Miracle-dz/UniFAR-FTUs huggingface dataset:
No support for oxytocin modulation of reward-related brain function in autism: evidence from a randomized controlled trial

Autism spectrum disorder (ASD) is characterized by difficulties in social communication and interaction, which have been related to atypical neural processing of rewards, especially in the social domain. Since intranasal oxytocin has been shown to modulate activation of the brain's reward circuit, oxytocin could be a useful tool to ameliorate the processing of social rewards in ASD and thus improve social difficulties. In this randomized, double-blind, placebo-controlled, crossover fMRI study, we examined effects of a 24 IU dose of intranasal oxytocin on reward-related brain function in 37 men with an ASD diagnosis and 37 age- and IQ-matched control participants. Participants performed an incentive delay task that allows the investigation of neural activity associated with the anticipation and receipt of monetary and social rewards. Apart from a specific interaction effect in a single voxel within the left amygdala during the receipt of rewards, oxytocin did not influence neural processes related to the anticipation or consumption of social or monetary rewards in either group. Exploratory analyses suggested that oxytocin may increase ventral striatum sensitivity to monetary, but not social rewards, in individuals with high levels of self-reported anxiety, depression, alexithymia, and autistic traits irrespective of an ASD diagnosis. There were no significant differences in reward-related brain function between the two groups under placebo. Overall, our results do not support the hypothesis that intranasal oxytocin generally enhances activation of reward-related neural circuits in men with and without ASD without intellectual impairment. How and if oxytocin can be beneficial in the treatment of social difficulties in ASD needs to be addressed by examining moderating influences of individual differences and context on reward-related oxytocin effects.
In-person schooling and COVID-19 transmission in Canada's three largest cities

In North America and Europe, the Fall 2020 school term has coincided with the beginning of the second wave of the novel coronavirus (COVID-19) pandemic, sparking a heated debate about the role of in-person schooling for community transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This issue has immediate policy relevance for deciding how to operate schools safely as the pandemic unfolds, new variants of SARS-CoV-2 are circulating, and immunization coverage remains low. We contribute to this debate by presenting data on trends in COVID-19 weekly incidence among school-aged children 0-19 years old vis-a-vis other age groups during Fall 2020 in Canada's three largest cities: Montreal, Toronto and Calgary. We interpret these trends in light of the different back-to-school policies and other public health measures implemented in the three cities over the observation period.
Age-related changes in the upper respiratory microbiome are associated with SARS-CoV-2 susceptibility and illness severity

Children are less susceptible to SARS-CoV-2 and typically have milder illness courses than adults. We studied the nasopharyngeal microbiomes of 274 children, adolescents, and young adults with SARS-CoV-2 exposure using 16S rRNA gene sequencing. We find that higher abundances of Corynebacterium species are associated with SARS-CoV-2 infection and SARS-CoV-2-associated respiratory symptoms, while higher abundances of Dolosigranulum pigrum are present in SARS-CoV-2-infected individuals without respiratory symptoms. We also demonstrate that the abundances of these bacteria are strongly, and independently, associated with age, suggesting that the nasopharyngeal microbiome may be a potentially modifiable mechanism by which age influences SARS-CoV-2 susceptibility and severity.
Judd‐Ofelt Parameters, Hypersensitivity, and Emission Characteristics of Ln3+ (Nd3+, Ho3+, and Er3+) Ions Doped in PbO‐PbF2 Glasses

The Judd–Ofelt parameters, Ω2 and ΣΩλ (λ= 2, 4, 6), for Nd3+, Ho3+, and Er3+ doped in the oxyfluoride glass 30PbO70PbF2 lie intermediate between fluoride glasses and oxide glasses such as borate and phosphate glasses, providing evidence for the sensitivity of these parameters to the bonding environment. The variation of Ω2 unlike Ω4 and Ω6 for the lanthanide series is qualitatively different for glass matrices compared to crystalline matrices. Plots of oscillator strengths of hypersensitive transitions for these ions against ΣΩλ (λ= 2, 4, 6) are found useful in discerning the degree of hypersensitivity of these transitions due to change in the host matrix. The 5F23K85G6←5I8 transition of Ho3+ is found to be the most hypersensitive. The radiative parameters for the oxyfluoride glasses are close to fluoride glasses and the branching ratio of the important lasing transition, viz., 4F3/2→4I11/2, of Nd3+ is higher for the present case compared to fluoride glass. The results suggest that the oxyfluoride glasses may be used as hosts in the place of fluoride glasses wherever suitable as they are more stable and easy to prepare and have similar radiative properties.
1 1 M ay 2 01 6 TYPE Ĉ BRAUER LOOP SCHEMES AND LOOP MODEL WITH BOUNDARIES

