Miracle-dz/UniFAR-FTUs huggingface dataset:
Individual Differences in Conditioned Fear and Extinction in Female Rats

The inability to extinguish a traumatic memory is a key aspect of post-traumatic stress disorder (PTSD). While PTSD affects 10–20% of individuals who experience a trauma, women are particularly susceptible to developing the disorder. Despite this notable female vulnerability, few studies have investigated this particular resistance to fear extinction observed in females. Similar to humans, rodent models of Pavlovian fear learning and extinction show a wide range of individual differences in fear learning and extinction, although female rodents are considerably understudied. Therefore, the present study examined individual differences in fear responses, including freezing behavior and ultrasonic vocalizations (USVs), of female Long–Evans rats during acquisition of fear conditioning and cued fear extinction. Similar to prior studies in males, female rats displayed individual variation in freezing during cued fear extinction and were divided into extinction competent (EC) and extinction resistant (ER) phenotypes. Differences in freezing between ER and EC females were accompanied by shifts in rearing during extinction, but no darting was seen in any trial. Freezing behavior during fear learning did not differ between the EC and ER females. Vocalizations emitted in the 22 and 50 kHz ranges during fear learning and extinction were also examined. Unlike vocalizations seen in previous studies in males, very few 22 kHz distress vocalizations were emitted by female rats during fear acquisition and extinction, with no difference between ER and EC groups. Interestingly, all female rats produced significant levels of 50 kHz USVs, and EC females emitted significantly more 50 kHz USVs than ER rats. This difference in 50 kHz USVs was most apparent during initial exposure to the testing environment. These results suggest that like males, female rodents show individual differences in both freezing and USVs during fear extinction, although females appear to vocalize more in the 50 kHz range, especially during initial periods of exposure to the testing environment, and emit very few of the 22 kHz distress calls that are typically observed in males during fear learning or extinction paradigms. Overall, these findings show that female rodents display fear behavior repertoires divergent from males.
The Therapeutic Potential of Mesenchymal Stromal Cells for Regenerative Medicine: Current Knowledge and Future Understandings

In recent decades, research on the therapeutic potential of progenitor cells has advanced considerably. Among progenitor cells, mesenchymal stromal cells (MSCs) have attracted significant interest and have proven to be a promising tool for regenerative medicine. MSCs are isolated from various anatomical sites, including bone marrow, adipose tissue, and umbilical cord. Advances in separation, culture, and expansion techniques for MSCs have enabled their large-scale therapeutic application. This progress accompanied by the rapid improvement of transplantation practices has enhanced the utilization of MSCs in regenerative medicine. During tissue healing, MSCs may exhibit several therapeutic functions to support the repair and regeneration of injured tissue. The process underlying these effects likely involves the migration and homing of MSCs, as well as their immunotropic functions. The direct differentiation of MSCs as a cell replacement therapeutic mechanism is discussed. The fate and behavior of MSCs are further regulated by their microenvironment, which may consequently influence their repair potential. A paracrine pathway based on the release of different messengers, including regulatory factors, chemokines, cytokines, growth factors, and nucleic acids that can be secreted or packaged into extracellular vesicles, is also implicated in the therapeutic properties of MSCs. In this review, we will discuss relevant outcomes regarding the properties and roles of MSCs during tissue repair and regeneration. We will critically examine the influence of the local microenvironment, especially immunological and inflammatory signals, as well as the mechanisms underlying these therapeutic effects. Importantly, we will describe the interactions of local progenitor and immune cells with MSCs and their modulation during tissue injury. We will also highlight the crucial role of paracrine pathways, including the role of extracellular vesicles, in this healing process. Moreover, we will discuss the therapeutic potential of MSCs and MSC-derived extracellular vesicles in the treatment of COVID-19 (coronavirus disease 2019) patients. Overall, this review will provide a better understanding of MSC-based therapies as a novel immunoregenerative strategy. Graphical Abstract The road map of MSC review.
Efficient Bandit Convex Optimization: Beyond Linear Losses

