Miracle-dz/UniFAR-FTUs huggingface dataset:
Influence of anisotropic gas diffusion layers on transport phenomena in a proton exchange membrane fuel cell

The gas diffusion layer is an anisotropic porous medium, which provides pathways for the reactant gases and produced water, conducts the electrical current, removes the generated heat, and provides mechanical support. However, the gas diffusion layer is mostly considered as isotropic in numerical simulations. In the present study, a three‐dimensional, two‐phase flow, and non‐isothermal agglomerate model with consideration of anisotropic permeability, mass diffusivity, thermal conductivity, and electrical conductivity was developed and employed to investigate effects of anisotropic properties on the transport phenomena in a proton exchange membrane fuel cell. The temperature of the anisotropic case is less than that of the isotropic case, and the temperature difference increases with increasing current density. Furthermore, the distributions of the oxygen mass fraction, liquid water saturation, water content, and local current density for both cases are also compared and discussed in detail. The cell performance is over‐predicted by the isotropic model, and the current density of the isotropic case is greater than that of the anisotropic case by approximately 10% at an operating cell voltage of 0.3 V. Both the local transport characteristics and overall cell performance are different for the isotropic and anisotropic cases. Accordingly, it is concluded that the anisotropic properties of the gas diffusion layer must be taken into account in the mathematical model. Copyright © 2017 John Wiley & Sons, Ltd.
The Content of Phenolic Compounds in Gentiana cruciata L. Growing in the Territory of the Republic of the Tatarstan

The article presents the results of a study of the quantitative content of phenolic compounds in aerial shoots and underground organs of the cruciate gentian (Gentiana cruciata L.) growing in plant communities on the territory of Tatarstan. The quantitative analysis of medicinal plant materials carried out by the spectrophotometric method confirmed the results of earlier studies, according to which the aerial part of the cruciform gentian plants is 5-6 times richer in biologically active phenolic compounds than rhizomes with roots. In addition, it was found that plants growing on the territory of Laishevsky (meadow cenosis) and Chistopol districts (forest cenosis) of the Republic of Tatarstan accumulate different amounts of biologically active compounds, which is probably due to the ecological-cenotic growing conditions. Namely, the positive effect of the concentration of some elements of mineral nutrition (exchangeable potassium, mobile phosphorus) and the insolation level, as well as the negative influence of organic matter (humus) on the content of the sum of xanthones and flavones in the plant raw materials of the cruciform gentian was noted. It was found that the maximum content of phenolic compounds among all studied samples of raw materials is typical for the aboveground part of Gentiana cruciata plants collected on the territory of Laishevsky district.
Social Support Positively Correlated with Dietary Adherence among Patients with Hypertension in Primary Health Center

Dietary adherence is one of the requirements in chronic disease management, which the success of hypertension management may correlate with the social support perceived by the patients. The study aimed to identify the relationship between social support and dietary adherence among patients with hypertension in Primary Health Centre, Tangerang. The study was a descriptive correlational, cross-sectional study, in which 120 respondents were recruited by using purposive sampling. Multidimensional Perceived of Social Support (MSPSS) and Dietary Adherence questionnaire was used based upon validity and reliability to measure social support and adherence. Pearson ProductMoment Correlation Coefficient was used to examine the relationship between social support and dietary adherence. The analysis result indicated that respondents who reported low support were 51 respondents (42.5%), and low dietary adherence was 53 respondents (44.2%). The analysis results showed there was a significant positive relationship between social support and dietary adherence among patients with hypertension, in which the power of correlation was moderate (r = 0.49, p < 0.05). Patients with hypertension who perceived higher social support more likely to adhere to the dietary of hypertension. The result suggests that health care providers need to facilitate support from friends, family, and significant others of the patients to achieve optimum dietary adherence.
Anthropometric Characteristics and Mineral Distribution and Contamination in Artisanal Small-scale Gold Mining Site of Ciguha in Gunung Pongkor, Bogor

