(1)

Goodwin (1965), Griffith (1968), Othmer (1976), Selgrade (1979)...

The transcription rate function λ 1 § Inducible Operon : Repressors R interacts with both the Operator O and the Effector E , R `nE

K 1 é RE n , K 1 " RE n R ¨E n , O `R K 2 é OR , K 2 " OR O ¨R . With QSSA, and if O tot ! R tot , λ 1 pE q " O O tot " 1 `K1 E n 1 `K2 R tot `K1 E n .
(2) § Repressible Operon : Similar but

O `RE n K 2 é ORE n , K 2 " ORE n O ¨RE n . and we get λ 1 pE q " O O tot " 1 `K1 E n 1 `pK 1 `K2 R tot qE n .
(3)

' ' ' ' & ' ' ' ' % dx 1 dt " γ 1 rλ 1 px 3 q ´x1 s , dx 2 dt " γ 2 px 1 ´x2 q , dx 3 dt " γ 3 px 2 ´x3 q . ( 4
)
Here (5)

λ 1 pxq " κ d 1 `xn K `xn .

Outline

Goodwin where h is the burst size distribution, ş 8 0 hpx, y qdy " 1. If hpx, y q " ´ν1 px`y q νpxq , ν OE and ν Ñ 8 0,

Lf pxq " ´γpxqf 1 pxq `λpxq νpxq ż 8 x f 1 pzqνpzqdz (7)
Any stationary distribution satisfies ş 8 0 Lf pxqu ˚px qdx " 0, so that ż 8 0 "

´γpxqu ˚px q `νpxq ż x 0 λpy q νpy q u ˚py qdy

ı f 1 pxqdx " 0 . (8)
Hence

u ˚px q " νpxq C γpxq exp ´ż x λpy q γpy q dy ¯(9)
Bifurcation analysis in SDE

0 1 2 3 4 5 6 7 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 x § Inducible : Uni-modal or Bi-modal. § Repressible : Uni-modal. (under technical assumptions...) upt, xqdx " P xptq P rx, x `dxq ( converges as t Ñ 8 (in L 1 ) towards u ˚, du dx " " λpxq ´γp1 `x bpxq q  u ˚px q γx .
with bpxq " ´νpxq ν 1 pxq . and γpxq " γx. From the 3-stage to the bursting model

where Z " ř i Z i δ T i is a jump process, of jump rate αpy ptqq and jump size cumulative distribution of separated form

P y pT ì q ě z | y pT í q " y ( " exp ´´ż z y γ 1 β λ 1 λ 2 pw qdw ¯.

Remark

For a constitutive gene, b :" λ 1 λ 2 γ 1 β is the average number of proteins produced per Gene activation event. We performed an adiabatic reduction to make the problem analytical tractable. § We solved the reduced problem for arbitrary coefficients at equilibrium. § We performed an (deterministic-analogous) bifurcation study.

Remark

Outline

Goodwin's deterministic model

Stochastic gene expression model

Analytical results on a reduced model Ongoing work 1) Taking into account division Ongoing work 2) Recovering the burst statistics Similar results may be obtained for a 'bursting-division' model.

Lf pxq " dpxq ż x 0 pf py q ´f pxqqκpx, y qdy `λpxq ż 8 0 pf px `y q ´f pxqqhpx, y qdy

For instance, with uniform repartition kernel (κpx, y q " 1{x), constant division rate d and constant exponential burst size (hpx, y q " expp´y {bq),

d dy u ˚" " ´λ1 py q `d λpy q `d `λpy q λpy q `d ´1 x `1 b ¯´xb 2 bx `1 ´1 x  u ˚py q
This may be used to predict the long time behavior of a dividing cell population scenario 1

A cell tree

Protein -1-0.5 0 0.5 1 1.5 2 2.5 3 3. Inverse Problem :pu ˚q ñ pλ, γ, bq

For a constitutive gene, we can infer the burst rate (in protein lifetime unit) λ γ and the mean burst size b from the first two (stationary) moments bλ

γ " E " X ‰ , b " Var pX q E " X ‰ .
For an auto-regulated gene, we can inverse the formula for the stationary pdf :

pxu ˚px qq 1 u ˚px q " λpxq γ ´x bpxq .

Simulated data

Density reconstruction by Kernel Density Estimation

Fitting with estimating rates

Small recap' on the inverse problem § With the help of the full solution, we obtained a formula to find the parameter functions from the stationary density. § We applied this on simulated and real data. § The inverse problem is generally ill-posed (cannot find burst size b and burst rate λ at the same time). § Although the resulting pdf does usually 'fit' the data. § Work still on progess...

$

  (1957), Ko et al. (1990), Ozbudak et al. (2002), Elowitz et al. (2002), Raser & O'Shea (2004)...

  Here λpxq " κ b 1 `xn K `xn and bpxq " b (νpxq " expp´x{bq).

  on the forward problem (2/2) § Careful ! The two notions of deterministic bistability and 'stochastic bistability (bimodality) are in fact quiet different mean) Switching time : can quantify the 'stability' of each state.

  model Analytical results on a reduced model Ongoing work 1) Taking into account division Ongoing work 2) Recovering the burst statistics

  Single cell data on self-regulating gene § Synthetic Tet-Off in budding yeast. § Feedback modulated by an external

  § If the Gene active periods are short (β Ñ 8), we obtain the bursting model

		$ ' ' ' ' ' & ' ' ' ' ' % $ ' & ' %	dy dt pG " 0q dx dt pG " 0q dt dy	" αpy ptqq ÝÝÝÝá âÝÝÝÝ βpy ptqq " βpy ptqq ÝÝÝÝá âÝÝÝÝ αpy ptqq "	G ptq λ2λ1 γ1 py ptqq ´γ2 y ptq , pG " 1q . G ptqλ 1 py ptqq ´γ1 xptq , pG " 1q . λ 2 xptq ´γ2 y ptq ,	(12) (10)
	$ ' & ' %	dy dt pG " 0q	" βpy ptqq ÝÝÝÝá âÝÝÝÝ αpy ptqq	G ptq λ2λ1 γ1 py ptqq ´γ2 y ptq , pG " 1q .	(11)

§

If the mRNA lifetime si short (γ 1 Ñ 8), we can perform an adiabatic reduction (xptq « G ptq λ 1 γ 1 py ptqq) :

dy dt " Z ptq ´γ2 y ptq ,

Rigney & Schieve (1977), Berg (1978), Peccoud & Ycart (1995), Thattai & Van Oudenaarden (2001)...

Inferred bursting rate


On note x n une quantité physiologique d'une cellule aux instants de division t n , modélisé par

x n`1 " k ´1 `Q´1 pQpx n q `τn`1 q ˘(1) où Hpτ q " P τ n ě τ | x n " x ( , Qpzq " ż z ϕpy q{g py qdy .

(2)

Si x 0 a pour densité f 0 P L 1 pR `q, alors x n " f n où f n`1 pxq " Pf n pxq , Pf pxq " ż kpxq 0 κpx, y qf py qdy , κpx, y q " ´d dx H pQpkpxqq ´Qpy qq .

(3)

On vérifie que si Q, k sont des bijections, absolument continues de R `Ñ R `, et H de R `Ñ r0, 1s, alors P est un opérateur de Markov, i.e. laisse tf P L 1 , }f } " 1u invariant.

Theorem (Lasota, Mackey (84))

Si τ n a une densité h,

ş xhpxqdx ă 8, et lim xÑ8 Qpkpxqq Qpxq ą 1 , Dx 0 ě 0 t.q. hpxq ą 0, @x ą x 0 , (4)
alors pP n q est asymptotiquement stable sur L 1

Exemple : § ϕ " 1, g pxq " gx α , kpxq " 2x. § hpτ q " Ψpτ ´tb q1 τ ět b pτ q Alors Qp2xq{Qpxq " 2 1´α Þ Ñ asympt. stable pour α ă 1.

x n`1 " 1 2

" x 1´α n `g p1 ´αqτ n`1 ‰ 1{p1´αq . (5) Sur F " E ˆI , E Ď R d , | I |ă 8, à un PDMP Y t "
f P BpE q, u P L 1 , ż E f py qS 0 ptqupy q " ż E upy qT 0 ptqf py qdy , où T 0 ptqf py q " f pπ t y qe ´şt 0 ϕpπs y qds " E y " f pY t q1 tăt 1 ‰ .
Théorème de Perturbation (Philipps, Kato...)

