Miracle-dz/UniFAR-FTUs huggingface dataset:
A Social Analgesic? Acetaminophen (Paracetamol) Reduces Positive Empathy

Acetaminophen – a potent physical painkiller that also reduces empathy for other people’s suffering – blunts physical and social pain by reducing activation in brain areas (i.e. anterior insula and anterior cingulate) thought to be related to emotional awareness and motivation. Some neuroimaging research on positive empathy (i.e., the perception and sharing of positive affect in other people) suggests that the experience of positive empathy also recruits these paralimbic cortical brain areas. We thus hypothesized that acetaminophen may also impair affective processes related to the experience of positive empathy. We tested this hypothesis in a double-blind, placebo-controlled experiment. Specifically, we administered 1,000 mg acetaminophen or a placebo and measured effects on different measures of positive empathy while participants read scenarios about the uplifting experiences of other people. Results showed that acetaminophen reduced personal pleasure and other-directed empathic feelings in response to these scenarios. In contrast, effects on perceived positivity of the described experiences or perceived pleasure in scenario protagonists were not significant. These findings suggest that (1) acetaminophen reduces affective reactivity to other people’s positive experiences and (2) the experience of physical pain and positive empathy may have a more similar neurochemical basis than previously assumed. Because the experience of positive empathy is related to prosocial behavior, our findings also raise questions about the societal impact of excessive acetaminophen consumption.
Muse-ing on the Impact of Utterance Ordering on Crowdsourced Emotion Annotations

Emotion recognition algorithms rely on data annotated with high quality labels. However, emotion expression and perception are inherently subjective. There is generally not a single annotation that can be unambiguously declared "correct. " As a result, annotations are colored by the manner in which they were collected. In this paper, we conduct crowdsourcing experiments to investigate this impact on both the annotations themselves and on the performance of these algorithms. We focus on one critical question: the effect of context. We present a new emotion dataset, Multimodal Stressed Emotion (MuSE), and annotate the dataset using two conditions: randomized, in which annotators are presented with clips in random order, and contextualized, in which annotators are presented with clips in order. We find that contextual labeling schemes result in annotations that are more similar to a speaker’s own self-reported labels and that labels generated from randomized schemes are most easily predictable by automated systems.
On exactly incompressible DG FEM pressure splitting schemes for the Navier-Stokes equation

We compare three iterative pressure correction schemes for solving the Navier-Stokes equations with a focus on exactly divergence free solution with higher order discontinuous Galerkin discretisations. The investigated schemes are the incremental pressure correction scheme on the standard differential form (IPCS-D), the same scheme on algebraic form (IPCS-A), and the semi-implicit method for pressure linked equations (SIMPLE). We show algebraically and through numerical examples that the IPCS-A and SIMPLE schemes are exactly mass conserving due to the algebraic pressure correction, while the IPCS-D scheme cannot be exactly divergence free due to the stabilisation terms required in the pressure Poisson equation. The SIMPLE scheme requires a significantly higher number of pressure correction iterations to obtain converged results than the IPCS-A scheme, so for efficient and mass conserving simulation the IPCS-A method is the best option among the three evaluated schemes.
A fault-tolerant non-Clifford gate for the surface code in two dimensions

We complete a universal set of fault-tolerant quantum logic gates for a two-dimensional surface code architecture. Fault-tolerant logic gates will consume a large proportion of the resources of a two-dimensional quantum computing architecture. Here we show how to perform a fault-tolerant non-Clifford gate with the surface code; a quantum error-correcting code now under intensive development. This alleviates the need for distillation or higher-dimensional components to complete a universal gate set. The operation uses both local transversal gates and code deformations over a time that scales with the size of the qubit array. An important component of the gate is a just-in-time decoder. These decoding algorithms allow us to draw upon the advantages of three-dimensional models using only a two-dimensional array of live qubits. Our gate is completed using parity checks of weight no greater than four. We therefore expect it to be amenable with near-future technology. As the gate circumvents the need for magic-state distillation, it may reduce the resource overhead of surface-code quantum computation considerably.
Block stochastic gradient descent for large-scale tomographic reconstruction in a parallel network

Iterative algorithms have many advantages for linear tomographic image reconstruction when compared to back-projection based methods. However, iterative methods tend to have significantly higher computational complexity. To overcome this, parallel processing schemes that can utilise several computing nodes are desirable. Popular methods here are row action methods, which update the entire image simultaneously and column action methods, which require access to all measurements at each node. In large scale tomographic reconstruction with limited storage capacity of each node, data communication overheads between nodes becomes a significant performance limiting factor. To reduce this overhead, we proposed a row action method BSGD. The method is based on the stochastic gradient descent method but it does not update the entire image at each iteration, which reduces between node communication. To further increase convergence speeds, an importance sampling strategy is proposed. We compare BSGD to other existing stochastic methods and show its effectiveness and efficiency. Other properties of BSGD are also explored, including its ability to incorporate total variation (TV) regularization and automatic parameter tuning.
Incorporation of Omega-3 Fatty Acids Into Human Skeletal Muscle Sarcolemmal and Mitochondrial Membranes Following 12 Weeks of Fish Oil Supplementation