In this paper we study the Brauer loop model on a strip and the associated quantum Knizhnik–Zamolodchikov (qKZ) equation. We show that the minimal degree solution of the Brauer qKZ equation with one of four different possible boundary conditions, gives the multidegrees of the irreducible components of generalizations of the Brauer loop scheme of [16, Knutson–Zinn-Justin ’07] with one of four kinds of symplectic-type symmetry. This is accomplished by studying these irreducible components, which are indexed by link patterns, and describing the geometric action of Brauer generators on them. We also provide recurrence relations for the multidegrees and compute the sum rule (multidegree of the whole schemes).
Mechanism(s) of humic acid induced beneficial effects in salt-affected soils

There is enough evidence that humic acid (HA) helps to enhance crops yield by promoting certain physical, chemical and biological activities in soil-plant system. Indigenously produced coal derived, HA was added to saline-sodic silty clay soil to determine its effects on activities of alkaline phosphatase, urease, microbial activities, cation exchange capacity and moisture retention of soil. Results indicated that HA treatments significantly (p<0.05) increased alkaline phosphatase activities. Urease activities increased by 2.84, 5.71 and 20.73% with application of 0.5, 1.0 and 2.0 mg HA kg -1
Dynamics of Ising spins with antiferromagnetic bonds on a triangular lattice

Six‐coupled Ising spins on a triangular lattice with antiferromagnetic (AF) nearest neighbour and ferromagnetic (F) next‐nearest neighbour interactions are investigated by Glauber dynamics. The system is fully frustrated and has seven non‐trivial local energy minima in both clusters. The ground state has four degenerate states with energy –6. These states are separated by the energy barrier ΔE = 4.0 to invert spins in the ground state. The dynamics of AF‐F and F‐AF coupling clusters are solved exactly. Each cluster contributes as many long relaxation times as it has non‐trivial local energy minima. The barriers against inversion of the clusters take only two values, 0 and 4|J|. In the paramagnetic case (PM), there is no diverging relaxation time. In the ferromagnetic case (FM), there is only one long‐lived mode. The longest relaxation times follow an Arrhenius law. Both AF‐F and F‐AF clusters undergo a zero‐temperature phase transition. The real and imaginary parts of the dynamic susceptibility display maxima if plotted versus temperature. The frequency dependence of the susceptibilities explain the effect of frustration. The real part reveals two plateaus and the imaginary part displays two corresponding maxima if they are plotted as functions of the logarithm of frequency. The Argand plots show two overlapping semicircles for fixed frequency at low temperature, indicating the time separation of the modes.
Further Studies on the Formation of Nitrosamines in Cured Pork Products Packaged in Elastic Rubber Nettings

Traces of N-nitrosodi-n-butylamine (NDBA) and N-nitrosodiethylamine (NDEA) can form in cured pork products packaged in elastic rubber nettings due to the interaction of nitrite in meat and amines in the rubber. Studies were carried out to determine whether taking nettings off immediately after processing or placing a layer of collagen as a barrier between nettings and meat could prevent or minimize formation of the above nitrosamines. None was effective. However, cured pork products packaged in a reformulated rubber netting, made with different amine additives, contained either none or only negligible levels of NDBA or NDEA, but, instead, contained 8–104 ppb levels of a new nitrosamine, N-nitrosodibenzylamine which is reported to be noncarcinogenic.
IONIC SPECIES IN CERIC PERCHLORATE SOLUTIONS

