Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Human-to-Human-or-Object (H2O) Interactions with DIABOLO

Detecting human interactions is crucial for human behavior analysis. Many methods have been proposed to deal with Human-to-Object Interaction (HOI) detection, i.e., detecting in an image which person and object interact together and classifying the type of interaction. However, Human-to-Human Interactions, such as social and violent interactions, are generally not considered in available HOI training datasets. As we think these types of interactions cannot be ignored and decorrelated from HOI when analyzing human behavior, we propose a new interaction dataset to deal with both types of human interactions: Human-to-Human-or-Object (H2O). In addition, we introduce a novel taxonomy of verbs, intended to be closer to a description of human body attitude in relation to the surrounding targets of interaction, and more independent of the environment. Unlike some existing datasets, we strive to avoid defining synonymous verbs when their use highly depends on the target type or requires a high level of semantic interpretation. As H2O dataset includes V-COCO images annotated with this new taxonomy, images obviously contain more interactions. This can be an issue for HOI detection methods whose complexity depends on the number of people, targets or interactions. Thus, we propose DIABOLO (Detecting Inter Actions By Only Looking Once), an efficient subject-centric single-shot method to detect all interactions in one forward pass, with constant inference time independent of image content. In addition, this multi-task network simultaneously detects all people and objects. We show how sharing a network for these tasks does not only save computation resource but also improves performance collaboratively. Finally, DIABOLO is a strong baseline for the new proposed challenge of H2O- Interaction detection, as it outperforms all state-of-the-art methods when trained and evaluated on HOI dataset V-COCO. We hope that this new dataset and new baseline will foster future research. H2O is available on https:/lkalisteo.cea. fr/.
Angle and angular momentum -- new twist for an old pair

Reaching ultimate performance of quantum technologies requires the use of detection at quantum limits and access to all resources of the underlying physical system. We establish a full quantum analogy between the pair of angular momentum and exponential angular variable, and the structure of canonically conjugate position and momentum. This includes the notion of optimal simultaneous measurement of the angular momentum and angular variable, the identification of Einstein-Podolsky-Rosen-like variables and states, and finally a phase-space representation of quantum states. Our construction is based on close interconnection of the three concepts and may serve as a template for the treatment of other observables. This theory also provides a new testbed for implementation of quantum technologies combining discrete and continuous quantum variables.
Rediscussion of eclipsing binaries. Paper VIII. The doubly-eclipsing quadruple star system V498 Cygni

V498 Cyg is an early-B-type binary known to show eclipses on a period of 3.48 d, and two sets of spectral lines. We present the discovery of a second set of eclipses, on a 1.44-d period, in the light curve of this object from the Transiting Exoplanet Survey Satellite (TESS). We develop a model of the light curve to simultaneously fit the properties of both eclipsing binaries and apply this to the TESS observations. We are able to fit the light curve of the fainter system well, but the light curve fit for the brighter system is unable to reproduce either its asymmetric primary eclipse or its changing light curve shape. The available eclipse timing measurements are extremely scattered so we determine orbital ephemerides based only on the TESS data. We infer the physical properties of all four stars, estimating the masses of the components of the brighter binary to be 10 Msun and 11 Msun, and of the fainter binary to be 6.5 Msun and 3.5 Msun. The properties of the system may be reliably determined in future by obtaining radial velocity measurements of the component stars.
Offline Reinforcement Learning for Road Traffic Control

Traffic signal control is an important problem in urban mobility with a significant potential of economic and environmental impact. While there is a growing interest in Reinforcement Learning (RL) for traffic signal control, the work so far has focussed on learning through simulations which could lead to inaccuracies due to simplifying assumptions. Instead, real experience data on traffic is available and could be exploited at minimal costs. Recent progress in offline or batch RL has enabled just that. Model-based offline RL methods, in particular, have been shown to generalize from the experience data much better than others. We build a model-based learning framework which infers a Markov Decision Process (MDP) from a dataset collected using a cyclic traffic signal control policy that is both commonplace and easy to gather. The MDP is built with pessimistic costs to manage out-of-distribution scenarios using an adaptive shaping of rewards which is shown to provide better regularization compared to the prior related work in addition to being PAC-optimal. Our model is evaluated on a complex signalized roundabout showing that it is possible to build highly performant traffic control policies in a data efficient manner.
Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations

This article proposes a sparse computation-based method for optimizing neural networks for reinforcement learning (RL) tasks. This method combines two ideas: neural network pruning and taking into account input data correlations; it makes it possible to update neuron states only when changes in them exceed a certain threshold. It signiﬁcantly reduces the number of multiplications when running neural networks. We tested diﬀerent RL tasks and achieved 20-150x reduction in the number of multiplications. There were no substantial performance losses; sometimes the performance even improved.
An Empirical Bayes Approach to Controlling the False Discovery Exceedance

In large-scale multiple hypothesis testing problems, the false discovery exceedance (FDX) provides a desirable alternative to the widely used false discovery rate (FDR) when the false discovery proportion (FDP) is highly variable. We develop an empirical Bayes approach to control the FDX. We show that, for independent hypotheses from a two-group model and dependent hypotheses from a Gaussian model fulfilling the exchangeability condition, an oracle decision rule based on ranking and thresholding the local false discovery rate (lfdr) is optimal in the sense that the power is maximized subject to the FDX constraint. We propose a data-driven FDX procedure that uses carefully designed computational shortcuts to emulate the oracle rule. We investigate the empirical performance of the proposed method using both simulated and real data and study the merits of FDX control through an application for identifying abnormal stock trading strategies.
PAC-Bayesian Matrix Completion with a Spectral Scaled Student Prior

We study the problem of matrix completion in this paper. A spectral scaled Student prior is exploited to favour the underlying low-rank structure of the data matrix. We provide a thorough theoretical investigation for our approach through PAC-Bayesian bounds. More precisely, our PAC-Bayesian approach enjoys a minimax-optimal oracle inequality which guarantees that our method works well under model misspecification and under general sampling distribution. Interestingly, we also provide efficient gradient-based sampling implementations for our approach by using Langevin Monte Carlo. More specifically, we show that our algorithms are significantly faster than Gibbs sampler in this problem. To illustrate the attractive features of our inference strategy, some numerical simulations are conducted and an application to image inpainting is demonstrated.
Enhanced Phase Mixing of Torsional Alfv\'en Waves in Stratified and Divergent Solar Coronal Structures, Paper II: Nonlinear Simulations

We use MHD simulations to detect the nonlinear effects of torsional Alfvén wave propagation in a potential magnetic field with exponentially divergent field lines, embedded in a stratified solar corona. In Paper I we considered solutions to the linearised governing equations for torsional Alfvén wave propagation and showed, using a finite difference solver we developed named WiggleWave, that in certain scenarios wave damping is stronger than what would be predicted by our analytic solutions. In this paper we consider whether damping would be further enhanced by the presence of nonlinear effects. We begin by deriving the nonlinear governing equations for torsional Alfvén wave propagation and identifying the terms that cause coupling to magnetosonic perturbations. We then compare simulation outputs from an MHD solver called Lare3d, which solves the full set of nonlinear MHD equations, to the outputs fromWiggleWave to detect nonlinear effects such as: the excitation of magnetosonic waves by the Alfvén wave, self-interaction of the Alfvén wave through coupling to the induced magnetosonic waves, and the formation of shock waves higher in the atmosphere caused by the steepening of these compressive perturbations. We suggest that the presence of these nonlinear effects in the solar corona would lead to Alfvén wave heating that exceeds the expectation from the phase mixing alone.
Community recovery in non-binary and temporal stochastic block models

This article studies the estimation of latent community memberships from pairwise interactions in a network of N nodes, where the observed interactions can be of arbitrary type, including binary, categorical, and vector-valued, and not excluding even more general objects such as time series or spatial point patterns. As a generative model for such data, we introduce a stochastic block model with a general measurable interaction space S , for which we derive information-theoretic bounds for the minimum achievable error rate. These bounds yield sharp criteria for the existence of consistent and strongly consistent estimators in terms of data sparsity, statistical similarity between intra- and inter-block interaction distributions, and the shape and size of the interaction space. The general framework makes it possible to study temporal and multiplex networks with S = { 0 , 1 } T , in settings where both N → ∞ and T → ∞ , and the temporal interaction patterns are correlated over time. We present several estimation algorithms, both ofﬂine and online, which fully utilise the non-binary nature of the observed data. Numerical experiments show that an online algorithm of low complexity is capable of producing accurate estimates in only few steps starting from a blind random guess.
COVID-19 Vaccine-Induced Pneumonitis, Myositis and Myopericarditis

