Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-Based Control for Robots by a Fully Spiking Neural System Relying on Cerebellar Predictive Learning

The cerebellum plays a distinctive role within our motor control system to achieve fine and coordinated motions. While cerebellar lesions do not lead to a complete loss of motor functions, both action and perception are severally impacted. Hence, it is assumed that the cerebellum uses an internal forward model to provide anticipatory signals by learning from the error in sensory states. In some studies, it was demonstrated that the learning process relies on the joint-space error. However, this may not exist. This work proposes a novel fully spiking neural system that relies on a forward predictive learning by means of a cellular cerebellar model. The forward model is learnt thanks to the sensory feedback in task-space and it acts as a Smith predictor. The latter predicts sensory corrections in input to a differential mapping spiking neural network during a visual servoing task of a robot arm manipulator. In this paper, we promote the developed control system to achieve more accurate target reaching actions and reduce the motion execution time for the robotic reaching tasks thanks to the cerebellar predictive capabilities.
A review on minimax rates in change point detection and localisation.

This paper reviews recent developments in fundamental limits and optimal algorithms for change point analysis. We focus on minimax optimal rates in change point detection and localisation, in both parametric and nonparametric models. We start with the univariate mean change point analysis problem and review the state-of-the-art results in the literature. We then move on to more complex data types and investigate general principles behind the optimal procedures that lead to minimax rate-optimal results.
DNN-Based Mask Estimation for Distributed Speech Enhancement in Spatially Unconstrained Microphone Arrays

Deep neural network (DNN)-based speech enhancement algorithms in microphone arrays have now proven to be efficient solutions to speech understanding and speech recognition in noisy environments. However, in the context of ad-hoc microphone arrays, many challenges remain and raise the need for distributed processing. In this paper, we propose to extend a previously introduced distributed DNN-based time-frequency mask estimation scheme that can efficiently use spatial information in form of so-called compressed signals which are pre-filtered target estimations. We study the performance of this algorithm named Tango under realistic acoustic conditions and investigate practical aspects of its optimal application. We show that the nodes in the microphone array cooperate by taking profit of their spatial coverage in the room. We also propose to use the compressed signals not only to convey the target estimation but also the noise estimation in order to exploit the acoustic diversity recorded throughout the microphone array.
On the Computability of Continuous Maximum Entropy Distributions: Adjoint Orbits of Lie Groups

Given a point $A$ in the convex hull of a given adjoint orbit $\mathcal{O}(F)$ of a compact Lie group $G$, we give a polynomial time algorithm to compute the probability density supported on $\mathcal{O}(F)$ whose expectation is $A$ and that minimizes the Kullback-Leibler divergence to the $G$-invariant measure on $\mathcal{O}(F)$. This significantly extends the recent work of the authors (STOC 2020) who presented such a result for the manifold of rank $k$-projections which is a specific adjoint orbit of the unitary group $\mathrm{U}(n)$. Our result relies on the ellipsoid method-based framework proposed in prior work; however, to apply it to the general setting of compact Lie groups, we need tools from Lie theory. For instance, properties of the adjoint representation are used to find the defining equalities of the minimal affine space containing the convex hull of $\mathcal{O}(F)$, and to establish a bound on the optimal dual solution. Also, the Harish-Chandra integral formula is used to obtain an evaluation oracle for the dual objective function. While the Harish-Chandra integral formula allows us to write certain integrals over the adjoint orbit of a Lie group as a sum of a small number of determinants, it is only defined for elements of a chosen Cartan subalgebra of the Lie algebra $\mathfrak{g}$ of $G. $ We show how it can be applied to our setting with the help of Kostant's convexity theorem. Further, the convex hull of an adjoint orbit is a type of orbitope, and the orbitopes studied in this paper are known to be spectrahedral. Thus our main result can be viewed as extending the maximum entropy framework to a class of spectrahedra.
Highly turbulent gas on GMC scales in NGC 3256, the nearest luminous infrared galaxy

