC12D1000) and a dual 16-bit Digital to Analog converter (DAC) (Analog Devices AD9136). All converters are operated at 2 GSamples/s.

The FPGA is the center part of the carrier board and it is connected to the ADC, the DAC and the crate back-panel. The dual DAC and the ADC analog parts are connected directly to the front-end board via a dedicated high performance connector (SAMTEC QSE-040-01-F-D-A). The FPGA generates the excitation comb digital signal sent to the DAC and analyzes the returning signal sampled by the ADC. It is also in charge of interfacing the acquisition with the Gigabit Ethernet link though the MCH and the Clocking and Timing system via the CCSB. The FPGA is configured at boot time from dedicated flash memory that hosts the firmware. This flash memory can be reprogrammed in-situ through the Ethernet link, and thus allows remote firmware upgrades.

The main components are all clocked with a dedicated clocking circuitry which is referenced by the 10 MHz clock distributed by the CCSB through the back-panel. Thus all readout boards are all perfectly synchronous and uses a very stable timing reference.

As shown in figure 2, a large part of the carrier board is devoted to local power supply generation. Indeed, 15 different voltages must be generated to separate analog from digital supplies and to optimize the power consumption: five for the FPGA, two for the ADC, five for the DAC, one for the RF part and two for the clocking part.

A Module Management Controller (MMC) is implemented to comply with the microTCA standard. It is composed of an 8-bit micro-controller and hosts a dedicated firmware. Aside from the mandatory functionalities required by the standard, it provides board health monitoring such as local power supplies current and voltage monitoring, board temperatures (four distributed LM35B probes) and power supply sequencing.

Connected to the main board, a mezzanine contains the front-end analog shaping electronics, as shown in figure 3. At first a low pass filter stage, composed of the SMC component Then, the up-converted signal (EXC_OUT) goes through the cryostat, probes the state of the detectors (MEAS) and is down-converted at the input of the mezzanine thanks to a mixer (AD8342) with the same LO used previously. The resulting signal is then ready to be digitized after low-pass filtering.

Firmware description

An overview of the firmware is shown in figure 4. The center piece is the acquisition Finite State Machine (ACQ_FSM) which collects the 400 I,Q data generated by the band managers and tags them with precise timing thanks to the IRIG-B receiver. One data frame holds two 32-bit words (I,Q data) per LEKID resonator monitored (400 in total), the timing information on two 32-bit words, the frame number on one 32-bit word and eleven ADC/DAC monitoring 32-bit words (peak values observed). Thus, it is composed of 3256 bytes of data in total. The data collected are stored in an buffer (DAQ_FIFO), and waiting there to be shipped to the acquisition computer through Ethernet.

The IRIG-B receiver uses the demodulated signal and the reference clock provided through the microTCA backplane to extract the date and time from the serial frame and the Pulse Per Second pulse (PPS). This timing functionality is extended with a sub-counter incremented by the 10 MHz reference clock and reset every PPS, hence a theoretical timing resolution of 100 ns can be reached. In practice the resolution is limited to about 4 µs because of the analog demodulation technique used in the CTB. However this is sufficient to synchronize the data that are sampled at a frequency of about 4 kHz.

To synchronize the CONCERTO readout and control electronics, the acquisition and data recording is armed by software and started on the following PPS received. This results in having all boards embedded in the microTCA crates begin the data production simultaneously and have

Demodulated IRIGB

In magenta : 8 phases of data @250MSps => one data @2GSps In red : data @250MSps the same frame numbering. Then, when the astronomical observation is about to start, the external modulation sequence (EXT_modulator) is programmed to start at a given frame number, which is the same start number programmed in the MCMPM for initiating the movement of the mirror of the MPI. The EXT_modulator element is made of a FSM coupled to a memory table holding the modulation values to apply at each sampling step. The memory table is large enough to hold all values for the slowest MpI operating frequency, which is 1 Hz, i.e. 4096 steps of 1/4 kHz. The modulation values are transferred via I2C protocol to the 10-bit DAC (AD5311) installed on the RF front-end board. Both the KID_READOUT boards and MCMPM board possess this memory table so the modulation, which used for tuning [START_REF] Bounmy | CONCERTO : Digital processing for finding and tuning LEKIDs[END_REF] and scientific calibration [START_REF] Fasano | Accurate sky signal reconstruction for ground-based spectroscopy with kinetic inductance detectors[END_REF], does not interfere with the interferometry measurement. For example, among the 4096 steps, only the first 64 steps are modulation samples leaving the rest for observation purpose. These modulated data samples can be used to adjust each tone's probing frequency and as a reference response of the readout electronics to a known frequency shift of the LEKIDs [START_REF] Bounmy | CONCERTO : Digital processing for finding and tuning LEKIDs[END_REF].

The communication between the acquisition computer and the FPGA is achieved via Ethernet through a UDP layer. On the one hand, this layer allows us to set and monitor the board via the IPBUS protocol [START_REF] Larrea | IPbus: a flexible ethernet-based control system for xTCA hardware[END_REF], which is a secured communication channel that offers an easy parallel interface on the user side. On the other hand, it is used to send a dedicated UDP datagram (jumbo frame) every time at least one full acquisition frame is stored in the DAQ_FIFO. The use of large datagram maximizes the throughput and reduces the software overhead required to manage the data taking. The average data rate due to the two arrays readout is about 150 MB/s. To read-out the 400 LEKID resonators of a transmission line over a bandwidth of 1 GHz, the firmware features ten band managers that are in charge of instrumenting a slice of 100 MHz of the bandwidth. The motivations for this configuration were to have a more optimal polyphase filters implementation and a lower operating frequency (250 MHz) for the LEKID processors as it was the case in [START_REF] Bourrion | NIKEL_AMC: readout electronics for the NIKA2 experiment[END_REF]. As shown in figure 5, each band manager contains 40 tone managers. Each of these constructs the excitation signals thanks to a 10-bit CORDIC [START_REF] Volder | The cordic trigonometric computing technique[END_REF] generator coupled to a phase accumulator and analyzes the returning signal with a Digital Down Converter (DDC) [START_REF] Löhning | Digital down conversion in software radio terminals[END_REF].

The filters used in the DDC are Cascaded Integrated Comb filters whose decimation period is selected to be the same as one CORDIC phase accumulation (2 16 250 MHz clock cycles). This yields an I,Q sampling period of about 4 kHz. The tone manager operates in the 250 MHz clock domain and therefore the excitation signal must be up-converted to 2 GSamples per second and frequency shifted in its 100 MHz band, while the measurement signal must undergo the opposite processing.

On the excitation path, each sine and cosine signal are potentially attenuated with a digital attenuator by 1 16 𝑡 ℎ steps. The 40 resulting sine and cosine signals are then numerically summed-up to construct the partial excitation comb which is then modified by a digital gain (16-bit tuning word). The resulting signals are monitored by amplitude peak detector to ensure that no clipping occurs. These peaks values (max_I, max_Q) are stored in the acquisition frame. A clipping situation can be mitigated by either reloading the tone frequencies given the fact that each phase is quasi random and that an unlucky all-in-phase summation would then be removed, or alternatively by attenuating the gain for the concerned frequency band. Finally, the up-conversion operation is performed in each band manager. As depicted in figure 6, each sub-band is generated at 250 MSps and then up-converted at 2 GSPs in its appropriate frequency band. Each up-converter is composed of an up-sampler and a Numerically Controlled Oscillator (NCO) ensuring the appropriate frequency band-shifting. The ten band excitation signals are then numerically summed and fed to the dual DAC.

The signal (fakeAdc 1,2,3, ... in Fig. 4 ) used on the measurement path by each bandManager is provided by the adequate output of the polyphase filter as shown in figure 4. This filter takes the eight phases of the measurement signal digitized at 2 GSps, performs the ten required frequency shifting and the decimation to down-convert the full spectrum in ten equally spaced spectrum, each having a usable bandwidth of 100 MHz.

Resource usage

A significant effort was made to optimize overall the firmware design for the experiment. In particular, the polyphase filter and the up-converters were studied and designed to use the minimum number of DSP while providing adequate performances for the experiment. Consequently, the firmware uses about 214k (64.6%) Configurable Logic Block (CLB) Look Up Table ( From the table, it can be seen that the highest resource users are the band managers, each of them uses about 8% of the CLB resources and about 4% of the DSPs. In terms of DSP resources usage, the first users are the ten up-converters, for a total of 680 DSPs, and then the polyphase filter uses 418 DSPs.

Readout electronics performances

Power consumption

One KID_readout board uses about 37 W in total power to instrument 400 KIDs over the total bandwidth of 1 GHz. Four crates, each equipped with three readout boards, are required to readout the two arrays of 2152 pixels. Within each crate, the power consumption of one MCH+CCSB is about 12 W and each fan tray uses 17 W. Hence, each equipped crate uses 157 W of power from the 12 V back-plane distributed supply. Accounting for the power module efficiency, about 90%, each crate requires about 175 W for the main power supply. In the end the total power required for the four readout crate is about 700 W below 0.17 W per KID.

