Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Explainable Student Group Collaboration Assessment Models Using Temporal Representations of Individual Student Roles

Collaboration is identified as a required and necessary skill for students to be successful in the fields of Science, Technology, Engineering and Mathematics (STEM). However, due to growing student population and limited teaching staff it is difficult for teachers to provide constructive feedback and instill collaborative skills using instructional methods. Development of simple and easily explainable machine-learning-based automated systems can help address this problem. Improving upon our previous work, in this paper we propose using simple temporal-CNN deep-learning models to assess student group collaboration that take in temporal representations of individual student roles as input. We check the applicability of dynamically changing feature representations for student group collaboration assessment and how they impact the overall performance. We also use Grad-CAM visualizations to better understand and interpret the important temporal indices that led to the deep-learning model's decision.
Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases

Previous literatures show that pre-trained masked language models (MLMs) such as BERT can achieve competitive factual knowledge extraction performance on some datasets, indicating that MLMs can potentially be a reliable knowledge source. In this paper, we conduct a rigorous study to explore the underlying predicting mechanisms of MLMs over different extraction paradigms. By investigating the behaviors of MLMs, we find that previous decent performance mainly owes to the biased prompts which overfit dataset artifacts. Furthermore, incorporating illustrative cases and external contexts improve knowledge prediction mainly due to entity type guidance and golden answer leakage. Our findings shed light on the underlying predicting mechanisms of MLMs, and strongly question the previous conclusion that current MLMs can potentially serve as reliable factual knowledge bases.
Retrospective analysis of controversial topics on COVID-19 in Japan

For efficient policy-making, a thorough recognition of controversial topics is crucial because the cost of unmitigated controversies would be extremely high for society. However, identifying controversial topics is costly. In this paper, we proposed a framework to search for controversial topics comprehensively. We then conducted a retrospective analysis of the controversial topics of COVID-19 with data obtained via Twitter in Japan as a case study of the framework. The results show that the proposed framework can effectively detect controversial topics that reflect current reality. Controversial topics tend to be about the government, medical matters, economy, and education; moreover, the controversy score had a low correlation with the traditional indicators-scale and sentiment of the topics-which suggests that the controversy score is a potentially important indicator to be obtained. We also discussed the difference between highly controversial topics and less controversial ones despite their large scale and sentiment.
Variation Free Approach for Molecular Density Functional Theory: Data-driven Stochastic Optimization

Density functional theory (DFT) is an efficient instrument for describing a wide range of nanoscale phenomena: wetting transition, capillary condensation, adsorption, etc. In this paper, we suggest a method for obtaining the equilibrium molecular fluid density in a nanopore using DFT without calculating the free energy variation — Variation Free Density Functional Theory (VF-DFT). This technique can be used to explore confined fluids with a complex type of interactions, additional constraints and to speed up calculations, which might be crucial in an inverse problems. The fluid density in VF-DFT approach is represented as a decomposition over a limited set of basis functions. We applied Principal Component Analysis (PCA) to extract the basic patterns from the density function and take them into account in the construction of a set of basis functions. The decomposition coefficients of the fluid density by the basis were sought by stochastic optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO) to minimize the free energy of the system. In this work, two different fluids were studied: nitrogen at a temperature of 77.4 K and argon 87.3 K, at a pore of 3.6 nm, and the performance of optimization algorithms was compared. We also introduce the Hybrid Density Functional Theory (H-DFT) approach based on stochastic optimization methods and the classical Picard iteration method to find the equilibrium fluid density starting from the physically appropriate solution. The combination of Picard iteration and stochastic algorithms helps to significantly speed up the calculations of equilibrium density in the system without losing the quality of the solution, especially in cases with the high relative pressure and expressed layering structure.
Causal Contextual Prediction for Learned Image Compression