A 63-year-old male, with no significant past history and not on any regular medications previously, had mild respiratory symptoms post the first dose of the AstraZeneca (Cambridge, England) coronavirus disease 2019 (COVID-19) vaccine, which were self-limiting. Following the second dose of the vaccine, he arrived at the emergency department (ED) with worsening shortness of breath. During this admission, he was assumed to have interstitial lung disease due to a possible past history of occupational exposure. He responded to a short-term course of corticosteroids and antibiotics and was discharged home. However, he reported again to the emergency department three weeks later, with persistent dyspnoea along with myalgia. His blood tests and imaging from scans suggested myositis, pneumonitis, and myopericarditis. Since he recently had the COVID-19 AstraZeneca vaccine, it was postulated as the most likely cause of the symptoms. He was managed with intravenous (IV) corticosteroids followed by oral corticosteroids with symptom resolution.
Integrated weighted gene co‐expression network analysis reveals biomarkers associated with prognosis of high‐grade serous ovarian cancer

Ovarian cancer is the gynecologic tumor with the highest fatality rate, and high‐grade serous ovarian cancer (HGSOC) is the most common and malignant type of ovarian cancer. One important reason for the poor prognosis of HGSOC is the lack of effective diagnostic and prognostic biomarkers. New biomarkers are necessary for the improvement of treatment strategies and to ensure appropriate healthcare decisions.
The establishment and maintenance of dominance hierarchies

Animal groups are often organized hierarchically, with dominant individuals gaining priority access to resources and reproduction over subordinate individuals. Initial dominance hierarchy formation may be influenced by multiple interacting factors, including an animal's individual attributes, conventions and self-organizing social dynamics. After establishment, hierarchies are typically maintained over the long-term because individuals save time, energy and reduce the risk of injury by recognizing and abiding by established dominance relationships. A separate set of behaviours are used to maintain dominance relationships within groups, including behaviours that stabilize ranks (punishment, threats, behavioural asymmetry), as well as signals that provide information about dominance rank (individual identity signals, signals of dominance). In this review, we describe the behaviours used to establish and maintain dominance hierarchies across different taxa and types of societies. We also review opportunities for future research including: testing how self-organizing behavioural dynamics interact with other factors to mediate dominance hierarchy formation, measuring the long-term stability of social hierarchies and the factors that disrupt hierarchy stability, incorporating phenotypic plasticity into our understanding of the behavioural dynamics of hierarchies and considering how cognition coevolves with the behaviours used to establish and maintain hierarchies. This article is part of the theme issue ‘The centennial of the pecking order: current state and future prospects for the study of dominance hierarchies’.
Identifying SARS-CoV-2 regional introductions and transmission clusters in real time

The unprecedented SARS-CoV-2 global sequencing effort has suffered from an analytical bottleneck. Many existing methods for phylogenetic analysis are designed for sparse, static datasets and are too computationally expensive to apply to densely sampled, rapidly expanding datasets when results are needed immediately to inform public health action. For example, public health is often concerned with identifying clusters of closely related samples, but the sheer scale of the data prevents manual inspection and the current computational models are often too expensive in time and resources. Even when results are available, intuitive data exploration tools are of critical importance to effective public health interpretation and action. To help address this need, we present a phylogenetic summary statistic which quickly and efficiently identifies newly introduced strains in a region, resulting clusters of infected individuals, and their putative geographic origins. We show that this approach performs well on simulated data and is congruent with a more sophisticated analysis performed during the pandemic. We also introduce Cluster Tracker (https://clustertracker.gi.ucsc.edu/), a novel interactive web-based tool to facilitate effective and intuitive SARS-CoV-2 geographic data exploration and visualization. Cluster-Tracker is updated daily and automatically identifies and highlights groups of closely related SARS-CoV-2 infections resulting from inter-regional transmission across the United States, streamlining public health tracking of local viral diversity and emerging infection clusters. The combination of these open-source tools will empower detailed investigations of the geographic origins and spread of SARS-CoV-2 and other densely-sampled pathogens.
Experimental identification of the subjective reception of external stimuli during wheelchair driving

Abstract The aim of this article is identification of the subjective reception of external stimuli during wheelchair driving by analyses of vibration signals obtained from measurements. The identification concerns the impact of vibrations generated during crossing various types of pavements on the discomfort feelings of the selected human body parts (mainly the spine). The identification used the measurements of the whole body vibrations received by the user of the wheelchair. The research focuses mainly on the analysis in the frequency ranges corresponding to the vibration resonance of the spine. It is because respondents of the conducted surveys selected the spine as one of the most sensitive parts of the body.
CAM: Double Standard Security Mechanism In The Management Cloud Based Mobile Application

