The role of disorder in the dynamics of critical fluctuations of mean field models

The purpose of this paper is to analyze how the disorder affects the dynamics of critical fluctuations for two different types of interacting particle system: the Curie-Weiss and Kuramoto model. The models under consideration are a collection of spins and rotators respectively. They both are subject to a mean field interaction and embedded in a site-dependent, i.i.d. random environment. As the number of particles goes to infinity their limiting dynamics become deterministic and exhibit phase transition. The main result concern the fluctuations around this deterministic limit at the critical point in the thermodynamic limit. From a qualitative point of view, it indicates that when disorder is added spin and rotator systems belong to two different classes of universality, which is not the case for the homogeneous models (i.e., without disorder).Comment: 41 page

Collective periodicity in mean-field models of cooperative behavior

We propose a way to break symmetry in stochastic dynamics by introducing a dissipation term. We show in a specific mean-field model, that if the reversible model undergoes a phase transition of ferromagnetic type, then its dissipative counterpart exhibits periodic orbits in the thermodynamic limit.Comment: 19 pages, 3 figure

Efficacy of a standardized training on horse welfare indicators: a preliminary study

Harmonized data collection is essential to obtain a reliable picture of equine welfare conditions. Effective education on how to assess and score welfare indicators plays a critical role in terms of inter-observer reliability. The Horse Grimace Scale (HGS), a facial-expression-based pain coding system, is able to identify a range of acute pain conditions in horses. This study aimed at evaluating the efficacy of a standardized training on HGS inter-observer reliability.Students in Veterinary Medicine from the University of Milan (N=46) and the University of Teramo (N=31) were recruited. Prior to any training, students were asked to score 10 pictures of horse faces using the six Facial Action Units (FAUs) of the HGS: Stiffly backwards ears, Orbital tightening, Tension above the eye area, Prominent strained chewing muscles, Mouth strained, Strained nostrils. Then, a 30-min training session was provided, including detailed descriptions and example pictures of each FAU, as well as a discussion of five pictures previously scored by an experienced assessor. After training, students scored other 10 pictures. To determine the inter-observer reliability pre and post-training, Intra-class Correlation Coefficient (ICC) was used.Students’ reliability was good even before training (ICC=0,986 for the overall HGS score), with Tension above the eye area, and Strained nostrils appearing more challenging to be scored reliably. Reliability improved after the 30 min training for the overall HGS score (ICC=0,992) and for each FAU (see table 1). According to Cicchetti (1994), an ICC score between 0.75 and 1.00 can be considered excellent.Our results suggest that the HGS scoring system is easy to apply even without any training; however, the training method applied proved useful to improve the reliability of HGS scores

Equine Transport-Related Problem Behaviors and Injuries: A Survey of Italian Horse Industry Members

An online survey was conducted to determine associations between equine transport management and transport-related injuries and problem behaviors in Italy. The survey was composed of four sections: respondents\u2019 demographic information and background, transport management practices, journey details and vehicle design, and transport injuries experienced by the horse in the previous two-year period. Univariable and multivariable logistic regression with a binary outcome variable was performed to explore associations between variables (respondents\u2019 and journeys\u2019 details and transport practices) and equine transport-related problem behaviors (TRPBs) and injuries. TRPBs were also considered an explanatory variable for injuries. The survey generated 201 responses; only 148 were complete and analyzed. TRPBs were reported by 14.45% of the respondents and the odds of TRPBs was linked to the respondent gender (p = 0.034), the use of tranquilizers prior to transport (p = 0.002), the use of a whip for loading (p = 0.049), the lack of protection equipment (p = 0.050), and shavings (p = 0.025) on the vehicle floor. Horse injuries (11.49%) were reported by more respondents who did not check the brakes of their transport vehicle before traveling (p = 0.043), had vehicles with padding on the chest bar (p = 0.038), and for horses reported to display TRPBs (p = 0.001). Finally, 10 respondents reported they were injured during horse transport (10/140; 7.14%), 50% simultaneously with their horses. The study findings should be interpreted with caution due to small sample size bias and participants\u2019 recall bias. Nevertheless, the results are in concordance with the literature, confirming that horse transport is a risk for the horse\u2019s and handler\u2019s health and well-being. Further studies are needed to identify best management practices to educate equine industry members on how to minimize transport-related problems

