Miracle-dz/UniFAR-FTUs huggingface dataset:
M3T: Multi-Modal Continuous Valence-Arousal Estimation in the Wild

This report describes a multi-modal multi-task ($M^3$T) approach underlying our submission to the valence-arousal estimation track of the Affective Behavior Analysis in-the-wild (ABAW) Challenge, held in conjunction with the IEEE International Conference on Automatic Face and Gesture Recognition (FG) 2020. In the proposed $M^3$T framework, we fuse both visual features from videos and acoustic features from the audio tracks to estimate the valence and arousal. The spatio-temporal visual features are extracted with a 3D convolutional network and a bidirectional recurrent neural network. Considering the correlations between valence / arousal, emotions, and facial actions, we also explores mechanisms to benefit from other tasks. We evaluated the $M^3$T framework on the validation set provided by ABAW and it significantly outperforms the baseline method.
Increased Nucleus Accumbens Volume in College Binge Drinkers - Preliminary Evidence From Manually Segmented MRI Analysis

Introduction Binge drinking (BD) is characterized by high alcohol intake in a short time followed by periods of withdrawal. This pattern is very common during adolescence and early adulthood, a developmental stage marked by the maturation of the fronto-striatal networks. The basal ganglia, specifically the nucleus accumbens (NAcc) and the caudate nucleus (CN), are part of the fronto-striatal limbic circuit involved in reward processes underlying addictive behaviors. Abnormal NAcc and CN morphometry has been noted in alcoholics and other drug abusers, however the effects of BD on these subcortical regions have been poorly explored. Accordingly, the main goal of the present study was to address potential morphological alterations in the NAcc and CN in a sample of college binge drinkers (BDs). Method Manual segmentation of the NAcc and the CN was performed in Magnetic Resonance Imaging (MRI) of 20 college BDs and 16 age-matched alcohol abstainers (18–23 years-old). Results A two-way mixed ANOVA revealed no group differences in the volumetry of the CN, whereas increased NAcc volume was observed in the BD group when compared to their abstinent control peers. Discussion These findings are in line with previous automatically segmented MRI reports highlighting abnormalities in a key region involved in drug rewarding processes in BDs.
The MADS-Box Gene CsSHP Participates in Fruit Maturation and Floral Organ Development in Cucumber

Cucumber is an important vegetable crop bearing fleshy pepo fruit harvested immature. Fruits left unpicked in time during summer production, as well as unfavorable environmental conditions during post-harvest shelf, will cause cucumber fruits to turn yellow and ripen, and thus impair the market value. Identification of maturity-related genes is of great agricultural and economic importance for cucumber production. Here, we isolated and characterized a MADS-box gene, Cucumis sativus SHATTERPROOF (CsSHP) in cucumber. Expression analysis indicated that CsSHP was specifically enriched in reproductive organs including stamens and carpels. Ectopic expression of CsSHP was unable to rescue the indehiscence silique phenotype of shp1 shp2 mutant plant in Arabidopsis. Instead, overexpression of CsSHP resulted in early flowering, precocious phenotypes, and capelloid organs in wild-type Arabidopsis. Biochemical analysis indicated that CsSHP directly interacted with cucumber SEPALLATA (SEP) proteins. CsSHP expression increased significantly during the yellowing stage of cucumber ripening, and was induced by exogenous application of abscisic acid (ABA). Therefore, CsSHP may participate in fruit maturation through the ABA pathway and floral organ specification via interaction with CsSEPs to form protein complex in cucumber.
Clemastine Fumarate Protects Against Myocardial Ischemia Reperfusion Injury by Activating the TLR4/PI3K/Akt Signaling Pathway

