Miracle-dz/UniFAR-FTUs huggingface dataset:
E-learning as an effective educational space in Poland: the benefits and disadvantages of studying using Moodle

Purpose: The purpose of this paper is to present the benefits and disadvantages of e-learning form of teaching in students’ opinion at Częstochowa University of Technology and Wrocław University of Technology. Findings: The main achievement relates to clear indication of main problems with an e-exams in both, students’ and staffs’ opinions. The biggest disadvantage of e-exams is taken into account only the final results, without taking into account the way of thinking. This article provides a practical way to solve this problem. The achievement is presentation structure of respondents’/students’ because of age, gender as well as type of studies. Valuable and original results in both cognitive and application related terms to the determination of education trends have been obtained. Research limitations/implications: Research and analysis, in spite of their interdisciplinary scope, have been limited in the field of educational processes. Due to the specificity of the issue, it is difficult to estimate the economic impact of the implementation of the proposed solution. Researches were carried out among 698 students from Czestochowa University of Technology and Wroclaw University of Technology located in Poland. Surveyed using a standard questionnaire on students experiences attending an e-learning courses and an e-exams. Practical implications: Within only a few years, the use of e-learning has increased rapidly in Poland. Although the advantages and disadvantages of e-learning have been discussed in various previous studies; it is a significant issue of better understanding the reasons why some students are dissatisfied with their e-learning practice. The practical application of the proposed solution is related to the increasing demand for e-learning form of study among students but also among teachers. The main aim of researches was to improve studying in the Moodle platform. Originality/value: Presented in this paper author’s opinion in the field of education trend is an original approach of the subject. The main factors’ in the field of education trends have been indicated.
USE OF Crescentia alata AND Guazuma ulmifolia FRUITS IN LAMB FEEDING IN SUBTROPICAL REGION OF GUERRERO, MEXICO Uso del fruto de Crescentia alata y Guazuma ulmifolia en la alimentación de corderos en la región subtropical de Guerrero, México

Chemical composition, in vitro gas production (PGIV) and digestibility of Crescentia alata and Guazuma ulmifolia fruits in Pungarabato, Guerrero, Mexico, were determined and were used to feeding lambs and evaluated the acceptability and animal response. Six and 15 male lambs Pelibuey x Dorper with body weight between 13.9 and 15.6 kg, respectively were used in the test of acceptability and productive response test. The acceptability study developed in 22 days (d), treatments were the two fruits. In the study of productive response developed in 55 d, treatments were: T1: oat hay and 50 g of corn, (Zea mays L.) T2: oat hay and G. ulmifolia fruit, T3: oat hay and C. alata fruit. The data, chemical composition, PGIV, digestibility of fruits, the number and duration of visits to the feeder (NVF and DVF), dry matter intake (DMI) and daily weight gain (DGW) were analyzed completely random. The data on the dynamics of fruit consumption were analyzed in a factorial design arrangement 2 x 6. In both designs Tukey test was used to compare means (P<0.05). The protein content (82.0 g kg –1 dry matter (DM); P<0.030), energy (5.6 MJ kg –1 DM; P<0.010) and total phenol (22.7 g kg –1 DM; P<0,001) was higher for G. ulmifolia. In the acceptability DMI, NVF and DVF was higher (P<0.05) about fruits of G. ulmifolia with 189.4 g –1 d –1 , 15.5 hits
A General Theory of the Stochastic Linear Bandit and Its Applications