To ensure safe operation, various temperatures are constantly monitored on the experimental site, thanks to the features provided by the KID readout boards and the microTCA crates. Indeed, each KID readout board is equipped with sensors probing two PCB temperatures and three others for the FPGA, DAC and ADC die temperatures, while the microTCA crates measure the air intake and exhaust temperatures. The plot of these temperatures is shown in figure 7 as a function of the board number. We can see that the maximum temperature elevation is about 29 °C for the FPGA and ADC components.

System transfer function

As a first characterisation of the electronics, an excitation signal composed of 400 tones evenly spaced in frequency was generated by the KID_readout board and measured with a Vector Network Analyzer (Rhode & Schwarz ZNL6). The Local Oscillator (LO) input was set at 1.2 GHz, the measured spectra can be seen in figure 8. The nice side band rejection (-30 dB), as well as the flatness (<2 dB) of the spectrum can be observed. In a second stage, using the same LO frequency, the excitation output of the readout board was looped-back in the measurement input with an external cable. Then using a direct ADC signal recording feature embedded in the FPGA firmware, two snapshots of the ADC output were recorded at 2 Gsamples/s, each featuring 65356 samples. The first snapshot was recorded with no excitation signal produced, while the second featured the 400 tones evenly spaced in frequency like previously. For each snapshot, a Fast Fourier Transform (FFT) was computed (see figure 9). On the spectrum of the ADC response with no excitation signal produced, we can see the harmonics of the 250 MHz (reference clock used by the board), the 500 MHz (ADC dual data rate clock frequency) and two peaks at 400 MHz and 800 MHz due to the inter-modulation products between the LO and the sampling frequency. The spectrum of ADC response featuring the excitation signal is highly similar to the one recorded directly at the excitation output (see figure 8), aside from the end of the bandwidth (above 950 MHz) where the drop in response is much more important. This can be explained by the additional low pass filter embedded on the mezzanine board (figure 3) used to filter the ADC analog input. The spectrum of the ADC response when no excitation signal is produced is shown on the l.h.s. On the r.h.s. is shown the ADC response spectrum when an excitation signal composed 400 tones evenly spaced in frequency is generated.

System noise

To assess the system noise the same frequency comb featuring 400 tones evenly spaced in frequency was used and 655360 IQ data were recorded for each tone. As previously, the excitation output of the readout board was looped-back in the measurement input with an external cable and the LO was set at 1.2 GHz. The power spectral density of amplitudes and phases were computed for one tone in each band (see figure 10). These plots show that the system noise floor is reached at around 100 Hz and that it is in the order of -100 dBc/Hz. We also observe two unwanted peaks at 763 Hz and 1527 Hz. Unfortunately, those are not yet explained, but they are sufficiently far from the scientific signal, which lies in the 110 Hz to 280 Hz range. Consequently, to have an overview of the phase and amplitude system noise in this area of interest for the full electronics bandwidth, the average noise of each tone between 110 Hz and 280 Hz is plotted in figure 11. We can see that the noise level stays around -100 dBc/Hz over the full bandwidth and experience a significant rise in the end of the spectrum (after 950 MHz), this is consistent with the fact that the transmission response drops in this area (see figure 9). The peaks observed at 62.5 MHz and 250 MHz are respectively sub-products of the Ethernet reference clock (125 MHz) and the ADC and DAC reference clock (250 MHz).

Motor Controller and Martin-Puplett Monitor (MCMPM) board

The Martin-Puplett Interferometer linear motors drivers and mirror position monitors are managed by a single dedicated board called the Motor Controller and Martin-Puplett Monitor (MCMPM) board (see figure 12). The two main requirements of this board are (i) to always move simultaneously and in opposite direction the mirror and its counter-weight to dampen the vibrations to a minimum and (ii) to drive the motors and monitor the positions synchronously with respect to the KID readout electronics.

The board was designed to be hosted in a microTCA crate. On the back-end side, through the back-panel connector, it communicates via Ethernet (Ipbus) with the DAQ software and receives the 10 MHz reference clock and the PPS signal. On the front-end side it is connected to two linear motor drivers and to three position measuring devices via serial links. Two of these are used to measure the mirror position and its possible deformations, while the third is used to monitor vibrations of the polariser membrane, which changes the effective optical path difference [START_REF] Monfardini | CONCERTO at APEX: Installation and Technical Commissioning[END_REF]. The interface between the back-end and front-end is ensured by an XC7K70TFB676-1 Xilinx FPGA.

To avoid parasitic signals and unwanted smearing in the acquired interferograms and hence ease the data analysis, the motor movements and various position measurements must be done synchronously to the data acquisition [START_REF] Fasano | CONCERTO: a breakthrough in wide field-of-view spectroscopy at millimeter wavelengths[END_REF]. Consequently, the two stepper driver (linMOT E1450) An overview of the firmware is shown in figure 13. On the right side, the interfaces with the position monitoring devices and the linear motor driver are shown. In the center, sits the central Finite State Machine (FSM), which is in charge of synchronously acquiring the data. On the left side, the buffer used to smooth the data flow sent toward the Ethernet (IPBus) and its content are shown.

Thanks to the 10 MHz reference clock, the DAQ_sync block produces the enable signal used to perform the acquisition cycles at the same rate as the KID data readout, i.e. at 250 2 16 ∼ 4 kHz. However, the position measuring devices are limited to a maximum acquisition rate of 2 kHz, so the LASER_interface are triggered at half the acquisition rate. Consequently, to up-sample the laser positions, each position measured is duplicated in two consecutive data blocks as those are generated at about 4 kHz in the firmware.

The acquisition process, and hence the frame numbering, is started on the subsequent PPS received after the electronics is armed by software. Using the same methodology for all electronic boards involved in the acquisition provides a simple way to start synchronously the acquisition from a GPS receiver. In parallel, the decoded IRIG-B date and time code along with the sub-second counter are inserted in the data block by the DAQ FSM. This measurement, along with the frame number, permits (i) to uniquely time-tag each data block, but also (ii) to check the delay between the various readout electronics which must remain constant. Indeed, the same IRIG-B signal is distributed on the five crates, these precise timing information allow the detection of an abnormal condition or drift in the DAQ and control system. Block diagram of the linear motor controller. The core component of the controller is a large memory (32768 elements) that holds the steps frequency to generate along with the direction of the movement. The motor movements are started upon the fulfilment of start conditions asserted by a dedicated Finite State Machine (FSM).

The linear motor controllers (LINMOT0/1) are further detailed in figure 14. The core component of the controller is a large memory (32768 elements) that holds the steps frequency to generate along with the direction of the movement. The memory depth was chosen in order to allow slow mirror frequency, i.e as low as 0.1 Hz. As illustrated in the two plots below the block diagram shown in figure 14, the motor speed as a function of time can be derived from the mirror position as a function of the time. The number of steps to produce per time interval is computed using the linear motor conversion factors, with the time interval being the duration of one acquisition cycle. Finally, the motor step curve profiles are computed in advance and loaded in the MCMPM. Given the fact that the mirror and its counter-weight must be moved simultaneously and in opposite direction to dampen the vibrations to a minimum, the curves are computed to be symmetric. The curve computing also uses the optical zero path difference offset and the maximum achievable acceleration (limitation) as input parameters.

At each time interval, the frequency word (named increment) provided by the memory is used by a 16-bit accumulator operating at 250 MHz whose Most Significant Bit (MSB) is used to generate the steps needed by the motor power drivers. The step frequency is given by the following formula:

f step = 250 MHz 2 16 × increment 2
. Per manufacturer specification, the motor power drivers can accept a maximum step frequency of 2 MHz, the increment width is limited to 8-bit, which allows the step frequency to be tuned between 0 Hz and 1953 kHz with a resolution of 7.6 kHz.

The memory readout pointer is driven by a Finite State Machine, which starts its operation when armed by software and upon receipt of a frame number matching the configured start number. This latter synchronization is required as the acquisition must be already running before starting mirror movements and we want the interferometer cycle to start just after the 3-point frequency modulation executed at each start of scan. Indeed this frequency modulation is mandatory before each interferogram acquisition as it allows to calibrate the KIDs response to a known frequency shift [START_REF] Fasano | Accurate sky signal reconstruction for ground-based spectroscopy with kinetic inductance detectors[END_REF]. The frequency modulation is injected by the KID readout board which is synchronized with the same technique as described here.

The firmware described above use a low percentage of the FPGA available resources, it uses 6805 (16.6%) LUTs, 6953 (8.5%) FFs and 36 (26%) block RAMs. The latter are mostly used for the IPBUS protocol as multi-buffering is required to allow lost packets recovery (16 block RAMs) and for the two speed tables (9 block RAM each).

