ReFace: Improving Clothes-Changing Re-Identification With Face Features

Person re-identification (ReID) has been an active research field for many years. Despite that, models addressing this problem tend to perform poorly when the task is to re-identify the same people over a prolonged time, due to appearance changes such as different clothes and hairstyles. In this work, we introduce a new method that takes full advantage of the ability of existing ReID models to extract appearance-related features and combines it with a face feature extraction model to achieve new state-of-the-art results, both on image-based and video-based benchmarks. Moreover, we show how our method could be used for an application in which multiple people of interest, under clothes-changing settings, should be re-identified given an unseen video and a limited amount of labeled data. We claim that current ReID benchmarks do not represent such real-world scenarios, and publish a new dataset, 42Street, based on a theater play as an example of such an application. We show that our proposed method outperforms existing models also on this dataset while using only pre-trained modules and without any further training

Decision S4: Efficient Sequence-Based RL via State Spaces Layers

Recently, sequence learning methods have been applied to the problem of off-policy Reinforcement Learning, including the seminal work on Decision Transformers, which employs transformers for this task. Since transformers are parameter-heavy, cannot benefit from history longer than a fixed window size, and are not computed using recurrence, we set out to investigate the suitability of the S4 family of models, which are based on state-space layers and have been shown to outperform transformers, especially in modeling long-range dependencies. In this work we present two main algorithms: (i) an off-policy training procedure that works with trajectories, while still maintaining the training efficiency of the S4 model. (ii) An on-policy training procedure that is trained in a recurrent manner, benefits from long-range dependencies, and is based on a novel stable actor-critic mechanism. Our results indicate that our method outperforms multiple variants of decision transformers, as well as the other baseline methods on most tasks, while reducing the latency, number of parameters, and training time by several orders of magnitude, making our approach more suitable for real-world RL.Comment: 21 pages,13 figure

Contact Distribution Encodes Frictional Strength

The static friction coefficient, $\mu$, is a central quantity in modeling mechanical phenomena. However, experiments show that it is highly variable, even for a single interface under carefully controlled experimental conditions. Traditionally, this inconsistency is attributed to fluctuations in the real area of contact between samples, $A_R$. In this work, we perform a variety of experimental protocols on three pairs of solid blocks while imaging the contact interface and measuring $\mu$. Using linear regression and images of the interface taken prior to tangential loading, we predict the static friction coefficient. Our model strongly outperforms two benchmarks, the Bowden and Tabor picture ($\mu \propto A_R$) and prediction using experimental variables, indicating that a large portion of the observed variance in the initialization of slip is encoded in the contact plane. We perform the same analysis using only sub-sections of the interface, and find that regions as small as $1\%$ of the interface can still can beat both benchmarks. However, bigger sub-sections of the interface, even when comprised of many small regions with bad individual predictive power, outperform the best small regions alone, suggesting that the interfacial state is not dependent on any single point, but is rather distributed across the contact ensemble.Comment: 5 pages 4 figure

Structural basis for the membrane fusion step in Hantavirus entry

Hantaviruses are important emerging human pathogens and are the causative agents of serious diseases in humans with high mortality rates. Like other members in the Bunyaviridae family their M segment encodes two glycoproteins, GN and GC, which are responsible for the early events of the viral infection. Hantaviruses deliver their tripartite genome into the cytoplasm by fusion of the viral and endosomal membranes in response to the reduced pH of the endosome. Unlike phleboviruses (e.g. Rift valley fever virus), that have an icosahedral glycoprotein envelope, hantaviruses display a pleomorphic virion morphology as GN and GC assemble into spikes with apparent four- fold symmetry organized in a grid-like pattern on the viral membrane. We determined the crystal structure of glycoprotein C (GC) from Puumala virus (PUUV), a representative member of the Hantavirus genus. The crystal structure shows GC as the membrane fusion effector of PUUV and it presents a class II membrane fusion protein fold. Furthermore, GC was crystallized in its post-fusion trimeric conformation that until now had been observed only in Flavi- and Togaviridae family members. The PUUV G C structure together with our functional data provides new mechanistic insights into class II membrane fusion proteins and reveals new targets for membrane fusion inhibitors against these important pathogens. Both similarities and differences to other class II membrane fusion proteins implies a revise paradigm for the evolution of these unique proteins