Self-Attention Temporal Convolutional Network for Long-Term Daily Living Activity Detection

International audienceIn this paper, we address the detection of daily living activities in long-term untrimmed videos. The detection of daily living activities is challenging due to their long temporal components, low inter-class variation and high intra-class variation. To tackle these challenges, recent approaches based on Temporal Convolutional Networks (TCNs) have been proposed. Such methods can capture long-term temporal patterns using a hierarchy of temporal convolutional filters, pooling and up sampling steps. However, as one of the important features of con-volutional networks, TCNs process a local neighborhood across time which leads to inefficiency in modeling the long-range dependencies between these temporal patterns of the video. In this paper, we propose Self-Attention-Temporal Convolutional Network (SA-TCN), which is able to capture both complex activity patterns and their dependencies within long-term untrimmed videos. We evaluate our proposed model on DAily Home LIfe Activity Dataset (DAHLIA) and Breakfast datasets. Our proposed method achieves state-of-the-art performance on both DAHLIA and Breakfast dataset

A simple mean field model for social interactions: dynamics, fluctuations, criticality

We study the dynamics of a spin-flip model with a mean field interaction. The system is non reversible, spacially inhomogeneous, and it is designed to model social interactions. We obtain the limiting behavior of the empirical averages in the limit of infinitely many interacting individuals, and show that phase transition occurs. Then, after having obtained the dynamics of normal fluctuations around this limit, we analize long time fluctuations for critical values of the parameters. We show that random inhomogeneities produce critical fluctuations at a shorter time scale compared to the homogeneous system.Comment: 37 pages, 2 figure

Selective Spatio-Temporal Aggregation Based Pose Refinement System: Towards Understanding Human Activities in Real-World Videos

Taking advantage of human pose data for understanding human activities has attracted much attention these days. However, state-of-the-art pose estimators struggle in obtaining high-quality 2D or 3D pose data due to occlusion, truncation and low-resolution in real-world un-annotated videos. Hence, in this work, we propose 1) a Selective Spatio-Temporal Aggregation mechanism, named SST-A, that refines and smooths the keypoint locations extracted by multiple expert pose estimators, 2) an effective weakly-supervised self-training framework which leverages the aggregated poses as pseudo ground-truth instead of handcrafted annotations for real-world pose estimation. Extensive experiments are conducted for evaluating not only the upstream pose refinement but also the downstream action recognition performance on four datasets, Toyota Smarthome, NTU-RGB+D, Charades, and Kinetics-50. We demonstrate that the skeleton data refined by our Pose-Refinement system (SSTA-PRS) is effective at boosting various existing action recognition models, which achieves competitive or state-of-the-art performance.Comment: WACV202

Use of Qualitative Behaviour Assessment as an Indicator of Welfare in Donkeys

One of the objectives of the Animal Welfare Indicators project was to develop animal-based indicators to assess donkey welfare, including their emotional state. This study aimed to develop a fixed rating scale of Qualitative Behaviour Assessment (QBA) for donkeys, to evaluate the inter-observer reliability when applied on-farm, and to assess whether the QBA outcomes correlate to other welfare measures. A fixed list of 16 descriptors was designed on the basis of a consultation in a focus group. The fixed list was then used by four trained observers to score nine 2 min videos of groups of donkeys owned by six farms and on-farm to score 11 donkey facilities representative of the most common type of donkey facilities in Western Europe. On each farm one experienced assessor collected different welfare measures on all the adult donkeys. The QBA scores and welfare measures were analysed using Principal Component Analysis (PCA, correlation matrix, no rotation). Kendall’s W and ANOVA were used to assess inter-observer reliability. PCA revealed three main components explaining 79% of total variation between them. PC1ranged from at ease/relaxed to aggressive/uncomfortable, suggesting that this Component is important in the description of the valence of donkeys’ affective states. PC2 was more related to the level of arousal of donkeys, ranging from apathetic to distressed/responsive. The four assessors showed a good level of agreement on the first two dimensions of the PCA (Kendall’s W varying from 0.61 to 0.90), and there was no significant effect of observer on donkey QBA scores (ANOVA p \u3e 0.05), both for the videos and on-farm. PCA of all measures together showed positive QBA descriptors on PC1 (relaxed, at ease, happy, friendly) to be associated with positive human–donkey interaction indicators (absence of tail tuck, no avoidance, and positive reaction to an assessor walking down the side of the donkey). Our findings suggest that QBA is a suitable tool to identify the emotional state of donkeys on-farm. A fixed list of descriptors can be used consistently by different trained assessors as a valid addition to a number of animal welfare assessment indicators

