Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of intermittent fasting and meal timing in weight management and metabolic health

Obesity remains a major public health concern and intermittent fasting is a popular strategy for weight loss, which may present independent health benefits. However, the number of diet books advising how fasting can be incorporated into our daily lives is several orders of magnitude greater than the number of trials examining whether fasting should be encouraged at all. This review will consider the state of current understanding regarding various forms of intermittent fasting (e.g. 5:2, time-restricted feeding and alternate-day fasting). The efficacy of these temporally defined approaches appears broadly equivalent to that of standard daily energy restriction, although many of these models of intermittent fasting do not involve fed-fasted cycles every other 24 h sleep–wake cycle and/or permit some limited energy intake outside of prescribed feeding times. Accordingly, the intervention period therefore may not regularly alternate, may not span all or even most of any given day, and may not even involve absolute fasting. This is important because potentially advantageous physiological mechanisms may only be initiated if a post-absorptive state is sustained by uninterrupted fasting for a more prolonged duration than applied in many trials. Indeed, promising effects on fat mass and insulin sensitivity have been reported when fasting duration is routinely extended beyond sixteen consecutive hours. Further progress will require such models to be tested with appropriate controls to isolate whether any possible health effects of intermittent fasting are primarily attributable to regularly protracted post-absorptive periods, or simply to the net negative energy balance indirectly elicited by any form of dietary restriction.
Emulating and Evaluating Hybrid Memory for Managed Languages on NUMA Hardware

Non-volatile memory (NVM) has the potential to become a mainstream memory technology and challenge DRAM. Researchers evaluating the speed, endurance, and abstractions of hybrid memories with DRAM and NVM typically use simulation, making it easy to evaluate the impact of different hardware technologies and parameters. Simulation is, however, extremely slow, limiting the applications and datasets in the evaluation. Simulation also precludes critical workloads, especially those written in managed languages such as Java and C#. Good methodology embraces a variety of techniques for evaluating new ideas, expanding the experimental scope, and uncovering new insights. This paper introduces a platform to emulate hybrid memory for managed languages using commodity NUMA servers. Emulation complements simulation but offers richer software experimentation. We use a thread-local socket to emulate DRAM and a remote socket to emulate NVM. We use standard C library routines to allocate heap memory on the DRAM and NVM sockets for use with explicit memory management or garbage collection. We evaluate the emulator using various configurations of write-rationing garbage collectors that improve NVM lifetimes by limiting writes to NVM, using 15 applications and various datasets and workload configurations. We show emulation and simulation confirm each other's trends in terms of writes to NVM for different software configurations, increasing our confidence in predicting future system effects. Emulation brings novel insights, such as the non-linear effects of multi-programmed workloads on NVM writes, and that Java applications write significantly more than their C++ equivalents. We make our software infrastructure publicly available to advance the evaluation of novel memory management schemes on hybrid memories.
Comparative split‐face study of durational changes in hyaluronic acid fillers for mid‐face volume augmentation

Subsequent changes after injection should be considered when determining the precise volume of injected dermal filler. Several studies have used scoring systems to evaluate facial volumes; however, these scoring systems are not particularly objective. This present study aimed to evaluate the volumetric changes over time on three‐dimensional (3D) images and the maintenance potential of various hyaluronic acid (HA) fillers used for mid‐face volume augmentation. This split‐face clinical study included nine Korean subjects who each received a mid‐facial injection of the test filler (B) on one side and a random control filler (J, R, or Y) on the contralateral side. Global, photographic, and 3D scanning assessments were conducted at baseline and after 30 min, 3 days, and 2, 4, 12, and 24 weeks. In all nine cases, the 3D images revealed the largest differences in height where the test filler (B) was injected. The results of subjective scoring systems correlated with the results of 3D imaging. The volumes of monophasic fillers (B and J) were maintained for longer periods of time than those of biphasic fillers (R and Y). The B filler yielded excellent volumizing and spreading effects and good injectability. This filler would be suitable for injection into high‐pressure areas, such as the lateral cheek, chin, and nasolabial fold. Moreover, the 3D imaging analysis provided objective and digitized data. The present authors hope that their data will allow physicians to better understand the durational changes in HA fillers and, thus, provide accurate predictions to their patients.
Performance Anomalies of Smartphone-based Mobile Wi-Fi Hotspots