We present the highest resolution CO (2-1) observations obtained to date (0.25") of NGC 3256 and use them to determine the detailed properties of the molecular interstellar medium in the central 6 kpc of this merger. Distributions of physical quantities are reported from pixel-by-pixel measurements at 55 and 120 pc scales and compared to disc galaxies observed by PHANGS-ALMA. Mass surface densities range from 8 to 5500 M$_{\odot}$ pc$^{-2}$ and velocity dispersions from 10 to 200 km s$^{-1}$. Peak brightness temperatures as large as 37 K are measured, indicating the gas in NGC 3256 may be hotter than all regions in nearby disc galaxies measured by PHANGS-ALMA. Brightness temperatures even surpass those in the overlap region of NGC 4038/9 at the same scales. The majority of the gas appears unbound with median virial parameters of 7 to 19, although external pressure may bind some of the gas. High internal turbulent pressures of 10$^{5}$ to 10$^{10}$ K cm$^{-3}$ are found. Given the lack of significant trends in surface density, brightness temperature, and velocity dispersion with physical scale we argue the molecular gas is made up of a smooth medium down to 55 pc scales, unlike the more structured medium found in the PHANGS-ALMA disc galaxies.
The Impact of Individual's Ecological Factors on the Dynamics of Alcohol Drinking among Arizona State University Students: An Application of the Survey Data-driven Agent-based Model

College-aged students are one of the most vulnerable populations to high-risk alcohol drinking behaviors that could cause them consequences such as injury or sexual assault. An important factor that may influence college students' decision on alcohol drinking behavior is socializing at certain contexts across university environment. The present study aims to identify and better understand ecological conditions driving the dynamics of distribution of alcohol use among college-aged students. To this end, a pilot study is conducted to evaluate students' movement patterns to different contexts across the Arizona State University (ASU) campus, and to use its results to develop an agent-based simulation model designed for examining the role of environmental factors on development and maintenance of alcohol drinking behavior by a representative sample of ASU students. The proposed model that resembles an approximate reaction-diffusion model accounts for movement of agents to various contexts (i.e. diffusion) and alcohol drinking influences within those contexts (i.e., reaction) via a SIR-type model. Of the four most visited contexts at ASU Tempe campus -- Library, Memorial Union, Fitness Center, and Dorm -- the context with the highest visiting probability, Memorial Union, is the most influential and most sensitive context (around $16$ times higher impact of alcohol related influences than the other contexts) on spreading alcohol drinking behavior. Our findings highlight the crucial role of socialization at local environments on the dynamics of students' alcohol use as well as on the long-term prediction of the college drinking prevalence.
Learning Visual Representations for Transfer Learning by Suppressing Texture

Recent literature has shown that features obtained from supervised training of CNNs may over-emphasize texture rather than encoding high-level information. In self-supervised learning in particular, texture as a low-level cue may provide shortcuts that prevent the network from learning higher level representations. To address these problems we propose to use classic methods based on anisotropic diffusion to augment training using images with suppressed texture. This simple method helps retain important edge information and suppress texture at the same time. We empirically show that our method achieves state-of-the-art results on object detection and image classification with eight diverse datasets in either supervised or self-supervised learning tasks such as MoCoV2 and Jigsaw. Our method is particularly effective for transfer learning tasks and we observed improved performance on five standard transfer learning datasets. The large improvements (up to 11.49\%) on the Sketch-ImageNet dataset, DTD dataset and additional visual analyses with saliency maps suggest that our approach helps in learning better representations that better transfer.
Meta-learning Transferable Representations with a Single Target Domain

Recent works found that fine-tuning and joint training---two popular approaches for transfer learning---do not always improve accuracy on downstream tasks. First, we aim to understand more about when and why fine-tuning and joint training can be suboptimal or even harmful for transfer learning. We design semi-synthetic datasets where the source task can be solved by either source-specific features or transferable features. We observe that (1) pre-training may not have incentive to learn transferable features and (2) joint training may simultaneously learn source-specific features and overfit to the target. Second, to improve over fine-tuning and joint training, we propose Meta Representation Learning (MeRLin) to learn transferable features. MeRLin meta-learns representations by ensuring that a head fit on top of the representations with target training data also performs well on target validation data. We also prove that MeRLin recovers the target ground-truth model with a quadratic neural net parameterization and a source distribution that contains both transferable and source-specific features. On the same distribution, pre-training and joint training provably fail to learn transferable features. MeRLin empirically outperforms previous state-of-the-art transfer learning algorithms on various real-world vision and NLP transfer learning benchmarks.
Massive black hole binary systems and the NANOGrav 12.5 yr results