Fish oil (FO) supplementation in humans results in the incorporation of omega-3 fatty acids (FAs) eicosapentaenoic acid (EPA; C20:5) and docosahexaenoic acid (DHA; C20:6) into skeletal muscle membranes. However, despite the importance of membrane composition in structure–function relationships, a paucity of information exists regarding how different muscle membranes/organelles respond to FO supplementation. Therefore, the purpose of the present study was to determine the effects 12 weeks of FO supplementation (3g EPA/2g DHA daily) on the phospholipid composition of sarcolemmal and mitochondrial fractions, as well as whole muscle responses, in healthy young males. FO supplementation increased the total phospholipid content in whole muscle (57%; p < 0.05) and the sarcolemma (38%; p = 0.05), but did not alter the content in mitochondria. The content of omega-3 FAs, EPA and DHA, were increased (+3-fold) in whole muscle, and mitochondrial membranes, and as a result the omega-6/omega-3 ratios were dramatically decreased (-3-fold), while conversely the unsaturation indexes were increased. Intriguingly, before supplementation the unsaturation index (UI) of sarcolemmal membranes was ∼3 times lower (p < 0.001) than either whole muscle or mitochondrial membranes. While supplementation also increased DHA within sarcolemmal membranes, EPA was not altered, and as a result the omega-6/omega-3 ratio and UI of these membranes were not altered. All together, these data revealed that mitochondrial and sarcolemmal membranes display unique phospholipid compositions and responses to FO supplementation.
Wasserstein Dependency Measure for Representation Learning

Mutual information maximization has emerged as a powerful learning objective for unsupervised representation learning obtaining state-of-the-art performance in applications such as object recognition, speech recognition, and reinforcement learning. However, such approaches are fundamentally limited since a tight lower bound of mutual information requires sample size exponential in the mutual information. This limits the applicability of these approaches for prediction tasks with high mutual information, such as in video understanding or reinforcement learning. In these settings, such techniques are prone to overfit, both in theory and in practice, and capture only a few of the relevant factors of variation. This leads to incomplete representations that are not optimal for downstream tasks. In this work, we empirically demonstrate that mutual information-based representation learning approaches do fail to learn complete representations on a number of designed and real-world tasks. To mitigate these problems we introduce the Wasserstein dependency measure, which learns more complete representations by using the Wasserstein distance instead of the KL divergence in the mutual information estimator. We show that a practical approximation to this theoretically motivated solution, constructed using Lipschitz constraint techniques from the GAN literature, achieves substantially improved results on tasks where incomplete representations are a major challenge.
Distributed Algorithms for Fully Personalized PageRank on Large Graphs

Personalized PageRank (PPR) has enormous applications, such as link prediction and recommendation systems for social networks, which often require the fully PPR to be known. Besides, most of real-life graphs are edge-weighted, e.g., the interaction between users on the Facebook network. However, it is computationally difficult to compute the fully PPR, especially on large graphs, not to mention that most existing approaches do not consider the weights of edges. In particular, the existing approach cannot handle graphs with billion edges on a moderate-size cluster. To address this problem, this paper presents a novel study on the computation of fully edge-weighted PPR on large graphs using the distributed computing framework. Specifically, we employ the Monte Carlo approximation that performs a large number of random walks from each node of the graph, and exploits the parallel pipeline framework to reduce the overall running time of the fully PPR. Based on that, we develop several optimization techniques which (i) alleviate the issue of large nodes that could explode the memory space, (ii) pre-compute short walks for small nodes that largely speedup the computation of random walks, and (iii) optimize the amount of random walks to compute in each pipeline that significantly reduces the overhead. With extensive experiments on a variety of real-life graph datasets, we demonstrate that our solution is several orders of magnitude faster than the state-of-the-arts, and meanwhile, largely outperforms the baseline algorithms in terms of accuracy.
Developmental trajectory of Caenorhabditis elegans nervous system governs its structural organization

A central problem of neuroscience involves uncovering the principles governing the organization of nervous systems which ensure robustness in brain development. The nematode Caenorhabditis elegans provides us with a model organism for studying this question. In this paper, we focus on the invariant connection structure and spatial arrangement of the neurons comprising the somatic neuronal network of this organism to understand the key developmental constraints underlying its design. We observe that neurons with certain shared characteristics—such as, neural process lengths, birth time cohort, lineage and bilateral symmetry—exhibit a preference for connecting to each other. Recognizing the existence of such homophily and their relative degree of importance in determining connection probability within neurons (for example, in synapses, symmetric pairing is the most dominant factor followed by birth time cohort, process length and lineage) helps in connecting specific neuronal attributes to the topological organization of the network. Further, the functional identities of neurons appear to dictate the temporal hierarchy of their appearance during the course of development. Providing crucial insights into principles that may be common across many organisms, our study shows how the trajectory in the developmental landscape constrains the structural organization of a nervous system.
A Geometric Variational Approach to Bayesian Inference