In this work, we tackle the ubiquitous provision of internet connections for users in smart cities by analyzing the mobile Wi-Fi hotspots and their performance from several aspects. We first discuss the capabilities and operational characteristics of these networks and the limitations they have when compared with other similar IEEE802.11 modes. We then conduct experiments to analyze their performance considering the following: operational frequency band, channel bandwidth, maximum number of connected devices, received signal strength, coverage and connection speed. The obtained results show many performance anomalies for such networks.
Histopathological Imaging–Environment Interactions in Cancer Modeling

Histopathological imaging has been routinely conducted in cancer diagnosis and recently used for modeling other cancer outcomes/phenotypes such as prognosis. Clinical/environmental factors have long been extensively used in cancer modeling. However, there is still a lack of study exploring possible interactions of histopathological imaging features and clinical/environmental risk factors in cancer modeling. In this article, we explore such a possibility and conduct both marginal and joint interaction analysis. Novel statistical methods, which are “borrowed” from gene–environment interaction analysis, are employed. Analysis of The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) data is conducted. More specifically, we examine a biomarker of lung function as well as overall survival. Possible interaction effects are identified. Overall, this study can suggest an alternative way of cancer modeling that innovatively combines histopathological imaging and clinical/environmental data.
Hybrid one-shot depth measuring for stereo-view structured light systems

In this paper, we propose a hybrid scheme to measure depth values for a stereo structured light system with only a single shot. We design a dual-frequency monochromatic pattern, based on which depth values are computed in three steps. Firstly, phases and coarse depth maps are computed by following the idea of Fourier transform profilometry, where a novel phase unwrapping method is proposed. Afterward, errors in coarse depth maps are detected according to cross-view geometry consistency. Finally, spatial stereo matching is conducted to refine the detected errors. Experiments demonstrate that the proposed scheme can generate accurate stereo depth maps with only one shot, which can be used in a range of real-time applications.
Smart Parking System Based on Internet of Things: A Review

The growing population in cities has led to increasing number of vehicles on road. More vehicles circulate in cities in search of parking due to which it increases the traffic in densely populated areas. To solve such parking issues there is a strong urge to introduce smart parking system. Smart parking system helps the driver in finding the parking slot in particular area resulting in reduced traffic and time required to search a parking slot. In this paper the parking related problems are introduced wherein IOT plays an important role. The algorithms, system and techniques related to smart parking are discussed and reviewed
Attribute-and-Identity Correspondence Network for Clothes Search

Clothes search is a challenging task, because the uncontrolled capture condition and massive styles of clothing images make the accurate retrieval difficult. To address the problem, we design a novel Attribute-and-Identity Correspondence Network (AICN) to obtain more robust features for clothes retrieval. The attribute information of clothes includes category, color, shape and so forth. The images of the same clothing pictured under different pose or background conditions have the same identity. Specifically, the AICN is comprised of two sub-branches: the attribute one and the identity one, and the two branches share the low level features. The former exploits attribute information to learn the discriminative feature, while the latter is to handle the variations of images with the same identity. Furthermore, we design a compactness loss to force the feature spaces of the two branches to become close, since their semantic spaces have overlap intuitively. Experiments on the benchmark and practical dataset demonstrate the effectiveness and priority of our method.
Assessing the Effects of Low Voltage in Branch Prediction Units

Branch prediction units are key performance components in modern microprocessors as they are widely used to address control hazards and minimize misprediction stalls. The continuous urge of high performance has led designers to integrate highly sophisticated predictors with complex prediction algorithms and large storage requirements. As a result, BPUs in modern microprocessors consume large amounts of power. But when a system is under a limited power budget, critical decisions are required in order to achieve an equilibrium point between the BPU and the rest of the microprocessor. In this work, we present a comprehensive analysis of the effects of low voltage configuration Branch Prediction Units (BPU). We propose a design with separate voltage domain for the BPU, which exploits the speculative nature of the BPU (which is self-correcting) that allows reduction of power without affecting functional correctness. Our study explores how several branch predictor implementations behave when aggressively undervolted, the performance impact of BTB as well as in which cases it is more efficient to reduce the BP and BTB size instead of undervolting. We also show that protection of BPU SRAM arrays has limited potential to further increase the energy savings, showcasing a realistic protection implementation. Our results show that BPU undervolting can result in power savings up to 69%, while the microprocessor energy savings can be up to 12%, before the penalty of the performance degradation overcomes the benefits of low voltage. Neither smaller predictor sizes nor protection mechanisms can further improve energy consumption.
Compressed Sensing OFDM Time Delay Estimation Based on Atomic Evolution and Elimination