Ceric ion has been shown to exist in perchloric acid solution as a mixture of Ce4+, CeOH3+, and a dimer, probably (Ce-O-Ce)6+. From a spectral study between 395 and 430 mμ, the values of the equilibrium ratios have been determined, along with the molar extinction coefficients of each ion over this region of wave length. The thermodynamic quantities ΔH, ΔF°, and ΔS° have been determined for each reaction.
Electrospray deposition and characterization of Cu2O thin films with ring-shaped 2-D network structure

Graduate School of Energy Science, Kyoto University, Yoshida Honmachi, Sakyo-ku, Kyoto 606–8501, Japan *Faculty of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki 305–8573, Japan **Institute of Multidisciplinary Research for Advanced Materials (IMRAM), Tohoku University, 2–1–1 Katahira, Aoba-ku, Sendai 980–8577, Japan ***Université de Toulon / CNRS, IM2NP (UMR 7334), BP 20132, F-83957 La Garde, France
Hydrophile-Lipophile Balance Values for O/W Emulsions Stabilized by Nonionic Surfactants. II. “Required Hydrophile-Lipophile Balance Values” of the Oil Mixture

To study the additivity of the HLB values of an oil mixture from its component and the characteristics of the oil mixture forming a component of the emulsion, the required HLB values of several oil mixtures were determined by means of the emulsification tests proposed by Griffin. The oil mixtures examined in this series of experiments consist of two oils, one being a mineral oil of fixed components and the other an oil to be studied as the additive, which is added in varying ratios to the fixed-component mineral oil. The additivity of the required HLB values in oil mixtures was shown to hold to some extent for the case of saturated fatty acids, n-fatty alcohols, and some acetylated esters. For the rest of the additives, the additivity is not valid. The required HLB values of these additive oils were remarkably large in the case of fatty acids, alcohols and some acetylated esters, excepting the anomalous value for oleic acid. The structural effects on the increase of required HLB values seem to be in the f.
Effect of chain structure on the melt rheology of modified polypropylene

The effect of molecular structure of polypropylene (PP) on the melt rheological properties were investigated for electron irradiated polymer and di-2-ethylhexyl peroxy dicarbonate (EHPC)-treated polymer. The modifications were examined in terms of the rheological behaviors, molecular weight distribution, and the degree of branching. The high melt strength PP was obtained by irradiating with 50 and 80 kGy and adding EHPC. The modified PPs showed the strain hardening in the uniaxial elongational viscosity, though the linear elongational viscosity was lower than that of the unmodified PP. Low angle laser light-scattering measurements of the modified PPs showed the interesting results; high irradiation doses such as 50 and 80 kGy caused higher molecular weight chains branching. Nevertheless, the long branching chains were not detected for the EHPC modified PP, which also showed the strain hardening in uniaxial elongational flow. In this article, the relation between chain structure and rheological properties is discussed. © 1999 John Wiley & Sons, Inc. J Appl Polym Sci 73: 1493–1500, 1999
Metabolic Impact of Nigella sativa extracts on Experimental Menopause Induced Rats

The aim of current study was to investigate the effects of different extracts of Nigella sativa on metabolic profile of ovariectomized rats. Forty ovariectomized Sprague Dawley rats were used in the study and randomly allotted into one of five experimental groups: treated with Supercritical Fluid Extraction (SFE), Methanol Extract and Hexane Extract of N.S (300mg/kg/day) compared to vehicle control (Olive oil 1ml) and estrogen group (0.2mg/kg Conjugated Equine Estrogen) by intra-gastric gavage as negative and positive control group respectively for 21 days. Food and water intake were measured daily and body weight and biochemical parameters were measured at baseline, 11th day and at the end of experiment. The treatment groups showed significant (P < 0.05) improvement with reference to low density lipoprotein cholesterol (LDL-C) and blood glucose (P < 0.05). There were no significant differences between groups in total cholesterol, high density lipoprotein and serum triglyceride concentration. These results suggested that treatment with Nigella sativa extracts exert a therapeutic and protective effect by modifying weight gain, improving lipid profile and blood glucose as well as hormonal level which is believed to play an important role in the pathogenesis of metabolic syndrome during menopause.
Protocol to identify a topological superconducting phase in a three-terminal device