Abstract We propose a novel Riemannian geometric framework for variational inference in Bayesian models based on the nonparametric Fisher–Rao metric on the manifold of probability density functions. Under the square-root density representation, the manifold can be identified with the positive orthant of the unit hypersphere in , and the Fisher–Rao metric reduces to the standard metric. Exploiting such a Riemannian structure, we formulate the task of approximating the posterior distribution as a variational problem on the hypersphere based on the α-divergence. This provides a tighter lower bound on the marginal distribution when compared to, and a corresponding upper bound unavailable with, approaches based on the Kullback–Leibler divergence. We propose a novel gradient-based algorithm for the variational problem based on Fréchet derivative operators motivated by the geometry of , and examine its properties. Through simulations and real data applications, we demonstrate the utility of the proposed geometric framework and algorithm on several Bayesian models. Supplementary materials for this article are available online.
Optimization of Inf-Convolution Regularized Nonconvex Composite Problems

In this work, we consider nonconvex composite problems that involve inf-convolution with a Legendre function, which gives rise to an anisotropic generalization of the proximal mapping and Moreau-envelope. In a convex setting such problems can be solved via alternating minimization of a splitting formulation, where the consensus constraint is penalized with a Legendre function. In contrast, for nonconvex models it is in general unclear that this approach yields stationary points to the infimal convolution problem. To this end we analytically investigate local regularity properties of the Moreau-envelope function under prox-regularity, which allows us to establish the equivalence between stationary points of the splitting model and the original inf-convolution model. We apply our theory to characterize stationary points of the penalty objective, which is minimized by the elastic averaging SGD (EASGD) method for distributed training. Numerically, we demonstrate the practical relevance of the proposed approach on the important task of distributed training of deep neural networks.
A Multimodal Emotion Sensing Platform for Building Emotion-Aware Applications

Humans use a host of signals to infer the emotional state of others. In general, computer systems that leverage signals from multiple modalities will be more robust and accurate in the same task. We present a multimodal affect and context sensing platform. The system is composed of video, audio and application analysis pipelines that leverage ubiquitous sensors (camera and microphone) to log and broadcast emotion data in real-time. The platform is designed to enable easy prototyping of novel computer interfaces that sense, respond and adapt to human emotion. This paper describes the different audio, visual and application processing components and explains how the data is stored and/or broadcast for other applications to consume. We hope that this platform helps advance the state-of-the-art in affective computing by enabling development of novel human-computer interfaces.
The effectiveness of seed priming and foliar application of zinc- amino acid chelates in comparison with zinc sulfate on yield and grain nutritional quality of common bean

Abstract The efficacy of seed priming and foliar application of zinc-amino acid chelates including zinc-histidine [Zn(His)2] and zinc-methionine [Zn(Met)2] in comparison with zinc sulfate (ZnSO4) on yield and grain nutritional quality of two common bean cultivars (Phaseolus vulgaris L., cvs Talash and Sadri) was investigated in a severely Zn-deficient calcareous soil (DTPA-Zn: 0.38 mg kg−1 soil) in a pot experiment. Bean response to Zn application varied depending on the Zn fertilizer, application method and cultivar. In ‘Talash’, seed priming with [Zn(His)2] and [Zn(Met)2] led to 24.1 and 11.6% increase in the grain yield of bean in comparison with ZnSO4 treatment, respectively. In both cultivars, foliar application of [Zn(His)2] led to significant increase in the grain yield in comparison with ZnSO4. The highest grain Zn concentration was obtained by seed priming with [Zn(Met)2] in ‘Sadri’ and [Zn(His)2] in ‘Talash’, respectively. For Zn-amino acid chelates, seed priming was more effective than foliar application in increasing grain yield and Zn concentration. Foliar application of [Zn(His)2] and [Zn(Met)2] in ‘Sadri’ and [Zn(Met)2] in ‘Talash’ resulted in higher protein content in bean grain as compared with ZnSO4. In both cultivars, foliar application of [Zn(Met)2] was the more effective than seed priming to increase grain protein content. The highest water-soluble carbohydrates concentration of grain was obtained by seed priming with [Zn(Met)2] and [Zn(His)2] in ‘Sadri’ and ‘Talash’ cultivars, respectively. Therefore, seed priming with [Zn(His)2] and ZnSO4 in ‘Sadri’ and [Zn(Met)2] in ‘Talash’ can effectively be used for improving yield of common bean in Zn-deficient calcareous soils.
Sogou Machine Reading Comprehension Toolkit