The total number and mass of SARS-CoV-2 virions in an infected person

Quantitatively describing the time course of the SARS-CoV-2 infection within an infected individual is important for understanding the current global pandemic and possible ways to combat it. Here we integrate the best current knowledge about the abundance of potential SARS-CoV-2 host cells and typical concentrations of virions in bodily fluids to estimate the total number and mass of SARS-CoV-2 virions in an infected person. We estimate that each infected person carries 109-1011 virions during peak infection, with a total mass of about 1 µg-0.1 mg, which curiously implies that all SARS-CoV-2 virions currently in the world have a mass of only 0.1-1 kg. Knowledge of the absolute number of virions in an infected individual can put into perspective parameters of the immune system response, minimal infectious doses and limits of detection in testing

The Rayleigh-Lamb wave propagation in dielectric elastomer layers subjected to large deformations

The propagation of waves in soft dielectric elastomer layers is investigated. To this end incremental motions superimposed on homogeneous finite deformations induced by bias electric fields and pre-stretch are determined. First we examine the case of mechanically traction-free layer, which is an extension of the Rayleigh-Lamb problem in the purely elastic case. Two other loading configurations are accounted for too. Subsequently, numerical examples for the dispersion relations are evaluated for a dielectric solid governed by an augmented neo-Hookean strain energy. It is found that the the phase speeds and frequencies strongly depend on the electric excitation and pre-stretch. These findings lend themselves at the possibility of controlling the propagation velocity as well as filtering particular frequencies with suitable choices of the electric bias field

Machine Learning in a data-limited regime: Augmenting experiments with synthetic data uncovers order in crumpled sheets

Machine learning has gained widespread attention as a powerful tool to identify structure in complex, high-dimensional data. However, these techniques are ostensibly inapplicable for experimental systems where data is scarce or expensive to obtain. Here we introduce a strategy to resolve this impasse by augmenting the experimental dataset with synthetically generated data of a much simpler sister system. Specifically, we study spontaneously emerging local order in crease networks of crumpled thin sheets, a paradigmatic example of spatial complexity, and show that machine learning techniques can be effective even in a data-limited regime. This is achieved by augmenting the scarce experimental dataset with inexhaustible amounts of simulated data of rigid flat-folded sheets, which are simple to simulate and share common statistical properties. This significantly improves the predictive power in a test problem of pattern completion and demonstrates the usefulness of machine learning in bench-top experiments where data is good but scarce.Comment: 8 pages, 5 figures (+ Supplemental Materials: 5 pages, 6 figures

CYP450 2D6 Genotype and Flecainide Efficacy in the Treatment of Patients with Lone Atrial Fibrillation

Background: CYP2D6 has been linked to one of four phenotypes: a) ultra-rapid metabolizers (UM), with multiple gene copies; b) extensive metabolizers (EM), with a single wild type gene copy, considered normal; c) intermediate (IM) metabolizer, with decreased enzymatic activity; and d) poor metabolizers (PM) with no detectable enzymatic activity.  By altering the drug dose-plasma concentration relationship, these differences may lead to severe toxicity and or therapeutic failure.Objectives: The aim of this study was to determine the correlation between CYP2D6 polymorphisms and both efficacy and magnitude of adverse reactions of flecainide, a class IC antiarrhythmic agent.Methods: Patients with Lone Atrial fibrillation (AF) were enrolled in a 2-arm prospective study: patients started on flecainide at the initial visit, then were followed up at 3 and 6 months intervals (arm 1) or exhibited AF recurrences on flecainide, defined as treatment failure (arm 2).  Data about recurrence of AF, side effects, and demographics were collected. Genotyping was performed using AmpliChipTM CYP450.Results: A total of 26 lone AF patients were enrolled (12 in arm 1, and 14 in arm 2).  The mean age was 47± 10.8 years and 56.2 ± 10.8 years respectively.  Among the analyzed phenotypes the following distribution was found: 1/26 (3.8%) UM, 19/26 (73%) EM, 5/26 (19%) IM, 1/26 (3.8%) PM.Conclusions: In this small series of patients with lone atrial fibrillation, most patients were found to be extensive metabolizers of flecainide.  There was no statistically significant correlation between the patients' genotype, and flecainide efficacy / side effects