Miracle-dz/UniFAR-FTUs huggingface dataset:
A Game Based Power Allocation in Cloud Computing Data Centers

The emergence of smart systems based on Internet of Things (IoT) and new technologies has led to use more cloud computing services. This incentivizes to build more geographically distributed data centers. However, the data centers consume a tremendous amount of electricity which significantly increases load on power grid. There are broad concerns about the impact that this huge consumption may cause to the power grid. Moreover, the data centers are competing to get the maximum of power from the smart grid in a selfish way, which also has a negative impact on both the smart grid and the other data centers. In this paper, we model the power allocation problem between the smart grid and cloud data centers as a non-cooperative game. The basic idea of our approach is to determine the optimal quantity of power that will be assigned to each data center, in order to have a fair power allocation. To do so, we consider the data center priority in terms of number of active servers, state of energy charge and number of running critical applications. Moreover, we prove the existence and uniqueness of Nash equilibrium, and compute the optimal quantity of power using Lagrange multipliers and KarushKuhnTucker (KKT) conditions. Simulation results confirm the effectiveness of the proposed approach, and show that our scheme can reduce the load on the power grid up to 80%.
Simultaneous Sensing of Vapor Concentration and Temperature Utilizing Multimode of a MEMS Resonator

Most gas sensors suffer from the cross sensitivity to environmental temperature, which significantly reduces the accuracy and reliability of measurements. Current solutions require the fabrication of a thermometer in close proximity to the gas sensor or an identical reference sensor to compensate for the sensor drift due to temperature. This increases the device size, fabrication cost, and the power required to operate the sensor; and also adds to the complexity of the device circuit for signal processing. Here, we demonstrate a single resonant gas sensor, based on a microbeam uniformly coated with metal-organic frameworks (MOFs), capable of simultaneously measuring environmental temperature and gas concentration (water vapor). Using the electrostatic harmonic voltage, we actuate the microbeam simultaneously near the first and second vibration modes. The frequency shifts of these two modes due to physical stimuli changes are monitored in real time. The lower electrode of the clamped-clamped microbeam resonator is perforated to reduce the effect of squeeze film damping, thereby allowing operation under atmospheric pressure. We demonstrate experimentally the effectiveness of this technique to measure the environmental temperature and gas concentration.
Fixing Defects in Integrity Constraints via Constraint Mutation

Defining appropriate integrity constraints (ICs) for the domain model of a software system is a complex and error-prone task. Both over-constraining and under-constraining the information base are undesirable. In this paper, we consider a systematic approach to explore the most suitable ICs for a software system. The inputs of this approach are an initial tentative set of ICs described in OCL (Object Constraint Language) plus a sample information base which is incorrectly forbidden (allowed) by them. Then, this method generates candidate weaker (stronger) versions of the ICs by mutating them in an structured way. Modelers can then replace the original defective set with the alternative versions to improve the quality of the domain model.
Time-Domain Approach for Analog Circuits: Fine-Resolution TDC and Quick-Start CDR Circuits

As the VLSI process technology scales further and further, analog circuit designs are facing a difficulty in maintaining its dynamic range mainly due to the reduced supply voltage. On the other hand, a time-domain approach, which is a new paradigm of realizing analog functionalities, is becoming superior to the conventional voltage/current-domain circuits thanks to the sharp signal transitions with high-speed transistors. In this paper, first we will briefly discuss the advantage of the time-domain approach in terms of its dynamic range. Then as examples of time-domain circuit designs, a fine-resolution time-to-digital converter based on pulse-shrinking scheme utilizing a built-in offset pulse and a reference-clock-less quick-start clock data recovery circuit based on self-tunable digitally-controlled delay lines will be reviewed.
Depth Control of Underwater Robots Using Sliding Modes and Gaussian Process Regression

The development of accurate control systems for underwater robotic vehicles relies on the adequate compensation for hydrodynamic effects. In this work, a new robust control scheme is presented for remotely operated underwater vehicles. In order to meet both robustness and tracking requirements, sliding mode control is combined with Gaussian process regression. The convergence properties of the closed-loop signals are analytically proven. Numerical results confirm the stronger improved performance of the proposed control scheme.
Loss Comparison of Two Bidirectional Isolated DC/DC Converters for Reversible Solid Oxide Fuel Cell Systems

The reversible solid oxide fuel cell (RSOFC) is a promising solution for future storage system in power grid, but the wide voltage range makes it difficult to design the DC/DC converter for RSOFC stack. In this paper, the losses of two DC/DC topologies, CLLC cascade interleaved Buck(two-stage) converter and dual-active-bridge(DAB) converter, are compared as the interface DC/DC converter for RSOFC. Firstly, the topology and control method is introduced for two selected DC/DC converters. Secondly, the losses of two converters are analyzed and corresponding loss models are presented. The loss models are also extended for multi-port topology. At last based on 1kW single port prototype, the experiments are conducted and the total loss are measured for two topologies. Both the model and experimental results indicate that the two-stage topology has higher efficiency and is selected for the interface DC/DC converter for RSOFC stack.
Time Series Analysis for Encrypted Traffic Classification: A Deep Learning Approach

