Miracle-dz/UniFAR-FTUs huggingface dataset:
When Virtual Reality Meets Internet of Things in the Gym

With the advent of immersive virtual reality (VR) head-mounted displays (HMD), we envision that immersive VR will revolutionize the personal fitness experience in our daily lives. Toward this vision, we present JARVIS, a virtual exercise assistant that is able to provide an immersive and interactive gym exercise experience to a user. JARVIS is enabled by the synergy between Internet of Things (IoT) and immersive VR. JARVIS employs miniature IoT sensing devices removably attachable to exercise machines to track a multitude of exercise information including exercise types, repetition counts, and progress within each repetition in real time. Based on the tracked exercise information, JARVIS shows the user the proper way of doing the exercise in the virtual exercise environment, thereby helping the user to better focus on the target muscle group. We have conducted both in-lab experiments and a pilot user study to evaluate the performance and effectiveness of JARVIS, respectively. Our in-lab experiments with fifteen participants show that JARVIS is able to segment exercise repetitions with an average accuracy of 97.96% and recognize exercise types with an average accuracy of 99.08%. Our pilot user study with ten participants shows statistically significant improvements in perceived enjoyment, competence, and usefulness with JARVIS compared to a traditional machine exercise setting (p < 0.05). Finally, our surface electromyography (sEMG) signal analysis conducted during the pilot user study shows statistically significant improvement in terms of muscle activation (p < 0.01), indicating the potential of JARVIS in providing an engaging and effective guidance for machine exercises.
Comparing the Effects of Contact-Based Education and Acceptance and Commitment-Based Training on Empathy toward Mental Illnesses among Nursing Students

Objective: Empathy is an important and valuable tool in therapeutic communication. Improvement barriers of empathy in psychiatric nursing education are associated with challenges, such as stress due to negative attitudes toward psychiatric disorders. The current study aimed at comparing the effects of contact-based education and commitment and acceptance-based training on empathy toward mental illnesses among nursing students. Method : In this clinical trial, 111 nursing students were selected using cluster and quota sampling methods in Mashhad, Iran. They were divided into 3 groups: (1) contact-based education (interpersonal contact among individuals with improved mental illnesses), (2) acceptance and commitment-based training, and (3) control group. The study tool was Jefferson Nurses Empathy Questionnaire, which was completed in 3 stages of pretest, posttest, and follow- up. Data were analyzed by repeated- measures ANOVA. Results: There was no significant difference between contact-based education and acceptance and commitment-based training groups in increasing the average score of total empathy during pretest, posttest, and 1-month follow- up (p = 0/92). However, a significant difference was found between contact-based education and control group (p = 0/004) and between acceptance and commitment-based training and control group (p = 0/02). Conclusion: Both methods of contact-based education and acceptance and commitment-based therapy were effective in increasing the level of empathy into mental illnesses in nursing students.
Multiobjective evolutionary polygonal approximation for identifying crude oil reservoirs

This work formalizes a multi-objective evolutionary approach for the segmentation issue according to Piecewise Linear Representation. It consists in the approximation of a given digital curve by a set of linear models minimizing the representation error and the number of such models. This solution allows the final user to decide from the best array of best found solutions considering the different objectives jointly. The proposed approach eliminates the difficult a-priori parameter choices in order to satisfy the user restrictions (the solution choice is performed a-posteriori, from the obtained array of solutions) and allows the algorithm to be run a single time (since the whole Pareto front is obtained with a single run and different solutions may be chosen at different times from that Pareto front in order to satisfy different requirements). This solution will be applied to Petroleum Industry in particular to the problem of identifying resources from extraction areas in order to optimize their operational costs and production capacity.
Predict Demographic Information Using Word2vec on Spatial Trajectories

