Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel attention-based hybrid CNN-RNN architecture for sEMG-based gesture recognition

The surface electromyography (sEMG)-based gesture recognition with deep learning approach plays an increasingly important role in human-computer interaction. Existing deep learning architectures are mainly based on Convolutional Neural Network (CNN) architecture which captures spatial information of electromyogram signal. Motivated by the sequential nature of electromyogram signal, we propose an attention-based hybrid CNN and RNN (CNN-RNN) architecture to better capture temporal properties of electromyogram signal for gesture recognition problem. Moreover, we present a new sEMG image representation method based on a traditional feature vector which enables deep learning architectures to extract implicit correlations between different channels for sparse multi-channel electromyogram signal. Extensive experiments on five sEMG benchmark databases show that the proposed method outperforms all reported state-of-the-art methods on both sparse multi-channel and high-density sEMG databases. To compare with the existing works, we set the window length to 200ms for NinaProDB1 and NinaProDB2, and 150ms for BioPatRec sub-database, CapgMyo sub-database, and csl-hdemg databases. The recognition accuracies of the aforementioned benchmark databases are 87.0%, 82.2%, 94.1%, 99.7% and 94.5%, which are 9.2%, 3.5%, 1.2%, 0.2% and 5.2% higher than the state-of-the-art performance, respectively.
Integrating complementary methods to improve diet analysis in fishery‐targeted species

Abstract Developing efficient, reliable, cost‐effective ways to identify diet is required to understand trophic ecology in complex ecosystems and improve food web models. A combination of techniques, each varying in their ability to provide robust, spatially and temporally explicit information can be applied to clarify diet data for ecological research. This study applied an integrative analysis of a fishery‐targeted species group—Plectropomus spp. in the central Great Barrier Reef, Australia, by comparing three diet‐identification approaches. Visual stomach content analysis provided poor identification with ~14% of stomachs sampled resulting in identification to family or lower. A molecular approach was successful with prey from ~80% of stomachs identified to genus or species, often with several unique prey in a stomach. Stable isotope mixing models utilizing experimentally derived assimilation data, identified similar prey as the molecular technique but at broader temporal scales, particularly when prior diet information was incorporated. Overall, Caesionidae and Pomacentridae were the most abundant prey families (>50% prey contribution) for all Plectropomus spp. , highlighting the importance of planktivorous prey. Less abundant prey categories differed among species/color phases indicating possible niche segregation. This study is one of the first to demonstrate the extent of taxonomic resolution provided by molecular techniques, and, like other studies, illustrates that temporal investigations of dietary patterns are more accessible in combination with stable isotopes. The consumption of mainly planktivorous prey within this species group has important implications within coral reef food webs and provides cautionary information regarding the effects that changing resources could have in reef ecosystems.
DIFFERENCES IN CERTAIN DIMENSIONS OF ANTHROPOLOGICAL STATUS OF YOUNG SOCCER PLAYERS OF DIFFERENT CHRONOLOGICAL, BIOLOGICAL AND TRAINING AGE

The main goal of the study was to determine the differences in anthropological status of young soccer players of different chronological, biological and training age. The subject sample included 47 young male soccer players whose mean chronological age was 14.79±1.17 years, mean biological age 14.67±1.20 and mean training age 7.09±1.47 years. Three morphological measurements were taken: body height, sitting height and body mass. Body mass index (BMI) and Peak height velocity (PHV) were calculated subsequently. By applying the K-means taxonomic analysis, two clusters were defined with categorical variables chronological age, indicator of biological age and training age, and by applying the univariate analysis of variance (ANOVA), significant differences were found in all the observed variables of anthropological status. All the applied variables differentiated the formed clusters significantly. Further analysis determined descriptive parameters of anthropometric characteristics, motor abilities and the results of functional test which was represented by three variables. The obtained results indicate that chronologically and biologically more mature soccer player, who have been included in the training process longer, have greater body and sitting height, they are heavier and have better results in all the measured motor abilities, as well as the functional test applied.
Co-Metric Learning for Person Re-Identification