Over the past several years, we have witnessed impressive progress in the field of learned image compression. Recent learned image codecs are commonly based on autoencoders, that first encode an image into low-dimensional latent representations and then decode them for reconstruction purposes. To capture spatial dependencies in the latent space, prior works exploit hyperprior and spatial context model to build an entropy model, which estimates the bit-rate for end-to-end rate-distortion optimization. However, such an entropy model is suboptimal from two aspects: (1) It fails to capture global-scope spatial correlations among the latents. (2) Cross-channel relationships of the latents remain unexplored. In this paper, we propose the concept of separate entropy coding to leverage a serial decoding process for causal contextual entropy prediction in the latent space. A causal context model is proposed that separates the latents across channels and makes use of channel-wise relationships to generate highly informative adjacent contexts. Furthermore, we propose a causal global prediction model to find global reference points for accurate predictions of undecoded points. Both these two models facilitate entropy estimation without the transmission of overhead. In addition, we further adopt a new group-separated attention module to build more powerful transform networks. Experimental results demonstrate that our full image compression model outperforms standard VVC/H.266 codec on Kodak dataset in terms of both PSNR and MS-SSIM, yielding the state-of-the-art rate-distortion performance.
Association Between Different Obesity Patterns and the Risk of Developing Type 2 Diabetes Mellitus Among Adults in Eastern China: A Cross-Sectional Study

Purpose Obesity has become a public health challenge worldwide and can lead to the development of diabetes. However, studies examining the associations between different obesity patterns and the development of type 2 diabetes mellitus (T2DM) in China are limited. Therefore, this study aimed to explore the associations between three obesity patterns and the risk of T2DM development in Eastern China. Methods A cross-sectional study was conducted at our health examination center, involving 5860 adults, from June to December 2019. Data, including sociodemographic information, lifestyle, and biochemical measurements, were collected, and obesity was classified into three patterns: overweight and general obesity, abdominal obesity, and compound obesity. Multivariate logistic regression was used to assess the associations between different obesity patterns and T2DM risk after adjustment for confounding factors. Subgroup analysis was used to further explore the associations between obesity patterns and T2DM risk. Results A total of 5860 subjects were enrolled in this study. A significant difference in the T2DM incidence was observed between men and women with normal weight or overweight and general obesity (p < 0.05); however, no significant differences were observed between men and women with abdominal obesity and compound obesity. After multivariable adjustment, multivariate logistic regression analysis showed that the odds ratios (ORs) [95% confidence interval (CI)] for T2DM in individuals with abdominal and compound obesity were 1.55 [1.08–2.24] and 1.85 [1.25–2.73], respectively, compared with the normal-weight group. Subgroup analysis showed that different obesity patterns were not independent risk factors for T2DM development among adults aged ≥ 60 years, whereas abdominal and compound obesity were highly associated with the risk of T2DM development among individuals who report current smoking or alcohol drinking. Conclusion Abdominal obesity and compound obesity are risk factors for T2DM. More attention should be paid to obesity prevention among individuals younger than 60 years and improving control of cigarette and alcohol abuse.
Distributed ledger technology based robust access control and real-time synchronization for consumer electronics

Background Consumer electronics or daily use home appliances are the basic necessity of every household. With the adoption of IoT in consumer electronics, this industry is set to rise exponentially. In recent times, the demand for consumer electronics rises amidst the pandemic due to a paradigm shift from in-office culture to work from home. Despite intelligent IoT devices, smart home configuration, and appliances at our disposal, the rudimentary client-server architecture fails to provide facilities like full access control of data and devices, transparency, secured communication, and synchronization between multiple devices, etc. to the users. Methods To overcome these limitations, Blockchain technology has been adopted in recent years, however, it has its own set of limitations in its widespread implementation. Hence, we propose a methodology using the IOTA platform, a distributed ledger technology (DLT) for secured communication between consumer electronics devices and appliances. Results The implementation provides access control, interoperability, data storage, and management with an exploratory insight towards a decentralized micro-payment use-case between Electric cars and charging stations.
The Relationship Between Employee’s Status Perception and Organizational Citizenship Behaviors: A Psychological Path of Work Vitality