The North American Nanohertz Observatory for Gravitational Waves (NANOGrav) recently reported evidence for the presence of a common stochastic signal across their array of pulsars. The origin of this signal is still unclear. One possibility is that it is due to a stochastic gravitational-wave background (SGWB) in the ∼1–10 nHz frequency region. Taking the NANOGrav observational result at face value, we show that this signal would be fully consistent with an SGWB produced by an unresolved population of in-spiralling massive black hole binaries (MBHBs) predicted by current theoretical models. Considering an astrophysically agnostic model, the MBHB merger rate is loosely constrained. Including additional constraints from galaxy pairing fraction and MBH–bulge scaling relations, we find that the MBHB merger rate is ${1.2\times 10^{-5}}{\rm -}{4.5\times 10^{-4}}\, \mathrm{Mpc}^{-3}\, \mathrm{Gyr}^{-1}$ , the MBHB merger time-scale is $\le 2.7\, \mathrm{Gyr}$, and the norm of the MBH−Mbulge relation is $\ge 1.2\times 10^{8}\, {\rm M}_\odot$ (all quoted at 90 per cent credible intervals). Regardless of the astrophysical details of MBHB assembly, the NANOGrav result would imply that a sufficiently large population of massive black holes pair up, form binaries and merge within a Hubble time.
The Use of Probiotic Containing Lactic Acid Bacteria to the Rescue of Antibiotics in Broiler Production

The use of Probiotic Containing Lactic Acid Bacteria to the 1 Rescue of Antibiotics in Broiler Production 2 3 4 5 ABSTRACT 6 The research was aimed to study the effect of Lactobacillus fermentum, Lactobacillus plantenrun 7 and Weissalla ciberia on growth performance, feed conversion ratio and mortality of broiler 8 chicken. This was design to find a possible alternative to antibiotics in broiler production. The 9 study was carried out at the Department of microbiology, faculty of sciences Kaduna State 10 University, Kaduna between January to April 2018. A total of ten raw milk samples were 11 screened for the isolation of Lactic Acid Bacteria (LAB) and 21 day old chicks were 12 administered probiotics (Lactobacillus fermentum, Lactobacillus plantenrun and Weissalla 13 ciberia) in water at 10cells/milliliters/isolates/birds/day for six weeks. Body Weight (BW), 14 Weight Gain (WG) and Feed Intake (FI) were measured weekly just as feed conversion ratio was 15 calculated and mortality was recorded throughout the duration of the experiment. The results 16 showed the identification of Lactobacillus fermentum, Lactobacillus plantenrun and Weissalla 17 ciberia that were used as probiotics. Significant differences was observe between treatment on 18 BW at day 14 P=0.0292 WG, P=0.0004 and FI P=0.0176, day 21 P=0.0329, WG P=0.0004 and 19 FI P=0.0176, day 28 P=0.0025, WG P=0.0053 and FI P=0.0189, day 42 WG P=0.0112 and FI 20 P=0.0006 and day 49 BW P=0.011, WG P=0.5289 and FI P=0.0006. Probiotics group showed a 21 better body weight and weight gain with a lower feed intake and highest feed conversion ratio 22 compared with antibiotic and control group. There was a progressive increase in weight gain 23 from the first to the fourth but decreases from week five and six. The LAB group recorded 5%, 24 mortality rate, antibiotics group recorded 10% and the control group recorded 0%. Probiotic 25 lactic acid bacteria showed promising tendencies to replace antibiotics in broiler production as 26 illustrated in this research work. 27
Meta-Learning for Natural Language Understanding under Continual Learning Framework

Neural network has been recognized with its accomplishments on tackling various natural language understanding (NLU) tasks. Methods have been developed to train a robust model to handle multiple tasks to gain a general representation of text. In this paper, we implement the model-agnostic meta-learning (MAML) and Online aware Meta-learning (OML) meta-objective under the continual framework for NLU tasks. We validate our methods on selected SuperGLUE and GLUE benchmark.
Communication-Efficient Distributed Machine Learning over Strategic Networks: A Two-Layer Game Approach