On a (formule de variation de la constante)

Pptqu " S 0 ptqu `ż t 0 S 0 pt ´sqBPpsquds (8) Si B est borné, ou si B : DpA 0 q Ñ L 1 , et ż E
Aupy q `Bupy qdy " 0 , @u P DpA 0 qp " L 1 ϕ q alors on peut définir un semi-groupe 'minimal' solution de (8), sous-stochastique, donné par

Pptqu " 8 ÿ n"0 S n ptqu , S n`1 ptqu " ż t 0 S 0 pt
T n ptq1 B pxq " T 0 ptq1 B pxq `ż t 0 T 0 psq pϕK ˚pT n´1 ptq1 B qq pxq .
Ainsi, par dualité,

ż E T n ptq1 B pxqupxqdx " ż B S 0 ptqupxqdx `ż t 0 ż E T n´1 pt ´sq1 B pxqK pϕS 0 psquq .
Par récurrence, on a

ż B n ÿ i"0 S i ptqupxqdx " ż E T n ptq1 B pxqupxqdx .
L'approche 'thm de perturbation' permet de donner des conditions pour obtenir un semi-groupe stochastique (autre que t 8 " 8). Par exemple, § Pour tout u P L 1 , lim Si kpx, y q " ´ν1 px`y q νpxq , ν ą 0, ν OE et ν Ñ 8 0, alors v ˚px q " ´ν1 pxqe ´Qpxq et u ˚px q " νpxq C γpxq exp ´şx λpy q γpy q dy ¯sont des densités stationnaires "candidates". Si Qp0q " G p0q " 8 et x ϕpy qupt, y q ν 1 pxq νpy q dy , En prenant ϕpxq " αx β´1 `xβ`1 g pxq " x β νpxq " x, pour 0 ď β ď 1, 0 ă α ă 1, on a convergence en temps long vers u ˚px q " νpxq cg pxq e ´şx x ϕpy q g py q dy , mais ϕ g R L 1

1 n ř n k"0 pK pϕRpλ, A 0 qqq k u exists in L 1 § J " lim λÑ0 K pϕRpλ, A 0 qqu
Application : bifurcation with bpxq ´νpxq ν 1 pxq . and γpxq " γx. For an auto-regulated gene, we can inverse the formula for the stationary pdf : u ˚px q " νpxq C γpxq exp ´şx λpy q γpy q dy ¯

  de montrer que Pptq est partiellement à noyau si ş ş kpx, y qϕpxqdxdy ą 0 en utilisant Pptqu ě S 1 ptqu .

  ´Qpxq dx ă 8, alors pour toute densité initiale u 0 , la solution Pptqu converge en norme L 1 quand t Ñ 8 vers l'unique solution stationnaire donnée par u ˚. § Remarque : on peut utiliser ce résultat pour prouver une convergence en temps long dans des cas 'non-triviaux'.En particulier, dans le modèle de croissance-fragmentation, avec

  Here λpxq " κ b 1 `xn K `xn and bpxq " b (νpxq " expp´x{bq). 2 `λ1 x 1 , Et x 1 effectue des sauts positifs de loi expo (ν " e ´x1 {b ) à tauxϕ " ϕpx 2 q " ϕ 1 `ϕ2 x N 2 1 `ϕ3 x N2 . § Voir [Tyran-Kamińska et al. 2015] pour l'étude en temps long (selon N, ϕ 3 etc...) § En cours : étude des 'oscillations'.Inverse Problem :pu ˚q ñ pλ, γ, bq For a constitutive gene, we can infer the burst rate (in protein lifetime unit) λ γ and the mean burst size b from the first two (

  pX t , i t q de caractéristique pg , ϕ, κq, on associe le semi-groupe Pptq sur L 1 pF q B pπ t y qH pQpπ t y q ´Qpy qq upy qdy

	On construit Pptq avec un théorème de perturbation
	solution de où A 0 u " ´Bpguq Bx ´ϕu génère un semi-groupe S 0 Bu Bt " ´Bpguq ´ϕu `K pϕuq , du " A 0 u `Bu , dt Bx où K est un opérateur de Markov donné par, @B P BpE q, u P L 1 , (7) (6) sous-stochastique, donné par
	ż	ż	ż	ż
	B	B S 0 ptqupy qdy " Kupy qdy " E 1 B pπ t y qe E ´şt κpy , Bqupy qdy . 0 ϕpπs y qds upy qdy
			ˆ" ż		Ȯn
	On remarque que @f P BpE q, u P L 1 ,
		ż		ż
	f py qKupy q " remarque que @	upy qK ˚f py qdy ,
		E		E
	où
		ż
		K ˚f py q "	f pzqκpy , dzq " E " f pY pτ 1 qq | Y pτ 1 q " y	‰	.
		E

E 1

  est ergodique en moyenne. Remarque : J est un opérateur stochastique associé au noyau Avec F " E " R `, g " ´γpxq ă 0, et κpx, ¨q à densité kpx, y qdy .Si Pptq est stochastique, partiellement à noyau, et possède une unique densité invariante alors Pptq est asymptotiquement stable.

		cell cycle PDMP Burst cell cycle PDMP Burst
	Rpλ, Aqu " L'étude en temps long repose sur des résultats du type Temps long ż 8 x 1 γpxq e Q λ py q´Q λ pxq upy qdy
	où Q λ " Q `λG , et Qpxq " Theorem (Pichór, Rudnicki (2000)) ż x γpzq dz , G pxq " ϕpzq	ż
					ż y 0	1 p0,xq pzqkpz, y q	ϕpzq γpzq	e Qpxq´Qpzq dz
	J px, Bq " Theorem (Mackey, Tyran-Kamińska, Y.) κpπ s x, Bqϕpπ s xqe ż 8	´şt 0 ϕpπs y qds
				0
	i.e. `J˚f py q "	ş E f pzqJ py , dzq ˘ ş 8 x py ´xqkpx, y q est loc. borné, et si
	lim sup xÑ8	ż x 0	ˆm1 pzq	ϕpzq γpzq	´1˙e

x 1 γpzq dz J px, dy q " jpx, y qdy , jpx, y q " Si Qp0q " G p0q " 8, et si l'une des deux conditions est vraie § J a une mesure invariant v ˚ą 0 a.e. § Si k est borné, m 1 pxq " Qpxq´Qpzq dz ă 0 alors Pptq est un opérateur stochastique. Partiellement à noyau : il existe t 0 ą 0 et p s.t.

Single cell data on self-regulating gene


R R Linn

J M Canfield

J Sauer
email: jsauer@lanl.gov

M Finney
email: mfinney@fs.fed.us

J Frothofer
email: jaforthofer@fs.fed.us

J L Winterkamp

C H Sieg
email: csieg@fs.fed.us

F Pimont
email: pimont@avignon.inra.fr

J L Dupuy
email: dupuy@avignon.inra.fr

Advances in Forest Fire Research -Page

Keywords: coupled fire/atmosphere, FIRETEC, grassfire, wildfire behaviour, vorticity, vortex

Idealized FIRETEC grassfire simulations were used to study some of the roles that three-dimensional aspects of coupled atmosphere/fire interactions play on fire behavior. Domains of various widths that were periodic in the cross-stream direction, simulating an infinite-length fireline, were used to isolate local fireline-scale threedimensional effects. Two-dimensional vertical plane (zero width) simulations were performed for comparison. Idealized finite-length fire simulations were used to study the larger fire-scale three-dimensional effects. In the infinitely long simulations the fireline remains fairly straight on a macroscale while significant heterogeneities develop along the fireline at fireline-scales. These simulations suggest that the nature of atmosphere-fire coupling and these heterogeneities are influenced by wind speed. At low wind speed, the spread rate is not significantly affected by the width of the domain (for domains greater than 10 m wide). For higher wind speeds, the flank of the simulated firelines is fingered and the front of the fireline exhibits lobes. The average spread rates vary by approximately 20% for the different domain widths. In the finite-length fireline simulations, the fireline shape was fairly parabolic and the headfire rate of spread (ROS) increased with wind speed and length of ignition line. The curvature of the fire was also influenced by the length of the ignition line. The indrafts of the headfire and flanking fire lines compete for upstream wind. For wider fires, the separation between the flanking fire lines is larger and more of the upstream wind is able to reach the headfire. These finite-length simulations also suggest that there might be an overall negative pressure gradient from upwind of the fireline to downwind of the fireline near the ground. This pressure gradient is believed to be tied to the penetration of wind through the fireline and the convective heating of unburned fuel. Streamwise-vorticity pairs contribute to the heterogeneous appearance of the fire front by feeding upward momentum in the location where towers are seen on the fireline and thrusting hot gases through the fireline and down to the unburned fuel in the troughs between the firelines. These streamwise vorticies have also been recognized in the periodic infinite length and finite-length simulated firelines as well as laboratory tests. In the two-dimensional simulations with wind speeds above 3 m/s, one major effect of the two-dimensional restrictions is to preclude the nominally streamwise-vorticity structures from forming upstream of the fireline. This thus diminishes the ability of the wind to mix through the heated plume and entrain hot gases down into the fuels ahead of the fire. These simulations suggest that caution should be used when attempting to use one or two-dimensional models to simulate wildland fires. These simulations also suggest the significant value of experiments in which details of both flow and fire dynamics can be studied at scales ranging from fireline scale to overall fire geometry scales in order to better understand fire behavior. The hypothesis generated here can help provide insight in support of experimental design and analysis.

