Nonperturbative Functional Renormalization Group for Random Field Models. III: Superfield formalism and ground-state dominance

We reformulate the nonperturbative functional renormalization group for the random field Ising model in a superfield formalism, extending the supersymmetric description of the critical behavior of the system first proposed by Parisi and Sourlas [Phys. Rev. Lett. 43, 744 (1979)]. We show that the two crucial ingredients for this extension are the introduction of a weighting factor, which accounts for ground-state dominance when multiple metastable states are present, and of multiple copies of the original system, which allows one to access the full functional dependence of the cumulants of the renormalized disorder and to describe rare events. We then derive exact renormalization group equations for the flow of the renormalized cumulants associated with the effective average action.Comment: 28 page

Simultaneous segmentation and pose estimation of humans using dynamic graph cuts

This paper presents a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other state of the art methods which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Our method works by optimizing a cost function based on a Conditional Random Field (CRF). This has the advantage that all information in the image (edges, background and foreground appearances), as well as the prior information on the shape and pose of the subject can be combined and used in a Bayesian framework. Optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any rigid, deformable or articulated object

Measuring Happiness: From Fluctuating Happiness to Authentic–Durable Happiness

On the basis of the theoretical distinction between self-centeredness and selflessness (Dambrun and Ricard, 2011), the main goal of this research was to develop two new scales assessing distinct dimensions of happiness. By trying to maximize pleasures and to avoid displeasures, we propose that a self-centered functioning induces a fluctuating happiness in which phases of pleasure and displeasure alternate repeatedly (i.e., Fluctuating Happiness). In contrast, a selfless psychological functioning postulates the existence of a state of durable plenitude that is less dependent upon circumstances but rather is related to a person’s inner resources and abilities to deal with whatever comes his way in life (i.e., Authentic–Durable Happiness). Using various samples (n = 735), we developed a 10-item Scale measuring Subjective Fluctuating Happiness (SFHS) and a 13-item scale assessing Subjective Authentic–Durable Happiness (SA–DHS). Results indicated high internal consistencies, satisfactory test–retest validities, and adequate convergent and discriminant validities with various constructs including a biological marker of stress (salivary cortisol). Consistent with our theoretical framework, while self-enhancement values were related only to fluctuating happiness, self-transcendence values were related only to authentic–durable happiness. Support for the distinction between contentment and inner-peace, two related markers of authentic happiness, also was found

Rings and rigidity transitions in network glasses

Three elastic phases of covalent networks, (I) floppy, (II) isostatically rigid and (III) stressed-rigid have now been identified in glasses at specific degrees of cross-linking (or chemical composition) both in theory and experiments. Here we use size-increasing cluster combinatorics and constraint counting algorithms to study analytically possible consequences of self-organization. In the presence of small rings that can be locally I, II or III, we obtain two transitions instead of the previously reported single percolative transition at the mean coordination number $\bar r=2.4$, one from a floppy to an isostatic rigid phase, and a second one from an isostatic to a stressed rigid phase. The width of the intermediate phase $~ \bar r$ and the order of the phase transitions depend on the nature of medium range order (relative ring fractions). We compare the results to the Group IV chalcogenides, such as Ge-Se and Si-Se, for which evidence of an intermediate phase has been obtained, and for which estimates of ring fractions can be made from structures of high T crystalline phases.Comment: 29 pages, revtex, 7 eps figure

Natural Variation in Arabidopsis thaliana as a Tool for Highlighting Differential Drought Responses

To test whether natural variation in Arabidopsis could be used to dissect out the genetic basis of responses to drought stress, we characterised a number of accessions. Most of the accessions belong to a core collection that was shown to maximise the genetic diversity captured for a given number of individual accessions in Arabidopsis thaliana. We measured total leaf area (TLA), Electrolyte Leakage (EL), Relative Water Content (RWC), and Cut Rosette Water Loss (CRWL) in control and mild water deficit conditions. A Principal Component Analysis revealed which traits explain most of the variation and showed that some accessions behave differently compared to the others in drought conditions, these included Ita-0, Cvi-0 and Shahdara. This study relied on genetic variation found naturally within the species, in which populations are assumed to be adapted to their environment. Overall, Arabidopsis thaliana showed interesting phenotypic variations in response to mild water deficit that can be exploited to identify genes and alleles important for this complex trait

Recognition of Gestures in the Context of Speech

The scope of this paper is the interpretation of a user's intention via a video camera and a speech recognizer. In comparison to previous work which only takes into account gesture recognition, we demonstrate that by including speech, system comprehension increases. For the gesture recognition, the user must wear a colored glove, then we extract the velocity of the center of gravity of the hand. A Hidden Markov Model (HMM) is learned for each gesture that we want to recognize. In a dynamic action, to know if a gesture has been performed or not, we implement a threshold model below which the gesture is not detected. The off line tests for gesture recognition have a success rate exceeding 85% for each gesture. The combination of speech and gestures is realized using Bayesian theory

Posecut: Simultaneous segmentation and 3d pose estimation of humans using dynamic graph-cuts

Abstract. We present a novel algorithm for performing integrated segmentation and 3D pose estimation of a human body from multiple views. Unlike other related state of the art techniques which focus on either segmentation or pose estimation individually, our approach tackles these two tasks together. Normally, when optimizing for pose, it is traditional to use some fixed set of features, e.g. edges or chamfer maps. In contrast, our novel approach consists of optimizing a cost function based on a Markov Random Field (MRF). This has the advantage that we can use all the information in the image: edges, background and foreground appearances, as well as the prior information on the shape and pose of the subject and combine them in a Bayesian framework. Previously, optimizing such a cost function would have been computationally infeasible. However, our recent research in dynamic graph cuts allows this to be done much more efficiently than before. We demonstrate the efficacy of our approach on challenging motion sequences. Note that although we target the human pose inference problem in the paper, our method is completely generic and can be used to segment and infer the pose of any specified rigid, deformable or articulated object.

Smart particle filtering for 3D hand tracking

Solving the tracking of an articulated structure in a reasonable time is a complex task mainly due to the high dimensionality of the problem. A new optimization method, called Stochastic Meta-Descent (SMD), based on gradient descent with adaptive and parameter specific step sizes was introduced recently [1] to solve this challenging problem. While the local optimization works very well, reaching the global optimum is not guaranteed. We therefore propose a novel algorithm which combines the SMD optimization with a particle filter to form ‘smart particles‘. After propagating the particles, SMD is performed and the resulting new particle set is included such that the original Bayesian distribution is not altered. The resulting ‘smart particle filter‘ (SPF) tracks high dimensional articulated structures with far fewer samples than previous methods. Additionally, it can handle multiple hypotheses, clutter and occlusion which pure optimization approaches have problems. The performance of the SMD particle filter is illustrated in challenging 3D hand tracking sequences demonstrating a better robustness and accuracy than those of a single SMD optimization or an annealed particle filter