Purpose Studies have shown that status-based rankings exist within almost every human social group and influence most aspects of organizational life. However, few studies have discussed the relationship between employees’ status and organizational citizenship behaviors (OCBs). Based on social cognitive theory, this paper explores the relationship between employees’ status perception and two types of OCBs: challenging and affiliative, as well as the mechanism underlying this relationship by introducing work vitality as the mediator and dominance motivation as the moderator. Methods We collected the empirical data from different enterprises located in major cities in China following a two-stage sampling procedure. The final sample consists of 330 employees. Hierarchical multiple regression analysis was used to test the hypothesis. Results Employee status perception is positively related to work vitality (b = 0.103, p = 0.027), challenging OCBs (b = 0.160, p < 0.001) and affiliative OCBs (b = 0.105, p = 0.006). Work vitality mediates the relationship between employee status perception and challenging OCBs with 95% bias-corrected confidence intervals [0.004, 0.063], and it also mediates the relationship between employee status perception and affiliative OCBs with 95% bias-corrected confidence intervals [0.004, 0.049]. The interaction of status perception and dominance motivation is significantly related to work vitality (b = 0.121, p = 0.041). Specifically, when dominance motivation is at low level, the effect of status perception on work vitality is −0.008 (non-significant); when dominance motivation is high level, the effect is 0.175 (p = 0.005). Conclusion The result suggests that employees’ perceptions of status are positively and significantly related to their challenging and affiliative OCBs, and employee’s work vitality mediates this relationship. It further indicates that dominance motivation moderates the relation between status perception and work vitality. Specifically, the positive relationship between employee status perception and work vitality is stronger when an employee has high dominance motivation than low dominance motivation.
Multisource noninvasive genetics of brown bears (Ursus arctos) in Greece reveals a highly structured population and a new matrilineal contact zone in southern Europe

Abstract In human‐dominated landscapes, connectivity is crucial for maintaining demographically stable mammalian populations. Here, we provide a comprehensive noninvasive genetic study for the brown bear population in the Hellenic Peninsula. We analyze its population structuring and connectivity, estimate its population size throughout its distribution, and describe its phylogeography in detail for the first time. Our results, based on 150 multilocus genotypes and on 244‐bp sequences of the mtDNA control region, show the population is comprised by three highly differentiated genetic clusters, consistent with geographical populations of Pindos, Peristeri, and Rhodope. By detecting two male bears with Rhodopean ancestry in the western demes, we provide strong evidence for the ongoing genetic connectivity of the geographically fragmented eastern and western distributions, which suggests connectivity of the larger East Balkan and Pindos‐Dinara populations. Total effective population size (N e) was estimated to be 199 individuals, and total combined population size (N C) was 499, with each cluster showing a relatively high level of genetic variability, suggesting that migration has been sufficient to counteract genetic erosion. The mtNDA results were congruent with the microsatellite data, and the three genetic clusters were matched predominantly with an equal number of mtDNA haplotypes that belong to the brown bear Western mitochondrial lineage (Clade 1), with two haplotypes being globally new and endemic. The detection of a fourth haplotype that belongs to the Eastern lineage (Clade 3a1) in three bears from the western distribution places the southernmost secondary contact zone between the Eastern and Western lineages in Greece and generates new hypotheses about postglacial maxima migration routes. This work indicates that the genetic composition and diversity of Europe's low‐latitude fringe population are the outcome of ancient and historical events and highlight its importance for the connectivity and long‐term persistence of the species in the Balkans.
Mapping scrub vegetation cover from photogrammetric point clouds: A useful tool in reserve management

Abstract Scrub vegetation is a valuable habitat and resource for wildlife, but if unmanaged can encroach and dominate adjacent habitats, reducing biodiversity value. A primary task in the management of terrestrial nature reserves in the UK is monitoring and controlling scrub. The methods used to monitor and assess scrub cover are often basic, relying on qualitative assessment. Inaccurate assessments may fail to inform appropriate management of the habitats and lead to loss or degradation of important ecological features. Scrub can be monitored using UAV or satellite‐derived imagery, but it can be difficult to distinguish between other vegetation types without using high‐cost hyperspectral sensors. An alternative method using high‐resolution surface models from photogrammetric point clouds enables the isolation of vegetation types based on height. Scrub can be isolated from woodland, hedgerows, and tall ground vegetation. In this study, we calculate scrub cover using a photogrammetric point cloud modeling approach using UAVs. We illustrate the method with two case studies from the UK. The scrub cover at Daneway Banks, a calcareous grassland site in Gloucestershire, was calculated at 21.8% of the site. The scrub cover at Flat Holm Island, a maritime grassland in the Severn Estuary, was calculated at 7%. This approach enabled the scrub layer to be readily measured and if required, modeled to provide a visual guide of what a projected management objective would look like. This approach provides a new tool in reserve management, enabling habitat management strategies to be informed, and progress toward objectives monitored.
Analysis of the electric field development and the relaxation of electron velocity distribution function for nanosecond breakdown in air