Dmitry I. Pikulin, 2 Bernard van Heck, 3 Torsten Karzig, Esteban A. Martinez, Bas Nijholt, Tom Laeven, Georg W. Winkler, John D. Watson, Sebastian Heedt, Mine Temurhan, Vicky Svidenko, Roman M. Lutchyn, Mason Thomas, Gijs de Lange, Lucas Casparis, and Chetan Nayak 5 Microsoft Quantum, Station Q, University of California, Santa Barbara, CA 93106, USA Microsoft Quantum, Redmond, Washington 98052, USA Microsoft Quantum Lab Delft, Delft University of Technology, 2600 GA Delft, The Netherlands Center for Quantum Devices, Niels Bohr Institute, University of Copenhagen, and Microsoft Quantum Copenhagen, Universitetsparken 5, 2100 Copenhagen, Denmark Department of Physics, University of California, Santa Barbara, CA 93106, USA (Dated: March 24, 2021)
Spatio-Temporal Sparsification for General Robust Graph Convolution Networks

Graph Neural Networks (GNNs) have attracted increasing attention due to its successful applications on various graph-structure data. However, recent studies have shown that adversarial attacks are threatening the functionality of GNNs. Although numerous works have been proposed to defend adversarial attacks from various perspectives, most of them can be robust against the attacks only on specific scenarios. To address this shortage of robust generalization, we propose to defend the adversarial attacks on GNN through applying the Spatio-Temporal sparsification (called ST-Sparse) on the GNN hidden node representation. ST-Sparse is similar to the Dropout regularization in spirit. Through intensive experiment evaluation with GCN as the target GNN model, we identify the benefits of ST-Sparse as follows: (1) ST-Sparse shows the defense performance improvement in most cases, as it can effectively increase the robust accuracy by up to 6\% improvement; (2) ST-Sparse illustrates its robust generalization capability by integrating with the existing defense methods, similar to the integration of Dropout into various deep learning models as a standard regularization technique; (3) ST-Sparse also shows its ordinary generalization capability on clean datasets, in that ST-SparseGCN (the integration of ST-Sparse and the original GCN) even outperform the original GCN, while the other three representative defense methods are inferior to the original GCN.
Generative Minimization Networks: Training GANs Without Competition

Many applications in machine learning can be framed as minimization problems and solved efficiently using gradient-based techniques. However, recent applications of generative models, particularly GANs, have triggered interest in solving min-max games for which standard optimization techniques are often not suitable. Among known problems experienced by practitioners is the lack of convergence guarantees or convergence to a non-optimum cycle. At the heart of these problems is the min-max structure of the GAN objective which creates non-trivial dependencies between the players. We propose to address this problem by optimizing a different objective that circumvents the min-max structure using the notion of duality gap from game theory. We provide novel convergence guarantees on this objective and demonstrate why the obtained limit point solves the problem better than known techniques.
Convergence of Finite Memory Q-Learning for POMDPs and Near Optimality of Learned Policies under Filter Stability

In this paper, for partially observed Markov decision problems (POMDPs), we provide the convergence of a Q learning algorithm for control policies using a finite history of past observations and control actions, and consequentially, we establish near optimality of such limit Q functions under explicit filter stability conditions. We present explicit error bounds relating the approximation error to the length of the finite history window. We establish the convergence of such Q learning iterations under mild ergodicity assumptions on the state process during the exploration phase. We further show that the limit fixed point equation gives an optimal solution for an approximate belief Markov decision problem (MDP). We then provide bounds on the performance of the policy obtained using the limit Q values compared with the performance of the optimal policy for the POMDP, in which we also present explicit conditions using recent results on filter stability in controlled POMDPs. Whereas there exist many experimental results, (i) the rigorous asymptotic convergence (to an approximate MDP value function) for such finite memory Q learning algorithms and (ii) the near optimality with an explicit rate of convergence (in the memory size) under filter stability are results that are new to the literature to our knowledge. Funding: This research was supported in part by the Natural Sciences and Engineering Research Council (NSERC) of Canada.
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement Learning