Person re-identification, aiming to identify the same pedestrian images across disjoint camera views, is a key technique of intelligent video surveillance. Although existing methods have developed both theories and experimental results, most of effective ones pertain to fully supervised training styles, which suffer the small sample size (SSS) problem a lot, especially in label-insufficient practical applications. To bridge SSS problem and learning model with small labels, a novel semisupervised co-metric learning framework is proposed to learn a discriminative Mahalanobis-like distance matrix for label-insufficient person re-identification. Different from typical co-training task that contains multiview data originally, single-view person images are firstly decomposed into pseudo two views, and then metric learning models are produced and jointly updated based on both pseudo-labels and references iteratively. Experiments carried out on three representative person re-identification datasets show that the proposed method performs better than state of the art and possesses low label sensitivity.
Learning to Represent Edits

We introduce the problem of learning distributed representations of edits. By combining a "neural editor" with an "edit encoder", our models learn to represent the salient information of an edit and can be used to apply edits to new inputs. We experiment on natural language and source code edit data. Our evaluation yields promising results that suggest that our neural network models learn to capture the structure and semantics of edits. We hope that this interesting task and data source will inspire other researchers to work further on this problem.
Ab Initio Evaluation of Henry Coefficients Using Importance Sampling

We present a new algorithm that allows for an efficient evaluation of the Henry coefficient of a guest molecule inside a porous material, which permits to use ab initio energy calculations. The Widom insertion method, which is currently used to compute these Henry coefficients, typically requires millions of energy evaluations. Our new methodology reduces this number by more than 1 order of magnitude, enabling the use of an ab initio potential energy surface. The methodology we propose is reminiscent of the well-known importance sampling technique which is frequently used in Monte Carlo integrations. First, a conventional Widom insertion simulation is performed using a force field. In the second step, the Widom results are used to select a limited number of configurations and only for these configurations the ab initio evaluation of the energy is required. Finally, by appropriately reweighting the latter energies, an accurate estimation of the ab initio Henry coefficient is possible at a moderate computational cost. We apply our methodology to the adsorption of CO2 in Mg-MOF-74, a prototypical case where interactions of a polar guest molecule with unsaturated metal sites dominate the adsorption mechanism. In this case generic force fields such as UFF or Dreiding are inappropriate and the use of ab initio methods is indispensable. In a second case study, we compute Henry coefficients of methane in UiO-66 using different levels of theory. We pay particular attention to the influence of the dispersion corrections and the role of many-body effects. For the final example, we qualitatively investigate adsorption features for a series of functionalized UiO-66 frameworks. Overall the cases we present show that accurate computations of Henry coefficients is extremely challenging, as different levels of theory provide strongly varying results. At the same time ab initio calculations have added value compared to force fields, as they provide a physically more sound description of the adsorption mechanism and in some cases clearly improve correspondence with experiment.
Clustering-Enhanced Stochastic Gradient MCMC for Hidden Markov Models with Rare States

MCMC algorithms for hidden Markov models, which often rely on the forward-backward sampler, suffer with large sample size due to the temporal dependence inherent in the data. Recently, a number of approaches have been developed for posterior inference which make use of the mixing of the hidden Markov process to approximate the full posterior by using small chunks of the data. However, in the presence of imbalanced data resulting from rare latent states, the proposed minibatch estimates will often exclude rare state data resulting in poor inference of the associated emission parameters and inaccurate prediction or detection of rare events. Here, we propose to use a preliminary clustering to over-sample the rare clusters and reduce variance in gradient estimation within Stochastic Gradient MCMC. We demonstrate very substantial gains in predictive and inferential accuracy on real and synthetic examples.
Soft conductive micropillar electrode arrays for biologically relevant electrophysiological recording