Introduction

A variety of wildfire models have been developed in recent years to predict wildfire behavior, assess wildfire risk or simply gain insight into cause and effect relationships and the sensitivity of wildfires to their environment. Some of the models are empirical-based, while others are process-model based so they can be used to explicitly simulate some of the processes that control wildfire behavior. In order to reduce complexity or save computational cost, some researchers have tried to develop wildfire behavior models using one-or two-dimensional formulations. In the case of empirical models, local fire-line scale three-dimensional effects can be inherently represented through extensive data collection in ensembles of experiments. However, without a good understanding of the phenomenology driving multi-scale fire dynamics, it is very difficult for these types of models to capture the larger fire-scale three-dimensional effects caused by fireline shape or end effects due to the complexity and number of experiments and/or field observations required to parameterize these effects over the possible size and shape of fires in various weather and topographic conditions. Some researchers have attempted to use computational fluid dynamics techniques to explore fire behavior using a two-dimensional x-z plane, where x is the direction of the wind and spread and z is the vertical direction. Unfortunately, these approaches inherently neglect three-dimensional features of wildfire behavior due to the combination of the two-dimensional formulation and process-based approach. While the potential benefits of physics-based modeling includes the ability to look at the interplay between processes and explore various cause and effect relationships in fire, this approach has the downside of omitting those processes that are not resolved or explicitly modeled as unresolved processes. In two-dimensional approaches, fluctuations in the third direction are omitted unless explicitly modeled by sub-grid parameterizations. However, such parameterizations have not yet been adequately developed for wildfire behavior. Better understanding of three-dimensional aspects of coupled fire/atmosphere interactions could suggest new ways to represent the effects of threedimensionality without explicitly resolving them by providing insight into the relative sensitivity of fire behavior to key variables, suggesting functional forms and reducing the number of parameters that must be obtained from experiments. [START_REF] Canfield | A numerical investigation of the interplay between fireline length, geometry, and rate of spread[END_REF] considered this three-dimensionality in two size-based categories: local fireline-scale aspects such as fireline undulations, which were the main focus of [START_REF] Linn | Using periodic line fires to gain a new perspective on multi-dimensional aspects of forward fire spread[END_REF], and larger fire-scale aspects such as fire shape. In order to understand the implications of omitting these aspects of fire behavior, a three-dimensional physics-based simulation tool was used to explore the significance of local fireline-scale heterogeneities [START_REF] Linn | Using periodic line fires to gain a new perspective on multi-dimensional aspects of forward fire spread[END_REF] as well as larger fire-scale three-dimensionality [START_REF] Canfield | A numerical investigation of the interplay between fireline length, geometry, and rate of spread[END_REF] of coupled fire/atmosphere fireline dynamics. These explorations are intended to compliment experiments by feeding the development of new hypotheses and additional mechanistic perspectives that are very difficult to isolate in experiments.

Methods

Idealized simulations with a physics-based fire/atmosphere model, FIRETEC, were used to explore the impacts of the local three-dimensionality of coupled fire/atmosphere interactions on fire behavior [START_REF] Linn | Using periodic line fires to gain a new perspective on multi-dimensional aspects of forward fire spread[END_REF]. FIRETEC [START_REF] Linn | A transport model for prediction of wildfire behavior[END_REF][START_REF] Linn | Studying wildfire behavior using FIRETEC[END_REF][START_REF] Linn | Numerical simulations of grass fires using a coupled atmospherefire model: basic fire behavior and dependence on wind speed[END_REF] was developed as an attempt to represent multi-phase chemistry and physics in a bulk sense by resolving quantities at approximately meter-scale. FIRETEC is coupled to a fully compressible, non-hydrostatic, atmospheric-dynamics model, HIGRAD [START_REF] Reisner | Coupled atmospheric-fire modeling employing the method of averages[END_REF], that was specifically designed to operate at higher resolution as compared to traditional mesoscale models. In all of the simulations described in this paper, the grid spacing in both of the horizontal directions is uniform and equal to 2 m. The grid spacing in the vertical direction is nonuniform, with a value of approximately 1.5 m near the ground increasing to about 30 m at the top of the domain (z = 615 m). The fuel bed load is specified to be similar to tall grass of height 0.7 m, with a load of 0.7 kg m -2 , contained within the first grid cell above the ground. Within the fuel bed, the surface area per unit volume is specified as 4000 m -1 . In order to isolate local fireline-scale three-dimensionality from larger three-dimensionality such as fireline curvature and end effects, a series of simulations were performed using periodic boundary conditions in the cross-stream direction (Figure 1) [START_REF] Linn | Using periodic line fires to gain a new perspective on multi-dimensional aspects of forward fire spread[END_REF]. Fires were uniformly ignited over the cross-stream width of several domain sizes: 10, 20, 40, 80, and 160 m. Since the domains were periodic in the cross-stream direction, these fires could be considered infinite in length with some periodic restrictions on them. In order to further isolate the effects of fire/atmosphere coupling from ambient upstream turbulence or the effects of eterogeneous vegetation, these idealized scenarios included homogeneous grass fuels with no cross-stream or time-dependent ambient wind variations at the domain boundary 100 m upwind of the fire. Two-dimensional, vertical plane (zero crossstream width) simulations were performed with the same model for comparison. Simulations with six different ambient wind speeds -1 m s -1 , 3 m s -1 , 6 m s -1 , 9 m s -1 , 12 m s -1 , and15 m s -

. Top down view of 6 m s-1 wind 40 m wide fire simulation a) (in red dotted box) and the use of periodic boundary conditions to conceptually approximate an infinite-length fireline a) and b). In these images the colors indicate gas temperatures (Dark Blue is 300 K and red is greater than 1100K.) These images show the fingering behind the fireline and lobes in front of the fireline. This structure was ftypical for the infinite fireline simulations with 6 m s-1 and stronger winds.

Idealized FIRETEC simulations were also used to study the interactions between fireline shape and rate of spread [START_REF] Canfield | A numerical investigation of the interplay between fireline length, geometry, and rate of spread[END_REF]. Idealizations/simplifications similar to the infinite fireline approach described above were used except that these fires were finite in length and not periodic in the span-wise direction. These simulations, which were performed with ambient wind speeds of 3 m s -1 and 6 m s -1 , included finite-length ignition lines ranging from 20 to 400 m in length. In these simulations, the lateral boundary conditions were at least 100 m away from the ends of the ignition line. The winds at the boundaries were relaxed to the upstream flow velocity profile.

Results

All of the infinite-length simulations that were periodic in the stream-wise direction [START_REF] Linn | Using periodic line fires to gain a new perspective on multi-dimensional aspects of forward fire spread[END_REF] showed some heterogeneity in the crossstream direction, even though the upstream winds, fuel, and ignition were uniform in this direction. This series of simulations suggest that the nature of atmosphere/fire coupling and the variability along the length of the fireline are influenced by wind speed. At low wind speed, variation along the length of the fireline exists but the fireline remains fairly straight and the spread rate is not significantly affected by the width of the domain (for domains greater than 10 m wide) for the duration of the simulations. For higher wind speeds, the upwind side of the simulated firelines are fingered and the downstream edge of the firelines exhibit lobes. The average spread rates vary by approximately 20% for the different domain widths (Table 1). This may indicate that the variety of sizes of atmosphere-fire structures influence spread rates and become affected by periodic domain constraints as the domain gets smaller. At higher ambient wind speeds, the structure of the wind and temperature fields also show evidence of streamwise vorticity. In the periodic infinite-length simulations, rate of spread increases with wind speed as seen in previous investigations with this model [START_REF] Linn | Numerical simulations of grass fires using a coupled atmospherefire model: basic fire behavior and dependence on wind speed[END_REF] as well as other numerical works and observations. The magnitudes of the spread rates for the periodic simulations were in general larger than those seen in the Linn and Cunningham (2005) finite-length fireline simulations. This result suggests a reduction of rate of spread due to fire-scale three-dimensional features such as end effects and fireline curvature that were present in the [START_REF] Linn | Numerical simulations of grass fires using a coupled atmospherefire model: basic fire behavior and dependence on wind speed[END_REF] simulations.

In the finite-length fireline simulations [START_REF] Canfield | A numerical investigation of the interplay between fireline length, geometry, and rate of spread[END_REF] the fires did not stay relatively straight as they did in the periodic simulations (Figure 2). Instead, they took on a shape that was paraboliclike. In these simulations, rate of spread increased with wind speed and length of ignition line (Table 2) as expected and shown by observations [START_REF] Cheney | The influence of fuel, weather and fireshape variables on fire-spread in grasslands[END_REF] and previous numerical studies [START_REF] Linn | Numerical simulations of grass fires using a coupled atmospherefire model: basic fire behavior and dependence on wind speed[END_REF][START_REF] Mell | A physics-based approach to modeling grassland fires[END_REF]. Flanking fire behavior was also influenced by ignition line length. As a result, fireline shape was affected by ignition line length.

Table 2. Rates of spread for finite-length fireline simulations of various widths and 3 m s -1 and 6 m s -1 wind speeds (measured 10 m above ground) (Canfield et al., 2012).

Wind 3.1. Three-dimensional effects at fireline scales A predominant feature of both the relatively straight periodic infinite-length and the curved finitelength firelines is the presence of stream-wise vorticies that originate well behind the fireline and extend into the fireline itself. Such structures have also been identified in laboratory experiments at the USDA Missoula Fire Lab and are believed to be very important to the spread of fires. These streamwise vorticies form alternating pairs and occasionally combine into larger vorticies. The development and strengthening of these vorticies is linked to the buoyancy of the sustained burning, which itself is heterogeneous in the crosswind direction. These pairs contribute to the heterogeneous appearance of the heading-fire front by feeding upward momentum in the location where flaming towers are seen on the fireline. The helical nature of the vortices thrust hot gases through the fireline and down to the unburned fuel in the troughs between the towers (Figure 3).