Progress in deep reinforcement learning (RL) research is largely enabled by benchmark task environments. However, analyzing the nature of those environments is often overlooked. In particular, we still do not have agreeable ways to measure the difficulty or solvability of a task, given that each has fundamentally different actions, observations, dynamics, rewards, and can be tackled with diverse RL algorithms. In this work, we propose policy information capacity (PIC) -- the mutual information between policy parameters and episodic return -- and policy-optimal information capacity (POIC) -- between policy parameters and episodic optimality -- as two environment-agnostic, algorithm-agnostic quantitative metrics for task difficulty. Evaluating our metrics across toy environments as well as continuous control benchmark tasks from OpenAI Gym and DeepMind Control Suite, we empirically demonstrate that these information-theoretic metrics have higher correlations with normalized task solvability scores than a variety of alternatives. Lastly, we show that these metrics can also be used for fast and compute-efficient optimizations of key design parameters such as reward shaping, policy architectures, and MDP properties for better solvability by RL algorithms without ever running full RL experiments.
Multilingual Autoregressive Entity Linking

We present mGENRE, a sequence-to- sequence system for the Multilingual Entity Linking (MEL) problem—the task of resolving language-specific mentions to a multilingual Knowledge Base (KB). For a mention in a given language, mGENRE predicts the name of the target entity left-to-right, token-by-token in an autoregressive fashion. The autoregressive formulation allows us to effectively cross-encode mention string and entity names to capture more interactions than the standard dot product between mention and entity vectors. It also enables fast search within a large KB even for mentions that do not appear in mention tables and with no need for large-scale vector indices. While prior MEL works use a single representation for each entity, we match against entity names of as many languages as possible, which allows exploiting language connections between source input and target name. Moreover, in a zero-shot setting on languages with no training data at all, mGENRE treats the target language as a latent variable that is marginalized at prediction time. This leads to over 50% improvements in average accuracy. We show the efficacy of our approach through extensive evaluation including experiments on three popular MEL benchmarks where we establish new state-of-the-art results. Source code available at https://github.com/facebookresearch/GENRE.
Benign Overfitting of Constant-Stepsize SGD for Linear Regression

There is an increasing realization that algorithmic inductive biases are central in preventing overfitting; empirically, we often see a benign overfitting phenomenon in overparameterized settings for natural learning algorithms, such as stochastic gradient descent (SGD), where little to no explicit regularization has been employed. This work considers this issue in arguably the most basic setting: constant-stepsize SGD (with iterate averaging) for linear regression in the overparameterized regime. Our main result provides a sharp excess risk bound, stated in terms of the full eigenspectrum of the data covariance matrix, that reveals a bias-variance decomposition characterizing when generalization is possible: (i) the variance bound is characterized in terms of an effective dimension (specific for SGD) and (ii) the bias bound provides a sharp geometric characterization in terms of the location of the initial iterate (and how it aligns with the data covariance matrix). We reflect on a number of notable differences between the algorithmic regularization afforded by (unregularized) SGD in comparison to ordinary least squares (minimumnorm interpolation) and ridge regression.
Are Neural Language Models Good Plagiarists? A Benchmark for Neural Paraphrase Detection

Neural language models such as BERT allow for human-like text paraphrasing. This ability threatens academic integrity, as it aggravates identifying machine-obfuscated plagiarism. We make two contributions to foster the research on detecting these novel machine-paraphrases. First, we provide the first large-scale dataset of documents paraphrased using the Transformer-based models BERT, RoBERTa, and Longformer. The dataset includes paragraphs from scientific papers on arXiv, theses, and Wikipedia articles and their paraphrased counterparts (1.5M paragraphs in total). We show the paraphrased text maintains the semantics of the original source. Second, we benchmark how well neural classification models can distinguish the original and paraphrased text. The dataset and source code of our study are publicly available.
The Success of AdaBoost and Its Application in Portfolio Management

We develop a novel approach to explain why AdaBoost is a successful classifier. By introducing a measure of the influence of the noise points (ION) in the training data for the binary classification problem, we prove that there is a strong connection between the ION and the test error. We further identify that the ION of AdaBoost decreases as the iteration number or the complexity of the base learners increases. We confirm that it is impossible to obtain a consistent classifier without deep trees as the base learners of AdaBoost in some complicated situations. We apply AdaBoost in portfolio management via empirical studies in the Chinese market, which corroborates our theoretical propositions.