Miracle-dz/UniFAR-FTUs huggingface dataset:
Wrist-Worn Older Adults' Function: Observational Study.

Background: Heart rate variability (HRV), or variation in beat-to-beat intervals of the heart, is a quantitative measure of autonomic regulation of the cardiovascular system. Low HRV derived from electrocardiogram (ECG) recordings is reported to be related to physical frailty in older adults. Recent advances in wearable technology offer opportunities to more easily integrate monitoring of HRV into regular clinical geriatric health assessments. However, signals obtained from ECG versus wearable photoplethysmography (PPG) devices are different, and a critical first step preceding their widespread use is to determine whether HRV metrics derived from PPG devices also relate to older adults’ physical function. Objective: This study aimed to investigate associations between HRV measured with a wrist-worn PPG device, the Empatica E4 sensor, and validated clinical measures of both objective and self-reported physical function in a cohort of older adults living independently within a continuing care senior housing community. Conclusions: The HRV TI measured with a relatively novel wrist-worn PPG device was related to both objective (SPPB and TUG) and self-reported (SF-36 physical composite) measures of physical function. In addition, the HRV TI explained additional variance in self-reported physical function and cumulative illness severity beyond traditionally measured aspects of physical health. Future steps include longitudinal tracking of changes in both HRV and physical function, which will add important insights regarding the predictive value of HRV as a biomarker of physical health in older adults.
Programmable Data Planes meets In-Network Computing: State of the Art, Challenges, and Research Directions

— Improving network trafﬁc in networks is one of the concerns between networking researchers and network operators since the architecture of modern networks still faces challenges to process large data trafﬁc without the cost of consuming a signiﬁcant amount of resources not related to computing speciﬁcally. At the same time, network programmability has enabled the development of new applications and network services, from software-deﬁned networking to domain-speciﬁc languages created to program network devices and specify their behavior. The development of programmable hardware and hardware accelerators like FPGAs, GPUs, and CPUs help this new paradigm go one step further. Using the artifact of programmability of these devices to solve problems, such as improve the processing of data trafﬁc is the key of in-network computing. It offers the opportunity to execute programs typically running on end-hosts within programmable network devices already incorporated on the network, thus being capable of providing a reduction on the in-network processing load and requiring no extra cost, since operations can be concluded using a fewer amount of devices of the network and no extra device are needed. In this paper, we survey in-network computing, as well as we suggest classifying related works to in-network computing according to the hardware accelerator used. Also, we discuss challenges and research directions.
Local-to-global learning for iterative training of production SLU models on new features

In production SLU systems, new training data becomes available with time so that ML models need to be updated on a regular basis. Specifically, releasing new features adds new classes of data while the old data remains constant. However, retraining the full model each time from scratch is computationally expensive. To address this problem, we propose to consider production releases from the curriculum learning perspective and to adapt the local-to-global learning (LGL) schedule (Cheng et. al, 2019) for a statistical model that starts with fewer output classes and adds more classes with each iteration. We report experiments for the tasks of intent classification and slot filling in the context of a production voice-assistant. First, we apply the original LGL schedule on our data and then adapt LGL to the production setting where the full data is not available at initial training iterations. We demonstrate that our method improves model error rates by 7.3% and saves up to 25% training time for individual iterations.
Computational Model for Electrical Motors Condition Analysis and Monitoring

Predictive maintenance uses several methods to monitor the conditions of electric motors applied in industrial plants. Among these methods, vibration analysis stands out as a widely used method due to the possibility of identifying a wide chance of failures. This work presents the development of a computational model endowed with a classifying algorithm to receive vibration readings from electric motors and determine if the machine has a fault behavior and, if so, which fault. For the algorithm to detect which type of failure, a dataset with readings from several engines in different failure conditions should be developed for training the model.
Improved Classification Model using CNN for Detection of Alzheimer’s Disease