Figure 2. Top down view of 6 m s -1 wind 200 m-ignition fire. This image illustrates the parabolic shape of the progressing fire and the white streamlines illustrate the interaction between the upstream air flow and the competing indrafts of the flanking and heading firelines

In the two-dimensional simulations there were no cross-stream variations. These scenarios are more representative of a fire burning between two very-close plates of glass with wind blowing between them than they are of a two-dimensional slice through an infinitely long fireline. With ambient wind speeds of 1 m s -1 , the general structure of the plume appeared similar to those seen in the 1 m s -1 threedimensional periodic simulation, but the spread rate was more than twice as fast as in the threedimensional simulations. For wind speeds above 3 m s -1 , a significant effect of the two-dimensional restrictions was to preclude stream-wise and vertically oriented vortex structures. These structures occurred and proved to be important in the three-dimensional calculations, but are prevented in twodimensional calculations because they require heterogeneity in the cross-stream direction. The elimination of the three-dimensional vorticity under two-dimensional constraints diminishes the ability of the wind to mix through the heated plume and entrain hot gases down into the fuels ahead of the fire. This causes a much slower spread rate in the two-dimensional simulations compared to the periodic three-dimensional simulations for ambient winds of 6 m s -1 and higher.

Three-dimensional effects at fire scales

In the finite-length fireline simulations, the curvature of the fireline and ability for winds to move around the fire lead to different fire behaviour than seen in the infinite-length fire scenarios. These differences are exacerbated by a couple of three-dimensional fire-scale interactions between the fire and the atmosphere. The head and flanking sections of the fire influence each other through their fireinduced indrafts. Indrafts from the flanking portions of the fireline compete with each other for the supply of upstream wind. For example, flanking portions of the fire draw from the airflow upstream, leaving a reduced amount of stream-wise wind to push the head fire forward and thus slowing down the forward spread rate. For wider fires, the separation between the flanking fires is large enough that competition between flanking fires is a non-issue. In scenarios where the flanking portions of the fire are relatively short compared to the length of the head fire, the wind can more effectively reach the headfire and transport hot gases to the unburned fuel in front of the fire. Similarly, as the competition between flanking fire and headfire sections of the fireline decreases, the wind arriving at the inside of the flanking fire sections can increase, enabling more effective heat transfer to the unburned fuel on the flanks as well. The competition between the indrafts of flanking and head fire and the net impacts on spread rate should be investigated experimentally in the future, as it has the potential to tie observable fireline geometry characteristics to corrections for rate of spread calculations. The idealized finite-length grass fire simulations also suggest that there might be an overall negative pressure gradient from upwind of the fireline to downwind of the fireline. This pressure gradient may affect the penetration of wind through the fireline and the convective heating of unburned fuel. The magnitude of the effective pressure gradient across the fireline is influenced by the upstream in-draft competition between flanking and headfires. These firelines did not extend across the entire domain

Figure 3 -Head on FIRETEC simulation of finite-length fireline (looking from in front of the fire as it approaches). This image illustrates the significance of the heterogeneity along the fireline in the cross-stream direction (left and right in the image). The fuel surface is colored by convective heating of the fuel where red is heating and blue is cooling. The vectors are in a y-z plane extending vertically above the white line and the vectors are colored by stream-wise velocity and scaled by total velocity

and air was free to move around them, unlike in the periodic infinite-length simulations described above where air cannot move around the firelines. The wind flow around the fires in the finite-fire simulations alters the upstream to downstream pressure gradient and therefore can contribute to the differences in spread rate between the infinite firelines and the finite length firelines. This effect contributes to the difference between the finite-length firelines and infinite-length fires where the firelines become much deeper (in the streamwise direction) firelines and have larger rates of spread.

In the periodic infinite-fireline scenarios, the larger pressure gradient across the firelines and inability of the winds to move around the fireline increase the flow through the fireline and the heat transfer to the unburned fuel in front of the fire.

Conclusions

This work suggests ways that the three-dimensionality of coupled fire/atmosphere behavior at a variety of scales affect fireline dynamics. The three-dimensional phenomena outlined here (described in detail in [START_REF] Canfield | A numerical investigation of the interplay between fireline length, geometry, and rate of spread[END_REF][START_REF] Linn | Using periodic line fires to gain a new perspective on multi-dimensional aspects of forward fire spread[END_REF] provide the basis for hypotheses that need to be tested in laboratory and field experiments. The three-dimensionality of the fire/atmosphere interaction also has significant implications for measurement strategies in such experiments. The analysis of any point measurements should include both knowledge of the position of the measurement with respect to the shape of the fireline as well as the context of the measurement with respect to the towers and troughs of the fireline itself. These simulations suggest that caution be taken when attempting to use a two-dimensional assumption for wildland fire spread modelling. This is especially important when attempting to interpret results in the context of a process-based model such as that of [START_REF] Linn | A transport model for prediction of wildfire behavior[END_REF], where the two-dimensional nature of the calculations are believed to have significant impacts on the simulated fire behavior. Care should be taken to account for the combined impacts of the variations in the flow field and plume structure in the third dimension. It is possible that the effects of local three-dimensionality could be represented in a manner similar to how turbulence kinetic energy is modelled without actually resolving the velocity fluctuations; however, this is not a trivial task and has not been accomplished to date. Such advances will likely come from a combination of modelling, laboratory experiments and field observations.

  Figure 1. Top down view of 6 m s-1 wind 40 m wide fire simulation a) (in red dotted box) and the use of periodic boundary conditions to conceptually approximate an infinite-length fireline a) and b). In these images the colors indicate gas temperatures (Dark Blue is 300 K and red is greater than 1100K.) These images show the fingering behind the fireline and lobes in front of the fireline. This structure was ftypical for the infinite fireline simulationswith 6 m s-1 and stronger winds.

Table 1 . Rates of spread for periodic infinite-length fireline simulations of various widths and various wind speeds (measured 10 m above ground) (Linn et al., 2012).

 1

	Wind Speed	2D	10 m	20 m	40 m	80 m	160 m
	U10 (m s -1 )
	1	.57	.25	.23	.22	.22	.22
	3	1.10	1.33	1.35	1.25	1.18	1.14
	6	1.24	2.15	2.27	2.14	1.91	1.92
	9	.77	2.23	2.81	2.41	2.49	2.28
	12	.76	2.67	3.14	2.96	2.94	3.01
	15	1.63	3.17	3.31	3.56	3.47	3.44


est destinée au dépôt

  RealCOW, a warping approach for the systematic determination of markers in GC-MS dataSaïd Abou El Karam, Jérémy Ratel, Nathalie Kondjoyan, Erwan Engel To cite this version: Saïd Abou El Karam, Jérémy Ratel, Nathalie Kondjoyan, Erwan Engel. RealCOW, a warping approach for the systematic determination of markers in GC-MS data. 38. International Symposium on Capillary Chromatography (ISCC), May 2014, Riva del Garda, Italy. 1 p., 2014. hal-


Filip Novokmet
email: filip.novokmet@psemail.eu

Thomas Piketty
email: piketty@psemail.eu

AND Li I Yang
email: li.yang@psemail.eu

Gabriel Zucman
email: zucman@berkeley.edu

Emmanuel Saez

World Inequality Lab Working papers n°2018/2 "From Communism to Capitalism: Private Versus Public Property and Inequality in China and Russia"

Keywords: Communism, Capitalism, Private wealh, Public Property, Inequality, China, Russia World Inequality Lab

In this paper, we compare our recent findings on private and public wealth accumulation in China and Russia, and discuss the impact of the different privatization strategies followed in the two countries on income inequality.

Privatization and Rise of Private Wealth

Private Wealth vs. Public Wealth

The ratio of private wealth to national income has increased in many countries in recent decades. This can be attributed to a number of factors, including high saving rates, the privatization of public assets, and a general rise in asset prices, itself due to a complex combination of factors (including changes in policies and institutions such as rent control, financial regulation, bargaining power of unions vs. shareholders, etc.). China and Russia can be viewed as extreme cases of this general evolution. (Russia) and 30-35% (China) in 2015. The Chinese share is higher but not incomparable to that observed in Western high-income countries during the "mixed economy" period . In other words, China and Russia have ceased to be communist in that public ownership is no longer the dominant form of property. However, these countries still have much more significant public wealth than Western high-income economies, due largely to lower public debts and greater public assets.

Income Inequality

Income inequality has increased markedly in both China and Russia since the beginning of their respective transitions towards marketorientated economies. Notes: Total billionaire wealth (as recorded by Forbes global list of dollar billionaires) divided by national income (measured at market exchange rates). For other countries, we only report citizen billionaires (numbers for resident billionaires are virtually identical).

Source: Novokmet, Piketty, and Zucman ( 2017)

A further insight can be obtained by looking at the distributional impact of growth in Table 1. The economic transformation has produced much higher growth in China than in Russia.

Although in both countries growth has not been equally shared, the outstanding growth experienced in China has very substantially lifted the living standards of the poorest. On the contrary, the bulk of the post-communist growth in Russia has been captured by the top.

Over the 1989-2016 period, the top 1% captured more than two-thirds of the total growth in Russia, while the bottom 50% actually saw a decline in its income.

Conclusion

The dramatic economic transformations in

  Since 1980, many economies around the world have experienced two trends: rising aggregate private wealth-income ratio and increasing income inequality (Piketty and Zucman 2014; Piketty 2014). These trends have been particularly spectacular in China and Russia since their transitions from communism to more capitalist orientated economic systems (Piketty, Yang, and Zucman 2017; Novokmet, Piketty, and Zucman 2017). The transition to a mixed economy has taken different economic and political forms in China and Russia-with different privatization strategies for public assets, in particular. These different strategies have had a large impact on inequality and wealth ownership. In China, the transition has involved gradual but nevertheless wideranging reforms. The reforms were implemented progressively, from special economic zones in coastal cities towards inland provincial regions, and in sectoral waves. By contrast, Russia opted for a "bigbang" transition after the fall of the Soviet Union in 1990-1991, with a rapid transfer of public assets to the private sector and the hasty introduction of free market economic principles.