Conclusion

In this paper we have presented a dedicated KID readout and control electronics for the CONCERTO experiment installed at the APEX 12-m telescope. These electronics ensure the KID readout and the synchronous operation and monitoring of the CONCERTO MPI. It consists of a total of five microTCA crates: four crates (three boards each) for KID readout and one crate for control and monitoring of the instrument. With respect to previous KID readout electronics used in the NIKA and NIKA2 experiments it represents a major step forward. It ensures similar noise properties with a much larger operation sampling rate (4 kHz instead of tens of Hz), a doubled bandwidth (1 GHz instead of 500 MHz) and a more compact design with lower power consumption (35 W per readout board). Furthermore, by using a single DAC, it provides an homogeneous readout of the 400 tones avoiding the sub-bands uncorrelated noise contribution observed in the previous version featuring five DACs [START_REF] Adam | Observation des amas de galaxies par effet Sunyaev-Zel'dovich et de la polarisation du fond diffus cosmologique : de Planck à NIKA[END_REF]. In addition, it allows us to synchronously and accurately control the MPI motors, as well as monitor the position of the MPI mirrors and the vibrations of the MPI main polariser, that are critical for the scientific measurements. Finally, these electronics have been successfully operated in real conditions (regular scientific CONCERTO observations are carried out since July 2021) [START_REF] Monfardini | CONCERTO at APEX: Installation and Technical Commissioning[END_REF][START_REF] Catalano | CONCERTO at APEX: Installation and first phase of on-sky commissioning[END_REF]. Thus, we conclude that the CONCERTO electronics are fully operational and fulfill the technical and scientific requirements.

FS745NetworkFigure 1 .

 1 Figure 1. Five microTCA crates are used to host most of the warm electronics (twelves readout boards and two monitoring and control boards). The timing and control box is in charge of distributing the 10 MHz reference clock and the demodulated Inter-Range Instrumentation Group standard B (IRIG-B) signals to all crates. The Clocking and Timing Box distributes the reference clock and modulated IRIG-B provided by the reference. The Data Acquisition (DAQ) computer is responsible for setting up the instrument at startup and then to record data during observations.

Figure 2 .

 2 Figure 2. Overview of the KID_READOUT board shown on the l.h.s, the main active components are shown (FPGA, A/D and D/A converters, interface to the radio-frequency front-end board and power supplies). On the r.h.s, an actual board equipped with the heat sinks and the front-end board is shown.

Figure 3 .

 3 Figure 3. Simplified schematic of the mezzanine board for signal shaping. At first a low pass filter stage, composed of the SMC component LP0BA0790A7TR250 from AVX, suppresses the sampling frequency of the DAC and rejects the image response. The low pass filters have a cutoff frequency of about 950 MHz. The differential IQ signals filtered are mixed by an IQ mixer (ADL5375) that up-converts frequencies from DC-1 GHz bandwidth to the LEKID matrix frequency range of 1.5 GHz-2.5 GHz, thanks to the Local Oscillator of 1.5 GHz. Then, the up-converted signal (EXC_OUT) goes through the cryostat, probes the state of the detectors (MEAS) and is down-converted at the input of the mezzanine thanks to a mixer (AD8342) with the same LO used previously.

Figure 4 .

 4 Figure 4. Overview of the FPGA firmware used for the KID readout.

Figure 5 .

 5 Figure 5. Band manager architecture. One band is composed of 40 tone managers that construct the excitation signals thanks to a CORDIC generator coupled to a phase accumulator and analyze the returning signal with a Digital Down Converter (DDC).

Figure 6 .

 6 Figure 6. Illustration of the excitation frequency comb construction. The full spectrum is composed of ten sub-bands having a width of 100 MHz. Each sub-band is generated at 250 MSps and then up-converted at 2 GSPs in its appropriate frequency band. Each up-converter is composed of an up-sampler and a Numerically Controlled Oscillator (NCO) ensuring the appropriate frequency band-shifting.

Figure 7 .

 7 Figure 7. Temperatures plot as a function of the board number. For each KID readout board, two PCB temperatures and FPGA, DAC and ADC die temperatures are shown. MicroTCA air intake and exhaust temperatures for each crate are also shown. A maximum temperature elevation of about 29 °C is observed for the FPGA and ADC components.

Figure 8 .

 8 Figure 8. Excitation output spectrum measurement with Local Oscillator (LO) at 1.2 GHz. The spectrum on the l.h.s was taken with a frequency span of 2 GHz, while the one on the r.h.s. was taken with a frequency span of 1 GHz and with dilated amplitude scale. The flatness of the frequency response can be seen, as well as the steep analog low pass filter effect induced at base band (DAC output, ADC input). With up and down conversion, this translates to steep drop from LO+950 MHz and above.

Figure 9 .

 9 Figure 9. FFT plots of the ADC data recorded at 2 Gsamples/s when operating in external loop-back mode.The spectrum of the ADC response when no excitation signal is produced is shown on the l.h.s. On the r.h.s. is shown the ADC response spectrum when an excitation signal composed 400 tones evenly spaced in frequency is generated.

Figure 10 .

 10 Figure10. Power spectral density plots showing amplitude (top) and phase noise (bottom) for one tone in each band. The Welch method was used to compute the noise power spectra. The window lengths were adapted to the frequency interval (smaller/larger window lengths at high/low frequency) to smooth the power spectra.

Figure 11 .

 11 Figure 11. Average noise per tone between 110 Hz and 280 Hz. The 400 tones are evenly distributed over the 1000 MHz bandwidth.

Figure 13 .

 13 Figure 13. MCMPM firmware overview. Interfaces with the three position monitoring devices and the two linear motor drivers are shown. The FIFO interface, as well as one data frame format are shown on the r.h.s.

Table 1 .

 1 LUT), 345k (52.1%) CLB Flip-flops (FF) and 1973 (71.5%) Digital Signal Processors (DSP) of the available resources. The detailed allocation of resources is shown in table1. Table summarizing the FPGA resource usage of the KID READOUT board. The various firmware components and their relative contribution to the total are shown. The overall FPGA resource consumption with respect to the maximum available is also given.

	Main block name	Sub-block portion	CLB LUT		CLB FF		DSP
			count	% of total used	count	% of total used	count	% of total used
	Ethernet layer	total	4532	2,1	4053	1,2	0	0,0
		UDP/IP stack	2730	1,3	2309	0,7	0	0,0
		DHCP	639	0,3	647	0,2	0	0,0
		ipbus	1163	0,5	1097	0,3	0	0,0
	ADC interface	total	748	0,3	2244	0,6	0	0,0
	LO modulator	total	142	0,1	134	0,0	0	0,0
	Polyphase filter	total	18736	8,7	37674	10,9	418	21,2
	DAC interface	total	1071	0,5	2192	0,6	0	0,0
	IRIGB RX	total	236	0,1	502	0,1	0	0,0
	Full tone manager	total	187524	87,6	288701	83,5	1555	78,8
		1 band manager (out of 10)	18050	8,4	27121	7,8	82	4,2
		band adder	2656	1,2	2672	0,8	0	0,0
		misc (unused for concerto)	2334	1,1	2421	0,7	3	0,2
		1 up-converter (out of 10)	190	0,1	1219	0,4	68	3,4
		daq_fifo	134	0,1	208	0,1	0	0,0
	Miscellaneous	total	1168	0,5	10398	3,0	0	0,0
	Firmware	total	214157	100,0	345898	100,0	1973	100,0
		FPGA resources/type	Available	Used	% used
			CLB LUT	331680	214157	64,6
			CLB FF	663360	345898	52,1
			DSP	2760	1973	71,5

microTCA crate (double width, midsize)

  Overview of the Martin Puplett Interferometer control electronics. The board is inserted in the microTCA crate and communicate via IPBUS with the DAQ software. On the front-end side, it is connected to three position measuring devices via serial links and to two linear motor drivers.are driven with an incremental code (A/B) generated by the MCMPM. At the same time, the mirror top and bottom positions are measured with LASER (Greyline ODS 120) measuring devices that are triggered and read-out by the MCMPM via a communication serial link (RS232 protocol). The linear motor driver parameters are configured through Ethernet with a driving software.