This paper considers a game-theoretic framework for distributed learning problems over networks where communications between nodes are costly. In the proposed game, players decide both the learning parameters and the network structure for communications. The Nash equilibrium characterizes the tradeoff between the local performance and the global agreement of the learned classifiers. We introduce a two-layer algorithm to find the equilibrium. The algorithm features a joint learning process that integrates the iterative learning at each node and the network formation. We show that our game is equivalent to a generalized potential game in the setting of symmetric networks. We study the convergence of the proposed algorithm, analyze the network structures determined by our game, and show the improvement of the social welfare in comparison with the distributed learning over non-strategic networks. In the case study, we deal with streaming data and use telemonitoring of Parkinson's disease to corroborate the results.
Epigallocatechin Gallate Effectively Affects Senescence and Anti-SASP via SIRT3 in 3T3-L1 Preadipocytes in Comparison with Other Bioactive Substances

Aim We investigated different bioactive compounds including epigallocatechin gallate (EGCG), anthocyanidin, resveratrol, phloretin, spermidine, butyrate, and β-hydroxybutyrate with regard to their effect on SIRT3 via NRF2 and modulation of the proinflammatory senescence-associated secretory phenotype (SASP) in senescence induced 3T3-L1 preadipocytes. Methods For induction of senescence, 3T3-L1 preadipocytes were incubated with bromodeoxyuridine (BrdU) for 8 days. Cell cycle inhibition was observed, and β-galactosidase activity was measured. After BrdU treatment, cells were treated with different bioactive compounds in various concentrations for 96 h. ELISA was used for determining proinflammatory cytokine IL6 in SASP cells. Results CDKN1a increased significantly after BrdU incubation compared to untreated control (p < 0.01). All secondary plant ingredients used for treatment, but not anthocyanidin 50 μM, decrease CDKN1a expression (p < 0.05), whereas most endogenous substances did not attenuate CDKN1a. IL6 secretion positively correlated with CDKN1a (p < 0.01), whereas EGCG could diminish both, IL6 and CDKN1a with the strongest effect (p < 0.01). Although NRF2 positively correlated with SIRT3 activation (p < 0.05), only resveratrol (p < 0.01) and anthocyanidin (p < 0.05) could activate NRF2 significantly. Solely anthocyanidin 50 μM (p < 0.05) and 100 μM (p < 0.01) and EGCG 50 μM (p < 0.01) could increase SIRT3 expression. Activation of SIRT3 with EGCG correlated with lowered IL6 secretion significantly (p < 0.05) but not with anthocyanidin. Conclusion Accumulation of senescent cells in adipose tissue plays an important role in obesity and age-related diseases. SIRT3, located in the mitochondria, can regulate ROS via different pathways. Thus, targeting SIRT3 activating compounds such as EGCG may delay senescence of cells and senescence induced inflammatory processes.
Isolation and evolutionary analyses of porcine epidemic diarrhea virus in Asia

Porcine epidemic diarrhea virus (PEDV) is a leading cause of diarrhea in pigs worldwide. Virus isolation and genetic evolutionary analysis allow investigations into the prevalence of epidemic strains and provide data for the clinical diagnosis and vaccine development. In this study, we investigated the genetic characteristics of PEDV circulation in Asia through virus isolation and comparative genomics analysis. APEDV strain designated HB2018 was isolated from a pig in a farm experiencing a diarrhea outbreak. The complete genome sequence of HB2018 was 28,138 bp in length. Phylogenetic analysis of HB2018 and 207 PEDVs in Asia showed that most PEDV strains circulating in Asia after 2010 belong to genotype GII, particularly GII-a. The PEDV vaccine strain CV777 belonged to GI, and thus, unmatched genotypes between CV777 and GII-a variants might partially explain incomplete protection by the CV777-derived vaccine against PEDV variants in China. In addition, we found the S protein of variant strains contained numerous mutations compared to the S protein of CV777, and these mutations occurred in the N-terminal domain of the S protein. These mutations may influence the antigenicity, pathogenicity, and neutralization properties of the variant strains.
Epigenetic Regulation of Dental Pulp Stem Cell Fate