Our pilot studies have shown that clemastine fumarate (CLE) can protect against myocardial ischemia-reperfusion injury (MIRI) through regulation of toll like receptor 4 (TLR4). However, the protective mechanism of CLE and related signaling pathways for MIRI remains unclear. The objective of this study is to determine the mechanism by which CLE relieves MIRI in cardiomyocytes and its relationship with the TLR4/PI3K/Akt signaling pathway. CCK8 analysis was used to test the optimal concentration of TLR4 inhibitor CLI-095 and TLR4 agonist lipopolysaccharide (LPS) on MIRI. The expression of inflammatory factors, oxidative stress response, cell damage, and intracellular calcium redistribution of cardiomyocytes were examined using the ELISA kits, Total Superoxide Dismutase Assay Kit with WST-8 and Lipid Peroxidation MDA Assay Kit, LDH Cytotoxicity Assay Kit, and laser scanning confocal microscope. The expression of TLR4/PI3K/Akt and cleaved caspase-3 were determined by Western blotting and immunofluorescent staining. Our results showed that MIRI aggravated the inflammatory response, oxidative stress, cellular damage of cardiomyocytes, and caused redistribution of intracellular calcium, upregulated the expression of TLR4 protein, cleaved caspase-3 protein, and down-regulated the expression of PI3K/Akt protein. After treatment with CLE, the inflammatory response, oxidative stress, and cellular damage of cardiomyocytes were alleviated, and intracellular calcium ion accumulation decreased. The expression of TLR4 protein, cleaved caspase-3 protein declined, but PI3K/Akt protein expression increased in cardiomyocytes treated with CLE. In addition, after treatment with the TLR4 inhibitor CLI-095, the results were similar to those of CLE treatment. The TLR4 agonist LPS aggravated the reactions caused by MIRI. The role of LPS was reversed after CLE treatment. These results suggested that CLE can attenuate MIRI by activating the TLR4/PI3K/Akt signaling pathway.
The Effect of Forest Management Options on Forest Resilience to Pathogens

Invasive pathogens threaten the ability of forests globally to produce a range of valuable ecosystem services over time. However, the ability to detect such pathogen invasions—and thus to produce appropriate and timely management responses—is relatively low. We argue that a promising approach is to plan and manage forests in a way that increases their resilience to invasive pathogens not yet present or ubiquitous in the forest. This paper is based on a systematic search and critical review of empirical evidence of the effect of a wide range of forest management options on the primary and secondary infection rates of forest pathogens, and on subsequent forest recovery. Our goals are to inform forest management decision making to increase forest resilience, and to identify the most important evidence gaps for future research. The management options for which there is the strongest evidence that they increase forest resilience to pathogens are: reduced forest connectivity, removal or treatment of inoculum sources such as cut stumps, reduced tree density, removal of diseased trees and increased tree species diversity. In all cases the effect of these options on infection dynamics differs greatly amongst tree and pathogen species and between forest environments. However, the lack of consistent effects of silvicultural systems or of thinning, pruning or coppicing treatments is notable. There is also a lack of evidence of how the effects of treatments are influenced by the scale at which they are applied, e.g., the mixture of tree species. An overall conclusion is that forest managers often need to trade-off increased resilience to tree pathogens against other benefits obtained from forests.
Need Satisfaction as a Mediator of Associations between Interparental Relationship Dimensions and Autonomy Supportive Parenting: A Weekly Diary Study.

Guided by the self-determination theory, this weekly diary study tested a process model in which week-to-week mother-reported interparental conflict and perceived partner responsiveness were associated with maternal autonomy support by means of maternal psychological need satisfaction. During six consecutive weeks, 258 mothers (Mage = 41.71 years) and their 157 adolescents (51.4% females, Mage = 14.92 years) from Turkey provided weekly reports of the study variables via an online survey. Multilevel analyses showed that maternal need satisfaction was predicted by lower levels of interparental conflict and greater levels of perceived partner responsiveness. Maternal need satisfaction, in turn, was positively associated with maternal and adolescent reports of maternal autonomy support. Further, these week-to-week associations were partly moderated by maternal perfectionism. The results underscore the dynamic nature of the intra-family relationships, the important role of particular conditions in which mothers may become more autonomy supportive, and the necessity to consider mother's personal characteristics while examining these dynamics.
Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A Systematic Scoping Review.