					Greyline ODS120 (laser)		100 mm
				Linear motor			Linear motor
		polarizer					weight
	Greyline ODS120 (laser) RS232 @2 kHz	mirror 100 mm	Ethernet LinMOT E1450 A/B (driver)	RS232 Greyline ODS120 (laser) @2 kHz RS232 @2 kHz	LinMOT E1450 (driver) A/B Ethernet
	Ethernet			MCMPM
	IRIG-B
			en_DAQ PPS	DAQ_sync	top_daq (@ 4kHz) sync_las (@ 2kHz)
		IRIGB
	RXFROLAS0 LAS_SYNC0 RXFROLAS1 LAS_SYNC1 B1 A1 B0 A0	interface (UART) interface LASER (UART) LASER LINMOT0 @clk_250 sync drive LINMOT0 @clk_250 sync drive date/time	sync_las sync_las 18 las_dat1 18 las_dat0 cur_pos1 24 15 cur_idx1 cur_pos0 24 15 cur_idx0	DAQ FSM	DAQ FIFO (2050 x 32)	9=> IRIGB_WORD1 8=> IRIGB_WORD0 7=> MOT1 STEPS 6=> MOT1 IDX 5=> MOT0 STEPS 4=> MOT0 IDX IPB bus One data block 0=> frame_count 1=> LAS0 (mirror_bot) 2=> LAS1 (mirror_top) 3=> LAS2 (polarizer)
	RXFROLAS2 LAS_SYNC2	LASER (UART) interface	sync_las las_dat2 18

mirror Figure 12.

Acknowledgments

This project has received funding from the European Research Council (ERC) under the European's Horizon 2020 research and innovation programme (grant agreement No 788212), from the Excellence Initiative of Aix-Marseille University-A*Midex, a French "Investissements d'Avenir" programme, and LabEx FOCUS (France).


Maël Houbre

Florian Boudin

Béatrice Daille

A Large-Scale Dataset for Biomedical Keyphrase Generation

Keyphrase generation is the task consisting in generating a set of words or phrases that highlight the main topics of a document. There are few datasets for keyphrase generation in the biomedical domain and they do not meet the expectations in terms of size for training generative models. In this paper, we introduce kp-biomed, the first large-scale biomedical keyphrase generation dataset with more than 5M documents collected from PubMed abstracts. We train and release several generative models and conduct a series of experiments showing that using large scale datasets improves significantly the performances for present and absent keyphrase generation.

Introduction

Keyphrase generation aims at automatically generating a set of keyphrases, that is, words and phrases that summarize a given document. Since they distill the important information from documents, keyphrases have showed to be useful in many applications, most notably in information retrieval [START_REF] Fagan | Automatic phrase indexing for document retrieval[END_REF]Zhai, 1997;[START_REF] Jones | Phrasier: A system for interactive document retrieval using keyphrases[END_REF][START_REF] Song | Keyphrase extraction-based query expansion in digital libraries[END_REF][START_REF] Boudin | Keyphrase generation for scientific document retrieval[END_REF] and summarization (Zha, 2002;[START_REF] Wan | Towards an iterative reinforcement approach for simultaneous document summarization and keyword extraction[END_REF][START_REF] Vahed Qazvinian | Citation summarization through keyphrase extraction[END_REF].

Current models for generating keyphrases are built upon the sequence-to-sequence architecture [START_REF] Sutskever | Sequence to sequence learning with neural networks[END_REF] and are able to generate absent keyphrases that is, keyphrases that do not appear in the source text. However, training these models require large amounts of labeled data [START_REF] Meng | An empirical study on neural keyphrase generation[END_REF]. Unfortunately, such data is only available for limited domains and languages which greatly limits the applicability of these models [START_REF] Ye | Semi-supervised learning for neural keyphrase generation[END_REF]. This work addresses this issue and introduces kp-biomed, the first large-scale dataset for keyphrase generation in the biomedical domain.

Creating labeled data for keyphrase generation is a challenging task, requiring expert annotators and great effort [START_REF] Su | SemEval-2010 task 5 : Automatic keyphrase extraction from scientific articles[END_REF][START_REF] Augenstein | SemEval 2017 task 10: ScienceIE -extracting keyphrases and relations from scientific publications[END_REF]. A commonly-used approach to cope with this task is to collect scientific abstracts and use keyphrases provided by authors as a proxy for expert annotations. Authors provide keyphrases without any vocabulary constraint to highlight important points of their article; whereas indexers use a specific vocabulary and focus on indexing the article within a collection [START_REF] Névéol | Author keywords in biomedical journal articles[END_REF]. Therefore, keyphrases may differ from MeSH headings which are another indexing resource in the biomedical domain. Fortunately, author keyphrases are becoming increasingly available in the biomedical domain [START_REF] Névéol | Author keywords in biomedical journal articles[END_REF], since they can be incorporated into search strategies in PubMed to improve retrieval effectiveness [START_REF] Lu | Understanding the retrieval effectiveness of collaborative tags and author keywords in different retrieval environments: An experimental study on medical collections[END_REF]. Despite this, the largest keyphrase-labeled biomedical dataset that we know of has about 3k abstracts, all of which are labeled with present-only keyphrases [START_REF] Gero | Namedkeys: Unsupervised keyphrase extraction for biomedical documents[END_REF]. In this paper, we take advantage of the expansive PubMed database to build a sufficiently large dataset to train biomedical keyphrase generation models1 . We then compare models trained with different training set sizes to highlight the impact of dataset sizes in keyphrase generation. Our contributions are as follows:

• kp-biomed, a large, publicly available dataset for keyphrase generation in the biomedical domain, available through the Huggingface dataset platform2 ;

• Transformer-based models for biomedical keyphrase generation, providing open bench-marks to stimulate further work in the area3 ;

• Performance analysis of our models, which provides valuable insights into their generalization ability to other domains.

Dataset

We employ the December 2021 baseline set of MEDLINE/PubMed citation records4 as a resource for collecting abstracts, which contains over 33 million records. We extracted all the records (5.9 million) that include a title, an abstract and some author keyphrases. Records of papers published between 1939 and 2011 only account for a small fraction of these extracted records (3%) and were further filtered out to avoid possible diachronic issues. Last, we went through the remaining records to split the semicolon-separated list of author keyphrases and discard those having keyphrases with punctuation in it. The resulting dataset is composed of 5.6 million abstracts and was randomly and evenly divided by publishing year into training, validation and test splits. To investigate the impact of the amount of training data on the quality of the generated keyphrases, the training split was further divided into increasingly large subsets: small (500k), medium (2M) and large (5.6M). The training splits are also evenly divided by publishing year. Statistics of the kp-biomed dataset are detailed in Table 1 along with other commonly-used datasets for keyphrase generation and extraction. We are aware of only two datasets in the biomedical domain: NamedKeys [START_REF] Gero | Namedkeys: Unsupervised keyphrase extraction for biomedical documents[END_REF] which is made up of MEDLINE/PubMed abstracts and is therefore mostly included in kp-biomed, and Schutz [START_REF] Alexander | Keyphrase extraction from single documents in the open domain exploiting linguistic and statistical methods[END_REF] which is composed of full-text articles from the same source. It is worth noting that these datasets are very limited in size (3k and 1.3k documents respectively) compared to recent keyphrase generation datasets KP20k [START_REF] Meng | Deep keyphrase generation[END_REF], KPTimes [START_REF] Gallina | KPTimes: A large-scale dataset for keyphrase generation on news documents[END_REF] and LDKP10k [START_REF] Mahata | LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents[END_REF]. Table 1 shows that thanks to the amount of papers available in MEDLINE/PubMed, kp-biomed is the largest of all aforementioned datasets, being more than 10 times larget than KP20k which is the current reference dataset for keyphrase generation. The average number of keyphrases per document (#kp) in kp-biomed is roughly the same than in KP20k and LDKP10k which have their keyphrases assigned by authors as well. However, we see that this number is way below the average number of keyphrases assigned by professional indexers like in Inspec [START_REF] Hulth | Improved automatic keyword extraction given more linguistic knowledge[END_REF] or when authors' keyphrases are combined with readers' as in SemEval-2010[START_REF] Su | SemEval-2010 task 5 : Automatic keyphrase extraction from scientific articles[END_REF]. The unusually high number of keyphrases per document in NamedKeys, despite having author assigned keyphrases, is because of two restrictive criteria. Indeed, each article has at least 5 keyphrases all of which have to occur in the source text. The average number of words per keyphrase (#kp_len) is also comparable for all scientific datasets regardless of the kind of annotators.

Using keyphrases as proxies for indexing or expanding documents with queries composed of words that do not appear in the source text, has been proven more useful to enhance document retrieval than using words occurring in the text [START_REF] Boudin | Keyphrase generation for scientific document retrieval[END_REF][START_REF] Nogueira | Document Expansion by Query Prediction[END_REF]. In keyphrase generation, we call those keyphrases absent keyphrases, for which several definitions are being used. We refer to the definition from [START_REF] Meng | Deep keyphrase generation[END_REF] "we denote phrases that do not match any contiguous subsequence of source text as absent keyphrases" which was then precised in [START_REF] Boudin | Redefining absent keyphrases and their effect on retrieval effectiveness[END_REF]. In [START_REF] Gero | Namedkeys: Unsupervised keyphrase extraction for biomedical documents[END_REF] the keyphrase "anesthesia" is considered present if the word "postanesthesia" is in the source text. In our case, it is considered absent which is why NamedKeys does not appear with 100% present keyphrases in Table 1. The main difference between kp-biomed and NamedKeys, despite the number of documents, is the proportion of absent keyphrases. kp-biomed contains about 34% of absent keyphrases which is in the same range as scientific datasets KP20k and LDKP10k that were designed to train neural generative approaches [START_REF] Meng | Deep keyphrase generation[END_REF][START_REF] Mahata | LDKP: A Dataset for Identifying Keyphrases from Long Scientific Documents[END_REF].