  The transitions from planned to marketbased economies in China and Russia brought about large rises in the countries' private wealth-income ratios. While these increases are not unexpected-as a large proportion of public wealth was transferred to the private sector-the magnitude of the rise is particularly striking. At the time of the "opening-up" policy reforms in 1978, private wealth in China amounted to just over 110% of national income. By 2015, this figure had reached 490%. Russia's transition began twelve years later in 1990, but the change since then has been no less spectacular. Over this shorter period of time, Russia's private wealth-income ratio more than tripled from around 120% to 370%, largely at the expense of public wealth. In Russia, public assets were transferred to the private sector following the "voucher privatization" strategy. Citizens were given a book of vouchers that represented potential shares in former state-owned enterprises and public housing, which could be traded or sold. This voucher privatization strategy led to a rapid and huge reduction in net public wealth, from around 300% of national income in 1990 to 70% in 2000, rising slightly thereafter to 90% by 2015. In contrast, the gradual process of privatization of public wealth in China led to a slight overall fall in the value of public wealth as a proportion of national income, from just over 250% of national income in 1978 to approximately 230% in 2015, in a context of rapidly rising asset prices. (See Figure 1).

FIGURE 1 .

 1 FIGURE 1. PUBLIC VS PRIVATE PROPERTY IN CHINA AND RUSSIA 1978-2015 (% NATIONAL INCOME) Source: China: Piketty, Yang, and Zucman (2017); Russia: Novokmet, Piketty, and Zucman (2017).

FIGURE 2 .

 2 FIGURE 2. COMPOSITION OF PRIVATE WEALTH (% NATIONAL INCOME) Source: China: Piketty, Yang, and Zucman (2017); Russia: Novokmet, Piketty, and Zucman (2017).The value of households' financial assets differs markedly in China and Russia.According to the officially published balance

FIGURE 3 .

 3 FIGURE 3. THE DECLINE OF PUBLIC PROPERTY: CHINA VS RUSSIA VS OTHER COUNTRIES (SHARE OF PUBLIC WEALTH IN NATIONAL WEALTH) Source: China: Piketty, Yang, and Zucman (2017); Russia: Novokmet, Piketty, and Zucman (2017); Other countries: Piketty and Zucman (2014).

  Figure 4 displays the income inequality dynamics in China and Russia since 1978 by looking at the evolutions of the top 10% and the bottom 50% income shares (Panel A) and the evolution of the top 1% income share (Panel B). According to our estimates, inequality was somewhat higher in China than in Russia in 1978, but has now become substantially higher in Russia. In particular, it is striking to note the strikingly fast increase in income inequality in Russia after the fall of the Soviet Union. The top 10% income share in Russia rose from less than 25% in 1990-1991 to more than 45% in 1996, and has remained around 45-50% since then. This enormous rise came together with a massive collapse of the bottom 50% share, which dropped from about 30% of total income in 1990-1991 to less than 10% in 1996, before gradually returning to about 18% by 2015.In China, the rise in income inequality has been substantial between 1978 and 2015. The top 10% income share rose from 27% to 41% and the bottom 50% income share declined from 27% to 15%. But it has occurred in a much more gradual manner than in Russia.

FIGURE 4 .

 4 FIGURE 4. DISTRIBUTION OF INCOME IN CHINA, RUSSIA, UNITED STATES, AND FRANCE Notes: Distribution of pretax national income (before taxes and transfers, except pensions and unempl. insurance) among adults. Corrected estimates combine survey, fiscal, wealth and national accounts data. Raw estimates in China and Russia rely only on selfreported survey data. Equal-split-adults series (income of married couples divided by two). Source: China: Piketty, Yang, and Zucman (2017); Russia: Novokmet, Piketty, and Zucman (2017); USA and France: WID.world.

FIGURE 5 .

 5 FIGURE 5. TOTAL FORBES BILLIONAIRE WEALTH: RUSSIA VS OTHER COUNTRIES, 1990-2016 (% NATIONAL INCOME)

  Russian capitalism places few constraints on top incomes (partly coming from outright plundering of the country's natural resources and foreign reserves), and shows high tolerance for extreme inequality after the failure of Soviet communism and its egalitarian ideology. The enormous political and ideological shift that occurred in Russia could be seen as an extreme version of the ideological reversal in the United States since the 1980s. The development model of China is subject to stronger ideological constraints that may limit the rise in inequality to extreme levels in the future.

TABLE 1 :

 1 AVERAGEANNUAL GROWTH RATES 1978-2015

	Notes: Distribution of pre-tax national income among equal-split
	adults. The unit is the adult individual. Fractiles are defined relative
	to the total number of adult individuals in the population. Corrected
	estimates combine survey, fiscal, wealth and national accounts data.
	Source: China: Piketty, Yang and Zucman (2017). Russia: Novokmet,
	Piketty and Zucman USA: Piketty-Saez-Zucman (2016).


Damien Bonal

Charlotte Grossiord

André Granier

Tree diversity does not always improve resistance of forest ecosystems to drought

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Tree diversity does not always improve resistance of forest ecosystems to drought

Results

• Tree species diversity influences the functioning of forest ecosystems in terms of water and carbon acquisition and use

• More diverse forests may be more resistant to soil drought than pure ones, but not necessarily

• Favoring mixed forest ecosystems could be preferred in certain regions, not only to preserve biodiversity or sustain productivity in the context of climate changes, but also to delay the negative effect of drought

Conclusions

••

  Selection of 25 to 42 stands (30×30m) varying in species richness in each studied region • Extraction of wood cores from a selection of dominant and codominant trees of each species in each stand • Selection of years (tree rings) with contrasting climatic conditions (dry vs. wet) • Analyses of carbon isotope composition (δ 13 C) of latewood of each ring • Calculation of plot-level δ The difference in plot-level δ 13 C between the DRY and the WET year can be used as a proxy of drought exposure If difference in δ 13 C is high, low resistance If difference in δ 13 C is low, high resistance

Thanks for your attention


The impact of voluntary, social & environmental, standards on smallholder access to markets • Specifically for organic standards: national legistation in both exporting and importing counties

FAO and INRA Collaboration

• 2013 -Open call for proposals for innovations linking sustainable practices with markets  87 proposals, 15 case studies selected.  Prioritized cases written by the innovators themselves.

• 2014-2015 -field visits, interviews with authors and other stakeholders, document analysis, and peer-review.  Analysis based on institutional innovations and actors' roles in performing functions in innovation systems  12 cases on agro-ecological practices and 3 using good agricultural practices.

A collective research project

Participatory approaches to learning from innovators What are 'Institutional Innovations' ?

• Institutional innovations are:

• new rules and forms of interaction.

• They help redefine sustainable practices for the local level and bring together food systems actors that have not traditionally worked together. How do market mechanisms incentivize the adoption of sustainable practices?

Muliti-actor Innovation Platform

• Greater communication between producers and consumers ▫ Identifying/communicating market demand for specific 'qualities' of the products (e.g., safe, organic, GAP)

• Multi-Actor Innovation Platforms and Community Supported Agriculture:

▫ The incentives are found in the creation of local networks that integrate knowledge (creation and sharing), markets, resources and policy support at multiple levels (municipal, national, international trade)

• Participatory guarantee systems: Alternative certification mechanisms reduce costs of compliance with standards ▫ Small farmer inclusion in the value chain is critical -not only as a producer, but also as an auditor and researcher -increases trust

• Shifting roles and sharing responsibilities between producers, consumers, researchers, intermediaries and public officialsreciprocity rather than only solidarity

Conclusions

• Incentives for adopting sustainable practices can come from the autonomy created when local actors develop innovative rules for market interactions.

• Local actors rely upon social values (e.g., trustworthiness, health (nutrition and safety), food sovereignty, youth development, farmer and community livelihoods) to adapt sustainable practices to local contexts and create new market outlets for their products which are core components of institutional innovations.

• Even when private actors (farmers, consumers, cooperatives, firms, etc.) are leading the innovations, partnerships with public actors and civil society are fundamental for legitimating political and physical spaces where sustainable agricultural knowledge, practices and products are exchanged through market interactions.

Contact:

• allison.loconto@fao.org; amloconto@versailles.inra.fr

Book

Card URL:

• http://www.fao.org/documents/card/en/c/53d 39282-ddd7-460c-a27f-3d5015eea7ca/ PDF URL: • http://www.fao.org/3/a-i5907e.pdf A policy brief that accompanies the book can be downloaded here: • http://www.fao.org/3/a-i5398e.pdf

The report of the Researcher-Practitioner Workshop that we held in Bogotá, Colombia, in collaboration with FAO Colombia, can be downloaded in both Spanish and English: • http://www.fao.org/3/a-az561e.pdf • http://www.fao.org/3/a-az561s.pdf

••

  Begins with partnerships located within local research, training or extension bodies and includes farmers • Uses national and international knowledge to promote organic or sustainable agriculture practices • Initial legitimacy comes from outside of the group, then builds internally • The focus is on specific technologies and farmer-led experimentation • A new local market is created as an outlet for supply: on-farm sales • Changes seen in the rules for training, extension, production, and the allocation of responsibilities among these actors Begins with partnerships between between farmers, consumers and intermediaries (including service providers, organic movements) • Uses local and national knowledge (and harmonized international organic standards) • Initial legitimacy comes from within the group, then outside recognition • The focus is on an alternative form of certification (based on free or low-cost peer review) and farmer-led experimentation • New local markets are created based on direct contact with consumers: farm visits, farmers'markets, internet sales and supermarkets used • Changes seen in the rules for organic production, internal organization and the sharing of roles and responsibilities among different people within the groups How does the PGS innovation work?

