Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Feature Adaptation and Graph Adaptive Label Propagation for Cross-Subject Emotion Recognition From EEG Signals

Though Electroencephalogram (EEG) could objectively reflect emotional states of our human beings, its weak, non-stationary, and low signal-to-noise properties easily cause the individual differences. To enhance the universality of affective brain-computer interface systems, transfer learning has been widely used to alleviate the data distribution discrepancies among subjects. However, most of existing approaches focused mainly on the domain-invariant feature learning, which is not unified together with the recognition process. In this paper, we propose a joint feature adaptation and graph adaptive label propagation model (JAGP) for cross-subject emotion recognition from EEG signals, which seamlessly unifies the three components of domain-invariant feature learning, emotional state estimation and optimal graph learning together into a single objective. We conduct extensive experiments on two benchmark SEED_IV and SEED_V data sets and the results reveal that 1) the recognition performance is greatly improved, indicating the effectiveness of the triple unification mode; 2) the emotion metric of EEG samples are gradually optimized during model training, showing the necessity of optimal graph learning, and 3) the projection matrix-induced feature importance is obtained based on which the critical frequency bands and brain regions corresponding to subject-invariant features can be automatically identified, demonstrating the superiority of the learned shared subspace.
Rationales and Support for Norms in the Context of Covid-19

This study empirically tests whether people invoke moral and prudential rationales when evaluating behavior in a novel context—the early months of the Covid-19 pandemic in the United States—and whether those rationales are associated with their support for a norm. We use data from two online vignette experiments that describe key health behaviors—staying home and masking—and find substantial support. Given the politicization of these behaviors in the U.S. context, we also explore the role of political orientation and find that liberal participants react more strongly to the behaviors.
Computer Vision for Identifying and Classifying Green Coffee Beans: A Review

Coffee is widely consumed around the world, also considered one of the most important beverages today. Factors contributing to the quality of coffee beans such as color, texture, size, aroma, etc. and other processes along the production chain such as plant, roasting, and grinding. Those processes will be worthless if the quality of the coffee bean is low. It is important to only use the best quality coffee beans. Therefore, the challenge is to develop a system that uses computer vision to either identify high quality beans or classify them by their species to ease the effort needed by all actors in the supply chain. Providing information for end customers is a defining factor to push forward the coffee industry. This paper aims to review literatures within the topic of using computer vision for coffee beans. After reviewing a selected number of studies which corresponds with the topic chosen in our paper, computer vision techniques were used for two main reasons, identification and classification. Researches on this topic are still limited. Hence, it can be concluded that there are still plenty of room for study on this topic. This study also aims to help provide research material for future researchers.
Cell Confluence Modulates TRPV4 Channel Activity in Response to Hypoxia

Transient receptor potential vanilloid 4 (TRPV4) is a polymodal Ca2+-permeable channel involved in various hypoxia-sensitive pathophysiological phenomena. Different tools are available to study channel activity, requiring cells to be cultured at specific optimal densities. In the present study, we examined if cell density may influence the effect of hypoxia on TRPV4 activity. Transiently TRPV4-transfected HEK293T cells were seeded at low or high densities corresponding to non-confluent or confluent cells, respectively, on the day of experiments, and cultured under in vitro normoxia or hypoxia. TRPV4-mediated cytosolic Ca2+ responses, single-channel currents, and Ca2+ influx through the channel were measured using Ca2+ imaging/microspectrofluorimetric assay, patch-clamp, and Bioluminescence Resonance Energy Transfer (BRET), respectively. TRPV4 plasma membrane translocation was studied using confocal microscopy, biotinylation of cell surface proteins, and BRET. Our results show that hypoxia exposure has a differential effect on TRPV4 activation depending on cell confluence. At low confluence levels, TRPV4 response is increased in hypoxia, whereas at high confluence levels, TRPV4 response is strongly inhibited, due to channel internalization. Thus, cell density appears to be a crucial parameter for TRPV4 channel activity.
Ultra-Voltage Gain Bidirectional DC–DC Converter With Reduced Switch Voltage Stress and Improved Efficiency

Bidirectional dc-dc converters (BDCs) play a significant role in battery/ultracapacitor-based applications. A novel ultra-gain BDC (UGBDC) is proposed with improved voltage transfer ratio (VTR), continuous source current and symmetrical VTR. The UGBDC architecture reduces voltage and current stress on MOSFETs and capacitors. The enhancement of VTR reduces the parasitic effect and the core saturation problem. Further, lowered voltage stress of the MOSFETs reduces the switching losses. The output voltage of the UGBDC is calculated for ideal and with parasitic. The component voltage stress, voltage gain and efficiency are compared to those of reported BDC topologies. A 500 W, 650 V, 50 kHz converter prototype is built, and steady-state waveforms are shown. The significant transfer functions are derived from the state-space model, and the PI controllers for step-up and stepdown operation are designed. The designed controller is implemented in Xilinx System Generator and closed loop experimental results are presented.
Evaluating keyphrase extraction algorithms for finding similar news articles using lexical similarity calculation and semantic relatedness measurement by word embedding