Ciguha in Gunung Pongkor, Bogor, had been an artisanal and small-scale gold mining site (ASGM) since 1998 where amalgamations were used intensively leading to pollution in water, soil, and foodstuffs. The present study assessed distribution and contamination level (CL) of minerals in a total of 69 environmental samples consisting of drinking water (n = 12), rice (n = 13), vegetable (n = 15), fruits (n = 5), fish (n = 14), and soil (n = 10). Chromium, iron, and manganese representing trace essential elements but toxic at high level, mercury representing heavy metal, arsenic representing toxic metalloid, and selenium representing essential metalloid which toxic at high level, were analyzed as mineral contaminants. Meanwhile, a total of 101 Ciguha residents consisting of 60 adults, 15 teenagers, and 26 school aged children were involved to charaterize athropometric exposure factors. The results shows that mercury has polluted soil, kangkung, thai squash, cassava leaves, and rice with contamination level (CL) of 604.43, 8.15, 6.01, 4.14, and 2.76 folds, respectively, while chromium has only polluted thai squash with CL of 1.18 folds. Amazingly, mercury distribution was only in the third position after iron and manganese, while the most distributed mineral was iron and the least was selenium. In overall environmental matrices, the detection frequencies of iron, manganese, mercury, chromium, arsenic, and selenium were 94.2, 56.2, 52.2, 47.8, 21.7, and 15.9 %, respectively. In conclusion, mercury was the most critical contaminant in ASGM site of Ciguha that has heavily polluted soil and grown vegetables, but the most distributed mineral was iron. Toxicologically, only mercury and chromium are important while arsenic, iron, manganese, and selenium are of less concern since the CL<1.
Diagnostic Evidence GAuge of Single cells (DEGAS): A flexible deep-transfer learning framework for prioritizing cells in relation to disease

We propose DEGAS (Diagnostic Evidence GAuge of Single cells), a novel deep transfer learning framework, to transfer disease information from patients to cells. We call such transferrable information “impressions,” which allow individual cells to be associated with disease attributes like diagnosis, prognosis, and response to therapy. Using simulated data and ten diverse single cell and patient bulk tissue transcriptomic datasets from Glioblastoma Multiforme (GBM), Alzheimer’s Disease (AD), and Multiple Myeloma (MM), we demonstrate the feasibility, flexibility, and broad applications of the DEGAS framework. DEGAS analysis on newly generated myeloma single cell transcriptomics led to the identification of PHF19high myeloma cells associated with progression.
Isolation and characterization of surface‐active fractions responsible for foam formation during anaerobic digestion of municipal wastes

A simple and rapid method to isolate surfactant‐like fractions responsible for foaming production during anaerobic digestion (AD) is proposed. Alkaline extraction of organic matter and subsequent fractionation of extracts into alkali‐soluble/acid‐insoluble (humic acid, HA) and alkali‐soluble/acid‐soluble fractions (fulvic acid, FA) were performed on samples directly taken from a full‐scale plant operating anaerobic digestion. Lab‐tests performed to measure foaming ability of the isolated organic fractions, allowed the separation of the fraction responsible for foam production, fulvic acid. Fraction isolation and subsequent chemical characterization revealed its specific chemical nature, i.e. high hydrophobicity, due to low carboxylic group content, and the presence of high N‐containing molecules. These characteristics agreed with those reported in the literature for molecules responsible for foam formation during anaerobic digestion, confirming the ability of this simple fractionation procedure to isolate the fraction responsible for foam formation.
Transcription of macromolecular N degrading genes provides a window into organic nitrogen decomposition in soil

Background: Nitrogen (N) is commonly limiting in soil in part because most N is present in macromolecular organic compounds and not directly available to plants. Using extracellular enzymes, the soil microbial community present near roots (rhizosphere) is largely responsible for transforming organic substrates, which yields nitrogen in forms that are plant-available. Microbial genetic potential (genes) and the cycling of inorganic nitrogen pools have been extensively documented and widely explored in rhizospere soil. But there is scarce information on microbial gene expression for macromolecular nitrogen decomposition and how it relates in space and time to life stages of plant roots. Here we use a suite of time-resolved metatranscriptomes from rhizosphere and bulk soil to follow bacterial and fungal extracellular protease and chitinase expression during rhizosphere aging. We also explore the effect of adding plant root litter as a distinct source of macromolecular carbon and nitrogen. Results: Expression of extracellular proteases in rhizosphere soil increased with plant growth in the absence of litter, whereas chitinase ( chit1 ) was highly upregulated in the detritusphere. Structural groups of proteases were dominated by serineproteases, despite the higher abundance of metalloproteases previously observed in soil and aquatic systems. Extracellular proteases of Betaprotebacteria were more highly expressed in the rhizosphere, whereas those of Deltaroteobacteria and Fungi responded strongly to the presence of litter. We identified distinct functional guilds of taxa specializing in decomposition of proteins in the rhizosphere, detritusphere and in the vicinity of aging roots. We also identify a sub-guild that appears to specialize in protein decomposition in the presence of growing roots as well as litter that increases its activity in aging rhizosphere soil, which may be particularly N limited. Taxonomic membership of these guilds partially overlapped with functional guilds identified by carbon decomposing enzymes, making up multifunctional guilds. Conclusions: We found organic N decomposers have distinct spatiotemporal preferences that are selected during plant growth. Rhizosphere microbes which could potentially benefit plants by degrading macromolecular nitrogen were not comparatively active in bulk soil, even in the presence of root litter, implying that they are enhanced near plant roots and less competitive in bulk soils.
New design approach of Front-End Electronics for high-accuracy time measurement systems