Corresponding Author: Ragavamsi Davuluri Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India E-mail: raaga.vamsi@gmail.com Abstract: Alzheimer’s Disease (AD) is commonly called a neurodegenerative disorder and it is a common form of dementia. There is no permanent cure for this brain disease hence the early diagnosis of such disease using medical imaging system is highly significant. Machine learning models play a vital role in the detection of AD. Since most of the conventional machine learning models find it difficult to detect the essential features to classify the disease, an advanced deep learning framework called Convolutional Neural Network (CNN) is used in this study to detect essential features automatically and classify the disease. The building components of the proposed CNN-based classification method include convolution layer, batch normalization process, ReLU, and Maxpooling operation. The main objective of this CNN-based classification method is to predict whether the patient is suffering from Alzheimer's disease through the analysis of brain MRI. The proposed methodology implemented is identical to a classification-based system that undergoes training, evaluation, and testing process. Finally, the softmax layer is applied for classification, and the Adam optimization technique is applied for reducing the loss, and by applying Adam quicker convergence can be achieved. The proposed improved CNN classification method achieves an accuracy of 97.8%.
Sports Med 2007; 37 (1): 31-46

31 1. Effects of Exercise Training on Fat Mass . 33 1.1 Effects of Endurance Training on Fat-Mass Loss During Caloric Restriction . 33 1.2 Site Specific Fat-Mass Loss . 35 1.3 Predictive Factors for Fat-Mass Loss . 35 2. Endurance Training and Energy Balance . 36 2.1 Energy Balance . 36 2.2 Energy Expenditure and Physical Exercise . 36 2.3 Basal Metabolic Rate . 37 2.4 Fat Oxidation . 37 3. Influences of Training Modalities on Fat-Mass Loss . 39 3.1 Exercise Intensity . 39 3.2 Amount of Exercise . 39 3.3 Type of Endurance Exercise . 41 3.4 Resistance Exercise . 41 4. Future Directions . 42 5. Conclusions . 43 Dietary restriction combined with endurance exercise training represents an Abstract effective strategy to promote weight loss and reduce fat mass in obese patients. Exercise programmes without dietary restriction are less efficient. However, addition of exercise to a dietary restriction programme does not induce a greater fat-mass loss than dietary restriction alone. The latter is likely attributed to a compensatory reduction in daily physical activity following the implementation of exercise training. Nonetheless, inclusion of an exercise training programme is important to prevent a decrease in fat-free mass, increase relative visceral fat-mass loss, improve dietary compliance and eventually maintain long-term weight control. Obese male patients with the highest fat mass are most likely to lose the largest amount of fat mass in such lifestyle intervention programmes. Influences
Learning to Advise and Learning from Advice in Cooperative Multiagent Reinforcement Learning

Learning to coordinate is a daunting problem in multi-agent reinforcement learning (MARL). Previous works have explored it from many facets, including cognition between agents, credit assignment, communication, expert demonstration, etc. However, less attention were paid to agents' decision structure and the hierarchy of coordination. In this paper, we explore the spatiotemporal structure of agents' decisions and consider the hierarchy of coordination from the perspective of multilevel emergence dynamics, based on which a novel approach, Learning to Advise and Learning from Advice (LALA), is proposed to improve MARL. Specifically, by distinguishing the hierarchy of coordination, we propose to enhance decision coordination at meso level with an advisor and leverage a policy discriminator to advise agents' learning at micro level. The advisor learns to aggregate decision information in both spatial and temporal domains and generates coordinated decisions by employing a spatiotemporal dual graph convolutional neural network with a task-oriented objective function. Each agent learns from the advice via a policy generative adversarial learning method where a discriminator distinguishes between the policies of the agent and the advisor and boosts both of them based on its judgement. Experimental results indicate the advantage of LALA over baseline approaches in terms of both learning efficiency and coordination capability. Coordination mechanism is investigated from the perspective of multilevel emergence dynamics and mutual information point of view, which provides a novel perspective and method to analyze and improve MARL algorithms.
BnPC: A Corpus for Paraphrase Detection in Bangla