Experiments

Models

In keyphrase generation, the architectures are currently mainly based on autoencoders with Recurrent Neural Networks [START_REF] Meng | Deep keyphrase generation[END_REF][START_REF] Chen | Keyphrase Generation with Correlation Constraints[END_REF][START_REF] Chen | Title-Guided Encoding for Keyphrase Generation[END_REF][START_REF] Hou Pong Chan | Neural Keyphrase Generation via Reinforcement Learning with Adaptive Rewards[END_REF] or Transformers [START_REF] Meng | An empirical study on neural keyphrase generation[END_REF][START_REF] Ahmad | Select, extract and generate: Neural keyphrase generation with layer-wise coverage attention[END_REF].

Following the work of [START_REF] Meng | An empirical study on neural keyphrase generation[END_REF] ers, we used two different generative BART models [START_REF] Lewis | BART: Denoising Sequence-to-Sequence Pretraining for Natural Language Generation, Translation, and Comprehension[END_REF]) and compared their performances on different domains. However, in this article we did not seek to get state-of-theart results, but rather introduce kp-biomed to the community with results on well known baselines, which is why we employed pre-trained models that we just fine-tuned for keyphrase generation [START_REF] Faisal | Applying a Generic Sequenceto-Sequence Model for Simple and Effective Keyphrase Generation[END_REF]. The models are BioBART-base [START_REF] Yuan | BioBART: Pretraining and evaluation of a biomedical generative language model[END_REF] which is already pre-trained on PubMed and BART-base [START_REF] Lewis | BART: Denoising Sequence-to-Sequence Pretraining for Natural Language Generation, Translation, and Comprehension[END_REF] which is pre-trained on news, books and webtext. To the best of our knowledge, there is no generic scientific BART model. Therefore, we chose BioBART for fine-tuning on scientific datasets rather than BART. Models are available via the huggingface platform.

For comparison with extractive approaches, we considered MultipartiteRank [START_REF] Boudin | Unsupervised Keyphrase Extraction with Multipartite Graphs[END_REF] as a baseline, which is state-of-the-art in unsupervised graph-based keyphrase extraction. We used the implementation available in the keyphrase extraction toolkit pke 5 with the default settings.

Experimental settings

We followed the One2Seq paradigm [START_REF] Meng | An empirical study on neural keyphrase generation[END_REF] for training which consists of generating the keyphrases of an input article as a single sequence. For each article, we concatenated the ground truth keyphrases as a single sequence with a special delimiter. Following [START_REF] Meng | An empirical study on neural keyphrase generation[END_REF], present keyphrases were ordered by their first occurrence in the source text followed by the absent ones.

We trained each model for 10 epochs with a 5 https://github.com/boudinfl/pke batch size of 128. We set the input length limit at 512 tokens for the text and 128 tokens for the reference keyphrase sequence. All the parameters and the training were handled with the huggingface trainer API6 . Hyperparameters and hardware details are available in appendix A. Training the BioBART-base model on the small training split for 10 epochs took about 9 hours and about 110 hours on the large training split. Once models were trained, we over-generated keyphrase sequences using beam search with a beam width of 20 for evaluation. Inference on test sets took around 50 minutes each.

Evaluation

We evaluated our models on 3 datasets, kp-biomed for biomedical data, KP20k for generic scientific documents and KPTimes for news articles. We did not use NamedKeys as a test set as we noticed a substantial overlap with our training set. We evaluated present and absent keyphrase generation separately to get better insights of our models' performances. To that end, we only compared each model's output to the present (respectively absent) keyphrases of the ground truth. For present keyphrases we employed F1@M and F1@10. F1@M is the F1 measure applied on the first keyphrase sequence generated by the model whereas F1@10 evaluates the top ten generated keyphrases. We evaluated absent keyphrase generation with R@10 which is the recall on the top 10 generated keyphrases. As F1@10 and R@10 require 10 keyphrases, if we did not have enough unique keyphrases with Model kp-biomed KP20k KPTimes F1@10 F1@M F1@10 F1@M F1@10 F1@M MultipartiteRank 15. our over generation, we added the token "<unk>" until we reached 10 keyphrases. The generated keyphrases and the reference were stemmed with the Porter Stemmer to reduce matching errors.

To measure statistical significance, we opted for Student's t-test at p < 0.01.

Results

The macro-averaged results of the evaluation are reported in Table 2 andTable 3. BioBART-KP20k (respectively BART-KPTimes) stands for the Bio-BART (respectively BART) model which has been fine-tuned on KP20k (respectively KPTimes).

For BioBART models, we add the size of the kp-biomed training split in the name for clarity.

Model kp-biomed KP20k KPTimes R@10 R@10 R@10 BioBART-small 3.3 1.8 2.6 BioBART-medium 3.6 † 1.9 2.7 BioBART-large 4.1 † 1.9 2.1 BioBART-KP20k 2.9 5.5 † 1.6 BART-KPTimes 1.5 0.8 39.1 † Table 3: Performances of the models on absent keyphrase generation. †means significant improvements over BioBART-small. Second best results are underlined.

Transformer based approaches achieve the best results but only on the datasets they were trained on as previously showed for RNN based approaches in [START_REF] Gallina | KPTimes: A large-scale dataset for keyphrase generation on news documents[END_REF]. For present keyphrase generation, BioBART-large achieves significant improvements compared to its small and medium counterparts in all datasets. This shows that using more data does improve the performances of the generative approaches in predicting present keyphrases in in and out of domain data. The performance drop of BioBART-KP20K on kp-biomed is interestingly much more controlled than BioBART models' on KP20k. Compared to BioBART-small which has been trained on the same amount of data, the drop in F1@M is only of 7.5% relative for BioBART-KP20k when it is of 16.6% relative for BioBART-small. We think that BioBART's pretraining may be beneficial for BioBART-KP20k on kp-biomed. On news articles though, BioBART-KP20k shows a relative drop of 35%, when it is only of 25% relative for BioBART-small. When used on out of domain data, BART-KPTimes performs even worse than MultipartiteRank.

In absent keyphrase generation, models fail in attaining significant improvements outside of their domain. Using more data does not seem to help for out of domain absent keyphrase generation. We can explain the high results of BART-KPTimes on its test set by the fact that many of the absent keyphrases are common to numerous articles.

We also think that the keyphrase order that we chose for training is one reason for the models' poor abstractive results. To verify this hypothesis, we compute the average percentage of the models' predictions appearing in the source text. Results are reported in Table 4. For @10, we removed all the added <unk> tokens before computing. It is clear that the extraction percentage of each model decreases when using top 10 predictions on all datasets. This shows that models prioritize generating present keyphrases which can then lead to low quality absent candidates.

Model

kp-biomed KP20k KPTimes @M @10 @M @10 @M @10 BioBART-large 96. This paper introduces kp-biomed, the first large scale dataset for biomedical keyphrase generation. We hope this new dataset will stimulate new research in biomedical keyphrase generation. Several generation models have been trained on this dataset and showed that having more data significantly improves the performances for present and absent keyphrase generation. However, models still perform very poorly on absent keyphrase generation even when using larger amounts of data. In future work, we will focus on how to use kp-biomed to improve biomedical absent keyphrase generation.

Broader Impact and Ethics

kp-biomed contains some abstracts that are part of copyright protected articles. As the "all rights reserved" statement is optional to be copyright protected, removing articles with this statement does not solve the problem (i.e no copyright does not mean free of use data). To be able to collect, work with these data and share the dataset to the research community, we complied with the conditions of US fair use and the exceptions from the 2019/79 EU guideline on using copyright content in text and data mining for research purposes. One of those criteria was to not use the data for commercial purposes which is why we opted for the Creative Commons Non Commercial use license CC-BY-NC v4.0.

Table 1 :

 1 that obtained state-of-the-art results with Transform-Statistics of the proposed dataset. For comparison purposes, we also report statistics of commonly-used and other biomedical datasets. Columns P and A are respectively the percentage of keyphrases occurring in the source text and absent ones.