Aiming at the problem of time delay estimation for orthogonal frequency division multiplexing (OFDM) signals with small samples, a new compressed sensing time delay estimation method based on atomic evolution and elimination is proposed. This method introduces the idea of evolution and elimination from Darwin’s evolutionism, abandons the fixed and invariable way of grid points in the traditional algorithm, takes grid points as adjustable parameters, and updates grid points in each iteration process to promote the evolution of atoms. In order to deal with the off-grid effect, the exponential function is directly modified instead of linear approximation, which effectively eliminates the modeling error caused by the original off-grid algorithm. In the process of calculation, the atomic elimination mechanism is adopted, which reduces the amount of calculation and improves the speed of it. The simulation results show that the proposed algorithm can significantly improve the performance of time delay estimation, especially when using coarse grids. Based on the universal software radio peripheral (USRP), the validity of the proposed algorithm is verified by the actual signal.
Q-Learning Based Physical-Layer Secure Game Against Multiagent Attacks

In this paper, we consider a Q-learning-based power allocation strategy for a secure physical-layer system under dynamic radio environments. In such a system, the transmitter sends the information to the receiver threatened by <inline-formula> <tex-math notation="LaTeX">${M (M \geq 2)}$ </tex-math></inline-formula> intelligent attackers which have several attack modes and will bring out the severe issue of information security. To safeguard the system security, we formulate the insecure problem as a stochastic game which consists of <inline-formula> <tex-math notation="LaTeX">${M+1}$ </tex-math></inline-formula> players: the transmitter which can flexibly choose its transmit power, and <inline-formula> <tex-math notation="LaTeX">${M}$ </tex-math></inline-formula> smart attackers that can determine their attack types. Then, the Nash equilibria (NEs) of the physical-layer secure game are derived, and their existence conditions are taken into account. The simulation results show that the proposed power allocation strategy in the stochastic game can efficiently suppress the attack rate of smart attackers even if there exist multiple smart attackers.
HMGB proteins are required for sexual development in Aspergillus nidulans

Aspergillus nidulans has three high mobility group box (HMGB) proteins, HmbA, HmbB and HmbC that are chromatin-associated architectural proteins involved in DNA-related functions. By creating and studying deletion strains in both veA+ and veA1 background, we have characterized the role of HmbA, HmbB and HmbC in sexual development. Expression of the mating-type MAT1-1 and MAT1-2 coding genes were found to be extremely down-regulated in all three mutants on day 4 of sexual development, which results in deficient ascospore production and/or ascospore viability in the mutants. In addition, we found that HmbA and HmbB play also a role in sensing of and response to environmental signals, while HmbC functionally interacts with VeA, a key regulator of the coordination of asexual and sexual development, as well as of secondary metabolism.
Fast HEVC Transrating using Random Forests

This article describes a fast transrating solution for HEVC based on classification and machine learning techniques. Two classifiers are trained to predict the range of CTU quadtree depths that will be searched to find the best CTU partitioning. Three approaches are proposed for reducing the number of features used by the classifiers, two based on feature selection, and one based on feature transformation using autoencoders. A full transrating framework based on x265 is built for model training and evaluation. Experimental results using the x265 encoder show that an average 41.81% computational complexity reduction can be achieved at the cost of a tolerable 0.29% Bjontegaard-Delta bitrate, outperforming competing methods.
Radio Tomographic Imaging Based on Low-Rank and Sparse Decomposition

Imaging artifacts induced by the multipath interference in Radio-Frequency sensing network usually significantly degrade the performance of Radio Tomographic Imaging (RTI) and thereby has become a major challenge in the Device-Free Localization (DFL). The multipath in the environment often invalidates the commonly used sparsity-regularized methods for RTI reconstruction because the sparse multipath-induced imaging artifacts may be misestimated as the sparse target-induced attenuation. To enhance the sensing ability of the target’s effect in RTI, for the first time, this paper utilized the Low-Rank and Sparse Decomposition (LRSD) to cope with the multipath-induced imaging artifacts and infer the sparse target-induced attenuation accurately. The experimental results demonstrated the significant advantages of the proposed LRSD method in the reconstructed RTI quality, DFL accuracy, and time complexity in comparison to the presenting commonly used methods, including Tikhonov Regularization and Bayesian Compressive Sensing (BCS). The above advantages make the proposed LRSD method highly expected to improve the RTI performance and also make it applicable for real-time DFL applications.
Audio-Noise Power Spectral Density Estimation Using Long Short-Term Memory