In a remote mHealth observing framework a customer could mastermind compact sensors in remote body sensor systems to gather different physiological information, for example, circulatory strain (BP), breathing rate (BR), Electrocardiogram (ECG/EKG), fringe oxygen immersion (SpO2) and blood glucose. Such physiological information could then be sent to a focal server which could then run a mixture of web therapeutic applications on these information to return opportune guidance to the customer. Cloud-helped mHealth observing could offer an incredible prospect to liven up the nature of social insurance administrations and conceivably decrease human services costs there is a reluctant square in making this innovation a reality. It is taking into account another option of key private intermediary re-encryption plan in which the organization just needs to accomplish encryption once at the setup stage while moving the rest computational errands to the cloud without bargaining security in addition diminishing the computational and correspondence load on customers and the cloud.
Application of Real Option Method in Enterprise Value Evaluation

Objective: Valuation of the underlying business is an important part of the Mergers and acquisitions (M&A) pricing process. In recent years, the domestic market has been highly volatile, leading to more pronounced uncertainty in the development of most businesses. The limitations of using traditional valuation methods to accurately value a business have gradually come to the fore. How to accurately assess the value of the subject business has become an important issue at present. Methodology: Real options approach can compensate for the inability of traditional methods to value from a dynamic perspective, among other things. This study analyses the application of the real options approach and the traditional income approach to specific M&A cases, and further compares the results of the two approaches in valuing 12 companies. Result: The conclusion that there is no significant difference between the appraised value of the Real Options Method and the market value of the subject enterprise, while there is a significant difference between the appraised value of the Income Method. On balance, the Real Option Method was found to be more suitable for the valuation of the subject enterprise.
How Does the Industrial Agglomeration Effect Affect Technological Innovation? A Moderated Mediation Model

Taking industrial agglomeration as the breakthrough point, this paper explores whether entrepreneurship can play a mediating role between agglomeration effect and enterprise technological innovation, puts forward a mediating model with moderating variables, and tests the moderating role of institutional environment in the whole process. Using survey data of 513 companies from Yangtze River delta, it demonstrates that industrial agglomeration effect is positively related to technological innovation and entrepreneurship plays a partial mediating role in this relationship. It also finds institutional environment plays a significant positive moderate role in the positive relationship of industrial agglomeration effect and entrepreneurship, entrepreneurship and technological innovation, and in the mediating effort of entrepreneurship.
Hsp47 Inhibitor Col003 Attenuates Collagen-Induced Platelet Activation and Cerebral Ischemic–Reperfusion Injury in Rats

Ischemic stroke is a major type of stroke worldwide currently without effective treatment, although antiplatelet therapy is an existing option for it. In previous studies, heat shock protein 47 (Hsp47) was found to be expressed on the surface of human and mice platelets and to strengthen the interaction between platelets and collagen. In recent years, Col003 was discovered to inhibit the interaction of Hsp47 with collagen. We evaluated whether the Hsp47 inhibitor Col003 is a promising therapeutic agent for ischemic stroke. Here, we first verified that Hsp47 is also expressed on the surface of rat platelets, and its inhibitor Col003 significantly inhibited thrombus formation in the FeCl3-induced rat carotid arterial thrombus model. Both Col003 and clopidogrel did not alter the bleeding time or coagulation parameters, while aspirin increased the tail-bleeding time (p < 0.05). The low cytotoxicity level of Col003 to rat platelets and human liver cells was similar to those of aspirin and clopidogrel. Col003 inhibited collagen-induced platelet aggregation, adhesion, [Ca2+]i mobilization, P-selectin expression, reactive oxygen species production and the downstream signal pathway of collagen receptors. The results of the middle cerebral artery occlusion model indicated that Col003 has a protective effect against cerebral ischemic–reperfusion injury in rats. The Hsp47 inhibitor Col003 exerted antiplatelet effect and protective effect against brain damage induced by ischemic stroke through the inhibition of glycoprotein VI (GPVI)and mitogen-activated protein kinase (MAPK) signaling events, which might yield a new antiplatelet agent and strategy to treat ischemic stroke.