In this paper, we present the first benchmark 001 dataset for paraphrase detection in Bangla lan-002 guage. Despite being the sixth most spoken 003 language 1 in the world, paraphrase identifi-004 cation in the Bangla language is barely ex-005 plored. Our dataset contains 8,787 human-006 annotated sentence pairs collected from a total 007 of 23 newspaper outlets’ headlines on four cate-008 gories. We explore different linguistic features 009 and pre-trained language models to benchmark 010 the dataset. We perform a human evaluation 011 experiment to obtain a better understanding of 012 the task’s constraints, which reveals intriguing 013 insights. We make our dataset and code pub-014 licly available. 2 015
Theory-Based Studies of Maritime Networks: A Literature Review

This article reviews the literature regarding maritime networks, including identifying the essential articles and authors and suggesting a categorization of associated topics. Network theory in logistics demonstrates strategic information about and competitive advantages of maritime transport between regions. We analyzed 145 technical/scientific studies of maritime networks, including articles, theses, patents, dissertations, and conference proceedings. After defining the inclusion and exclusion criteria, we selected 103 technical and scientific studies, whose analysis allowed us to suggest six categories of interest: Ports, Economy, Containerized Cargo, Maritime Safety, Clusters, and Technological Solutions and Innovations.
Reliability evaluation of high-performance, low-power FinFET standard cells based on mixed RBB/FBB technique

With shrinking transistor feature size, the fin-type field-effect transistor (FinFET) has become the most promising option in low-power circuit design due to its superior capability to suppress leakage. To support the VLSI digital system flow based on logic synthesis, we have designed an optimized high-performance low-power FinFET standard cell library based on employing the mixed FBB/RBB technique in the existing stacked structure of each cell. This paper presents the reliability evaluation of the optimized cells under process and operating environment variations based on Monte Carlo analysis. The variations are modelled with Gaussian distribution of the device parameters and 10000 sweeps are conducted in the simulation to obtain the statistical properties of the worst-case delay and input-dependent leakage for each cell. For comparison, a set of non-optimal cells that adopt the same topology without employing the mixed biasing technique is also generated. Experimental results show that the optimized cells achieve standard deviation reduction of 39.1% and 30.7% at most in worst-case delay and input-dependent leakage respectively while the normalized deviation shrinking in worst-case delay and input-dependent leakage can be up to 98.37% and 24.13%, respectively, which demonstrates that our optimized cells are less sensitive to variability and exhibit more reliability.
Enriching Hindi WordNet Using Knowledge Graph Completion Approach

Even though the use of WordNet in the Natural Language Processing domain is unquestionable, creating and maintaining WordNet is a cumbersome job and it is even difficult for low resource languages like Hindi. In this study, we aim to enrich the Hindi WordNet automatically by using state-of-the-art knowledge graph completion (KGC) approaches. We pose the automatic Hindi WordNet enrichment problem as a knowledge graph completion task and therefore we modify the WordNet structure to make it appropriate for applying KGC approaches. Second, we attempt five KGC approaches of three different genres and compare the performances for the task. Our study shows that ConvE is the best KGC methodology for this specific task compared to other KGC approaches.
Integer points close to a transcendental curve and correctly-rounded evaluation of a function

Despite several significant advances over the last 30 years, guaranteeing the correctly rounded evaluation of elementary functions, such as cos, exp, 3 √ · for instance, is still a difficult issue. This can be formulated as a Diophantine approximation problem, called the Table Maker’s Dilemma, which consists in determining points with integer coordinates that are close to a curve. In this article, we propose two algorithmic approaches to tackle this problem, closely related to a celebrated work by Bombieri and Pila and to the so-called Coppersmith’s method. We establish the underlying theoretical foundations, prove the algorithms, study their complexity and present practical experiments; we also compare our approach with previously existing ones. In particular, our results show that the development of a correctly rounded mathematical library for the binary128 format is now possible at a much smaller cost than with previously existing approaches.
DeepTEA: Effective and Efficient Online Time-dependent Trajectory Outlier Detection