	Domain	Dataset	#train #val #test #doc len #kp #kp len P A
		kp-biomed (ours)	5.6M 20k	20k	271	5.3	1.9
	Biomedical NamedKeys	-	-	3k	276 14.3	1.9
		Schutz	-	-1.3k	5.4k	5.4	1.9
	General scientific articles	KP20k SemEval-2010 Inspec LDKP10k	530k 20k 144 -1k 500 1.3M 10k	20k 100 500 10k	175 192 15.4 5.3 138 9.8 4.9k 6.9	2.1 2.1 2.3 2.1
	News	KPTimes	260k 10k	20k	921	5.0	1.5

Table 2 :

 2 Performances of the models on present keyphrase generation. †means significant improvements over BioBART-small. Second best results are underlined.

		3	-	12.9	-	16.7	-
	BioBART-small	31.4	32.5	25.2	27.1	22.0	24.4
	BioBART-medium	32.5 †	33.8 †	26.2 †	28.2 †	22.1	24.6
	BioBART-large	33.1 †	34.7 †	26.9 †	28.9 †	23.5 †	26.2 †
	BioBART-KP20k	28.2	29.5	28.6 †	31.9 †	16.8	19.2
	BART-KPTimes	9.1	9.6	3.6	2.7	29.7 †	39.4 †

Table 4 :

 4 Extraction percentage in top M and top 10 predictions

		3 92.2 94.8 88.5 93.5 84.6
	BioBART-KP20k 95.4 84.5 91.8 82.7 83.7 66.6
	BART-KPTimes	46.0 31.2 21.4 17.4 65.8 50.7

  Hongyuan Zha. 2002. Generic summarization and keyphrase extraction using mutual reinforcement principle and sentence clustering. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '02, page 113-120, New York, NY, USA. Association for Computing Machinery. Chengxiang Zhai. 1997. Fast statistical parsing of noun phrases for document indexing. In Fifth Conference Applied Natural Language Processing, pages 312-319, Washington, DC, USA. Association for Computational Linguistics.

KP20k is currently considered as the reference dataset size (≥ 500k) to train keyphrase generation models

https://huggingface.co/datasets/taln-ls2n/kpbiomed

https://huggingface.co/datasets/taln-ls2n/kpbiomedmodels

4 https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/

Our code is available for reproducibility. https://github.com/MHoubre/kpbiomed

Acknowledgements

We thank the anonymous reviewers for their valuable input on this article and our colleagues from the TALN team at LS2N for their proofreading and feedback. This work is part of the ANR DELICES project (ANR-19-CE38-0005) and was performed using HPC resources from GENCI-IDRIS (Grant 2022-[AD011013670]).

datasets/taln-


Samia Ben

Youssef Mnif

Values in Action (VIA) and Counseling Psychology

Keywords: Values in Action, Values in Action Inventory of Strengths, Counseling Psychology

Character strengths have been the subject of various researches and positive interventions in different fields of application of psychology: in the workplace, at school, in healthcare, etc. Yet, research on the application of character strengths in the field of counseling psychology is very discrete. The current article presents the relevance of the rapprochement between positive psychology and counselling psychology through the study of character strengths. It exposes the relevance of an approach based on character strengths that allow the individual to prosper and live subjective happy experiences, especially in the field of career guidance. The article concludes by the various positive interventions based on the model of character strengths that are applied to counselling, especially during periods of professional transition.

Introduction

The 21 st century is witnessing abundant and diverse publications in positive psychology [START_REF] Seligman | Positive Psychology: An Introduction[END_REF][START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF][START_REF] Peterson | A Primer in Positive Psychology[END_REF][START_REF] Martin-Krumm | Traité de psychologie positive[END_REF][START_REF] Niemiec | VIA Character Strengths: Research and Practice (The First 10 Years)[END_REF][START_REF] Mcgrath | Scale-and Item-Level Factor Analyses of the VIA Inventory of Strengths[END_REF][START_REF] Mcgrath | The VIA Assessment Suite for Adults (p. 63)[END_REF]. This new approach studies man as a whole through his subjectivity, personal characteristics, and the strengths that specify him and the environment in which he evolves. By consulting the site dedicated to the study of character strengths (www.viacharacter.org), we noticed that different fields of psychology have been the subject of strength experimentation. A section of the website is dedicated to each of the domains. We were challenged by the absence of a section dedicated to counselling psychology. Does this mean that it has not yet attracted the attention of researchers in positive psychology? Would not it be DOI: 10.4236/psych. 2019.106058 904 Psychology useful to use character strengths at different times of life requiring professional counselling? Or perhaps has there been no interest in studying career guidance outside the institutional context? Yet, counselling psychology increasingly covers individuals who seek to improve their well-being, to make sense of their lives while developing and building themselves throughout life. Far from the linear conception of counselling of the Parsons era (1909), where career paths used to be defined in advance, current career paths are fragmented and scattered, thus weakening the well-being of individuals. Focusing on one's own character strengths is a great opportunity to value one's self and optimize his functioning.

The importance of applying character strengths in the field of professional counselling is presented in the next section.

Positive Psychology and Character Strengths

Representing a cross-cutting approach that can serve the theories and interventions rather than a sub-discipline of psychology [START_REF] Bernaud | Psychologie de l'accompagnement[END_REF], positive psychology is the scientific study of human strengths and virtues that aim to develop what characterizes us best. It is a science devoted to the study of the vitality and virtues of ordinary people. Thus, it helps individuals, communities, and societies to develop and promote the qualities that lead to their own self-fulfillment and that of others [START_REF] Seligman | Positive Psychology: An Introduction[END_REF]. The definition that best describes the purpose of his study is that of Shelly Gable and Jonathan Haidt (2005: p. 104) for whom "positive psychology is the study of the conditions and processes that contribute to the flourishing or optimal functioning of people, groups, and institutions". Psychologist", [START_REF] Seligman | Positive Psychology: An Introduction[END_REF] of a special issue devoted to the current of positive psychology.

The main idea of the new approach is that one way of solving problems is to identify and exploit the strengths and assets of the individual [START_REF] Park | Positive Psychology and Character Strengths: Application to Strengths-Based School Counseling[END_REF][START_REF] Park | Positive Psychology and Therapy[END_REF]. The researchers found that some existing human strengths, to varying degrees, in all individuals prevented mental illness: courage, hope, faith, future orientation, perseverance, etc. [START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF]. These theories see individuals as decision-making beings who have choices, preferences, and the opportunity to learn and grow throughout life [START_REF] Martin-Krumm | Traité de psychologie positive[END_REF]. For this purpose, Values In Action (VIA) to this classification. They hoped to conceive for positive psychology a tool based on moral excellence (character strengths and virtues). The objective of positive psychology is, therefore, to promote the strengths of everyone in order to reach an optimal functioning that would be the origin of happiness.

Character Strengths and the Values in Action (VIA) Model

Character strengths have been defined as "the processes or mechanisms of psychological substrates that define virtues" (Peterson & Seligman, 2004: p. 13).

It is a classification of the good traits of the individual's personality. This definition is largely influenced by the moral personality and the virtues that constitute it. One of the definitions of virtue was given by Yearley (1990: p. 38): "a disposition to act, desire, and feel that involves the exercise of judgment and leads to a recognizable human excellence or instance of human flourishing."

Drawing on both the psychology of personality and moral values, the model is meant to bring together contemporary psychology with traditional moral philosophy. For this end, [START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF] initiated a research program based on the description of the most salient strengths of the individual and their evaluation as individual differences. This is about the Values In Action project (VIA) that presents itself under 24 strengths grouped in six virtues. The use of the term VIA is a terminology used to account for a psychological conception of the personal qualities of an individual that makes him morally admirable. The theoretical context from which the VIA project emerged is characterized by three elements: the spirit of positive psychology, the roots of trait theory and, in particular, religious philosophical traditions.

1) The classification of character strengths accentuates the effort of positive psychology to thwart traditional psychology, with its alleged negligence of positive human functioning. The VIA classification was specifically proposed in response to the most prevalent psychiatric classification of mental disorders, namely DSM (APA, 2000), and its predominance in psychology. Peterson and Seligman (2004: p. 8) hoped to "conceive, for positive psychology, a tool based on moral excellence (character strengths and virtues)".

2) The second pillar is the psychology of personality or, more specifically, the theory of traits [START_REF] Allport | Pattern and Growth in Personality[END_REF]. The working hypothesis that the authors have adopted considers the character as "plural". Character, therefore, is understood as "a family of largely valued traits, each of which exists to varying degrees and manifests itself in a category of thoughts, feelings, and behaviors" (Park et al., 2009: p. 137).

3) The last theoretical element is the ethics of virtue. [START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF] pp. 9-10) explicitly state that their classification is "grounded in a long philosophical tradition concerned with morality explained in terms of virtues".