We propose a method using a long short-term memory (LSTM) network to estimate the noise power spectral density (PSD) of single-channel audio signals represented in the short-time Fourier transform (STFT) domain. An LSTM network common to all frequency bands is trained, which processes each frequency band individually by mapping the noisy STFT magnitude sequence to its corresponding noise PSD sequence. Unlike deep-learning-based speech-enhancement methods, which learn the full-band spectral structure of speech segments, the proposed method exploits the sub-band STFT magnitude evolution of noise with long time dependence, in the spirit of the unsupervised noise estimators described in the literature. Speaker- and speech-independent experiments with different types of noise show that the proposed method outperforms the unsupervised estimators, and it generalizes well to noise types that are not present in the training set.
The relationship between initial threshold, learning, and generalization in perceptual learning

We investigated the origin of two previously reported general rules of perceptual learning. First, the initial discrimination thresholds and the amount of learning were found to be related through a Weber-like law. Second, increased training length negatively influenced the observer's ability to generalize the obtained knowledge to a new context. Using a five-day training protocol, separate groups of observers were trained to perform discrimination around two different reference values of either contrast (73% and 30%) or orientation (25° and 0°). In line with previous research, we found a Weber-like law between initial performance and the amount of learning, regardless of whether the tested attribute was contrast or orientation. However, we also showed that this relationship directly reflected observers' perceptual scaling function relating physical intensities to perceptual magnitudes, suggesting that participants learned equally on their internal perceptual space in all conditions. In addition, we found that with the typical five-day training period, the extent of generalization was proportional to the amount of learning, seemingly contradicting the previously reported diminishing generalization with practice. This result suggests that the negative link between generalization and the length of training found in earlier studies might have been due to overfitting after longer training and not directly due to the amount of learning per se.
Image Colorization Based on Self-Adaptive Mutation Particle Swarm Optimization and Support Vector Machine

Image colorization technology has been widely studied because of its strong practicability. In this paper, an image colorization method of strong robustness is proposed which is based on the self-adaptive mutation particle swarm optimization and support vector machine. Firstly, the parameters of support vector machine are optimized by self-adaptive mutation particle swarm optimization. And then, the image is edited by the optimized support vector machine to separate the foreground and background. Finally, the local features of the image are reconstructed according to the based-graph theory. The experimental results show that the method in this paper can realize colorization at high fidelity. (Abstract)
Graph Regularized and Label-matched Dictionary Learning for Video-based Person Re-identification

In recent years, video-based person re-identification has attracted more and more attention. However, most existing video-based methods do not fully consider the intrinsic structure and invariant information of the same person across different cameras. In this paper, we propose a graph regularized and label-matched dictionary learning (GRLDL) method to capture the intrinsic structure of the same person between two cameras. Firstly, in order to reduce the variations between different cameras, we use local Fisher discriminant analysis to transform the person videos from different cameras into a common feature space. A dictionary is learned from this common space. Then, we construct a graph regularization term to preserve the geometrical structure of the same person and enhance the discriminative ability of the learned dictionary. Finally, a projective matrix is introduced to map the coding coefficients into a label space, which is able to correlate and match the same person under different cameras. Experiments on the public iLIDS-VID and PRID 2011 datasets show the effectiveness of the proposed method.
Innovative Research in Computer and

: World Wide Web is an ever-growing public library with hundreds of millions of books without any central management system. Finding a piece of information without a proper directory is like finding a middle in a haystack. Various search engines solve this problem by indexing an amount of the complete content that is available in the internet. For accomplishing this job, search engines use an automated program, known as a web crawler. The most vital job of the web is information retrieval, that too with proper efficiency. Web Crawler helps to accomplish that, by helping search indexing or by helping in making archives. Web Crawler automatically visits all the available links which is further indexed. But, usage of web crawler is not limited to only search engines, but they can also be used for web scrapping, spam filtering, identifying unauthorized use of copyrighted content, identifying illegal and harmful web activities etc. Web Crawler faces various challenges while crawling deep web content, multimedia content etc. Various crawling techniques and various web crawlers are available and discussed in this paper.
High Power Quality Wide Range Power Source for B-H Meter Applications

measurement of magnetic material characteristics is one of the most important issues for electrical engineers who work in transformer and machine design. The measurement device known as B-H meter is designed for this purpose. One of the most important parts of the B-H meter is programmable power source. The accuracy and uncertainty of B-H meter device is mostly affected by the quality of output power source voltage. In this paper, a hybrid power amplifier including cascade H-bridge multilevel inverter and a linear power amplifier are presented for using in B-H meter devices. The proposed power amplifier reduce the total harmonic distortion, effectively. By considering high bandwidth of linear power amplifier, it can be used to generate harmonic components at the output voltage. The presented amplifier is simulated in the MATLAB software and the amplifier performance has been confirmed in terms of power quality characteristics.