This paper discusses a detailed design approach to determine the optimal input impedance ($Z_{in}$) and bandwidth (BW) for current preamplifiers in Front End Electronics (FEE) of high-accuracy time measurement systems used in particle detection. Our study shows the effect of $Z_{in}$ including the parasitic interconnection inductances of bonding wires between the detector and the FEE. We explain also the development of a new mathematical model for the estimation of the timing jitter of the current preamplifier in the case of using a low capacitor detector ($C_{d}$) as diamond. Tests and measurements of the response of a transimpedance amplifier (TIA) designed in a 130 nm 1P8M CMOS technology are used to demonstrates the accuracy of the design approach and the timing jitter estimation model.
Pronto: Federated Task Scheduling

We present a federated, asynchronous, memory-limited algorithm for online task scheduling across large-scale networks of hundreds of workers. This is achieved through recent advancements in federated edge computing that unlocks the ability to incrementally compute local model updates within each node separately. This local model is then used along with incoming data to generate a rejection signal which reflects the overall node responsiveness and if it is able to accept an incoming task without resulting in degraded performance. Through this innovation, we allow each node to execute scheduling decisions on whether to accept an incoming job independently based on the workload seen thus far. Further, using the aggregate of the iterates a global view of the system can be constructed, as needed, and could be used to produce a holistic perspective of the system. We complement our findings, by an empirical evaluation on a large-scale real-world dataset of traces from a virtualized production data center that shows, while using limited memory, that our algorithm exhibits state-of-the-art performance. Concretely, it is able to predict changes in the system responsiveness ahead of time based on the industry-standard CPU-Ready metric and, in turn, can lead to better scheduling decisions and overall utilization of the available resources. Finally, in the absence of communication latency, it exhibits attractive horizontal scalability.
Novel Approach to Unveil Quantum Phase Transitions Using Fidelity Map

Fidelity approach has been widely used to detect various types of quantum phase transitions, including some that are beyond the Landau symmetry breaking theory, in condensed matter models. However, challenges remain in locating the transition points with precision in several models with unconventional phases such as the quantum spin liquid phase in spin-1 Kitaev-Heisenberg model. In this work, we propose a novel approach, which we named the fidelity map, to detect quantum phase transitions with higher accuracy and sensitivity as compared to the conventional fidelity measures. Our scheme extends the fidelity concept from a single dimension quantity to a multi-dimensional quantity, and use a meta-heuristic algorithm to search for the critical points that globally maximized the fidelity within each phase. We test the scheme in three interacting condensed matter models, namely the spin-1 Kitaev Heisenberg model which consists of the quantum spin liquid phase and the topological Haldane phase, the spin-1/2 XXZ model which possesses a Berezinskii–Kosterlitz–Thouless transition, and the Su-Schrieffer–Heeger model that exhibits a topological quantum phase transition. The result shows that the fidelity map can capture a wide range of phase transitions accurately, thus providing a new tool to study phase transitions in unseen models without prior knowledge of the system’s symmetry.
Applying the Cz\'edli-Schmidt Sequences to congruence properties of planar semimodular lattices

Following G. Grätzer and E. Knapp, 2009, a planar semimodular lattice L is rectangular, if the left boundary chain has exactly one doublyirreducible element, cl, and the right boundary chain has exactly one doublyirreducible element, cr, and these elements are complementary. The Czédli-Schmidt Sequences, introduced in 2012, construct rectangular lattices. We use them to prove some structure theorems. In particular, we prove that for a slim (no M3 sublattice) rectangular lattice L, the congruence lattice ConL has exactly length[cl, 1] + length[cr, 1] dual atoms and a dual atom in ConL is a congruence with exactly two classes. We also describe the prime ideals in a slim rectangular lattice.
A Reinforcement Learning Environment for Polyhedral Optimizations

The polyhedral model allows a structured way of defining semantics-preserving transformations to improve the performance of a large class of loops. Finding profitable points in this space is a hard problem which is usually approached by heuristics that generalize from domain-expert knowledge. Existing problem formulations in state-of-the-art heuristics depend on the shape of particular loops, making it hard to leverage generic and more powerful optimization techniques from the machine learning domain. In this paper, we propose PolyGym, a shape-agnostic formulation for the space of legal transformations in the polyhedral model as a Markov Decision Process (MDP). Instead of using transformations, the formulation is based on an abstract space of possible schedules. In this formulation, states model partial schedules, which are constructed by actions that are reusable across different loops. With a simple heuristic to traverse the space, we demonstrate that our formulation is powerful enough to match and outperform state-of-the-art heuristics. On the Polybench benchmark suite, we found transformations that led to a speedup of 3.39x over LLVM O3, which is 1.83x better than the speedup achieved by ISL. Our generic MDP formulation enables using reinforcement learning to learn optimization policies over a wide range of loops. This also contributes to the emerging field of machine learning in compilers, as it exposes a novel problem formulation that can push the limits of existing methods.
Can the Wikipedia moderation model rescue the social marketplace of ideas?