We study the problem of online learning with bandit feedback, where a learner aims to minimize a sequence of adversarially generated loss functions, while only observing the value of each function at a single point. When the loss functions chosen by the adversary are convex and quadratic, we develop a new algorithm which achieves the optimal regret rate of Õ ( T 1/2 ) . Furthermore, our algorithm satisfies three important desiderata: (a) it is practical and can be efficiently implemented for high dimensional problems, (b) the regret bound holds with high probability even against adaptive adversaries whose decisions can depend on the learner’s previous actions, and (c) it is robust to model mis-specification; that is, the regret bound degrades gracefully when the loss functions deviate from convex quadratics. To the best of our knowledge, ours is the first algorithm for bandit convex optimization with quadratic losses which is efficiently implementable and achieves optimal regret guarantees. Existing algorithms for this problem either have sub-optimal regret guarantees (Flaxman et al., 2004; Saha and Tewari, 2011) or are computationally expensive and do not scale well to high-dimensional problems (Bubeck et al., 2017). Our algorithm is a bandit version of the classical regularized Newton’s method. It involves estimation of gradients and Hessians of the loss functions from single point feedback. A key caveat of working with Hessian estimates however is that they typically have large variance. In this work, we show that it is nonetheless possible to finesse this caveat by relying on the idea of “focus regions”, where we restrict the algorithm to choose actions from a subset of the action space in which the variance of our estimates can be controlled.
Open Problem: Tight Online Confidence Intervals for RKHS Elements

Confidence intervals are a crucial building block in the analysis of various online learning problems. The analysis of kernel based bandit and reinforcement learning problems utilize confidence intervals applicable to the elements of a reproducing kernel Hilbert space (RKHS). However, the existing confidence bounds do not appear to be tight, resulting in suboptimal regret bounds. In fact, the existing regret bounds for several kernelized bandit algorithms (e.g., GP-UCB, GP-TS, and their variants) may fail to even be sublinear. It is unclear whether the suboptimal regret bound is a fundamental shortcoming of these algorithms or an artifact of the proof, and the main challenge seems to stem from the online (sequential) nature of the observation points. We formalize the question of online confidence intervals in the RKHS setting and overview the existing results.
Parameter-Free Multi-Armed Bandit Algorithms with Hybrid Data-Dependent Regret Bounds

This paper presents multi-armed bandit (MAB) algorithms that work well in adversarial environments and that offer improved performance by exploiting inherent structures in such environments, as stochastic generative models, as well as small variations in loss vectors. The fundamental aim of this work is to overcome the limitation of worst-case analyses in MAB contexts. There can be found two basic approaches achieving this purpose: best-of-both-worlds algorithms that work well for both stochastic and adversarial settings, and data-dependent regret bounds that work well depending on certain difﬁculty indicators w.r. g. loss sequences. One remarkable study w.r. t. the best-of-both-worlds approach deals with the Tsallis-INF algorithm (Zimmert and Seldin, 2021), which achieves nearly optimal regret bounds up to small constants in both settings, though such bounds have remained unproven for a special case of a stochastic setting with multiple optimal arms. This paper offers two particular contributions: (i) We show that the Tsallis-INF algorithm enjoys a regret bound of a logarithmic order in the number of rounds for stochastic environments, even if the best arm is not unique. (ii) We provide a new algorithm with a new hybrid regret bound that implies logarithmic regret in the stochastic regime and multiple data-dependent regret bounds in the adversarial regime, including bounds dependent on cumulative loss, total variation, and loss-sequence path-length. Both our proposed algorithm and the Tsallis-INF algorithm are based on a follow-the-regularized-leader (FTRL) framework with a time-varying regularizer. The analyses in this paper rely on skewed Bregman divergence , which provides simple expressions of regret bounds for FTRL with a time-varying regularizer.
Learning Cloth Dynamics: 3D+Texture Garment Reconstruction Benchmark

Human avatars are important targets in many computer applications. Accurately tracking, capturing, reconstructing and animating the human body, face and garments in 3D are critical for human-computer interaction, gaming, special eﬀects and virtual reality. In the past, this has required extensive manual animation. Regardless of the advances in human body and face reconstruction, still modeling, learning and analyzing human dynamics need further attention. In this paper we plan to push the research in this direction, e.g. understanding human dynamics in 2D and 3D, with special attention to garments. We provide a large-scale dataset (more than 2M frames) of animated garments with variable topology and type, called CLOTH3D++ . The dataset contains RGBA video sequences paired with its corresponding 3D data. We pay special care to garment dynamics and realistic rendering of RGB data, including lighting, fabric type and texture. With this dataset, we hold a competition at NeurIPS2020. We design three tracks so participants can compete to develop the best method to perform 3D garment reconstruction in a sequence from (1) 3D-to-3D garments, (2) RGB-to-3D garments, and (3) RGB-to-3D garments plus texture. We also provide a baseline method, based on graph convolutional networks, for each track. Baseline results show that there is a lot of room for improvements. However, due to the challenging nature of the problem, no participant could outperform the baselines
Differentially Private Nonparametric Regression Under a Growth Condition