Inferring socio-demographic attributes of users is an important and challenging task that could help with personalization, recommendation, advertising, etc. Sensor data collected from mobile devices can be utilized for inferring such attributes. Previous works have focused on combining different types of sensors, such as applications, accelerometer, GPS, battery, and many others, to achieve this task. In this study, we were able to infer attributes, such as gender, age, marital status, and whether the user has children, using solely the GPS sensor. We suggest a novel inference technique, which learns an embedding representation of preprocessed spatial GPS trajectories using an adaption of the Word2vec approach. Based on the embedding representation, we later train multiple classification models to achieve the inference goals. Our empirical results indicate that the suggested embedding approach outperforms a classification approach which does not take into consideration the embedding patterns. Experiments on real datasets collected from Android devices show that the proposed method achieves over 80% accuracy for various demographic prediction tasks.
Insights Into Auditory Cortex Dynamics From Non-invasive Brain Stimulation

Non-invasive brain stimulation (NIBS) has been widely used as a research tool to modulate cortical excitability of motor as well as non-motor areas, including auditory or language-related areas. NIBS, especially transcranial magnetic stimulation (TMS) and transcranial direct current stimulation, have also been used in clinical settings, with however variable therapeutic outcome, highlighting the need to better understand the mechanisms underlying NIBS techniques. TMS was initially used to address causality between specific brain areas and related behavior, such as language production, providing non-invasive alternatives to lesion studies. Recent literature however suggests that the relationship is not as straightforward as originally thought, and that TMS can show both linear and non-linear modulation of brain responses, highlighting complex network dynamics. In particular, in the last decade, NIBS studies have enabled further advances in our understanding of auditory processing and its underlying functional organization. For instance, NIBS studies showed that even when only one auditory cortex is stimulated unilaterally, bilateral modulation may result, thereby highlighting the influence of functional connectivity between auditory cortices. Additional neuromodulation techniques such as transcranial alternating current stimulation or transcranial random noise stimulation have been used to target frequency-specific neural oscillations of the auditory cortex, thereby providing further insight into modulation of auditory functions. All these NIBS techniques offer different perspectives into the function and organization of auditory cortex. However, further research should be carried out to assess the mode of action and long-term effects of NIBS to optimize their use in clinical settings.
A New Variational Model for Binary Classification in the Supervised Learning Context

We examine the supervised learning problem in its continuous setting and give a general optimality condition through techniques of functional analysis and the calculus of variations. This enables us to solve the optimality condition for the desired function u numerically and make several comparisons with other widely utilized supervised learning models. We employ the accuracy and area under the receiver operating characteristic curve as metrics of the performance. Finally, 3 analyses are conducted based on these two mentioned metrics where we compare the models and make conclusions to determine whether or not our method is competitive.
Mitochondria and Calcium Regulation as Basis of Neurodegeneration Associated With Aging

Age is the main risk factor for the onset of neurodegenerative diseases. A decline of mitochondrial function has been observed in several age-dependent neurodegenerative diseases and may be a major contributing factor in their progression. Recent findings have shown that mitochondrial fitness is tightly regulated by Ca2+ signals, which are altered long before the onset of measurable histopathology hallmarks or cognitive deficits in several neurodegenerative diseases including Alzheimer’s disease (AD), the most frequent cause of dementia. The transfer of Ca2+ from the endoplasmic reticulum (ER) to the mitochondria, facilitated by the presence of mitochondria-associated membranes (MAMs), is essential for several physiological mitochondrial functions such as respiration. Ca2+ transfer to mitochondria must be finely regulated because excess Ca2+ will disturb oxidative phosphorylation (OXPHOS), thereby increasing the generation of reactive oxygen species (ROS) that leads to cellular damage observed in both aging and neurodegenerative diseases. In addition, excess Ca2+ and ROS trigger the opening of the mitochondrial transition pore mPTP, leading to loss of mitochondrial function and cell death. mPTP opening probably increases with age and its activity has been associated with several neurodegenerative diseases. As Ca2+ seems to be the initiator of the mitochondrial failure that contributes to the synaptic deficit observed during aging and neurodegeneration, in this review, we aim to look at current evidence for mitochondrial dysfunction caused by Ca2+ miscommunication in neuronal models of neurodegenerative disorders related to aging, with special emphasis on AD.
Are Mutation Scores Correlated with Real Fault Detection? A Large Scale Empirical Study on the Relationship Between Mutants and Real Faults