BACKGROUND Application of machine learning for classifying human behavior is increasingly common as access to raw accelerometer data improves. The aims of this scoping review are (1) to examine if machine-learning techniques can accurately identify human activity behaviors from raw accelerometer data and (2) to summarize the practical implications of these machine-learning techniques for future work. METHODS Keyword searches were performed in Scopus, Web of Science, and EBSCO databases in 2018. Studies that applied supervised machine-learning techniques to raw accelerometer data and estimated components of physical activity were included. Information on study characteristics, machine-learning techniques, and key study findings were extracted from included studies. RESULTS Of the 53 studies included in the review, 75% were published in the last 5 years. Most studies predicted postures and activity type, rather than intensity, and were conducted in controlled environments using 1 or 2 devices. The most common models were support vector machine, random forest, and artificial neural network. Overall, classification accuracy ranged from 62% to 99.8%, although nearly 80% of studies achieved an overall accuracy above 85%. CONCLUSIONS Machine-learning algorithms demonstrate good accuracy when predicting physical activity components; however, their application to free-living settings is currently uncertain.
Atorvastatin reduces cerebral vasospasm and infarction after aneurysmal subarachnoid hemorrhage in elderly Chinese adults

We explored whether acute atorvastatin treatment would improve clinical outcomes and reduce the incidence of cerebral vasospasm after aneurysmal subarachnoid hemorrhage in elderly Chinese adults. Patients (60 to 90 years old) were admitted to intensive care units after surgery to clip or embolize their aneurysms. We assessed 592 patients and assigned 159 to receive atorvastatin (20 mg/day) and 158 to receive placebo once daily for up to 14 days. The primary outcome was the Glasgow outcome scale at 6 months, and secondary outcomes were cerebral vasospasm, 30-days all-cause mortality, cerebral infarction, and delayed ischemic neurological deficit. The incidence of postoperative cerebral vasospasm (39.3% vs 56%, P =0.004) and cerebral infarction (18.7% vs 27.3%, P=0.027) were significantly lower in the atorvastatin group. The study did not detect benefits in the use of atorvastatin for 6 months clinical outcome or 30-day all-cause mortality, but it suggests that atorvastatin together with nimodipine can reduce cerebral vasospasm and cerebral infarction after subarachnoid hemorrhage.
Potential Molecular Mechanism of the NPPB Gene in Postischemic Heart Failure with and without T2DM

Background This study is aimed at investigating natriuretic peptide B (NPPB) coexpression genes and their pathways involved in heart failure (HF) among patients both with and without type 2 diabetes mellitus (T2DM). Methods The microarray dataset GSE26887, containing 19 postischemic HF patients' peripheral blood samples (7 with T2DM and 12 without T2DM), was examined to detect the genes coexpressed with NPPB using the corr.test function in the R packet. Furthermore, using online analytical tools, we determined the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, Gene Ontology (GO) annotation, and protein-protein interaction (PPI) network of the coexpression genes. The modules and hub genes of the PPI network were then identified using the Cytoscape software. Results In patients with T2DM, a total of 41 biological processes (BP), 20 cellular components (CC), 13 molecular functions (MF), and 41 pathways were identified. Furthermore, a total of 61 BPs, 16 CCs, 13 MFs, and 22 pathways in patients without T2DM were identified. In both groups of patients, 17 BPs, 10 CCs, 6 MFs, and 13 pathways were enriched. We also identified 173 intersectional coexpression genes (63 positively, 106 negatively, and 4 differently coexpressed in patients with and without T2DM, respectively) in both types of patients, which were enriched in 16 BPs, 8 CCs, 3 MFs, and 8 KEGG pathways. Moreover, the PPI network (containing 237 edges and 170 nodes) with the top module significantly enriched in 4 BPs (tricarboxylic acid metabolic process, citrate metabolic process, tricarboxylic acid cycle, and aerobic respiration) and 3 pathways (citrate cycle, malaria parasite metabolic pathway, and AGE-RAGE signaling pathway in diabetic complications) was constructed. DECR1, BGN, TIMP1, VCAN, and CTCF are the top hub genes. Conclusions Our findings may elucidate the functions and roles of the NPPB gene in patients with postischemic HF and facilitate HF management.
Embedding Cultivated Diversity in Society for Agro-Ecological Transition