In addition to the theoretical backgrounds presented above, the model is also DOI: 10.4236/psych.2019.106058 906 Psychology based on strictly quantitative empirical fundamentals, which are at the basis of classification. In developing the VIA, the authors conducted a thorough cross-cultural analysis through a study centered on "the most influential traditions of thought in human history". These studies have explored traditions in countries such as China, South Asia (mainly India) and the West (p. 34), although they have chosen Confucianism and Taoism more specifically for China, Buddhism and Hinduism for South Asia as well as Judeo-Christianity for Ancient Greece and Islam for the East [START_REF] Dahlsgaard | Shared Virtue: The Convergence of Valued Human Strengths across Culture and History[END_REF].

The results of the study led the authors to conclude that there were "great similarities between cultures (p. 36). In particular, they claimed to have found "a historical and intercultural convergence" (p. 50) of six fundamental virtues: wisdom, courage, justice, humanity, temperance, transcendence. Due to the surprising ubiquity of these character traits, the authors decided to consider them as omnipresent in all major religious and cultural traditions" (Seligman, 2002: p. 130). The outcomes of these different researches have resulted in a complete typology categorized into six virtues-wisdom, courage, humanity, justice, temperance, and transcendence-which have been identified as core characteristics by moral philosophers, religious thinkers across the history and cultures of the world [START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF]. Twenty-four corresponding character strengths-"psychological components" or pathways to these virtues-come from a long list of subjects that have been carefully researched [START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF]) (see Table 1).

The VIA model is a discovery in "the psychology of positive personality". It is a model that incorporates personality traits, moral values and skills. This classification allows us, therefore, to describe and study the "good morality" that paves the way to pleasure, commitment and, more generally, positive experiences [START_REF] Peterson | Strengths of Character, Orientations to Happiness, and Life Satisfaction[END_REF][START_REF] Seligman | Flourish[END_REF]. The work on the VIA model expands thinking about what is meant by the word character, which has different meanings within and across cultures. Traditional conceptions of character usually identify a few traits and claim that each person should strive to develop as much of this set of basic characteristics as possible [START_REF] Niemiec | VIA Character Strengths: Research and Practice (The First 10 Years)[END_REF]

The VIA-IS: A Flagship Tool for Strength Measurement

Even though two tools to measure character strengths currently exist: Realise2 [START_REF] Niemiec | VIA Character Strengths: Research and Practice (The First 10 Years)[END_REF]. This work on character strengths is one of the most important initiatives since the birth of positive psychology. The VIA classification research [START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF]) is flourishing and practitioners, ranging from psychologists and coaches to business leaders and educators, are eager to find ways to apply research to their practices. That said, applied research on the 24-character strengths-their use in practice, the effect of strength combinations, and the results of each-is a relatively new terrain. To what extent can character strength then be applied to professional counseling?

Character Strengths and Counseling Psychology

The first value that currently underpins counseling psychology research is concerned with the people's assets, their resources, strengths, mental health apart from their psychological disorders (Gelso et al., 2014a). We would like to point out that character strengths as a subject of study of positive psychology have long been studied and applied in the field of health care. [START_REF] Owens | A Comparison of Strengths and Interests Protocols in Career Assessment and Counseling[END_REF]. Nevertheless, if we look back into the history of psychology, we find a particular interest in character strengths in the field of professional counseling since the beginning of the twentieth century. Indeed, the concept associated with character strengths, namely "optimal functioning" has been, from the beginning, an integral part of the psychology of counseling and guidance (Hartung, Savickas, & Walsh, 2015). This orientation began with the approach of Donald [START_REF] Super | Transition: From Vocational Guidance to Counseling Psychology[END_REF] who perceived the counseling in the space of a life more from an angle of health than disease. Thus, counseling and guidance psychologists have begun to work on the strengths and potentials of individuals in order to develop them. This view of research is typical of the hope and optimism that underpins this approach:

"Individuals can change, have a life satisfaction, orient themselves and find ways to use their resources, otherwise they will be caught up in and overwhelmed by limiting beliefs and feelings, a slowed down maturation, bad luck in opportunities, and diseases" (Jordaan, Myers, Layton, & Morgan, 1968: p. 2).

The focus on strengths is more noticeable in its developmental role of helping individuals "feel better rather than feel good" [START_REF] Menninger | The Vital Balance: The Life Process in Mental Health and Illness[END_REF]. Adherence to this state of mind has been noted over the past 15 -20 years prior to the 1950s, as evidenced by the work of the Joint Commission on Mental Illness and Mental Health (1961). For this purpose, the reflections of [START_REF] Jahoda | Current Concepts of Positive Mental Health[END_REF] within this commission have attracted attention:

"We commonly use 'mental health' as an interchangeable term with 'mental illness' in the same euphemistically insinuating direction that public health generally refers to the prevention and monitoring of the disease by massive methods. Behavioral scientists who joined the mental health team and made a major contribution to the mental health movement expressed dissatisfaction with the primary goal of studying 'pathological behavior'. They proposed new, broader perspectives to consider mental health as a positive force and make it a clear and useful concept in practice."

During this period, important contributions and concepts related to the high level of optimal functioning were developed: [START_REF] Rogers | On Becoming a Person[END_REF] with the optimal functioning of the person, [START_REF] Allport | Behavioral Science, Religion, and Mental Health[END_REF] with mature personality, [START_REF] Heath | On the Social Psychology of Agency Relationships: Lay Theories of Motivation Overemphasize Extrinsic Incentives[END_REF] with the reasonable adventurer, [START_REF] Maslow | Motivation and Personality[END_REF] with self-actualization. However, these models have not been developed in the context of counseling psychology or guidance even though some have tried to incorporate them into their work.

Strengths represent the almost perfect performance of a task by combining talents, knowledge and skills. The optimal use of human strengths has been the goal of counseling psychology since the work of Frank Parsons, founder of the discipline. Parsons' goal was to help people make a "wise" choice of their vocation. He strove to do this by implementing a three-step process, which included self-exploration, exploring the world of work, and selecting opportunities that fit both perfectly [START_REF] Robitschek | Vocational Psychology: Using One of Counseling Psychology's Strengths to Foster Human Strength[END_REF]. Following Parsons' initiative, counseling psychology has long emphasized individual strengths (Robitschek & DOI: 10.4236/psych.2019.106058 909 Psychology Woodson, 2006). This positive focus can easily be seen through the positive language used in current research, "improving job satisfaction", "well-being at work", and "hope at work".

Although no theory in counseling psychology explicitly claims to borrow elements from positive psychology, the themes that unite them are very similar and often share a focus on self-esteem and self-efficacy [START_REF] Robitschek | Vocational Psychology: Using One of Counseling Psychology's Strengths to Foster Human Strength[END_REF]. [START_REF] Savickas | The Theory and Practice of Career Construction[END_REF] has described a taxonomy of attitudes, beliefs, and coping skills that subsumes the virtues identified by [START_REF] Seligman | The President's Address[END_REF], including courage, optimism, interpersonal skills, hope, perseverance, and ethics at work. Savickas

(2011) has argued that career education and counseling can help strengthen the character strengths of customers. Thus, identifying and promoting assets offers a unique perspective to career seekers or those who have already started it. Before the career exploration process (for example, asking questions, gathering information and interacting with various work environments; [START_REF] Super | A Life-Span, Life-Space Approach to Career Development[END_REF], it would probably be useful to identify strengths to determine how to apply them to professional opportunities. For Linley & Harrington (2006: p. 318), individuals can "build on their strengths" or adapt effectively to different environments using their character strengths. The extent to which a person can adapt to their roles and work environment is an essential part of maintaining life satisfaction. [START_REF] Savickas | Toward a Taxonomy of Human Strengths: Career Counseling's Contribution to Positive Psychology[END_REF] argued that psychology should resume work on its initial subject of study, which is based on the "construction of human strength", including in the field of counseling psychology. Positive contributions to counseling psychology, based on character strengths, would then enable individuals to deepen self-knowledge in order to make much more serene orientation choices. Following these, some contributions based on character strengths began to budge.

Development of Positive Interventions in Counseling Psychology

Counseling psychologists have paid considerable attention to the way in which consultants use and manage their strengths. Yet, until nowadays, there is little written literature and concrete empirical research that explains how advisors do to work on the strengths of consultants. It is only since the last ten years that research has focused its studies on the understanding and implementation of a support mechanism for the promotion of strengths, even if the arrangements in positive psychology centered on careers are almost exclusively based on therapies (Gelso et al., 2014b). These different interventions are presented in Table 2.

• The first approach, SCT, is a psychotherapeutic approach designed to enhance character strengths and virtues as defined by [START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF]. It helps to facilitate changes. The approach consists of four steps (1.

explicitizing, 2. envisioning, 3. empowering, 4. evolving). [START_REF] Snyder | The Psychology of Hope: You Can Get There from Here[END_REF] and self-help growth. This is a three-session process to promote the intentional use of strengths.

