ESTSS at 20 years: "a phoenix gently rising from a lava flow of European trauma"

Roderick J. Ørner, who was President between 1997 and 1999, traces the phoenix-like origins of the European Society for Traumatic Stress Studies (ESTSS) from an informal business meeting called during the 1st European Conference on Traumatic Stress (ECOTS) in 1987 to its emergence into a formally constituted society. He dwells on the challenges of tendering a trauma society within a continent where trauma has been and remains endemic. ESTSS successes are noted along with a number of personal reflections on activities that give rise to concern for the present as well as its future prospects. Denial of survivors' experiences and turning away from survivors' narratives by reframing their experiences to accommodate helpers' theory-driven imperatives are viewed with alarm. Arguments are presented for making human rights, memory, and ethics core elements of a distinctive European psycho traumatology, which will secure current ESTSS viability and future integrity

Reflexive and preparatory selection and suppression of salient information in the right and left posterior parietal cortex

Attentional cues can trigger activity in the parietal cortex in anticipation of visual displays, and this activity may, in turn, induce changes in other areas of the visual cortex, hence, implementing attentional selection. In a recent TMS study [Mevorach, C., Humphreys, G. W., & Shalev, L. Opposite biases in salience-based selection for the left and right posterior parietal cortex. Nature Neuroscience, 9, 740-742, 2006b], it was shown that the posterior parietal cortex (PPC) can utilize the relative saliency (a nonspatial property) of a target and a distractor to bias visual selection. Furthermore, selection was lateralized so that the right PPC is engaged when salient information must be selected and the left PPC when the salient information must be ignored. However, it is not clear how the PPC implements these complementary forms of selection. Here we used on-line triple-pulse TMS over the right or left PPC prior to or after the onset of global/local displays. When delivered after the onset of the display, TMS to the right PPC disrupted the selection of the more salient aspect of the hierarchical letter. In contrast, left PPC TMS delivered prior to the onset of the stimulus disrupted responses to the lower saliency stimulus. These findings suggest that selection and suppression of saliency, rather than being "two sides of the same coin," are fundamentally different processes. Selection of saliency seems to operate reflexively, whereas suppression of saliency relies on a preparatory phase that "sets up" the system in order to effectively ignore saliency

Synapsin selectively controls the mobility of resting pool vesicles at hippocampal terminals

Presynaptic terminals are specialized sites for information transmission where vesicles fuse with the plasma membrane and are locally recycled. Recent work has extended this classical view, with the observation that a subset of functional vesicles is dynamically shared between adjacent terminals by lateral axonal transport. Conceptually, such transport would be expected to disrupt vesicle retention around the active zone, yet terminals are characterized by a high-density vesicle cluster, suggesting that counteracting stabilizing mechanisms must operate against this tendency. The synapsins are a family of proteins that associate with synaptic vesicles and determine vesicle numbers at the terminal, but their specific function remains controversial. Here, using multiple quantitative fluorescence-based approaches and electron microscopy, we show that synapsin is instrumental for resisting vesicle dispersion and serves as a regulatory element for controlling lateral vesicle sharing between synapses. Deleting synapsin disrupts the organization of presynaptic vesicle clusters, making their boundaries hard to define. Concurrently, the fraction of vesicles amenable to transport is increased, and more vesicles are translocated to the axon. Importantly, in neurons from synapsin knock-out mice the resting and recycling pools are equally mobile. Synapsin, when present, specifically restricts the mobility of resting pool vesicles without affecting the division of vesicles between these pools. Specific expression of synapsin IIa, the sole isoform affecting synaptic depression, rescues the knock-out phenotype. Together, our results show that synapsin is pivotal for maintaining synaptic vesicle cluster integrity and that it contributes to the regulated sharing of vesicles between terminals

Spectral Sparsification and Regret Minimization Beyond Matrix Multiplicative Updates