Facebook announced a community review program in December 2019 and Twitter launched a community-based platform to address misinformation, called Birdwatch, in January 2021. We provide an overview of the potential affordances of such community based approaches to content moderation based on past research. While our analysis generally supports a community-based approach to content moderation, it also warns against potential pitfalls, particularly when the implementation of the new infrastructures does not promote diversity. We call for more multidisciplinary research utilizing methods from complex systems studies, behavioural sociology, and computational social science to advance the research on crowd-based content moderation.
HOTR: End-to-End Human-Object Interaction Detection with Transformers

Human-Object Interaction (HOI) detection is a task of identifying "a set of interactions" in an image, which involves the i) localization of the subject (i.e., humans) and target (i.e., objects) of interaction, and ii) the classification of the interaction labels. Most existing methods have indirectly addressed this task by detecting human and object instances and individually inferring every pair of the detected instances. In this paper, we present a novel framework, referred by HOTR, which directly predicts a set of 〈human, object, interaction〉 triplets from an image based on a transformer encoder-decoder architecture. Through the set prediction, our method effectively exploits the inherent semantic relationships in an image and does not require time-consuming post-processing which is the main bottleneck of existing methods. Our proposed algorithm achieves the state-of-the-art performance in two HOI detection benchmarks with an inference time under 1 ms after object detection.
Reconstructing nodal pressures in water distribution systems with graph neural networks

Knowing the pressure at all times in each node of a water distribution system (WDS) facilitates safe and efficient operation. Yet, complete measurement data cannot be collected due to the limited number of instruments in a real-life WDS. The data-driven methodology of reconstructing all the nodal pressures by observing only a limited number of nodes is presented in the paper. The reconstruction method is based on K-localized spectral graph filters, wherewith graph convolution on water networks is possible. The effect of the number of layers, layer depth and the degree of the Chebyshev-polynomial applied in the kernel is discussed taking into account the peculiarities of the application. In addition, a weighting method is shown, wherewith information on friction loss can be embed into the spectral graph filters through the adjacency matrix. The performance of the proposed model is presented on 3 WDSs at different number of nodes observed compared to the total number of nodes. The weighted connections prove no benefit over the binary connections, but the proposed model reconstructs the nodal pressure with at most 5% relative error on average at an observation ratio of 5% at least. The results are achieved with shallow graph neural networks by following the considerations discussed in the paper.
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization

Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action using a multivariate Gaussian with a diagonal covariance structure. This modeling choice assumes that different dimensions of the next state and reward are conditionally independent given the current state and action and may be driven by the fact that fully observable physics-based simulation environments entail deterministic transition dynamics. In this paper, we challenge this conditional independence assumption and propose a family of expressive autoregressive dynamics models that generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We demonstrate that autoregressive dynamics models indeed outperform standard feedforward models in log-likelihood on heldout transitions. Furthermore, we compare different model-based and model-free off-policy evaluation (OPE) methods on RL Unplugged, a suite of offline MuJoCo datasets, and find that autoregressive dynamics models consistently outperform all baselines, achieving a new state-of-the-art. Finally, we show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer through data augmentation and improving performance using model-based planning.
The Algonauts Project 2021 Challenge: How the Human Brain Makes Sense of a World in Motion

The sciences of natural and artificial intelligence are fundamentally connected. Brain-inspired human-engineered AI are now the standard for predicting human brain responses during vision, and conversely, the brain continues to inspire invention in AI. To promote even deeper connections between these fields, we here release the 2021 edition of the Algonauts Project Challenge: How the Human Brain Makes Sense of a World in Motion (http://algonauts.csail.mit.edu/). We provide whole-brain fMRI responses recorded while 10 human participants viewed a rich set of over 1,000 short video clips depicting everyday events. The goal of the challenge is to accurately predict brain responses to these video clips. The format of our challenge ensures rapid development, makes results directly comparable and transparent, and is open to all. In this way it facilitates interdisciplinary collaboration towards a common goal of understanding visual intelligence. The 2021 Algonauts Project is conducted in collaboration with the Cognitive Computational Neuroscience (CCN) conference.