Significance The large mechanical discrepancy at the cell–electrode interface (∼6 orders of magnitude difference in Young’s moduli) is known to significantly affect the cell’s behavior and its mechanotransduction pathways. To date, the Young’s moduli of the electrode materials do not match those of the cells, due to the limited availability of required electronic materials for microfabrication. Here, we present a soft micropillar electrode array made of electronically conductive hydrogel as a general tool for electrophysiological recording. The three-dimensional microelectrode provides biologically relevant mechanical microenvironment to the cells during electrophysiological recording with higher amplitude and signal-to-noise ratio compared with a state-of-the-art inorganic microelectrode. Multielectrode arrays (MEAs) are essential tools in neural and cardiac research as they provide a means for noninvasive, multiplexed recording of extracellular field potentials with high temporal resolution. To date, the mechanical properties of the electrode material, e.g., its Young’s modulus, have not been taken into consideration in most MEA designs leaving hard materials as the default choice due to their established fabrication processes. However, the cell–electrode interface is known to significantly affect some aspects of the cell’s behavior. In this paper, we describe the fabrication of a soft 3D micropillar electrode array. Using this array, we proceed to successfully record action potentials from monolayer cell cultures. Specifically, our conductive hydrogel micropillar electrode showed improved signal amplitude and signal-to-noise ratio, compared with conventional hard iridium oxide micropillar electrodes of the same diameter. Taken together, our fabricated soft micropillar electrode array will provide a tissue-like Young’s modulus and thus a relevant mechanical microenvironment to fundamental cardiac and neural studies.
Reinforcement Learning of Phase Oscillators for Fast Adaptation to Moving Targets

Online movement generation in tasks involving real humanoid robots interacting with fast-moving targets is extremely difficult. This paper approaches this problem via imitation and reinforcement learning using phase variables. Imitation learning is used to acquire primitive trajectories of the demonstrator interacting with the target. The temporal progress of the robot is represented as a function of the target’s phase. Using a phase oscillator formulation, reinforcement learning optimizes a temporal policy such that the robot can quickly react to large/unexpected changes in the target movement. The phase representation decouples the temporal and spatial problems allowing the use of fast online solutions. The methodology is applicable in both cyclic and single-stroke movements. We applied the proposed method on a real bi-manual humanoid upper body with 14 degrees-of-freedom where the robot had to repeatedly push a ball hanging in front of it. In simulation, we show a human-robot interaction scenario where the robot changed its role from giver to receiver as a function of the interaction reward.
Tolerance strategies revealed in tree peony (Paeonia suffruticosa; Paeoniaceae) ecotypes differentially adapted to desiccation

Premise of the Study Tree peony (Paeonia suffruticosa; Paeoniaceae) is well known for its ornamental value, edible oil, and medicinal properties. However, its growing area has been limited by drought that has been exacerbated by global climate change. Methods Gene expression profiles of a drought‐tolerant cultivar and a drought‐sensitive cultivar during dehydration and rehydration were investigated by transcriptome analysis. Expression patterns of unigenes related to drought and recovery response and unrelated to either cultivar were classified by hierarchical clustering and real‐time quantitative PCR (qPCR). Results A total of 81,725 unigenes with a mean length of 762 nucleotides that may play roles in drought response were identified. Unigenes were characterized as being involved in lipid transport metabolism, proline metabolism, and photosynthesis. In addition, plant hormone signaling pathway genes were also characterized as potentially being involved in drought response. Expression patterns of the 20 drought‐responsive unigenes verified by qPCR showed a differential expression pattern under either the drought or recovery treatment. Discussion This is the first report to identify and verify unigenes of tree peonies with differing water sensitivity during dehydration and rehydration. This study offers a valuable resource for candidate genes involved in drought and provides insight into the breeding of drought‐resistant tree peony cultivars.
On Fast Leverage Score Sampling and Optimal Learning