Given a real-valued hypothesis class $\mathcal{H}$, we investigate under what conditions there is a differentially private algorithm which learns an optimal hypothesis from $\mathcal{H}$ given i.i. d. data. Inspired by recent results for the related setting of binary classification (Alon et al., 2019; Bun et al., 2020), where it was shown that online learnability of a binary class is necessary and sufficient for its private learnability, Jung et al. (2020) showed that in the setting of regression, online learnability of $\mathcal{H}$ is necessary for private learnability. Here online learnability of $\mathcal{H}$ is characterized by the finiteness of its $\eta$-sequential fat shattering dimension, ${\rm sfat}_\eta(\mathcal{H})$, for all $\eta>0$. In terms of sufficient conditions for private learnability, Jung et al. (2020) showed that $\mathcal{H}$ is privately learnable if $\lim_{\eta \downarrow 0} {\rm sfat}_\eta(\mathcal{H})$ is finite, which is a fairly restrictive condition. We show that under the relaxed condition $\lim \inf_{\eta \downarrow 0} \eta \cdot {\rm sfat}_\eta(\mathcal{H}) = 0$, $\mathcal{H}$ is privately learnable, establishing the first nonparametric private learnability guarantee for classes $\mathcal{H}$ with ${\rm sfat}_\eta(\mathcal{H})$ diverging as $\eta \downarrow 0$. Our techniques involve a novel filtering procedure to output stable hypotheses for nonparametric function classes.
Near Optimal Distributed Learning of Halfspaces with Two Parties

Distributed learning protocols are designed to train on distributed data without gathering it all on a single centralized machine, thus contributing to the efﬁciency of the system and enhancing its privacy. We study a central problem in distributed learning, called distributed learning of halfspaces : let U ⊆ R d be a known domain of size n and let h : R d → R be an unknown target afﬁne function. 1 A set of examples { ( u, b ) } is distributed between several parties, where u ∈ U is a point and b = sign ( h ( u )) ∈ {± 1 } is its label. The parties’ goal is to agree on a classiﬁer f : U → {± 1 } such that f ( u ) = b for every input example ( u, b ) . We design a protocol for the distributed halfspace learning problem in the two-party setting, communicating only ˜ O ( d log n ) bits. To this end, we introduce a new tool called halfspace containers , that is closely related to bracketing numbers in statistics and to hyperplane cuttings in discrete geometry, and allows for a compressed approximate representation of every halfspace. We complement our upper bound result by an almost matching ˜Ω( d log n ) lower bound on the communication complexity of any such protocol. Since the distributed halfspace learning problem is closely related to the convex set disjointness problem in communication complexity and the problem of distributed linear programming in distributed optimization, we also derive upper and lower bounds of ˜ O ( d 2 log n ) and ˜Ω( d log n ) on the communication complexity of both of these basic problems.
The Hateful Memes Challenge: Competition Report

Machine learning and artiﬁcial intelligence play an ever more crucial role in mitigating important societal problems, such as the prevalence of hate speech. We describe the Hateful Memes Challenge competition, held at NeurIPS 2020, focusing on multimodal hate speech. The aim of the challenge is to facilitate further research into multimodal reasoning and understanding
Rhythm of the Night: Brain Activity and Performance on a Sustained Attention Task is Modulated by Circadian Typology and Time of Day