A textual data processing task that involves the automatic extraction of relevant and salient keyphrases from a document that expresses all the important concepts of the document is called keyphrase extraction. Due to technological advancements, the amount of textual information on the Internet is rapidly increasing as a lot of textual information is processed online in various domains such as offices, news portals, or for research purposes. Given the exponential increase of news articles on the Internet, manually searching for similar news articles by reading the entire news content that matches the user’s interests has become a time-consuming and tedious task. Therefore, automatically finding similar news articles can be a significant task in text processing. In this context, keyphrase extraction algorithms can extract information from news articles. However, selecting the most appropriate algorithm is also a problem. Therefore, this study analyzes various supervised and unsupervised keyphrase extraction algorithms, namely KEA, KP-Miner, YAKE, MultipartiteRank, TopicRank, and TeKET, which are used to extract keyphrases from news articles. The extracted keyphrases are used to compute lexical and semantic similarity to find similar news articles. The lexical similarity is calculated using the Cosine and Jaccard similarity techniques. In addition, semantic similarity is calculated using a word embedding technique called Word2Vec in combination with the Cosine similarity measure. The experimental results show that the KP-Miner keyphrase extraction algorithm, together with the Cosine similarity calculation using Word2Vec (Cosine-Word2Vec), outperforms the other combinations of keyphrase extraction algorithms and similarity calculation techniques to find similar news articles. The similar articles identified using KPMiner and the Cosine similarity measure with Word2Vec appear to be relevant to a particular news article and thus show satisfactory performance with a Normalized Discounted Cumulative Gain (NDCG) value of 0.97. This study proposes a method for finding similar news articles that can be used in conjunction with other methods already in use.
Design Framework for ReRAM-Based DNN Accelerators with Accuracy and Hardware Evaluation

To achieve faster design closure, there is a need to provide a design framework for the design of ReRAM-based DNN (deep neural network) accelerator at the early design stage. In this paper, we develop a high-level ReRAM-based DNN accelerator design framework. The proposed design framework has the following three features. First, we consider ReRAM’s non-linear properties, including lognormal distribution, leakage current, IR drop, and sneak path. Thus, model accuracy and circuit performance can be accurately evaluated. Second, we use SystemC with TLM modeling method to build our virtual platform. To our knowledge, the proposed design framework is the first behavior-level ReRAM deep learning accelerator simulator that can simulate real hardware behavior. Third, the proposed design framework can evaluate not only model accuracy but also hardware cost. As a result, the proposed design framework can be used for behavior-level design space exploration. In the experiments, we have deployed different DNN models on the virtual platform. Circuit performance can be easily evaluated on the proposed design framework. Furthermore, experiment results also show that the noise effects are different in different ReRAM array architectures. Based on the proposed design framework, we can easily mitigate noise effects by tuning architecture parameters.
Temporary Trapping of Dust Particles into Orbital Resonances with the Earth

We have studied the orbital evolution of interplanetary dust particles beyond the Earth's orbit under the action of Poynting-Robertson (PR) drag and gravitational perturbations of the Earth and Jupiter. The particle orbits were integrated by directly introducing in the equation of motion the position vectors of the Earth and Jupiter as obtained from the Richardson-Walker numerical simulation of the motion of the full planetary system (1). This allowed us to take account of the "real" evolution of the Earth's and Jupiter's orbits (under all the planetary perturbations) and, therefore, of their influence on the motion of the dust particles in Earth's neighborhood. In this respect, the present study differs and extends previous work on the orbital evolution of dust particles under PR drag with only a single or few perturbing planets (2,3,4) whose orbits were directly integrated.
Nonlinear thermal emission and visible thermometry

The control of thermal emission is of great importance for emerging applications in energy conversion and thermometric sensing. Usually, thermal emission at ambient temperature is limited to the mid-to far-infrared, according to the linear theory of Planck ’ s law. We experimentally demonstrate a broadband nonlinear thermal emission in the visible-NIR spectrum within a quadradic nonlinear medium, which emits visible thermal radiation through a pump-driven nonlinear upconversion from its mid-IR components even at room temperature, unlike its linear counterpart which requires ultrahigh temperature. The broadband emission is enabled by the crucial random quasi-phase-matching condition in our nonlinear nanocrystal powders. Moreover, nonlinear thermal emission also permits visible thermometry using traditional optical cameras instead of thermal ones. This scheme paves the way to understand thermal radiation dynamics with nonlinearity in many fields, such as nonlinear heat transfer and nonlinear thermodynamics.
EFFECT OF LUMBAR STABILIZATION EXERCISE AND LUMBAR EXTENSION EXERCISE IN THE TREATMENT OF CHRONIC LOW BACK PAIN