Machine reading comprehension have been intensively studied in recent years, and neural network-based models have shown dominant performances. In this paper, we present a Sogou Machine Reading Comprehension (SMRC) toolkit that can be used to provide the fast and efficient development of modern machine comprehension models, including both published models and original prototypes. To achieve this goal, the toolkit provides dataset readers, a flexible preprocessing pipeline, necessary neural network components, and built-in models, which make the whole process of data preparation, model construction, and training easier.
The Collection of Rhizosphere Microorganisms: its importance for the study of associative plant-bacterium interactions

Microbial culture collections are very important components of biological science. They provide researchers with material for studies and preserve biological resources. One such collection is the Collection of Rhizosphere Microorganisms, kept at the Institute of Biochemistry and Physiology of Plants and Microorganisms of the Russian Academy of Sciences, Saratov (IBPPM). Its activity is primarily directed toward the isolation and preservation of microorganisms from the plant root zone. The international research interest in microorganisms from this ecological niche is not waning, because they are very important for plant growth and development and, consequently, for plant breeding. The group of bacteria with properties of significance for plants has been given the name “plant-growth-promoting rhizobacteria” (PGPR). This group includes nitrogen-fixing soil alpha-proteobacteria of the genus Azospirillum, which form the core of the IBPPM collection. First discovered by Brazilian scientists in the 1970s, azospirilla are now a universally recognized model object for studying the molecular mechanisms underlying plant-bacterium interactions. The broad range of useful properties found in these microorganisms, including the fixation of atmospheric nitrogen, production of phytohormones, solubilization of phosphates, control of pathogens, and formation of induced systemic resistance in the colonized plants, make these bacteria an all-purpose tool that has been used for several decades in basic and applied research. This article reviews the current state of Azospirillum research, with emphasis on the results obtained at the IBPPM. Scientific expeditions across the Saratov region undertaken by IBPPM microbiologists in the early 1980s formed the basis for the unique collection of members of this bacterial taxon. Currently, the collection has more than 160 Azospirillum strains and is one of the largest collections in Europe. The research conducted at the IBPPM is centered mostly on the Azospirillum structures involved in associative symbiosis with plants, primarily extracellular polysaccharide-containing complexes and lectins. The development of immunochemical methods contributed much to our understanding of the overall organization of the surface of rhizosphere bacteria. The extensive studies of the Azospirillum genome largely deepened our understanding of the role of the aforesaid bacterial structures, motility, and biofilms in the colonization of host plant roots. Of interest are also applied studies focusing on agricultural and environmental technologies and on the “green” synthesis of Au, Ag, and Se nanoparticles. The Collection of Rhizosphere Microorganisms continues to grow, being continually supplemented with newly isolated strains. The data presented in this article show the great importance of specialized microbial culture repositories, such as the IBPPM collection, for the development and maintenance of the microbial research base and for the effective solution of basic and applied tasks in microbiology.
Using Gaussian process regression for efficient parameter reconstruction

Optical scatterometry is a method to measure the size and shape of periodic micro- or nanostructures on surfaces. For this purpose the geometry parameters of the structures are obtained by reproducing experimental measurement results through numerical simulations. We compare the performance of Bayesian optimization to different local minimization algorithms for this numerical optimization problem. Bayesian optimization uses Gaussian-process regression to find promising parameter values. We examine how pre-computed simulation results can be used to train the Gaussian process and to accelerate the optimization.
Improvement of Copper Metal Leaching in Sulfuric Acid Solution by Simultaneous Use of Oxygen and Cupric Ions

A new concept for copper (Cu) metal leaching by the simultaneous use of cupric ions (Cu2+) and oxygen (O2) was proposed to improve Cu metal leaching in sulfuric acid. According to this concept, Cu2+ oxidizes Cu metal into cuprous ion (Cu+), and O2 oxidizes Cu+ into Cu2+. The improvement in Cu leaching efficiency from Cu metal was investigated experimentally in the sulfuric acid solution using Cu2+ and O2 simultaneously. Furthermore, the result was compared with that for the sulfuric acid solution containing neither Cu2+ nor O2 and with the sulfuric acid solution without Cu2+ and O2. When both Cu2+ and O2 were used in the leaching solution, the leaching rate of Cu from Cu metal powder was higher than at other leaching conditions, and the leaching efficiency of Cu increased to more than 99.9% in 1 mol/L sulfuric acid solution at 400 rpm and 50 °C with ≤75 µm Cu metal powder, 1% pulp density, 10,000 mg/L initial Cu2+ concentration, and 100 cc/min O2 introduction. These results indicated that the leaching of Cu from Cu metal could be accelerated by adding Cu2+ and O2 in the sulfuric acid solution.