Using theoretical and experimental methods, the electric field and the electron multiplication in direct vicinity of a sharp cathode is analysed. The development of the electric field in the pre-breakdown phase of the atmospheric pressure air negative DC corona discharge in the Trichel pulse regime is determined. During the following ultra-fast electrical breakdown, the emission of selected spectral bands of the nitrogen molecule is recorded with high spatiotemporal resolution using the time-correlated single photon counting method. The emission of a Townsend discharge is used to calibrate the setup for the quantitative determination of electric field. Therefore, the Trichel pulse corona and Townsend discharge cell are arranged in the same single-table setup. This direct calibration procedure is described step-by-step including the discussion of known limitations. Finally, the electric field development of the positive streamer passing the 160 μm distance in less than two nanoseconds is determined. Due to the high spatiotemporal gradients of the electric field strength within the streamer breakdown, the local field approximation of the electron component is analysed by investigating numerically the temporal and spatial electron relaxation by means of the solution of the electron Boltzmann equation and Monte Carlo simulation. Results of these computations are given for several reduced electric field values and prove that the electrons are in a hydrodynamic equilibrium state for experimentally given space and time scales for reduced fields above 100 Td.
Environmental heterogeneity shapes physiological traits in tropical direct‐developing frogs

Abstract Tropical ectotherm species tend to have narrower physiological limits than species from temperate areas. As a consequence, tropical species are considered highly vulnerable to climate change since minor temperature increases can push them beyond their physiological thermal tolerance. Differences in physiological tolerances can also be seen at finer evolutionary scales, such as among populations of ectotherm species along elevation gradients, highlighting the physiological sensitivity of such organisms. Here, we analyze the influence of elevation and bioclimatic domains, defined by temperature and precipitation, on thermal sensitivities of a terrestrial direct‐developing frog (Craugastor loki) in a tropical gradient. We address the following questions: (a) Does preferred temperature vary with elevation and among bioclimatic domains? (b) Do thermal tolerance limits, that is, critical thermal maximum and critical thermal minimum vary with elevation and bioclimatic domains? and (c) Are populations from high elevations more vulnerable to climate warming? We found that along an elevation gradient body temperature decreases as environmental temperature increases. The preferred temperature tends to moderately increase with elevation within the sampled bioclimatic domains. Our results indicate that the ideal thermal landscape for this species is located at midelevations, where the thermal accuracy (db) and thermal quality of the environment (de) are suitable. The critical thermal maximum is variable across elevations and among the bioclimatic domains, decreasing as elevation increases. Conversely, the critical thermal minimum is not as variable as the critical thermal maximum. Populations from the lowlands may be more vulnerable to future increases in temperature. We highlight that the critical thermal maximum is related to high temperatures exhibited across the elevation gradient and within each bioclimatic domain; therefore, it is a response to high environmental temperatures.
The Current Evidence for the Use of Safinamide for the Treatment of Parkinson's Disease

Introduction Parkinson’s therapeutic interventions are only symptomatic. An optimal treatment should therefore address the largest number of motor and non-motor symptoms, to manage patients at best. Safinamide is one of the most recent approved drugs for fluctuating patients, in add-on to levodopa, that remains the gold standard treatment. It has a unique mechanism of action, both dopaminergic (as MAO-B inhibitor) and glutamatergic (through Na+ channel blockade). Results from Phase III trials, post-hoc analyses and real-life experiences suggest a beneficial effect on motor (such as tremor, bradykinesia, rigidity and gait) and non-motor (pain, mood, sleep) symptoms. Areas Covered Here, the authors discuss clinical efficacy and safety of safinamide, identifying the patients’ profiles that could benefit most. A search in PubMed was performed in September 2020, with no time limits. Publications’ abstracts were reviewed. Conclusion Safinamide is peculiar due to its double mechanism of action. Its benefits in improving motor functions and fluctuations, and some non-motor symptoms, could have a valuable impact on patients’ quality of life (QoL), together with its safety profile.