Leverage score sampling provides an appealing way to perform approximate computations for large matrices. Indeed, it allows to derive faithful approximations with a complexity adapted to the problem at hand. Yet, performing leverage scores sampling is a challenge in its own right requiring further approximations. In this paper, we study the problem of leverage score sampling for positive definite matrices defined by a kernel. Our contribution is twofold. First we provide a novel algorithm for leverage score sampling and second, we exploit the proposed method in statistical learning by deriving a novel solver for kernel ridge regression. Our main technical contribution is showing that the proposed algorithms are currently the most efficient and accurate for these problems.
Visual-GPS combined ‘follow-me’ tracking for selfie drones

ABSTRACT The ‘follow-me’ mode, where the drone autonomously follows and captures the user or target, is a new and attractive feature for camera drones, especially selfie drones. For this purpose, today’s commercial drones use the difference between two GPS data in the drone and user-side mobile GCS, e.g. a smartphone, but the targeting performance is often not satisfactory due to the inaccuracy of the GPS data, ranging from a few to tens of meters. Visual tracking can be considered for a solution to this problem, but the reliability of visual tracking is still questionable for long-term tracking in un-expected operating environments. The paper proposes a hybrid approach that combines the high accuracy of a visual tracking algorithm in short-term tracking and the reliability of GPS-based one in long-term tracking. The experiment with our prototype drone system demonstrates that the proposed combined approach can accomplish the follow-me operation very successfully, capturing the target in the center of video contents over 50% higher accuracy than the GPS-based ones. Also, the extreme scenario experiments verify the system can recover vision tracking failure and Wi-Fi failure quickly in a short-term, e.g. 3–7 s. GRAPHICAL ABSTRACT
Exome sequencing of sporadic childhood‐onset schizophrenia suggests the contribution of X‐linked genes in males

Childhood‐onset schizophrenia (COS) is a rare and severe form of schizophrenia, defined as having an onset before the age of 13. The male COS cases have a slightly younger age of onset than female cases. They also present with a higher rate of comorbid developmental disorders. These sex differences are not explained by the frequency of chromosomal abnormalities, and the contribution of other forms of genetic variations remains unestablished. Using a whole‐exome sequencing approach, we examined 12 COS trios where the unaffected parents had an affected male child. The sequencing data enabled us to test if the hemizygous variants, transmitted from the unaffected carrying mother, could mediate the phenotype (X‐linked recessive inheritance model). Our results revealed that affected children have a significantly greater number of X‐linked rare variants than their unaffected fathers. The variants identified in the male probands were mostly found in genes previously linked to other neuropsychiatric diseases like autism, intellectual disability, and epilepsy, including LUZP4, PCDH19, RPS6KA3, and OPHN1. The level of expression of the genes was assessed at different developmental periods in normal brain using the BrainSpan database. This approach revealed that some of them were expressed earlier in males than in females, consistent with the younger age of onset in male COS. In conclusion, this article suggests that X‐linked genes might play a role in the pathophysiology of COS. Candidate genes detailed here could explain the higher level of comorbidities and the earlier age of onset observed in a subset of the male COS cases.
Unsupervised Method for Processing Unstructured Dataset for Multilinguals

NLP –Natural language processing a major domain of processing unstructured dataset (text). One of such type of the task is to extract keyphrase or keyterms or keywords from huge sized text. The term Keywords/Keyphrases/Keyterms are the words which gives the incisive description of the content of the document.In spite of wide researches still this extraction function is in relatively poor performance.partly due to the selection of “correct” set of keyphrases. The fundamental difficulty lies in determining which keyphrases are the most relevant and provide the best coverage.In this paper, we propose an unsupervised method to extract keywords from a document. This method extracts unigram nouns (candidates) by applying preprocessing steps on the text. Then graph based Unsupervised algorithms is applied to find “frequency of cooccurrence or semantic relatedness” between candidates . It selects a number of keywords from the highest scored candidate. Keywords— Text mining; keywords extraction; co-occurrence;
Radiolabeling an Electronic Cigarette Aerosol Using Technetium Carbon Ultrafine Particles.