•

  Begins with grassroots entrepreneurial activities to resolve a community concern by relying upon community-based knowledge, and diffusing this through existing networks. • Resources are mobilized from within the community. • The CSA practices are reinforced through internal improvements over time, focalizing more on the purpose of the initiative and building both internal and external legitimacy • Market formation, often in the form of bringing the market into the community, is a result of these reinforcement mechanisms • Change seen in the rules for how the community creates a protected space to market their products within the local communities

So what is innovative about the PGS?

	Production	Participatory Trade Guarantee System	Consumption
	• Research	• Certification	• Retail
	• Inputs	• Commercialization	• Responsible
	• Support services/extension • Farming • Processing	• Farmer-led Research • Inputs • Support services/extension • Farming	consumption
		• Processing
		• Certification
		• Commercialization
		• Retail
		• Responsible Consumption

up and legitimize innovative initiatives through policy frameworks and recognition of ongoing initiatives

	Recommendation 4

• Scale-


Sam Blisard

Ted Carmichael

Li Ding

Tim Finin

Wende Frost

Arthur Graesser

Mirsad Hadzikadic

Lalana Kagal

Geert-Jan M Kruijff

Pat Langley

James Lester

Deborah L Mcguinness

Jack Mostow

Panagiotis Papadakis

Fiora Pirri

Rashmi Prasad

Svetlana Stoyanchev

Pradeep Varakantham

Reports of the AAAI 2011 Fall Symposia

Advances in Cognitive Systems

The goal of the AAAI Fall Symposium on Advances in Cognitive Systems was to bring together researchers who are interested in developing intelligent systems that demonstrate the full range of human cognitive abilities and to report progress on this daunting task.

The original aims of artificial intelligence, when it was launched in the late 1950s, were to explain intelligence in computational terms and to reproduce the entire range of human cognitive abilities in computational artifacts. Although the field has seen impressive advances in the last few decades, many researchers have, in the process, forgotten or abandoned these important goals. The purpose of the Fall Symposium on Advances in Cognitive Systems was to bring together scientists who remained committed to AI's original vision. The meeting received 50 paper submissions and it was attended by more than 75 participants, suggesting that there remains substantial interest in this view on the discipline.

Research in cognitive systems, as reflected by the contributors to the meeting, differs from what has become mainstream AI in five basic ways. First, it retains a concern with high-level aspects of cognition, such as the ability to engage in multistep infer-ence, understand the meaning of natural language, generate novel plans that achieve goals, and even reason about one's own reasoning. Second, cognitive systems research emphasizes the role of structured representations in intelligence and recognizes that both computers and humans are general symbol manipulators, not just numeric processors. Third, it approaches intelligence from a systems perspective, examining how different abilities fit together rather than focusing on component algorithms. Fourth, the field draws ideas and inspiration from findings about human cognition, both for candidate mechanisms and for challenging problems to drive research. Finally, it adopts flexible views on evaluation, welcoming papers that demonstrate new functionality, present novel approaches to established problems, analyze challenging cognitive tasks, and report on architectures for integrated intelligence. Together, these characteristics identify cognitive systems as a separate paradigm that remains committed to the original vision for artificial intelligence.

The symposium included 22 talks and 23 posters that reflected a number of distinct but connected themes. Some presentations dealt with flexible conceptual-inference mechanisms for using commonsense knowledge that move beyond traditional deductive approaches. A second theme dealt with deep language processing, at both the sentence and dialogue levels, that combines linguistic and domain knowledge to produce genuine representations of meaning. A related topic was reasoning about the mental states of other agents, an ability essential to dialogue and joint activity, with a number of frameworks relying on partitioned memories (divided into "worlds" or "contexts") to keep mental models distinct. A fourth emphasis involved mechanisms for acquiring structural knowledge from experience and instruction, most relying on some form of explanation to generate candidate structures. Together, these themes clarify the distinctive character of research on cognitive systems and its concern with producing humanlike computational artifacts.

Open discussions during the meeting focused on three important issues. One concerned criteria for evaluating research progress, and for publishing papers, on cognitive systems. There was general agreement that existing conferences are not supportive of work in the paradigm, and that the community should find ways to broaden existing venues or establish new ones so that submissions will receive appropriate evaluation. Another discussion topic dealt with challenges that might drive research in the area. Proposals included flexible and scalable inference mechanisms, more natural approaches to acquiring knowledge over extended periods, techniques that interface general-purpose reasoning with specialized methods for spatial and visual processing, and motivational mechanisms that influence reasoning and goal generation. Finally, participants discussed resources that would support the cognitive systems community, with the main candidate being a wiki that incorporates pointers to software, course material, challenge problems, and data sets.

There was general consensus among participants that the symposium served a genuine need within the AI community and that additional meetings were desirable. There was also a sense that, although the cognitive systems paradigm adopts the original aims of AI, its modern incarnation is relatively new and, to ensure its success as a scientific discipline, we must clarify its distinctive characteristics, foster a community of like-minded researchers, identify research challenges and make progress on them, establish venues for publishing results, and train promising new scientists. The symposium was only one step on the road toward the audacious goal of computational systems with the same broad forms of intelligence as humans, and this task will keep the community occupied for years to come.

Pat Langley served as the chair of the meeting. Paul Bello (Office of Naval Research), Nicholas Cassimatis (Rensselaer Polytechnic Institute), Kenneth Forbus (Northwestern University), John Laird (University of Michigan, Ann Arbor), and Sergei Nirenburg (University of Maryland, Baltimore County) were coorganizers of the symposium. Papers from the meeting appear in Technical Report FS-11-01 from AAAI Press.

Building Representations of Common Ground with Intelligent Agents

The goal of this symposium was to explore methods of explicity or implicity using aspects of common ground to interact with intelligent agents. Much of the success of natural language interaction is caused by the participants' mutual understanding of the circumstances surrounding the communication. The mutual understanding of the perceived context and joint beliefs of the partici-pants is termed common ground, and is made up of all of the background and shared information that will lead to the eventual success of the communication. Some measure of common ground is used in most, if not all, successful interactions between human actors. For humans to have a convincing and beneficial experience interacting with intelligent agents, the agents must have mechanisms that support the fundamentals of common ground. The goal of this symposium was to bring together researchers from diverse fields to examine the representations and models currently in use that will one day enable complete common ground interaction with intelligent agents. The participants of this symposium came from a variety of AI fields such as robotics, human-computer interaction, linguistics, and cognitive modeling. A major theme of the talks was the need to clarify other agents' beliefs and solidify joint knowledge by asking questions when a situation is ambiguous or unclear. Strategies for doing this and examples of when this might occur were given by Alex Djalali (Stanford University) and Matthew Marge (Carnegie Mellon University). Another major theme of the symposium was the implicit need to build common ground for recognizing and creating deception. This was demonstrated both in cases where the deception is caused by a human, as in a talk presented by Will Bridewell (Stanford Center for Biomedical Informatics Research) during the joint session with the Advances in Cognitive Systems symposium, and in cases when the deception is caused by an intelligent agent, as demonstrated in a paper by Micah Clark (NASA Jet Propulsion Laboratory).

Other topics touched upon by the symposium participants included social authority in dialogue, using outcome matrices for phrase selection, cognitive modeling of common ground, and modeling actual and nonactual states of modal verbs.

The symposium included two invited talks. The first talk was given by Geert-Jan Kruijff (German Research Center for Artificial Intelligence (DFKI)) and was titled "Common Ground in Human-Robot Teams in Urban Search and Rescue." The second talk was given by James Allen (University of Rochester/Institute for Human and Machine Cognition) and was titled "What Are We Doing? A Key Aspect of Common Ground." A joint session was held with the Advances in Cognitive Systems symposium, where topics that were of interest to both symposia were discussed.

The symposium was successful in generating interest among the participants in building the common ground community, which we hope to expand and preserve by future events and additional channels of communication.

Sam Blisard and Wende Frost served as cochairs of this symposium. The papers of the symposium were published as AAAI Press Technical Report FS-11-02.

Complex Adaptive Systems:

Energy, Information, and Intelligence

The goal of the Complex Adaptive Systems symposium was to bring together researchers and students who represent a wide diversity of disciplines, and use the common tools of complex adaptive systems to investigate the next set of fundamental processes in these systems from the perspective of multiple domains.

All complex systems require energy to develop and persist, but "energy" as a concept in complex adaptive systems (CAS) is often more than merely physical energy; it is anything that drives and constrains the system. Agents must cooperate or compete for limited resources, whether these resources are energy, power, food, money, or some other physical or metaphorical limitation. The success or failure of various agent strategies depends on how effective the agents are in acquiring and utilizing these resources.

In this quest for resources, the agent-level implementation of various strategies is enabled by the collection of local information by these agents. But "information," too, encompasses more than just words, ideas, or memes; it also includes many forms of nonverbal or nonhuman communication systems. We broadly define communication to be any information passing to an agent, from other agents or from its environment. Such flows of information may, in their own right, be the focus of CAS modeling, such as in models of political dissent, social contagion, or the dynamical flows across networks.