Epigenetic regulation, mainly involving DNA methylation, histone modification, and noncoding RNAs, affects gene expression without modifying the primary DNA sequence and modulates cell fate. Mesenchymal stem cells derived from dental pulp, also called dental pulp stem cells (DPSCs), exhibit multipotent differentiation capacity and can promote various biological processes, including odontogenesis, osteogenesis, angiogenesis, myogenesis, and chondrogenesis. Over the past decades, increased attention has been attracted by the use of DPSCs in the field of regenerative medicine. According to a series of studies, epigenetic regulation is essential for DPSCs to differentiate into specialized cells. In this review, we summarize the mechanisms involved in the epigenetic regulation of the fate of DPSCs.
Prevalence of antibodies against SARS-CoV-2 according to socioeconomic and ethnic status in a nationwide Brazilian survey

ABSTRACT Objectives. To investigate socioeconomic and ethnic group inequalities in prevalence of antibodies against SARS-CoV-2 in the 27 federative units of Brazil. Methods. In this cross-sectional study, three household surveys were carried out on May 14-21, June 4-7, and June 21-24, 2020 in 133 Brazilian urban areas. Multi-stage sampling was used to select 250 individuals in each city to undergo a rapid antibody test. Subjects answered a questionnaire on household assets, schooling and self-reported skin color/ethnicity using the standard Brazilian classification in five categories: white, black, brown, Asian or indigenous. Principal component analyses of assets was used to classify socioeconomic position into five wealth quintiles. Poisson regression was used for the analyses. Results. 25 025 subjects were tested in the first, 31 165 in the second, and 33 207 in the third wave of the survey, with prevalence of positive results equal to 1.4%, 2.4%, and 2.9% respectively. Individuals in the poorest quintile were 2.16 times (95% confidence interval 1.86; 2.51) more likely to test positive than those in the wealthiest quintile, and those with 12 or more years of schooling had lower prevalence than subjects with less education. Indigenous individuals had 4.71 (3.65; 6.08) times higher prevalence than whites, as did those with black or brown skin color. Adjustment for region of the country reduced the prevalence ratios according to wealth, education and ethnicity, but results remained statistically significant. Conclusions. The prevalence of antibodies against SARS-CoV-2 in Brazil shows steep class and ethnic gradients, with lowest risks among white, educated and wealthy individuals.
Laparoscopic Sleeve Gastrectomy-Induced Decreases in FT3 and TSH are Related to Fasting C-Peptide in Euthyroid Patients with Obesity

Objective Laparoscopic sleeve gastrectomy (LSG) is one of the most effective therapies to treat obesity. However, whether LSG affects thyroid function remains elusive. Due to a lack of longitudinal research, we explored changes in thyroid function in euthyroid patients with obesity before and after LSG. Methods In total, 109 participants (59 obese patients, 30 normal controls and 20 overweight subjects) were recruited from the Second Xiangya Hospital of Central South University (CSU). All patients underwent LSG, and metabolic indicators and free triiodothyronine (FT3), free thyroxine (FT4), and thyroid stimulating hormone (TSH) were evaluated at baseline, 6 and 12 months postoperatively. Results Compared to the normal control group, the concentrations of FT4 were decreased and TSH were increased in the obese group at baseline. Thyroid hormone levels in all participants were within the normal range during the 12 months after LSG. The concentrations of FT3 (4.83 ± 0.06 vs 5.03 ± 0.08, P = 0.023) and TSH (1.67 ± 0.11 vs 2.25 ± 0.18, P = 0.000) significantly decreased from baseline to 12 months postoperatively, while the concentrations of FT4 significantly increased (17.40 ± 0.52 vs 15.80 ± 0.32, P = 0.004). The decrease in fasting C-peptide (FCP) was related to the decline in FT3 and TSH during 12 months after LSG. Conclusion Obesity is closely related to thyroid function. LSG promoted a significant decrease in FT3 and TSH and a significant increase in FT4 in euthyroid patients with obesity after LSG. The decline in FCP may be involved in the decrease in FT3 and TSH after LSG.