In this paper, we study anomalous trajectory detection, which aims to extract abnormal movements of vehicles on the roads. This important problem, which facilitates understanding of traffic behavior and detection of taxi fraud, is challenging due to the varying traffic conditions at different times and locations. To tackle this problem, we propose the deep -probabilistic-based time-dependent anomaly detection algorithm ( DeepTEA ). This method, which employs deep-learning methods to obtain time-dependent outliners from a huge volume of trajectories, can handle complex traffic conditions and detect outliners accurately. We further develop a fast and approximation version of DeepTEA, in order to capture abnormal behaviors in real-time. Compared with state-of-the-art solutions, our method is 17.52% more accurate than seven competitors on average, and can handle millions of trajectories.
Closed-Form Solution of the Bending Two-Phase Integral Model of Euler-Bernoulli Nanobeams

: Recent developments have shown that the widely used simpliﬁed differential model of Eringen’s nonlocal elasticity in nanobeam analysis is not equivalent to the corresponding and initially proposed integral models, the pure integral model and the two-phase integral model, in all cases of loading and boundary conditions. This has resolved a paradox with solutions that are not in line with the expected softening effect of the nonlocal theory that appears in all other cases. In addition, it revived interest in the integral model and the two-phase integral model, which were not used due to their complexity in solving the relevant integral and integro-differential equations, respectively. In this article, we use a direct operator method for solving boundary value problems for n th order linear Volterra–Fredholm integro-differential equations of convolution type to construct closed-form solutions to the two-phase integral model of Euler–Bernoulli nanobeams in bending under transverse distributed load and various types of boundary conditions.
Energy Efficiency Oriented Design Method of Power Management Strategy for Range-Extended Electric Vehicles

The energy efficiency of the range-extended electric bus (REEB) developed by Tsinghua University must be improved; currently, the energy management strategy is a charge-deplete-charge-sustain (CDCS) strategy, which exhibits low energy efficiency on the demonstration model. To improve the energy efficiency and reduce the operating cost, a rule-based control strategy derived from the dynamic programming (DP) strategy is obtained for the Chinese urban bus driving cycle (CUBDC). This rule is extracted by the power-split-ratio (PSR) from the simulation results of the dynamic powertrain model using the DP strategy. By establishing the REEB dynamic models in Matlab/Simulink, the control rule can be achieved, and the power characteristic of powertrain, energy efficiency, operating cost, and computing time are analyzed. The simulation results show that the performance of the rule-based strategy presented in this paper is similar to that of the DP strategy. The energy efficiency can be improved greatly compared with that of the CDCS strategy, and the operating cost can also be reduced.
Penicillin-binding protein folding is dependent on the PrsA peptidyl-prolyl cis-trans isomerase in Bacillus subtilis Penicillin-binding protein folding is dependent on the PrsA peptidyl-prolyl cis-trans isomerase in Bacillus subtilis Peptidoglycan and muropeptide analysis

cis - trans isomerase in Bacillus Gram-positive the post-translocational folding of exported proteins and is essential for normal growth of B. subtilis . We the mechanism behind this indispensability. We construct a viable prsA null mutant in the of a high of the peptidoglycan cross-linkage index. Misfolded PBP2a was detected in PrsA-depleted cells, indicating that PrsA is required for the folding of this PBP either directly or indirectly. Furthermore, strongly increased uniform staining of cell wall with a ﬂuorescent vancomycin was observed in the absence of PrsA. We also demonstrated that PrsA is a dimeric or oligomeric proteinwhichislocalizedatdistinctspotsorganizedin a helical pattern along the cell membrane. These results suggest that PrsA is essential for normal growth most probably as PBP folding is dependent on this PPIase.
Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use