Recent growing adoption of experimentation in practice has led to a surge of attention to multiarmed bandits as a technique to reduce the opportunity cost of online experiments. In this setting, a decision-maker sequentially chooses among a set of given actions, observes their noisy rewards, and aims to maximize her cumulative expected reward (or minimize regret) over a horizon of length T . In this paper, we introduce a general analysis framework and a family of algorithms for the stochastic linear bandit problem that includes well-known algorithms such as the optimism-in-the-face-of-uncertainty-linear-bandit (OFUL) and Thompson sampling (TS) as special cases. Our analysis technique bridges several streams of prior literature and yields a number of new results. First, our new notion of optimism in expectation gives rise to a new algorithm, called sieved greedy (SG) that reduces the overexploration problem in OFUL. SG utilizes the data to discard actions with relatively low uncertainty and then choosing one among the remaining actions greedily. In addition to proving that SG is theoretically rate optimal, our empirical simulations show that SG outperforms existing benchmarks such as greedy, OFUL, and TS. The second application of our general framework is (to the best of our knowledge) the ﬁrst polylogarithmic (in T ) regret bounds for OFUL and TS, under similar conditions as the ones by Goldenshluger and Zeevi (2013). Finally, we obtain sharper regret bounds for the k -armed contextual MABs by a factor of √ k .
Simplicial Embeddings in Self-Supervised Learning and Downstream Classification

Simplicial Embeddings (SEM) are representations learned through self-supervised learning (SSL), wherein a representation is projected into $L$ simplices of $V$ dimensions each using a softmax operation. This procedure conditions the representation onto a constrained space during pretraining and imparts an inductive bias for group sparsity. For downstream classification, we formally prove that the SEM representation leads to better generalization than an unnormalized representation. Furthermore, we empirically demonstrate that SSL methods trained with SEMs have improved generalization on natural image datasets such as CIFAR-100 and ImageNet. Finally, when used in a downstream classification task, we show that SEM features exhibit emergent semantic coherence where small groups of learned features are distinctly predictive of semantically-relevant classes.
Parallel Proof-of-Work with Concrete Bounds

Authorization is challenging in distributed systems that cannot rely on the identification of nodes. Proof-of-work offers an alternative gate-keeping mechanism, but its probabilistic nature is incompatible with conventional security definitions. Recent related work establishes concrete bounds for the failure probability of Bitcoin's sequential proof-of-work mechanism. We propose a new family of state replication protocols that use parallel proof-of-work. Our bottom-up design from an agreement sub-protocol allows us to give concrete bounds for the failure probability in adversarial synchronous networks. State updates can be sufficiently secure to support commits after one block, removing the risk of double-spending in many applications. We offer guidance on the optimal choice of parameters for a wide range of network and attacker assumptions. Simulations show that the proposed construction is robust even against partial violations of our design assumptions.
Stereo Unstructured Magnification: Multiple Homography Image for View Synthesis

This paper studies the problem of view synthesis with certain amount of rotations from a pair of images, what we called stereo unstructured magnification. While the multi-plane image representation is well suited for view synthesis with depth invariant, how to generalize it to unstructured views remains a significant challenge. This is primarily due to the depth-dependency caused by camera frontal parallel representation. Here we propose a novel multiple homography image (MHI) representation, comprising of a set of scene planes with fixed normals and distances. A two-stage network is developed for novel view synthesis. Stage-1 is an MHI reconstruction module that predicts the MHIs and composites layered multi-normal images along the normal direction. Stage-2 is a normal-blending module to find blending weights. We also derive an angle-based cost to guide the blending of multi-normal images by exploiting per-normal geometry. Compared with the state-of-the-art methods, our method achieves superior performance for view synthesis qualitatively and quantitatively, especially for cases when the cameras undergo rotations.
Evaluation of Fake News Detection with Knowledge-Enhanced Language Models