Rhythm of the Night: Brain Activity and Performance on a Sustained Attention Task is Modulated by Circadian Typology and Time of Day Carly Cooper Director: Dr. Lee Baugh, Ph.D. The human circadian system plays an important role in biological and psychological processes in both health and disease. Circadian typology refers to individual differences in circadian rhythm and is categorized into three general chronotypes: morning, evening, and neither. Research suggests that an individual’s diurnal preference may be associated with differences in cognitive abilities, personality traits, and incidence of psychiatric disorders. In the present study, we utilized a Sustained Attention to Response Task (SART) and an electroencephalogram (EEG) in a desynchrony protocol. Morning-type and evening-type participants completed a SART task on two separate occasions during which brain activity was recorded. This allowed us to examine the difference in the underlying neural networks corresponding to alerting and sustained arousal when participants are in-phase versus out-of-phase of their diurnal preference. When examining both reaction times and response accuracy, performance and EEG differences were observed between participants’ optimal testing time, where we found decreased performance in out-of-phase testing sessions. This suggests that differences in task performance may be instantiated through transient changes in brain network function. These preliminary results may offer further insight into how task performance can change throughout the day, and the neural networks associated with those performance changes.
Effects of Vitamin C and Dimethylsulfoxide (Dmso) on Neuro-Behaviour, Apoptosis and oxidative stress in ischaemic stroke induced rats

Background: Researches over the years have identified excitotoxicity, oxidative stress, inflammation and cell death as key contributors underlying progression of lesion in ischaemic stroke (IS). This study reports the effect of vitamin C and dimethylsulfoxide (DMSO) in the management of ischaemic stroke (IS) in Wistar rats. Method: Forty rats were divided into four groups of ten rats each. Groups 1, 2 and 3 were induced with IS by middle cerebral artery occlusion (MCAO). Anxiety like behavior and locomotor activity assessments were carried out. Group 1was treated with vitamin C (67.5mg/kg) and group 2 with DMSO (67.5mg/kg) orally for two weeks. Group 4 was neither induced nor treated. Brain tissue homogenates (cerebral cortex) were assessed for the level of calcium binding protein (S100B), antioxidant enzymes [Superoxide dismutase (SOD), Catalae (CAT), Glutathion peroxidase (GPx)] gene expressions, BCL2, Bax and Caspase3. Result: The results of this study showed that in the IS rats, there was significant (P<0.05) increase in anxiety and a significant (P<0.05) decrease in the locomotor activity. There was also significant (P<0.05) increase in S100B, caspase 3 and Bax expressions, while there was significant (P<0.05) decrease in BCL2. Treatment with vitamin C and DMSO resulted in significant (P<0.05) increase in the locomotor activity and significant (P<0.05) decrease in anxiety. There was significant (P<0.05) increase in the expression of SOD, CAT and GPx genes. Also, there was significant (P<0.05) decrease in S100B level, caspase-3 and Bax expressions, while there was significant (P<0.05) increase in BCL2. Conclusion: Conclusively, the results of this research highlighted the potentials for the use of vitamin C and DMSO in the management of IS. Keywords: Stroke; Antioxidant; Locomotor; Neuro-Behavioural; Dimethylsulfoxide Apoptosis; Oxidative Stress
Sterile neutrinos in 
Λb0→(Λc+,p+)ℓ1−ℓ2−ℓ3+ν
 decays

We study lepton number violating and lepton number conserving semi-leptonic decays of heavy baryons $\Lambda_b^0$ to three charged leptons and a neutrino. The decays occur through two intermediate quasi-degenerate GeV-scale sterile neutrinos of either Majorana or Dirac type that can be on-shell. Interference between the intermediate heavy neutrinos leads to CP violation in the final states. Effect of neutrino oscillations between the heavy states are also considered in observables of interests, \emph{i.e.,} branching ratio, and the CP-asymmetry. Given the present constraints on the heavy-to-light mixing elements $|V_{eN}|$ and $|V_{\mu N}|$, CP-averaged branching ratio of $\Lambda_b^0\to (\Lambda_c,p)\mu\mu e\nu$ with intermediate Majorana neutrinos is almost two orders of magnitude larger than for the same with Dirac neutrinos, whereas, CP-averaged branching ratio of $\Lambda_b^0\to (\Lambda_c,p)ee\mu\nu$ is of the same order of magnitude for both Majorana and Dirac neutrino induced decays. CP-violation is found to be appreciable when the neutrino mass difference is comparable with the average decay widths.
Increasing number of hospital beds has inconsistent effects on delaying bed shortages due to COVID-19