Background. The behaviors and emotions associated with and reasons for nonmedical prescription drug use (NMPDU) are not well-captured through traditional instruments such as surveys and insurance claims. Publicly available NMPDU-related posts on social media can potentially be leveraged to study these aspects unobtrusively and at scale.Methods. We applied a machine learning classifier to detect self-reports of NMPDU on Twitter and extracted all public posts of the associated users. We analyzed approximately 137 million posts from 87,718 Twitter users in terms of expressed emotions, sentiments, concerns, and possible reasons for NMPDU via natural language processing.Results. Users in the NMPDU group express more negative emotions and less positive emotions, more concerns about family, the past, and body, and less concerns related to work, leisure, home, money, religion, health, and achievement compared to a control group (i.e., users who never reported NMPDU). NMPDU posts tend to be highly polarized, indicating potential emotional triggers. Gender-specific analyses show that female users in the NMPDU group express more content related to positive emotions, anticipation, sadness, joy, concerns about family, friends, home, health, and the past, and less about anger than males. The findings are consistent across distinct prescription drug categories (opioids, benzodiazepines, stimulants, and polysubstance). Conclusion. Our analyses of large-scale data show that substantial differences exist between the texts of the posts from users who self-report NMPDU on Twitter and those who do not, and between males and females who report NMPDU. Our findings can enrich our understanding of NMPDU and the population involved.
EQS Res-HBC: A 65-nm Electro-Quasistatic Resonant 5240 <italic></italic>W Human Whole-Body Powering and 2.19 <italic></italic>W Communication SoC With Automatic Maximum Resonant Power Tracking

Applications like connected healthcare through physiological signal monitoring and secure authentication using wearable keys can benefit greatly from the battery-less operation. Low-power communication along with energy harvesting is critical to sustain such perpetual battery-less operation. Human body channel is known to be a promising alternative to wireless radio wave communication for low power operation through human body communication (HBC), as well as very recently demonstrated as a medium for power transfer through body-coupled power transfer. However, channel length (L) dependency of the received power makes it inefficient for L>40 cm. In this article, we utilize electro-quasistatic resonant HBC (EQS Res-HBC) with maximum resonant power tracking (MRPT) to enable channel length-independent whole-body communication and powering. We design the first system to transfer power and data from a HUB device to wearable energy-harvested NODE through the human body to enable battery-less perpetual operation. Measurement results show 240, 28, and 5 μW power transfer through the body in a machine–machine (large devices with strong ground connection), tabletop (small devices kept on a table), and wearable–wearable (small form factor battery-operated wearable devices) scenario, respectively, independent of body channel length, while enabling communication at 2.19 μW. This enables >25× more power transfer with >100× more efficiency compared to the state of the art for 100-cm body channel in tabletop scenario by utilizing the benefits of EQS operation. The MRPT loop automatically tracks device and posture-dependent changes in the resonance point to maximize power transfer in all cases. Manuscript received July 29, 2021; revised November 4, 2021; accepted December 29, 2021. Date of publication January 25, 2022; date of current version February 24, 2022. This article was approved by Associate Editor Farhana Sheikh. This work was supported in part by the Air Force Office of Scientific Research YIP Award under Grant FA9550-17-1-0450 and in part by the National Science Foundation (NSF) Career Award under Grant 1944602. (Corresponding author: Nirmoy Modak.) This work involved human subjects or animals in its research. Approval of all ethical and experimental procedures and protocols was granted by the Purdue Institutional Review Board under IRB Protocol No. 1610018370. The authors are with the Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 USA (e-mail: nmodak@purdue.edu; shreyas@purdue.edu). Color versions of one or more figures in this article are available at https://doi.org/10.1109/JSSC.2022. 3142177. Digital Object Identifier 10.1109/JSSC.2022. 3142177