Recent advances in fake news detection have exploited the success of large-scale pre-trained language models (PLMs). The predominant state-of-the-art approaches are based on fine-tuning PLMs on labelled fake news datasets. However, large-scale PLMs are generally not trained on structured factual data and hence may not possess priors that are grounded in factually accurate knowledge. The use of existing knowledge bases (KBs) with rich human-curated factual information has thus the potential to make fake news detection more effective and robust. In this paper, we investigate the impact of knowledge integration into PLMs for fake news detection. We study several state-of-the-art approaches for knowledge integration, mostly using Wikidata as KB, on two popular fake news datasets - LIAR, a politics-based dataset, and COVID-19, a dataset of messages posted on social media relating to the COVID-19 pandemic. Our experiments show that knowledge-enhanced models can significantly improve fake news detection on LIAR where the KB is relevant and up-to-date. The mixed results on COVID-19 highlight the reliance on stylistic features and the importance of domain specific and current KBs. The code is available at https://github.com/chenxwh/fake-news-detection.
Noninteger conformal dimensions for type IIA flux vacua

We study the conformal dimensions of the would-be dual operators to the stabilized moduli for two non-supersymmetric DGKT vacua. For the ﬁrst of them, related to the standard SUSY DGKT vacuum by G 4 = − G SUSY4 , we obtain integer conformal dimensions. For the second of them, which has a non-zero harmonic component in G 2 , we obtain both integers and real numbers.
Model Predictive Control for Fluid Human-to-Robot Handovers

Human-robot handover is a fundamental yet challenging task in human-robot interaction and collaboration. Recently, remarkable progressions have been made in human-to-robot handovers of unknown objects by using learning-based grasp generators. However, how to responsively generate smooth motions to take an object from a human is still an open question. Specifically, planning motions that take human comfort into account is not a part of the human-robot handover process in most prior works. In this paper, we propose to generate smooth motions via an efficient model-predictive control (MPC) framework that integrates perception and complex domain-specific constraints into the optimization problem. We introduce a learning-based grasp reachability model to select candidate grasps which maximize the robot's manipulability, giving it more freedom to satisfy these constraints. Finally, we integrate a neural net force/torque classifier that detects contact events from noisy data. We conducted human-to-robot handover experiments on a diverse set of objects with several users ($N=4$) and performed a systematic evaluation of each module. The study shows that the users preferred our MPC approach over the baseline system by a large margin.
Dynamical Analysis of the Covarying Coupling Constants in Scalar-Tensor Gravity

A scalar–tensor theory of gravity was considered, wherein the gravitational coupling G and the speed of light c were admitted as space–time functions and combined to form the definition of the scalar field ϕ. The varying c participates in the definition of the variation of the matter part of the action; it is related to the effective stress–energy tensor, which is a result of the requirement of symmetry under general coordinate transformations in our gravity model. The effect of the cosmological coupling Λ is accommodated within a possible behavior of ϕ. We analyzed the dynamics of ϕ in the phase space, thereby showing the existence of an attractor point for reasonable hypotheses on the potential V(ϕ) and no particular assumption on the Hubble function. The phase space analysis was performed both with the linear stability theory and via the more general Lyapunov method. Either method led to the conclusion that the condition G˙/G=σc˙/c, where σ=3 must hold for the rest of cosmic evolution after the system arrives at the globally asymptotically stable fixed point and the dynamics of ϕ ceases. This result realized our main motivation: to provide a physical foundation for the phenomenological model admitting G/G0=c/c03, used recently to interpret cosmological and astrophysical data. The thus covarying couplings G and c impact the cosmic evolution after the dynamical system settles to equilibrium. The secondary goal of our work was to investigate how this impact occurs. This was performed by constructing the generalized continuity equation in our scalar–tensor model and considering two possible regimes for the varying speed of light—decreasing c and increasing c—while solving our modified Friedmann equations. The solutions to the latter equations make room for radiation- and matter-dominated eras that progress to a dark-energy-type of accelerated expansion.
Rethinking Position Bias Modeling with Knowledge Distillation for CTR Prediction