Thus, agents in a CAS acquire information from a limited communication radius, interpret what they see, hear, feel, or otherwise sense, and then act based on this knowledge. These actions and reactions spread through a CAS in patterns of correlated feedbacks. This process leads to the emergence of system-level properties, including global patterns of intelligence that no individual agent within that system possesses. Such system-level intelligence can arise even from simple agents, or agents with only a few abilities or limited knowledge. Therefore, these agents cannot be studied in isolation; understanding these complex systems requires understanding how the agents behave and interact with other agents, and in the context of the emergent, system-level properties.

Complex adaptive systems have proven to be a powerful tool for exploring these and other related phenomena. We characterize a general CAS model as having a large number of self-similar agents that: (1) Utilize one or more levels of feedback; (2) Exhibit emergent properties and self-organization; and (3) Produce nonlinear dynamic behavior. Advances in modeling and computing technology have led to a deeper understanding of complex systems in many areas, and have raised the possibility that similar fundamental principles may be at work across these systems, even though the underlying principles may manifest themselves differently.

In attendance were approximately 45 researchers from disciplines as diverse as computer science, philosophy, economics, political science, biology, public policy, cognitive science, literature, and ecology. Some 20 papers were presented that explored these themes from an equally diverse set of viewpoints. Among the highlights were Kiran Lakkaraju (Sandia National Laboratory), who presented on the diffusion of attitudes in a community; Patrick Grim (Stony Brook University), who presented an analysis of various network structures and how these affect the spread of germs, genes, and memes; and Aaron Bramson, who talked about methods for testing for emergence (University of Michigan). Erika Frydenlund and David Earnest (Old Dominion University) presented two agentbased models: one on community information sharing for improving public safety; and another that explored the resiliency of the international air transportation network. John Seymour and Joseph Tuzo of the University of Maryland, Baltimore County (and their advisor Marie desJardins) discussed two separate models that showed emergent features from the interactions of simple agents: one in the context of lane-changing strategies for highway driving, and the second using an ant colony optimization algorithm. Finally, Russ Abbott (California State University) presented research that compares and contrasts supply-driven ecological systems to demand-driven economic systems.

In addition to paper presentations, we held a participatory panel discussion that highlighted the work from an NEH-sponsored summer institute on simulation for the humanities. Humanists are not usually found at symposia hosted by a computer science organization, and so this was particularly useful in exemplifying how CAS tools and methods can be used in a wide variety of domains. This panel was chaired by Marvin Croy (University of North Carolina, Charlotte) and featured talks from Gillian Crozier (Laurentian University), Graham Sack (Columbia University), and Stephen Crowley (Boise State University).

As with previous years, we were fortunate to have two distinguished keynote speakers. Carl Simon is the founding director of the prestigious Center for the Study of Complex Systems at the University of Michigan. Simon gave us his perspective on the importance of solving practical problems. We also hosted Sean O'Brien, who is an executive vice-president and senior scientist at SAE, Inc., and a former program director from DARPA. O'Brien discussed various projects and methodologies that affect our armed forces, especially in the context of human, social, cultural, and behavioral (HSCB) dimensions.

Thus, our efforts this year can be summarized as strengthening the community of researchers from across a wide range of fields, and using the three fundamental properties of CAS-energy, information, and intelligence-as a bridge connecting these disciplines. Many of this year's attendees were also present at our first two AAAI CAS symposia, held in 2009 and 2010. Of particular note is the emphasis we place on student participation. Approximately half of the attendees were graduate students or undergrads, including multiple members of the organizing committee.

Mirsad Hadzikadic and Ted Carmichael served as the cochairs for this symposium, and the papers presented here can be found in AAAI Press Technical Report FSS-11-03.

Multiagent Coordination under Uncertainty

The goal of the Multiagent Coordination under Uncertainty symposium was to understand the latest research in multiagent coordination, new application domains, and some of the interesting future directions.

In domains ranging from Earth-observing sensor webs to collaborating ambulances or fire fighters during disaster rescue or software personal assistants scheduling meetings to "coordinators" assisting in executing military missions or exploration of underwater terrains using autonomous underwater vehicles to handling large-scale humanitarian logistics, multiple intelligent agents need to coordinate in the presence of uncertainty to achieve team goals. The goal of the symposium was to understand some of the latest application domains, look at interesting research problems in this area, and investigate future research directions.

The symposium was organized as a set of invited talks by pioneers of the area mixed with talks from researchers who have been working in this area. There were also two discussion sessions where topics raised in the talks and other interesting research questions were discussed. Typically, research in the area has focused on generic models such as DEC-(PO)MDP, DCOP, stochastic games. A key theme that was observed across most of the talks was that the research was based on interesting real-world problems: (1) coordinating security forces to thwart adversaries in airports, country borders; (2) decision support for taxi drivers in Sin-gapore for better performance (revenue for drivers and higher taxi availability for customers); (3) guiding customers in a theme park for reduced wait times at attractions; (4) building ad hoc robot teams to play soccer; (5) coordinating robot boats to observe interesting phenomenon (observing water temperature and floods to save fish in Philippines); (6) coordinating large-scale logistics services during disaster relief.

There were many exciting and interesting results presented during the talks on latest research. First, in certain large-scale multiagent coordination problems, the quality of solution strategies becomes closer to optimal as the number of agents increases (or the impact of an individual agent on the overall solution is reduced). This was illustrated on route guidance problems faced by cars travelling through a city and for customers at a theme park. Second, selective communication for multiagent coordination can sometimes lead to a fall in coordination performance. Third, multiagent coordination in certain domains with uncertainty can be improved by insights from nature. For instance, robots coordinating for museum surveillance were able to perform better with ant-colony optimization techniques than with standard coordination techniques. Fourth, it is possible to add a new robot to a robot soccer team and perform well without any precoordination between the new robot and the team. Fifth, in certain coordination problems (particularly ones where there is congestion over resources with uncertainty), game-theoretic solution concepts provide better social welfare than locally rational agents (greedy myopic behavior).

There was also considerable discussion on future research directions. An interesting direction raised during talks was about building ad hoc teams of agents in generic problem domains. As part of this, new agents can join teams without any coordination with the team. Reasoning about coordination (and communication for coordination) has typically been performed either completely offline or completely online. Pursuing a hybrid mechanism seems to be a direction for the future. Existing models for coordination under uncertainty (DEC-POMDPs, stochastic games, DCOPs) assume either completely cooperative or adversarial settings and it was agreed that there was need for models that considered partially adversarial and partially cooperative problems. Finally, considering the robust optimization (risk aware) criterion to reason about uncertainty seems to be a natural progression from expected value optimization.

Pradeep Varakantham (author of this report), William Yeoh, Paul Scerri, and Janusz Marecki served as cochairs of this symposium. There were no papers published from this symposium.

Open Government Knowledge: AI Opportunities and Challenges

The AAAI Fall Symposium on Open Government Knowledge focused on issues related to publishing government data as reusable knowledge on the web. More than 40 countries around the world are working on publishing government data as open and reusable world knowledge to improve transparency in government and to accumulate valuable social information. The Open Government Knowledge symposium brought together a diverse community to explore the governmental, business, scientific, and academic challenges and opportunities in the process of publishing, linking, mashing-up, and leveraging public government data.

The symposium featured a wide range of distinguished speakers from government, industry, and academia. Opening keynote talks were given by Jeanne Holm on driving global innovation through linked data and by Abdul Shaikh of NIH/NCI on crowd sourcing of the research enterprise. Invited talks were presented by Debbie Brodt-Giles of NERL on open energy information and by Curt Tilmes of NASA on scientific data and provenance. David Wood of 3roundstones gave a two-hour tutorial on linked data and associated semantic web standards providing the participants with a common understanding of the technology and context. Government practitioners from the United States, Canada, and Brazil reported on their progress and challenges in publishing open data of various kinds including health, energy, community, and education, as well as in preserving the data and its provenance. Speakers from Google (Sree Balakrishnan) and Microsoft (Lewis Shepherd) discussed two important aspects of open government knowledge: web-scale structured data integration and semantic processing to turn data into highquality knowledge. Several panels addressed advanced topics relating to the role of linked data in an open government knowledge ecosystem, possible "killer apps," and key technologies for growing business opportunities around open government knowledge.

The symposium generated much discussion among the participants and led to the collaborative development of a road map for linked open government data.

During the discussion, a principle in processing open government data was agreed upon: instead of expecting that all government data be at the highest level of quality, linked data technology can be used to enable incremental data publishing and refinement. Depending on the scale of government organization and the data, the organization might think it is sufficient to get raw data online or may choose to require that all data be directly published as linked data. Another important issue raised by our government practitioners is the lack of a global, scalable data catalog, which is required to overview the availability of government data and to promote the progressive quality refinement of the data. It was also noted that there are immense quantities of government data that remain in nondigital form. Two important scientific challenges involved with opening reusable and linked government data were identified: (1) management of social-economic issues such as privacy, licenses, access control, risk models, and provenance; and (2) management of computational issues such as uncertainty, correlated metadata and context, special treatment on the temporalspatial dimension, persistence, and portability.

In summary, open government data has a unique impact on artificial intelligence research.