In this paper, we provide a novel construction of the linear-sized spectral sparsifiers of Batson, Spielman and Srivastava [BSS14]. While previous constructions required $\Omega(n^4)$ running time [BSS14, Zou12], our sparsification routine can be implemented in almost-quadratic running time $O(n^{2+\varepsilon})$. The fundamental conceptual novelty of our work is the leveraging of a strong connection between sparsification and a regret minimization problem over density matrices. This connection was known to provide an interpretation of the randomized sparsifiers of Spielman and Srivastava [SS11] via the application of matrix multiplicative weight updates (MWU) [CHS11, Vis14]. In this paper, we explain how matrix MWU naturally arises as an instance of the Follow-the-Regularized-Leader framework and generalize this approach to yield a larger class of updates. This new class allows us to accelerate the construction of linear-sized spectral sparsifiers, and give novel insights on the motivation behind Batson, Spielman and Srivastava [BSS14]

Fields Medals and Nevanlinna Prize Presented at ICM-94 in Zurich

The Notices solicited the following five articles describing the work of the Fields Medalists and Nevanlinna Prize winner

Contextual Object Detection with a Few Relevant Neighbors

A natural way to improve the detection of objects is to consider the contextual constraints imposed by the detection of additional objects in a given scene. In this work, we exploit the spatial relations between objects in order to improve detection capacity, as well as analyze various properties of the contextual object detection problem. To precisely calculate context-based probabilities of objects, we developed a model that examines the interactions between objects in an exact probabilistic setting, in contrast to previous methods that typically utilize approximations based on pairwise interactions. Such a scheme is facilitated by the realistic assumption that the existence of an object in any given location is influenced by only few informative locations in space. Based on this assumption, we suggest a method for identifying these relevant locations and integrating them into a mostly exact calculation of probability based on their raw detector responses. This scheme is shown to improve detection results and provides unique insights about the process of contextual inference for object detection. We show that it is generally difficult to learn that a particular object reduces the probability of another, and that in cases when the context and detector strongly disagree this learning becomes virtually impossible for the purposes of improving the results of an object detector. Finally, we demonstrate improved detection results through use of our approach as applied to the PASCAL VOC and COCO datasets

Subgraphs and network motifs in geometric networks

Many real-world networks describe systems in which interactions decay with the distance between nodes. Examples include systems constrained in real space such as transportation and communication networks, as well as systems constrained in abstract spaces such as multivariate biological or economic datasets and models of social networks. These networks often display network motifs: subgraphs that recur in the network much more often than in randomized networks. To understand the origin of the network motifs in these networks, it is important to study the subgraphs and network motifs that arise solely from geometric constraints. To address this, we analyze geometric network models, in which nodes are arranged on a lattice and edges are formed with a probability that decays with the distance between nodes. We present analytical solutions for the numbers of all 3 and 4-node subgraphs, in both directed and non-directed geometric networks. We also analyze geometric networks with arbitrary degree sequences, and models with a field that biases for directed edges in one direction. Scaling rules for scaling of subgraph numbers with system size, lattice dimension and interaction range are given. Several invariant measures are found, such as the ratio of feedback and feed-forward loops, which do not depend on system size, dimension or connectivity function. We find that network motifs in many real-world networks, including social networks and neuronal networks, are not captured solely by these geometric models. This is in line with recent evidence that biological network motifs were selected as basic circuit elements with defined information-processing functions.Comment: 9 pages, 6 figure

Premise Selection for Mathematics by Corpus Analysis and Kernel Methods

Smart premise selection is essential when using automated reasoning as a tool for large-theory formal proof development. A good method for premise selection in complex mathematical libraries is the application of machine learning to large corpora of proofs. This work develops learning-based premise selection in two ways. First, a newly available minimal dependency analysis of existing high-level formal mathematical proofs is used to build a large knowledge base of proof dependencies, providing precise data for ATP-based re-verification and for training premise selection algorithms. Second, a new machine learning algorithm for premise selection based on kernel methods is proposed and implemented. To evaluate the impact of both techniques, a benchmark consisting of 2078 large-theory mathematical problems is constructed,extending the older MPTP Challenge benchmark. The combined effect of the techniques results in a 50% improvement on the benchmark over the Vampire/SInE state-of-the-art system for automated reasoning in large theories.Comment: 26 page