BACKGROUND Electronic cigarettes (ECIGs) are widely used, but their health effects are not well known. ECIG exposure is difficult to quantify, and a direct measurement of deposition would be beneficial to in vivo and in vitro toxicity studies. The aim of this study was to demonstrate effective radiolabeling of an ECIG. METHODS A technetium-99m-labeled carbon ultrafine (TCU) aerosol was generated and introduced to a fourth-generation ECIG before nucleation and aerosol formation. The aerosolized e-liquid was a commercially available strawberry flavor containing 1.2% nicotine in a 55% propylene glycol and 45% vegetable glycerine base. An ECIG power setting of 100 W was selected. Mass and radioactivity were measured on each stage within a Sierra Cascade Impactor at 14 L/min to verify the labeling technique using the calculated aerodynamic diameters. A strong positive correlation (R2 > 0.95) between the percent activity and percent mass deposition on each stage provides a reliable validation of colocation. RESULTS Unlabeled ECIG aerosol from the chosen e-liquid produced a mass median aerodynamic diameter (MMAD) of 0.85 μm. An ECIG labeled with TCU produced an aerosol with an activity median aerodynamic diameter of 0.84 μm and an MMAD of 0.84 μm. The relative mass versus radioactivity on each plate was highly correlated (average R2 = 0.973, p < 0.001). CONCLUSION A TCU radiolabel was generated and shown to associate with the mass of an aerosol produced by a typical commercially available ECIG. Thus, the radioactivity of the deposited aerosol may be used to determine ECIG aerosol deposition for the future in vivo and in vitro dosimetry studies of the third- and fourth-generation ECIGs.
Blood pressure-induced physiological strain variability modulates wall structure and function in aorta rings

Vascular smooth muscle cells respond to mechanical stretch by reorganizing their cytoskeletal and contractile elements. Recently, we showed that contractile forces in rat aorta rings were maintained when the rings were exposed to 4 h of physiological variability in cycle-by-cycle strain, called variable stretch (VS), mimicking beat-to-beat blood pressure variability. Contractility, however, was reduced when the aorta was exposed to monotonous stretch (MS) with an amplitude equal to the mean peak strain of VS. Objective: Here we reanalyzed the data to obtain wall stiffness as well as added new histologic and inhibitor studies to test the effects of VS on the extracellular matrix. Main results: The results demonstrate that while the stiffness of the aorta did not change during 4 h MS or VS, nonlinearity in mechanical behavior was slightly stronger following MS. The inhibitor studies also showed that mitochondrial energy production and cytoskeletal organization were involved in this fluctuation-driven mechanotransduction. Reorganization of β-actin in the smooth muscle layer quantified from immunohistochemically labeled images correlated with contractile forces during contraction. Histologic analysis of wall structure provided evidence of reorganization of elastin and collagen fibers following MS but less so following VS. The results suggested that the loss of muscle contraction in MS was compensated by reorganization of fiber structure leading to similar wall stiffness as in VS. Significance: We conclude that muscle tone modulated by variability in stretch plays a role in maintaining aortic wall structural and mechanical homeostasis with implications for vascular conditions characterized by a loss or an increase in blood pressure variability.
Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English

Recent research has shown that attention-based sequence-to-sequence models such as Listen, Attend, and Spell (LAS) yield comparable results to state-of-the-art ASR systems on various tasks. In this paper, we describe the development of such a system and demonstrate its performance on two tasks: first we achieve a new state-of-the-art word error rate of 3.43% on the test clean subset of LibriSpeech English data; second on non-native English speech, including both read speech and spontaneous speech, we obtain very competitive results compared to a conventional system built with the most updated Kaldi recipe.