The objective of this study is to determine the effect of lumbar stabilization exercise and lumbar extension exercise in chronic low back pain patients. The study design is Randomized clinical trial. 40 patients with chronic low back pain were selected from Gee Bee Physio Clinic, Chidambaram, Cuddalore District, Tamilnadu. They were randomly divided into 2 groups. Of them, 20 patients were given lumbar stabilization exercise and the remaining 20 were given lumbar extension exercise. They both were given interferential therapy and the results were analyzed. The outcome measures used were Numerical pain rating scale for pain and the Oswestry disability questionnaire for disability. The analysis was done using two way ANOVA. The analysis of the results shows that lumbar stabilization exercise showed much improvement in both pain and disability when compared to extension exercise group.
Automatic Classification of Foot Thermograms Using Machine Learning Techniques

Diabetic foot is one of the main complications observed in diabetic patients; it is associated with the development of foot ulcers and can lead to amputation. In order to diagnose these complications, specialists have to analyze several factors. To aid their decisions and help prevent mistakes, the resort to computer-assisted diagnostic systems using artificial intelligence techniques is gradually increasing. In this paper, two different models for the classification of thermograms of the feet of diabetic and healthy individuals are proposed and compared. In order to detect and classify abnormal changes in the plantar temperature, machine learning algorithms are used in both models. In the first model, the foot thermograms are classified into four classes: healthy and three categories for diabetics. The second model has two stages: in the first stage, the foot is classified as belonging to a diabetic or healthy individual, while, in the second stage, a classification refinement is conducted, classifying diabetic foot into three classes of progressive severity. The results show that both proposed models proved to be efficient, allowing us to classify a foot thermogram as belonging to a healthy or diabetic individual, with the diabetic ones divided into three classes; however, when compared, Model 2 outperforms Model 1 and allows for a better performance classification concerning the healthy category and the first class of diabetic individuals. These results demonstrate that the proposed methodology can be a tool to aid medical diagnosis.
The mass of molecular gas in the Galaxy

Information on the metallicity gradient in the Galaxy is assessed and used to make a new estimate of the distribution of molecular hydrogen (H2) in the Galaxy. it is found that the radial distribution of H2 is rather different than thought hitherto and not too far from that of atomic hydrogen (H1). Gamma-ray data above 35 MeV from the SAS II and COS B satellites have been used, together with the estimated masses of both H2 and H1, to determine the galactic distribution of cosmic rays under the assumption that the flux of gamma rays from unresolved sources is negligible. It is found that the intensity distribution (or electrons of several hundred MeV and protons of a few GeV) is similar to that of supernova remnants.
Mobile Device Usage before and during the COVID-19 Pandemic among Rural and Urban Adults

Technology has played a critical role during the COVID-19 pandemic. Despite affording a safe way for people to connect with others, the potential for problematic device usage (e.g., overuse, addiction) should be considered. The goal of this study was to examine mobile device use during the COVID-19 pandemic among rural and urban people in Canada. Based on an online survey conducted in the summer of 2021 in British Columbia (n = 465), participants self-reported spending more hours per day (M = 8.35 h) using technology during the pandemic compared to prior (M = 6.02 h), with higher increases among urban participants (p < 0.001). Mobile device usage scores were highest for reasons of social connectedness and productivity, with no rural/urban differences; however, urban participants reported higher use of mobile devices for their mental well-being (p = 0.001), but also reported higher, continuous use (p < 0.001), addiction (p < 0.001), and detrimental impacts on their physical health (p < 0.001) compared to rural participants. Because urban participants were more vulnerable to mobile device overuse and addiction during the pandemic, researchers and policy makers should consider the ongoing role and positive/negative impacts of mobile device use, paying particular attention to urban populations.
Formulation of a Consistent Multi‐Species Canopy Description for Hydrodynamic Models Embedded in Large‐Scale Land‐Surface Representations of Mixed‐Forests