SARS-CoV-2, the virus responsible for COVID-19, has killed hundreds of thousands of Americans. Physical distancing measures and record-setting vaccine roll out played a key role in slowing COVID-19 spread, but the advent of new SARS-CoV-2 variants remains a real threat. Implementing strategies to minimize COVID-19 hospitalizations will be key to controlling the toll of COVID-19 variants and future novel pathogens, but non-physical distancing strategies receive relatively little attention. We present a novel system of differential equations designed to predict the relative effects of hospitalizing fewer COVID-19 patients and increasing ICU bed availability on delaying ICU bed shortages. This model, which we call SEAQIRD, was calibrated to mortality data on two US states with different peak infection times from mid-March – mid-May 2020. It found that when the probability of hospitalization is already low, decreasing it further can have a large effect on delaying an ICU bed shortage in both states. Meanwhile, altering the proportion of ICU beds available to COVID-19 patients had markedly different effects on when a bed shortage was reached in the two states. This trend remained consistent when the model’s most sensitive parameters were altered.
Revision of the crystal structure and chemical formula of haiweeite, Ca(UO2)2(Si5O12)(OH)2·6H2O

Abstract The previously published crystal structure study suggested that haiweeite is orthorhombic, Cmcm, with a = 7.125(1), b = 17.937(2), c = 18.342(2) Å, and V = 2344.3(7) Å3, and an ideal chemical formula Ca[(UO2)2Si5O12(OH)2]·3H2O, with Z = 4. Using single-crystal X-ray diffraction and electron microprobe analysis we re-examined haiweeite from the Teofilo Otoni, Minas Gerais, Brazil. Our diffraction experiment provided weak reflections responsible for doubling of the b cell parameter (for the current space-group settings), leading finally to the choice of a different space group. Haiweeite is thus orthorhombic, the space group Pbcn, with the unit-cell parameters a = 18.3000(5), b = 14.2331(3), c = 17.9192(5) Å, V = 4667.3(2) Å3, and an ideal formula Ca[(UO2)2(SiO3OH)2(Si3O6)]·6H2O (6.25 H2O inferred from the thermal analysis; 7.50 H2O from the structure model), with Z = 8. The structure refinement yielded R1 = 0.0512 for 2498 observed reflections [Iobs > 3σ(I)] and wR2 = 0.1286 for all 6117 unique reflections. Structure solution confirmed by subsequent refinement provided a structure model with full occupancies for U, Si, and Ca atoms, contrasting to previous average structure model. Although the general topology of our structure resembles that reported previously, all Si and O sites in our structure are fully occupied, in contrast to the previous structure determination.
Evaluation of the effect of electromagnetic treatment on the sowing qualities of triticale seeds

Sirakov, K. , Álvarez, J. & Muhova, A. (2021). Evaluation of the effect of electromagnetic treatment on the sowing qualities of triticale seeds. Bulg. J. Agric. Sci., 27 (4), 699–711 The fact of applying energy stimulation is an excellent opportunity to search for alternative environmentally friendly methods and technologies to increase the yields of main crops. Pre-sowing electromagnetic treatment of triticale seeds of the cultivar Boomerang was performed according to a threefactor experiment and the symmetrical composition plan B3. The controllable factors of the treatment used, were voltage or tension (U), kilovolts (kV) between electrodes of the treatment device, exposure time (τ) in seconds and duration (T) in days of seed pre-treatment before sowing in laboratory conditions. In order to determine the optimal growth environment, which would lead to an overall positive impact on seed performance, a mathematical processing of the results obtained was carried out and mathematical models of all observed variables were built. Based on these, their corresponding surfaces and lines of response were compiled, and by using them, the optimal limits of the controllable factors for maximum stimulation of the sowing qualities of the pre-sowing treated seeds, were determined. Based on the determined levels of controllable factors of pre-sowing electromagnetic treatment, studied variables were stimulated: germinating vigor up to 3.0% and germination up to 6.0%, number of roots up to 9.4%, root length up to 33.0%, sprout length up to 7.6%, and fresh and dry masses up to 35.6% and 37.0%, respectively. The proposed electromagnetic treatment of triticale seeds could be applied both in conventional and organic farming to improve the seed sowing qualities and subsequently to accelerate the growth and development of plants and increase the yield.