Agroecology calls for a global approach, integrating scientific, practical, and advocacy dimensions, to redesign agricultural systems based on ecological and socio-cultural processes and emphasizing biodiversity. This review is grounded on the results of DIVERSIFOOD, a European H2020 multi-actor research project, and explores the concept of cultivated diversity using various dimensions relevant to foster sustainable organic food systems and agro-ecological transition. From the evaluation of underutilized genetic resources and forgotten crops, DIVERSIFOOD has proposed plant breeding strategies, on-farm experimentation, and statistical tools to create new populations, landraces, and organic cultivars with intra-varietal diversity. The added value of Community Seed Banks and forms of collective seed management in Europe have been described in terms of goals and activities, and their value for improving seed regulations, treaties, and genetic resources management is discussed. In the context of the current agro-food system characterized by standardization, DIVERSIFOOD raised awareness of qualities of ‘biodiverse food systems’ in which all actors have a role to play. It highlighted the critical capacity to preserve a diversity of cultural values embodied in ‘biodiverse products’, thereby involving consumers in collective strategies for reviving diversity, and empowering all actors of organic food systems to really and efficiently implement research within their farms and networks.
The Remnant of Neutron Star-White Dwarf Merger and the Repeating Fast Radio Bursts

Fast radio bursts (FRBs) at cosmological distances still hold concealed physical origins. Previously Liu (2018) proposes a scenario that the collision between a neutron star (NS) and a white dwarf (WD) can be one of the progenitors of non-repeating FRBs and notices that the repeating FRBs can also be explained if a magnetar formed after such NS-WD merger. In this paper, we investigate this channel of magnetar formation in more detail. We propose that the NS-WD post-merger, after cooling and angular momentum redistribution, may collapse to either a black hole or a new NS or even remains as a hybrid WDNS, depending on the total mass of the NS and WD. In particular, the newly formed NS can be a magnetar if the core of the WD collapsed into the NS while large quantities of degenerate electrons of the WD compressed to the outer layers of the new NS. A strong magnetic field can be formed by the electrons and positive charges with different angular velocities induced by the differential rotation of the newborn magnetar. Such a magnetar can power the repeating FRBs by the magnetic reconnections due to the crustal movements or starquakes.
Transfinite Milnor invariants for 3-manifolds

In his 1957 paper, John Milnor introduced link invariants which measure the homotopy class of the longitudes of a link relative to the lower central series of the link group. Consequently, these invariants determine the lower central series quotients of the link group. This work has driven decades of research with profound influence. One of Milnor's original problems remained unsolved: to extract similar invariants from the transfinite lower central series of the link group. We reformulate and extend Milnor's invariants in the broader setting of 3-manifolds, with his original invariants as special cases. We present a solution to Milnor's problem for general 3-manifold groups, developing a theory of transfinite invariants and realizing nontrivial values.
Cosmological models, observational data and tension in Hubble constant

We analyze how predictions of cosmological models depend on a choice of described observational data, restrictions on flatness, and how this choice can alleviate the $H_0$ tension. These effects are demonstrated in the $w$CDM model in comparison with the standard $\Lambda$CDM model. We describe the Pantheon sample observations of Type Ia supernovae, 31 Hubble parameter data points $H(z)$ from cosmic chronometers, the extended sample with 57 $H(z)$ data points and observational manifestations of cosmic microwave background radiation (CMB). For the $w$CDM and $\Lambda$CDM models in the flat case and with spatial curvature we calculate $\chi^2$ functions for all observed data in different combinations, estimate optimal values of model parameters and their expected intervals. For both considered models the results essentially depend on a choice of data sets. In particular, for the $w$CDM model with $H(z)$ data, supernovae and CMB the $1\sigma$ estimations may vary from $H_0=67.52^{+0.96}_{-0.95}$ km\,/(s$\cdot$Mpc) (for all $N_H=57$ Hubble parameter data points) up to $H_0=70.87^{+1.63}_{-1.62}$ km\,/(s$\cdot$Mpc) for the flat case ($k=0$) and $N_H=31$. These results might be a hint how to alleviate the problem of $H_0$ tension: different estimates of the Hubble constant may be connected with filters and a choice of observational data.
Non-linear interacting cosmological models after Planck 2018 legacy release and the H0 tension