The plant hydrodynamic approach represents a recent advancement to land surface modeling, in which stomatal conductance responds to water availability in the xylem rather than in the soil. To provide a realistic representation of tree hydrodynamics, hydrodynamic models must resolve processes at the level of a single modeled tree, and then scale the resulting fluxes to the canopy and land surface. While this tree‐to‐canopy scaling is trivial in a homogeneous canopy, mixed‐species canopies require careful representation of the species properties and a scaling approach that results in a realistic description of both the canopy and individual‐tree hydrodynamics, as well as leaf‐level fluxes from the canopy and their forcing. Here, we outline advantages and pitfalls of three commonly used approaches for representing mixed forests in land surface models and present a new framework for scaling vegetation characteristics and fluxes in mixed forests. The new formulation scales fluxes from the tree to canopy level in an energy‐ and mass‐conservative way and allows for a consistent multi‐species/multi‐type canopy description by hydrodynamic models.
A New Method for Centrality Measurement Using Generalized Fuzzy Graphs

The fuzzy graph is the foundation of many actual structures such as networking, picture planning, and so on. Generalized fuzzy graphs (GFG) are ideal for avoiding certain constraints of fuzzy graphs. Social networks are a useful and powerful way to link citizens worldwide. A central person in a social network is to deny the leading people in it. Different centrality steps have also been established over the years. We evaluated the centrality of the social network through a general fuzzy graph in this article. An application to detect the central person in any online group like WhatsApp is described in this study by using generalized fuzzy graphs. Also, a few important results are established in this study.
Distribution of mutational effects in indigenous chicken

The selection of the indigenous chicken (IC) has amounted to remarkable phenotypic changes traced back to the variations in their genome. These variations take many forms among them, insertions and deletions (INDELs). This study applied whole genome re-sequencing to characterize the distribution of INDELs in IC. Eight IC birds from eight ecotypes; Siaya, Kakamega, Narok, West Pokot, Taita-Taveta, Turkana, Bomet, and Lamu were re-sequenced. One representative bird per ecotype was selected based on the Principal Coordinate Analysis and heterozygosity from the microsatellite data. Variant calling process to identify SNPs and INDELs was done. Valid INDELs retained for analysis had a read dept (dp) >4 and Minimum quality (minQ) 20. A total of 2.7 million INDELs were discovered. Of this, 1,430,403 and 1,448,720 were insertions and deletions respectively. All ecotypes recorded more deletions compared to insertions. The total affected bases due to INDELs were 10,645,076. Deletions were generally longer (43bp) and insertions were shorter (28bp). The distribution of INDELs in IC genome both among the ecotypes and between the chromosomes within an ecotype proved to be different. The information generated from this study will render a new and deeper understanding into ways genetic variations mold IC phenotypic diversity.
Tree edit distance and common subtrees

The relationship between tree edit distance and maximum common subtrees is established, showing that a tree edit distance constrained by insertion and deletions on leaves only and by a simple condition on the cost of the tree edit operations, corresponds to a maximum commonsubtree isomorphism, allowing thus the use of known tree edit distance algorithms to solve the maximum common subtree problem, and viceversa. Further, a tree distance based on the size of a maximum common subtree is introduced.
Template-based classifiers for ERP-based BCIs

This talk aim to present pattern recognition techniques of graphic elements (e.g. event-related potential, auditory evoked potential, k-complex, sleep spindles, vertex waves) included in electro-encephalographic signals. More specifically, template-based classifiers will be introduced to robustly detect evoked potentials in a single trial from noisy and multi-sources electro-encephalographic signals.
Foreign direct investment and economic policy uncertainty in China

Abstract The trend of foreign direct investment (FDI) in China has switched from inflow to outflow following the rapid economic growth during the past several decades. Stimulated by government policies, in recent years China’s firms have actively sought overseas investment opportunities. This paper examines the relationship between economic policy uncertainty (EPU) and cross-border capital flow decisions for listed firms in China. Our findings demonstrate that EPU originating from China does not seem to dissuade FDI inflow into China, but it does curtail FDI outflow from the country. It appears that government policies and the uncertainty the policies bring are deterministic for FDI.
Use of botanical gardens as arks for conserving pollinators and plant-pollinator interactions: A case study from the United States Northern Great Plains

Botanical gardens have contributed to plant conservation through the maintenance of both living and preserved plant specimens for decades. However, there is still a large gap in the literature about the potential conservation value that botanical gardens could provide to local pollinators. We investigated how plant-pollinator interaction network structure and diversity may differ between botanical gardens and native habitats by sampling and comparing two environments: a restored native grassland patch within a local botanical garden and fifteen native, remnant temperate grassland sites in the Northern Great Plains. We found pollinator diversity within the restored botanical garden’s native grassland patch to be at the high end of the distribution of the remnant temperate grassland sites throughout the entire flowering season. However, plant diversity and network community metrics between the two environments remained similar throughout, except that remnant temperate grasslands have more links (higher connectance) with pollinators than the garden patch. Overall, our findings demonstrate the promising role restored native grassland patches in botanical gardens could play as reservoirs for local pollinator communities by supporting plant-pollinator interactions comparable to those found in native habitat remnants in the same region.