Unlike conventional text web pages, open government data is released and reused in a more structural way, demanding effective AI solutions to enable and enhance cross-domain data integration and web-scale data mash-ups. The huge amount of government data available from different countries, states, and cities further offers a realistic opportunity for evolving computationally and socially scalable AI solutions.

Li Ding, Tim Finin, Lalana Kagal, and Deborah L. McGuiness served as cochairs of this symposium. There were no papers published from this symposium.

Question Generation

Asking questions is a fundamental cognitive process that underlies higher-level cognitive abilities such as comprehension and reasoning. Ultimately, question generation (QG) allows humans, and in many cases artificial intelligence systems, to understand their environment and each other. Research on question generation has a long history in artificial intelligence, psychology, education, and natural language processing. One thread of research has been theoretical, with attempts to understand and specify the triggers and mechanisms underlying question generation. The other thread of research has focused on automated question generation, which has far-reaching applications in intelligent technologies, such as dialogue systems, question-answering systems, web search, intelligent tutoring systems, automated assessment systems, inquiry-based environments, adaptive intelligent agents and game-based learning environments.

The 2011 AAAI Symposium on Question Generation followed three previous workshops and a shared task and evaluation campaign. The goal of the 2011 symposium was to foster theoretical and applied research on computational and cognitive aspects of question generation, thus bringing together participants from diverse disciplines including natural language processing, artificial intelligence, linguistics, psychology, and education, and to create a forum for discussing and planning the future of question generation shared task and evaluation challenges.

The first part of the symposium featured paper and poster presentations, which represented three major themes. The first theme was human or cognitive perspectives on question generation, under which papers explored the importance of using human models and social interaction for promoting learning, the relationship between curiosity and question asking, and the efficacy of prompts for self-questioning in learning. An invited talk by Patricia Alexander explored the implications of asking questions when prompted or directed to do so by others versus when learners' questions arise spontaneously or intentionally. The second theme focused on various applications of question generation, including generation of multiple choice exams using limited human-judged data resources, generation of questions based on numerical entities, generation of mathematical word problems from ontologies in unrestricted domains, generation of cloze questions based on definitions, generation of questions to promote creativity, generation of questions in situated human-robot interaction, and using automatically generated question-answer pairs for augmenting conversational characters. An invited talk by Jack Mostow reviewed his team's research on automatically generating and administering questions in Project LISTEN's Reading Tutor. The final theme focused on technical approaches for question generation, where research included the generation of more specific questions, ranking of over-generated questions, and exploring the feasibility of using discourse parsing for question-generation tasks.

The second half of the symposium featured break-out working group discussion sessions that addressed evaluation of question generation, specifically through question generation shared task challenge and evaluation campaigns. Evaluating the quality of automatically generated natural language text is notoriously difficult. As natural language is rich and diverse, a correctly generated sentence (in this case, a question) may have multiple forms. This makes automatic evaluation, such as comparing system outputs against a gold-standard key set, difficult. Evaluating natural language generation (NLG) output often involves human judgments of the output in terms of grammatical and semantic correctness, relevance, or appropriateness.

The goal of the discussion sessions was to prepare for the next evaluation challenge. Vasile Rus gave a kickoff invited talk on the first questiongeneration campaign and future opportunities and challenges. Participants split into three groups and engaged in a creative discussion on the topics of question-generation evaluation. The organizers defined two tasks for the participants to discuss. Following the discussions, each group presented their ideas and everyone voted on their favorite proposal.

In the first task, participants had to design an enhanced evaluation criteria and process for question generation. Each group had access to the data set and were able to look at the outputs of the systems participating in the first campaign. The groups came up with creative ideas for using crowd-sourcing tools (Mechanical Turk) and enhancing the task to appeal to more participants.

In the second task, participants were asked to brainstorm a different method of evaluation: taskbased evaluation. Task-based evaluation measures the impact of an NLG system on how well a user can perform a task while assisted by the system. Task-based evaluation may be an online game, such as navigation in a three-dimensional environment assisted by generation of directions, or a task that participants perform in a lab, such as card matching. Task-based evaluation avoids subjective judgments and, instead, evaluates NLG systems indirectly, by analyzing user performance.

The participants came up with creative new ideas for task-based evaluation. One of the ideas involved an online game where players engage in a Turing test-like task, determining whether a question is generated by a human or by a computer. Correctness of their judgments and quickness of their responses allows evaluation to compare the quality of questions generated by different systems. This type of game may be implemented on popular websites such as Facebook that attract many players and allow for collection of large data sets.

Rashmi Prasad, Svetlana Stoyanchev, Jack Mostow, Arthur Graesser, and James Lester served as cochairs of the symposium. The papers of the symposium were published as AAAI Press Technical Report FS-11-04.

Robot-Human Teamwork in Dynamic Adverse Environments

Robots are gradually making their way into different aspects of our lives. We find them at home, on the factory floor, and, more and more, they are also performing missions in complex outdoor environments.

At the Robot-Human Teamwork in Dynamic Adverse Environments symposium we discussed issues in human-robot teamwork, set in environments that are dynamic and adverse. Typical examples here include urban search and rescue (USAR), or security missions. This is a timely topic: This is already happening. Humans are taking robots on such missions, using them in situations that are too dangerous for people to (immediately) enter. Where this is not happening yet, it is very likely to be happening soon.

Human-robot teaming in such environments is much more than just a technical issue. It is "people plus robots." This is a complex sociotechnical system in which humans and robots are trying to work together under very difficult circumstances. They are performing under heavy physical and mental stress, and we know that in such extreme situations, under such extreme conditions, human characteristics, behaviors, emotions are driven to extremes. People change in how they behave, act, what they pay attention to, and how they interact. If we use robots in such situations, will we actually make things better? Or will we make things worse? Can we make robots to assist humans to do better, to handle such situations better?

Looking at these problems from the viewpoint of the user (first responder, police officer, soldier, and so on) the symposium raised from anywhere between practical and fundamental questions. What is a team? What is a human-robot team? What is teamwork in such a context? When we consider mixed-initiative settings, do we actually want robots to take the lead? What makes for good teamwork, and what would be bad teamwork? What does it mean to share situation awareness, when humans and robots are typically geographically distributed, and each and every one has his or her own ways of looking at a situation, experiencing it subjectively? Also, amid all the hows and coulds, should we?

The take-home messages from the various presentations, and invited talks by Ron Arkin, Jeffrey Bradshaw, and Satoshi Tadokoro, were essentially that it's about working together. There is an inherent interdependency between humans and robots, and this requires much more than autonomy (even when we consider autonomy to be multidimensional). It's about the social dynamics of actors, roles, performing particular tasks. It's about situation awareness specific to interactions between roles, in specific task contexts; and above all, it is about making robots acceptable. As Satoshi Tadokoro aptly put it, it's perhaps better to turn AI from artificial intelligence into acceptable intelligence: robot intelligence that is clear, predictable, and acceptable in a given team context. Only then we may be able to succeed in turning robots into real team players.

Geert-Jan M. Kruijff, Panos Papadakis and Fiora Pirri served as cochairs of this symposium. Additional support for this symposium came from the Natural Human-Robot Collaboration in Dynamic Environments project. The papers of the symposium were published as AAAI Press Technical Report FS-11-05. Pradeep Varakantham is an assistant professor at the School of Information Systems, Singapore Management University.

Sam

  Blisard is a research scientist in the Interactive Systems section at the Naval Research Laboratory in Washington, D.C. Ted Carmichael is an assistant research professor in the department of Software and Information Systems at the University of North Carolina at Charlotte, and faculty member in the Complex Systems Institute. Li Ding is a staff engineer at Qualcomm Inc. Tim Finin is a professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County. Wende Frost is a research scientist in the Interactive Systems section at the Naval Research Laboratory in Washington, D.C., and a Ph.D. candidate in linguistics at Arizona State University. Arthur Graesser is a professor in the Department of Psychology, an adjunct professor in computer science, and codirector of the Institute of Intelligent Systems, University of Memphis. Mirsad Hadzikadic is a professor in the department of Software and Information Systems at the University of North Carolina at Charlotte, and the founding director of UNC Charlotte's Complex Systems Institute. Lalana Kagal is a research scientist in the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Geert-Jan M. Kruijff is a senior researcher at the Language Technology Lab, German Research Center for Artificial Intelligence (DFKI GmbH). Pat Langley is a professor of computer science and engineering at Arizona State University in Tempe, Arizona, and director of the Institute for the Study of Learning and Expertise in Palo Alto, California. James Lester is a professor in the Department of Computer Science, North Carolina State University. Deborah L. McGuinness a professor of computer science and cognitive science at Rensselaer Polytechnic Institute. Jack Mostow is a research professor in the School of Computer Science, Carnegie Mellon University. Panagiotis Papadakis is a postdoctoral researcher at the ALCOR lab (Auto Agent Laboratory for Cognitive Robotics), Department of Informatics and Systems, University of Sapienza, Rome. Fiora Pirri is a professor of computer science and head of the ALCOR lab, Department of Informatics and Systems, University of Sapienza, Rome. Rashmi Prasad is an assistant professor at the Department of Health Informatics and Administration, University of Wisconsin-Milwaukee. Svetlana Stoyanchev is a postdoctoral research scientist at Columbia University.

The Advances in Cognitive Systems AAAI Fall Symposium, held at the Westin Hotel in Arlington, Virginia.Photograph courtesy Will Bridewell.