Empirical validation of software testing studies is increasingly relying on mutants. This practice is motivated by the strong correlation between mutant scores and real fault detection that is reported in the literature. In contrast, our study shows that correlations are the results of the confounding effects of the test suite size. In particular, we investigate the relation between two independent variables, mutation score and test suite size, with one dependent variable the detection of (real) faults. We use two data sets, CoreBench and De-fects4J, with large C and Java programs and real faults and provide evidence that all correlations between mutation scores and real fault detection are weak when controlling for test suite size. We also found that both independent variables significantly influence the dependent one, with significantly better fits, but overall with relative low prediction power. By measuring the fault detection capability of the top ranked, according to mutation score, test suites (opposed to randomly selected test suites of the same size), we found that achieving higher mutation scores improves significantly the fault detection. Taken together, our data suggest that mutants provide good guidance for improving the fault detection of test suites, but their correlation with fault detection are weak.
Angiogenesis and vasculogenic mimicry are inhibited by 8-Br-cAMP through activation of the cAMP/PKA pathway in colorectal cancer

Introduction Vasculogenic mimicry (VM) describes the formation of an epithelial-independent tumor microcirculation system that differs from traditional angiogenesis. Angiogenesis and the formation of VM are closely related through the cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) pathway and the epithelial–mesenchymal transition (EMT) process. Materials and methods In this study, 8-Br-cAMP, a cAMP analog and PKA activator, was used to activate the cAMP/PKA pathway to evaluate the effects of cAMP/PKA on angiogenesis and VM in colorectal cancer (CRC) cells. We used a syngeneic model of CRC in BALB/c mice. Results We discovered that treatment with 8-Br-cAMP significantly reduced tumor number compared to control mice after the 7th, 14th, and 28th days of treatment. VM was evaluated by periodic acid–schiff (PAS)–CD31 staining, and we found that VM was inhibited by 8-Br-cAMP treatment in vivo. Immunohistochemistry confirmed the inhibition of vascular endothelial growth factor (VEGF) and cAMP and the activation of PKA by 8-Br-cAMP; quantitative real-time-PCR (qRT-PCR) demonstrated that 8-Br-cAMP regulated the expression of vascular endothelial (VE)-cadherin, matrix metalloproteinase 2 (MMP2), ephrin type-A receptor 2 (EphA2), and VEGF in vivo. Experiments in vitro revealed that treatment with 8-Br-cAMP and U0126 decreased VEGF expression through PKA–ERK in CT26 cells by qRT-PCR. We further confirmed that tube formation of human umbilical vein endothelial cells was inhibited by 8-Br-cAMP in vitro. Discussion This study demonstrates that angiogenesis and VM are inhibited by 8-Br-cAMP treatment. Our data indicate that 8-Br-cAMP acts through the cAMP/PKA–ERK pathway and through EMT processes in CRC. These findings provide an insight into mechanisms of CRC and suggest that the cAMP/PKA–ERK pathway is a novel potential therapeutic target for the treatment of CRC.
Multi-Template Mesiotemporal Lobe Segmentation: Effects of Surface and Volume Feature Modeling