Click-through rate (CTR) Prediction is of great importance in real-world online ads systems. One challenge for the CTR prediction task is to capture the real interest of users from their clicked items, which is inherently biased by presented positions of items, i.e., more front positions tend to obtain higher CTR values. A popular line of existing works focuses on explicitly estimating position bias by result randomization which is expensive and inefficient, or by inverse propensity weighting (IPW) which relies heavily on the quality of the propensity estimation. Another common solution is modeling position as features during offline training and simply adopting fixed value or dropout tricks when serving. However, training-inference inconsistency can lead to sub-optimal performance. Furthermore, post-click information such as position values is informative while less exploited in CTR prediction. This work proposes a simple yet efficient knowledge distillation framework to alleviate the impact of position bias and leverage position information to improve CTR prediction. We demonstrate the performance of our proposed method on a real-world production dataset and online A/B tests, achieving significant improvements over competing baseline models. The proposed method has been deployed in the real world online ads systems, serving main traffic on one of the world's largest e-commercial platforms.
A Misfired Outburst in the Neutron Star X-Ray Binary Centaurus X-4

We report on a long-term optical monitoring of the neutron star X-ray binary Centaurus X-4 performed during the last 13.5 yr. This source has been in quiescence since its outburst in 1979. Our monitoring reveals the overall evolution of the accretion disk; we detect short-duration flares, likely originating also in the disk, superimposed with a small-amplitude (<0.1 mag) ellipsoidal modulation from the companion star due to geometrical effects. A long-term (∼2300 days) downward trend, followed by a shorter (∼1000 days) upward one, is observed in the disk light curve. Such a rise in the optical has been observed for other X-ray binaries preceding outbursts, as predicted by the disk instability model. For Cen X-4, the rise of the optical flux proceeded for ∼3 yr, and culminated in a flux increase at all wavelengths (optical–UV–X-rays) at the end of 2020. This increase faded after ∼2 weeks, without giving rise to a full outburst. We suggest that the propagation of an inside-out heating front was ignited due to a partial ionization of hydrogen in the inner disk. The propagation might have stalled soon after the ignition due to the increasing surface density in the disk that the front encountered while propagating outward. The stall was likely eased by the low-level irradiation of the outer regions of the large accretion disk, as shown by the slope of the optical/X-ray correlation, suggesting that irradiation does not play a strong role in the optical, compared to other sources of emission.
Microbial dynamics in the postharvest supply chain of fresh produce---an observational study and simulation model framework

Quality and safety of fresh produce are important to public health and maintaining commerce between Mexico and USA. While preventive practices can reduce risks of contamination and are generally successful, the variable environment of the supply chain of fresh produce can be suitable for introduction or proliferation of pathogenic microorganisms. As routine surveillance of these pathogens is not practical, indicator microorganisms are used to assess the sanitary conditions of production and handling environments. An opportunity exists to use indicators on fresh produce to measure how handling and transport from field to market may affect microbial populations that contribute to their quality or safety. The objective was to quantify indicator microorganisms on tomatoes sampled along the supply chain during the harvest year, in order to observe the levels and changes of populations at different locations. Roma tomatoes (n=475) were taken from the same lots (n=28) at four locations of the postharvest supply chain over five months: at arrival to and departure from the packinghouse in México, at the distribution center in Texas, and at retail in USA. Samples were analyzed individually for four microbial populations: aerobic plate count (APC), total coliforms (TC), generic Escherichia coli, and yeasts and molds (YM). APC population differed (p<0.05) from 1.9±1.1, 1.7±1.1, 2.3±1.1 and 3.5±1.4 log CFU/g at postharvest, packing, distribution center and supermarket, respectively. TC populations were <1 log CFU/g at postharvest, increased at packing
Improving Biomarker-based HIV Incidence Estimation in the Treatment Era