• The fourth approach aims to promote strengths for the pursuit of happiness: 5 exercises, 3 of which aim at identifying and understanding character strengths. Knowledge of one's strengths allows individuals to consider opportunities for using them to promote well-being. The 4 th exercise is based on documenting the "blessings in life" (ask the subject to write 3 things they do well every day for a week and write down the reasons for doing it well. The fifth exercise is based on the moment when these people do things well and when they are at their best. b) During the second session, the clients are encouraged to recognize their strengths, according to the VIA classification. After defining their goals, the counseling process moves to the stage of linking strengths to achieving personal and professional goals. The counselor and client discuss the contribution of strengths to the client's life, while focusing on employment and how the client could use these strengths to advance and achieve the previously stated goals.

c) The third session begins with a review of the tasks assigned in the previous session and a discussion of the clients' progress. They identify the strengths that customers would like to develop for the future while reinforcing what has already been accomplished. d) In this closing session, the counselor and the client revisit the achievements made in the area of strength development. The counselor and the client identify significant changes that occurred during the strength development process. The advisor determines with clients how to use the personal resources available to face future challenges.

These different strength-based interventions have been conducted with different audiences and have proved their effectiveness [START_REF] Owens | A Comparison of Strengths and Interests Protocols in Career Assessment and Counseling[END_REF]. However, the interest for character strengths in the field of vocational counseling remains nascent but offers interesting prospects in relation to the positive effects they may have on the functioning. We hope to see many interventions in the field of counseling in the field of positive psychology [START_REF] Bernaud | Adaptive Counseling Theory: A New Perspective for Career Counseling[END_REF][START_REF] Bernaud | Le sens de la vie comme paradigme pour le conseil en orientation[END_REF].

Conclusion

The finding has been established that positive psychology is moving towards a future where it will be more and more present in the different spheres of life, affecting many aspects of the individual that are related to his functioning [START_REF] Martin-Krumm | Psychologie positive, quelle part d'innovation[END_REF][START_REF] Peterson | A Primer in Positive Psychology[END_REF][START_REF] Seligman | Positive Psychology: An Introduction[END_REF]. It has also been introduced a few years ago in the field of counseling psychology through four stages: the grouping of universal theories on human functioning, finding methods based on strengths that do not necessarily belong to the medical domain; working on the positive traits of people and positive experiences and emotions that allow them to be happy. Similarly, it is gradually finding a niche in the training of the guidance counselor [START_REF] Sovet | Life Design in Adolescence: The Role of Positive Psychological Resources[END_REF]. The applications of these positivist approaches have, in fact, very quickly been echoed in the field of counseling psychology [START_REF] Hirschi | Effects of Orientations to Happiness on Vocational Identity Achievement[END_REF][START_REF] Richardson | New Directions for Theory Development in Vocational Psychology[END_REF][START_REF] Rottinghaus | The Career Futures Inventory: A Measure of Career-Related Adaptability and Optimism[END_REF].

Character strengths as psychological resources can then be a cornerstone in the new paradigm of counseling. Identifying and promoting forces offers a new perspective for consultants and counselors to explore the field of counseling. The identification of strengths would probably be beneficial in determining how they can be applied to the opportunities offered to the individual in periods of professional transitions [START_REF] Kidd | You Have to Adapt Because You Have No Other Choice: The Stories of Strength and Resilience of Homeless Youth in New York and Toronto[END_REF]. Thus, it becomes indisputable that character strengths constitute, as psychological resources, a real engine for indi-

  The term "positive psychology" first appeared in 1954 in the final chapter of Abraham Maslow's book Personality & Motivation. The term is used to describe the characters a person needs to function in an optimal way. Long forgotten, the term "positive psychology" resurfaced in 1998 with Martin Seligman (professor at the University of Pennsylvania) at the time he was elected president of the APA (American Association of Psychology). Seligman positions positive psychology as a discipline in its own right whose field of study is the scientific study of optimal human functioning. The year 2000 witnessed the founding act of positive psychology through the publication in the American journal, "American

(

  [START_REF] Linley | Technical Manual and Statistical Properties of Realise 2[END_REF] and Clifton Strengths-Finde (CSF)[START_REF] Asplund | The Clifton Strengths Finder 2.0 Technical Report: Development and Validation[END_REF], the Values In Action Inventory of Strengths (VIA-IS,[START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF] is the most used tool. The inventory, which was elaborated as an antithesis to the DSM, is based on the belief that strengths are the real manifestation of virtues and are associated with well-being. After several trials, researchers[START_REF] Park | Moral Competence and Character Strengths among Adolescents: The Development and Validation of the Values in Action Inventory of Strengths for Youth[END_REF], Peterson, Park, & Seligman, 2005), elaborated a list of 24 forces organized into six virtues (wisdom and knowledge, courage, humanities, justice, temperance and transcendence). This inventory is designed to describe individual differences in character strengths as a continuum rather than a discontinuity. Initially, it gathers 240 items (10 for each force) on a 5-point Likert scale for a duration of 30 minutes. The scales showed a fairly satisfactory internal consistency[START_REF] Mcgrath | Scale-and Item-Level Factor Analyses of the VIA Inventory of Strengths[END_REF] and a test-retest validity after a period of 4 months.In terms of validity, the correlations are higher than expected. The VIA-IS Character Strength Inventory has been used by more than one million people and is a free online tool. The VIA-IS has been translated into 39 languages and is currently being translated into 20 other languages, including Urdu, Farsi and Portuguese (McGrath, 2017). The richness of these translations supports the need to explore character strengths and the ambition to generalize the model to all cultures and domains. The VIA classification and the VIA-IS Inventory of Character Strengths are widely used by researchers and practitioners around the world

•

  The SBCC developed by Littman-Ovadia, Lazar-Butbul, & Benjamin (2014), mobilizes four sessions. a) The first session includes a brief intake interview, gathering information on the clients' past work experiences, education and training. The counselor then helps clients articulate their employment-related counseling goals in clear, positive, and realistic terms.

  [START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF] have established a classification of personality traits that are morally valued in different cultures of the world. The authors attributed the name:

Peterson and S. Ben Youssef Mnif DOI: 10.4236/psych.2019.106058 905 Psychology

Table 1 .

 1 VIA classification[START_REF] Peterson | Character Strengths and Virtues: A Handbook S. Ben Youssef Mnif[END_REF]. sense of well-being and give meaning to our actions, particularly in societies characterized by instability. This model did not remain only conceptual but was also meant to be practical. The authors have thus constructed a tool to measure these character strengths.

	Virtues	Type of strength	Corresponding strengths
	Wisdom	Cognitive strengths	Creativity -Curiosity -Judgment -Love of Learning -Perspective
	Courage	Emotional strengths	Bravery -Honesty -Perseverance -Zest
	Humanity	Interpersonal strengths	Kindness -Love -Social Intelligence
	Justice	Civic strengths	Fairness -Leadership -Teamwork
	Temperance Regulation strength	Forgiveness -Humility -Prudence -Self regulation
	Transcendence	Spiritual	Appreciation of beauty -Gratitude -Hope -Humor -Spirituality

. If character strengths are then conceived as virtuous personality traits, it is because they DOI: 10.4236/psych.2019.106058 907 Psychology consequently provide a

•

  The second intervention (SBC) allows to develop strengths to help people better solve the problems of their lives. It orients the psychologist to know

	S. Ben Youssef Mnif
	DOI: 10.4236/psych.2019.106058	910	Psychology

Table 2 .

 2 Positive interventions based on strengths

	Approach	Population Authors + date	Measures
	1) Strengths Centered Therapy (SCT)	Adults	Wong, 2006	VIA-IS
	2) Strengths-Based Counseling (SBC)	Adolescents	Smith, 2006	None
	3) Strengths Mentoring (SM)	Students	Lopez et al., 2006	Clifton Strengths Finder (CSF)
	4) Positive Psychology Interventions (PPI)	Adults	Seligman et al., 2005	VIA-IS
	5) Strength-Based Career Counseling (SBCC)	Unemployed job-seeking	2014	VIA-IS
	and look for what distinguishes each adolescent from the others in terms of
	assets, know-how and how they succeed in doing so while others fail. This
	process takes place in 10 steps:
	a) Establish a therapeutic relationship for the importance of protocol + com-
	mitment,
	b) Identify strengths via a narrative method, in order to help individuals know
	their strengths and internalize them,
	c) Evaluate the problem presented in order to clarify the client's interest,
	d) Encourage hope,
	e) Framing solutions relies heavily on solution-focused strategies,
	f) Build strengths and foster skills,
	g) From 7 to 9: empowering, changing and building resilience in order to
	promote agency and facilitate goal pursuit,
	h) Evaluate
	• The third intervention (SM) was designed to develop student strategies and
	promote academic effectiveness feeling, hope

DOI: 10.4236/psych.2019.106058 912 Psychology viduals to build their lives by themselves and adapt to their context and environment, as long as these forces are virtuous and fulfilling.

Conflicts of Interest

The author declares no conflicts of interest regarding the publication of this paper.