Numerous neurological disorders are associated with atrophy of mesiotemporal lobe structures, including the hippocampus (HP), amygdala (AM), and entorhinal cortex (EC). Accurate segmentation of these structures is, therefore, necessary for understanding the disease process and patient management. Recent multiple-template segmentation algorithms have shown excellent performance in HP segmentation. Purely surface-based methods precisely describe structural boundary but their performance likely depends on a large template library, as segmentation suffers when the boundaries of template and individual MRI are not well aligned while volume-based methods are less dependent. So far only few algorithms attempted segmentation of entire mesiotemporal structures including the parahippocampus. We compared performance of surface- and volume-based approaches in segmenting the three mesiotemporal structures and assess the effects of different environments (i.e., size of templates, under pathology). We also proposed an algorithm that combined surface- with volume-derived similarity measures for optimal template selection. To further improve the method, we introduced two new modules: (1) a non-linear registration that is driven by volume-based intensities and features sampled on deformable template surfaces; (2) a shape averaging based on regional weighting using multi-scale global-to-local icosahedron sampling. Compared to manual segmentations, our approach, namely HybridMulti showed high accuracy in 40 healthy controls (mean Dice index for HP/AM/EC = 89.7/89.3/82.9%) and 135 patients with temporal lobe epilepsy (88.7/89.0/82.6%). This accuracy was comparable across two different datasets of 1.5T and 3T MRI. It resulted in the best performance among tested multi-template methods that were either based on volume or surface data alone in terms of accuracy and sensitivity to detect atrophy related to epilepsy. Moreover, unlike purely surface-based multi-template segmentation, HybridMulti could maintain accurate performance even with a 50% template library size.
Stability of a type 2 diabetes rat model induced by high-fat diet feeding with low-dose streptozotocin injection

The present study aims at determining the stability of a popular type 2 diabetes rat model induced by a high-fat diet combined with a low-dose streptozotocin injection. Wistar rats were fed with a high-fat diet for 8 weeks followed by a one-time injection of 25 or 35 mg/kg streptozotocin to induce type 2 diabetes. Then the diabetic rats were fed with regular diet/high-fat diet for 4 weeks. Changes in biochemical parameters were monitored during the 4 weeks. All the rats developed more severe dyslipidemia and hepatic dysfunction after streptozotocin injection. The features of 35 mg/kg streptozotocin rats more resembled type 1 diabetes with decreased body weight and blood insulin. Rats with 25 mg/kg streptozotocin followed by normal diet feeding showed normalized blood glucose level and pancreatic structure, indicating that normal diet might help recovery from certain symptoms of type 2 diabetes. In comparison, diabetic rats fed with high-fat diet presented decreased but relatively stable blood glucose level, and this was significantly higher than that of the control group (P>0.05). This model easily recovers with normal diet feeding. A high-fat diet is suggested as the background diet in future pharmacological studies using this model.
Revisiting the Hierarchical Multiscale LSTM

Hierarchical Multiscale LSTM (Chung et. al., 2016) is a state-of-the-art language model that learns interpretable structure from character-level input. Such models can provide fertile ground for (cognitive) computational linguistics studies. However, the high complexity of the architecture, training and implementations might hinder its applicability. We provide a detailed reproduction and ablation study of the architecture, shedding light on some of the potential caveats of re-purposing complex deep-learning architectures. We further show that simplifying certain aspects of the architecture can in fact improve its performance. We also investigate the linguistic units (segments) learned by various levels of the model, and argue that their quality does not correlate with the overall performance of the model on language modeling.
Scaffolding for an OLM for Long-Term Physical Activity Goals

An important role of open learner models (OLMs) is to support self-reflection. We explore how to do this for an OLM based on fine-grained long term physical activity tracker data that many people are accumulating. We aim to tackle two well-documented challenges that people face, in making effective use of an OLM for reflection. 1. We created a tutorial to scaffold sense-making needed to understand the meaning of the OLM. 2. We integrated an interface scaffold to help users consider key questions for effective reflection. We report the results of a qualitative think-aloud lab study with 21 participants viewing their own long term OLM. To evaluate the tutorial scaffolding, we split participants into an experimental group, who did a tutorial before exploring the OLM and a control group which explored the interface without the tutorial. To evaluate the reflection scaffolding, all participants first explored the interface as they wished. We then provided goal prompts to scaffold reflection. Our study revealed that, under lab conditions, the tutorial scaffolding was not needed - all participants in both groups could readily understand the OLM. However, we found that several of the goal prompts were important to help participants consider key questions for effective reflection. Our key contribution is insights into the design of scaffolding for reflection in a life-long learning context of gaining insights and setting goals for physical activity.
OS-Aware Automotive Controller Design Using Non-Uniform Sampling