O desenvolvimento do protocolo de avaliação de bem-estar AWIN (Animal Welfare Indicators) para jumentos

The donkey population has increased in the last 10 years, with an estimated 50 million donkeys currently worldwide. Donkey welfare, meanwhile, is an increasing global concern that receives close public scrutiny. However, multiple challenges are surrounding how donkey welfare is assessed and recorded. The Animal Welfare Indicators (AWIN) project is the first project, funded by the European Commission, intended to improve donkey welfare by developing a scientifically sound and practical on-farm welfare assessment protocol. The present study describes the procedure for the development of the AWIN welfare assessment protocol for donkeys: 1) selection of promising welfare indicators; 2) research to cover gaps in knowledge; 3) stakeholder consultation; 4) testing the prototype protocol on-farm. The proposed two-level strategy improved on-farm feasibility, while the AWIN donkey app enables the standardized collection of data with prompt results. Although limitations are linked with a relatively small reference population, the AWIN welfare assessment protocol represents the first scientific and standardized approach to evaluate donkey welfare on-farm.Na última década, a população de jumentos vem aumentando; estima-se que existam aproximadamente 50 milhões de em todo o mundo. O bem-estar dos jumentos é uma preocupação global crescente, que recebe um escrutínio público próximo. No entanto, existem vários desafios em torno de como o bem-estar do jumento é avaliado e registrado. O projeto Indicadores de Bem-Estar Animal (AWIN) foi o primeiro projeto, financiado pela Comissão Europeia, destinado a melhorar o bem-estar dos jumentos, desenvolvendo um protocolo de avaliação do bem-estar cientificamente válido e prático na fazenda. O presente estudo descreve o procedimento para o desenvolvimento do protocolo de avaliação de bem-estar AWIN para jumentos: 1) seleção de indicadores promissores de bem-estar; 2) pesquisa para cobrir lacunas no conhecimento; 3) consulta às partes interessadas; 4) testando o protocolo do protótipo em fazendas. A estratégia proposta em dois níveis de avaliação melhorou a viabilidade na fazenda, além disso, o aplicativo AWIN donkey permite coletar dados de maneira padronizada e mostrar resultados rapidamente. Embora a limitação esteja ligada a uma população de referência relativamente pequena, o protocolo de avaliação de bem-estar do AWIN representa a primeira abordagem científica e padronizada para avaliar o bem-estar de jumentos em fazendas

Self-Attention Temporal Convolutional Network for Long-Term Daily Living Activity Detection

International audienceIn this paper, we address the detection of daily living activities in long-term untrimmed videos. The detection of daily living activities is challenging due to their long temporal components, low inter-class variation and high intra-class variation. To tackle these challenges, recent approaches based on Temporal Convolutional Networks (TCNs) have been proposed. Such methods can capture long-term temporal patterns using a hierarchy of temporal convolutional filters, pooling and up sampling steps. However, as one of the important features of con-volutional networks, TCNs process a local neighborhood across time which leads to inefficiency in modeling the long-range dependencies between these temporal patterns of the video. In this paper, we propose Self-Attention-Temporal Convolutional Network (SA-TCN), which is able to capture both complex activity patterns and their dependencies within long-term untrimmed videos. We evaluate our proposed model on DAily Home LIfe Activity Dataset (DAHLIA) and Breakfast datasets. Our proposed method achieves state-of-the-art performance on both DAHLIA and Breakfast dataset