Background: Estimating HIV-1 incidence using biomarker assays in cross-sectional surveys is important for understanding the HIV pandemic. However, the utility of these estimates has been limited by uncertainty about what input parameters to use for false recency rate (FRR) and mean duration of recent infection (MDRI) after applying a recent infection testing algorithm (RITA). Methods: This article shows how testing and diagnosis reduce both FRR and mean duration of recent infection compared to a treatment-naive population. A new method is proposed for calculating appropriate context-specific estimates of FRR and mean duration of recent infection. The result of this is a new formula for incidence that depends only on reference FRR and mean duration of recent infection parameters derived in an undiagnosed, treatment-naive, nonelite controller, non-AIDS-progressed population. Results: Applying the methodology to eleven cross-sectional surveys in Africa results in good agreement with previous incidence estimates, except in 2 countries with very high reported testing rates. Conclusions: Incidence estimation equations can be adapted to account for the dynamics of treatment and recent infection testing algorithms. This provides a rigorous mathematical foundation for the application of HIV recency assays in cross-sectional surveys.
Efficient Active Learning with Abstention

The goal of active learning is to achieve the same accuracy achievable by passive learning, while using much fewer labels. Exponential savings in terms of label complexity have been proved in very special cases, but fundamental lower bounds show that such improvements are impossible in general. This suggests a need to explore alternative goals for active learning. Learning with abstention is one such alternative. In this setting, the active learning algorithm may abstain from prediction and incur an error that is marginally smaller than random guessing. We develop the first computationally efficient active learning algorithm with abstention. Our algorithm provably achieves $\mathsf{polylog}(\frac{1}{\varepsilon})$ label complexity, without any low noise conditions. Such performance guarantee reduces the label complexity by an exponential factor, relative to passive learning and active learning that is not allowed to abstain. Furthermore, our algorithm is guaranteed to only abstain on hard examples (where the true label distribution is close to a fair coin), a novel property we term \emph{proper abstention} that also leads to a host of other desirable characteristics (e.g., recovering minimax guarantees in the standard setting, and avoiding the undesirable ``noise-seeking'' behavior often seen in active learning). We also provide novel extensions of our algorithm that achieve \emph{constant} label complexity and deal with model misspecification.
Population Games With Erlang Clocks: Convergence to Nash Equilibria For Pairwise Comparison Dynamics

The prevailing methodology for analyzing population games and evolutionary dynamics in the large population limit assumes that a Poisson process (or clock) inherent to each agent determines when the agent can revise its strategy. Hence, such an approach presupposes exponentially distributed inter-revision intervals, and is inadequate for cases where each strategy entails a sequence of sub-tasks (sub-strategies) that must be completed before a new revision time occurs. This article proposes a methodology for such cases under the premise that a sub-strategy’s duration is exponentially distributed, leading to Erlang distributed inter-revision intervals. We assume that a so-called pairwise comparison protocol captures the agents’ revision preferences to render our analysis concrete. The presence of sub-strategies brings on additional dynamics that is incompatible with existing models and results. Our main contributions are twofold, both derived for a deterministic approximation valid for large populations. We prove convergence of the population’s state to the Nash equilibrium set when a potential game generates payoffs for the strategies. We use system-theoretic passivity to determine conditions under which this convergence is guaranteed for contractive games.
Fusing Interpretable Knowledge of Neural Network Learning Agents For Swarm-Guidance

Neural-based learning agents make decisions using internal artificial neural networks. In certain situations, it becomes pertinent that this knowledge is re-interpreted in a friendly form to both the human and the machine. These situations include: when agents are required to communicate the knowledge they learn to each other in a transparent way in the presence of an external human observer, in human-machine teaming settings where humans and machines need to collaborate on a task, or where there is a requirement to verify the knowledge exchanged between the agents. We propose an interpretable knowledge fusion framework suited for neural-based learning agents, and propose a Priority on Weak State Areas (PoWSA) retraining technique. We first test the proposed framework on a synthetic binary classification task before evaluating it on a shepherding-based multi-agent swarm guidance task. Results demonstrate that the proposed framework increases the success rate on the swarm-guidance environment by 11% and better stability in return for a modest increase in computational cost of 14.5% to achieve interpretability. Moreover, the framework presents the knowledge learnt by an agent in a human-friendly representation, leading to a better descriptive visual representation of an agent's knowledge.