Interacting dark energy models are widely renowned for giving an explanation to the cosmic coincidence problem as well as several observational issues. According to the recent observational data, and so far we are concerned with the literature, the choice of the interaction function between dark matter and dark energy is always questionable since there is no such underlying theory that could derive it. Thus, in this work we have raised this issue by proposing two new non-linear interaction functions and constrain them using cosmic microwave background (CMB) from Planck 2018, baryon acoustic oscillations (BAOs), dark energy survey and a measurement of the Hubble constant H0 from Hubble Space Telescope (HST) 2019. The dark energy equation of state is considered to be constant throughout the work and the geometry of the universe is assumed to be homogeneous and isotropic with zero spatial curvature. Our analyses report that a non-zero interaction is always allowed by the observational data and the dark energy equation of state is bent towards the phantom regime. In particular, when H0 from HST is added to Planck 2018+BAO, we find an evidence for a non-zero coupling at more than 2σ confidence level. Our analyses also report that for both the models, H0 is close to its local measurements and thus alleviating the H0 tension. In particular, one of the interacting models perfectly solves the H0 tension.
An initial investigation on optimizing tandem speaker verification and countermeasure systems using reinforcement learning

The spoofing countermeasure (CM) systems in automatic speaker verification (ASV) are not typically used in isolation of each other. These systems can be combined, for example, into a cascaded system where CM produces first a decision whether the input is synthetic or bona fide speech. In case the CM decides it is a bona fide sample, then the ASV system will consider it for speaker verification. End users of the system are not interested in the performance of the individual sub-modules, but instead are interested in the performance of the combined system. Such combination can be evaluated with tandem detection cost function (t-DCF) measure, yet the individual components are trained separately from each other using their own performance metrics. In this work we study training the ASV and CM components together for a better t-DCF measure by using reinforcement learning. We demonstrate that such training procedure indeed is able to improve the performance of the combined system, and does so with more reliable results than with the standard supervised learning techniques we compare against.
Understanding and Improving Knowledge Distillation

Knowledge distillation is a model-agnostic technique to improve model quality while having a fixed capacity budget. It is a commonly used technique for model compression, where a higher capacity teacher model with better quality is used to train a more compact student model with better inference efficiency. Through distillation, one hopes to benefit from student's compactness, without sacrificing too much on model quality. Despite the large success of knowledge distillation, better understanding of how it benefits student model's training dynamics remains under-explored. In this paper, we dissect the effects of knowledge distillation into three main factors: (1) benefits inherited from label smoothing, (2) example re-weighting based on teacher's confidence on ground-truth, and (3) prior knowledge of optimal output (logit) layer geometry. Using extensive systematic analyses and empirical studies on synthetic and real-world datasets, we confirm that the aforementioned three factors play a major role in knowledge distillation. Furthermore, based on our findings, we propose a simple, yet effective technique to improve knowledge distillation empirically.
Deriving Emotions and Sentiments from Visual Content: A Disaster Analysis Use Case

Sentiment analysis aims to extract and express a person's perception, opinions and emotions towards an entity, object, product and a service, enabling businesses to obtain feedback from the consumers. The increasing popularity of the social networks and users' tendency towards sharing their feelings, expressions and opinions in text, visual and audio content has opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis of images and videos is relatively new. This article introduces visual sentiment analysis and contrasts it with textual sentiment analysis with emphasis on the opportunities and challenges in this nascent research area. We also propose a deep visual sentiment analyzer for disaster-related images as a use-case, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation and evaluations. We believe such rigorous analysis will provide a baseline for future research in the domain.