Automotive functionalities typically consist of a large set of periodic/cyclic tasks scheduled under a real-time operating system (OS). Many of the tasks are feedback control applications with stringent performance requirements. OSEK/VDX is a common class of automotive OS that offers preemptive periodic schedules supporting a pre-configured set of periods. The feedback controllers implemented onto such OSEK/VDX-compliant systems need to use one of the pre-configured (sampling) periods. A shorter period is often desired for a higher control performance, and this implies a higher processor load. For a given performance requirement, the longest sampling period that meets this requirement is the optimal one. Given a limited set of pre-configured periods, such optimal sampling periods are often not available, and the practice is to choose a shorter available period—leading to a higher processor load. To address this, we propose a controller that cyclically switches among the available periods, thereby leading to an average sampling period closer to the optimal one. This way, we reduce the processor load and are able to pack more control applications on the same processor. The main challenge in this article is the design of such controllers that takes into account such cyclic switching of sampling periods (i.e., use non-uniform sampling). The controller needs to meet specified performance requirements (settling time) and system constraints (e.g., input saturation). Such a non-convex constrained controller optimization problem as raised in the OS-aware automotive systems design has not been addressed in the traditional optimal control literature. A novel approach based on adaptively parameterized particle swarm optimization (PSO) is proposed to solve it. Using the OS-aware controller design with non-uniform sampling, we show that a higher number of applications can be packed on a processor, which is of particular interest in the cost-sensitive automotive industry.
Male Sterility of an AHAS-Mutant Induced by Tribenuron-Methyl Solution Correlated With the Decrease of AHAS Activity in Brassica napus L.

Tribenuron-methyl (TBM), an acetohydroxyacid synthase (AHAS)-inhibiting herbicide, can be used as an efficient chemical hybridization agent to induce male sterility for practical utilization of heterosis in rapeseed (Brassica napus L.). Utilization of rapeseed mutants harboring herbicide-resistant AHAS alleles as the male parent can simplify the hybrid seed production protocol. Here we characterized a novel TBM-resistant mutant K5 derived from an elite rapeseed variety, Zhongshuang No. 9 (ZS9), by ethyl methyl sulfonate mutagenesis. Comparative analysis of three BnAHAS genes (BnAHAS1, BnAHAS2, and BnAHAS3) between the mutant K5 and ZS9 identified a C-to-T transition at 544 from the translation start site in BnAHAS1 in K5 (This resistant allele is referred to as BnAHAS1544T), which resulted in a substitution of proline with serine at 182 in BnAHAS1. Both ZS9 and K5 plants could be induced complete male sterility under TBM treatment (with 0.10 and 20 mg⋅L-1 of TBM, respectively). The relationship between TBM-induced male sterility (Y) and the relative AHAS activity of inflorescences (X) could be described as a modified logistic function, Y = 100-A/(1+Be(-KX)) for the both genotypes, although the obtained constants A, B, and K were different in the functions of ZS9 and K5. Transgenic Arabidopsis plants expressing BnAHAS1544T exhibited a higher TBM resistance of male reproductive organ than wild type, which confirmed that the Pro-182-Ser substitution in BnAHAS1 was responsible for higher TBM-resistance of male reproductive organs. Taken together, our findings provide a novel valuable rapeseed mutant for hybrid breeding by chemical hybridization agents and support the hypothesis that AHAS should be the target of the AHAS-inhibiting herbicide TBM when it is used as chemical hybridization agent in rapeseed.