We develop a novel time series feature extraction technique to address the encrypted traffic/application classification problem. The proposed method consists of two main steps. First, we propose a feature engineering technique to extract significant attributes of the encrypted network traffic behavior by analyzing the time series of receiving packets. In the second step, we develop a deep learning-based technique to exploit the correlation of time series data samples of the encrypted network applications. To evaluate the efficiency of the proposed solution on the encrypted traffic classification problem, we carry out intensive experiments on a raw network traffic dataset, namely VPN-nonVPN, with three conventional classifier metrics including Precision, Recall, and F1 score. The experimental results demonstrate that our proposed approach can significantly improve the performance in identifying encrypted application traffic in terms of accuracy and computation efficiency.
Automatic Inference of Task Parallelism in Task-Graph-Based Actor Models

Automatic inference of task level parallelism is fundamental for ensuring many kinds of safety and liveness properties of parallel applications. For example, two tasks running in parallel may be involved in data races when they have conflicting memory accesses, or one is affecting the termination of another by updating shared variables. In this paper, we have considered a task-graph-based actor model, used in signal processing applications (e.g., baseband processing in wireless communication, LTE uplink processing) that are deployed on many-core platforms, in which the actors, task-graphs, and tasks are the active entities running in parallel. The actors invoke task graphs, which in turn invoke tasks, and they communicate through message passing, thus creating different kinds of dependencies and parallelism in the application. We introduce a novel May Happen in Parallel (MHP) analysis for complex parallel applications based on our computational model. The MHP analysis consists of (i) data-flow analysis applicable to parallel control-flow structures inferring MHP facts representing pairs of tasks running in parallel, (ii) identification of all direct and indirect communication by generating a context-free grammar and enumerating valid strings representing parallelism and dependencies among active entities, and (iii) inferring MHP facts when multiple task-graphs communicate. Our analysis is applicable to other computational models (e.g. Cilk or X10) too. We have fully implemented our analysis and evaluated it on signal processing applications consisting of a maximum of 36.57 million lines of code representing 232 different tasks. The analysis took approximately 7 minutes to identify all communication information and 10.5 minutes to identify 12052 executable parallel task-pairs (to analyse for concurrency bugs) proving that our analysis is scalable for industrial-sized code-bases.
Learning Upper-Level Policy using Importance Sampling-based Policy Search Method

Policy search methods are a successful approach to reinforcement learning. These allow to learn upper-level policies whose main advantage is that these distributions explore directly in the parameter space. The contribution of this paper is to propose an algorithm based on importance sampling methods and local linear regression that uses the samples in an efficient way. In order to get this aim, we propose to include information of all the past samples in the learning process using importance sampling methods. Additionally, we use the gradient direction of the linear local model reward to explore regions where the prediction of the reward could be better.
Image Processing Algorithm for Virtual Chromoendoscopy (Tone Enhancement) in Clinical Decision Support System

Virtual chromoendoscopy is one of the demanded modern direction for increasing the diagnostic value of medical images. The most famous technics are I-SCAN and FICE, and image-processing algorithms that can leverage the unique characteristics of different spectral response in the endoscopic image are actual and relevant, such as TRI-SCAN. The new method of virtual chromoendoscopy based on digital processing of images obtained in the white light was proposed. The main feature of method is local nonlinear processing each color plane. Proposed method was tested on open database of endoscopic images KVASIR. Experiment shows that method can effectively improve color contrast. Proposed method realizes visual effect corresponding visual effect of images obtaining with modern technologies of virtual chromoendoscopy (I-SCAN and FICE) and give possibility to get visual effect superior than modern method of tone enhancement TRI-SCAN.
Visibility Prediction Based on Kilometric NWP Model Outputs Using Machine-Learning Regression

Low visibility conditions have a strong impact on air and road traffics and their prediction is still a challenge for meteorologists, particularly its spatial coverage. In this study, an estimated visibility product over the north of Morocco, from the operational NWP model AROME outputs using the state-of-the art of Machine-learning regression, has been developed. The performance of the developed model has been assessed, over the continental part only, based on real data collected at 37 synoptic stations over 2 years. Results analysis points out that the developed model for estimating visibility has shown a strong ability to differentiate between visibilities occurring during daytime and nighttime. However, the KDD-developed model have shown low performance of generality across time. The performance evaluation indicates a bias of -9m, a mean absolute error of 1349m with 0.87 correlation and a root mean-square error of 2150m.
Spatial Patterns of Offender Groups

A co-offending network, the network of offenders who have committed crimes together, is a prime source for crime investigation. Analyzing co-offending networks contributes to crime reduction and prevention strategies and tactics at different levels by extracting meaningful patterns and relationships. On a different track, spatial analysis of crime has recently enriched understanding of criminal activity extensively. This study integrates spatial and social network analysis to understand the role of spatial distance in forming criminal collaborations. First, we extract co-offending networks from a police-reported database and present a comprehensive study of the spatial properties of co-offending networks. Then, using community detection approaches, we detect offender groups as denser sub graphs of some co-offending network. Finally, we study the geography of offender groups as an important characteristic of such groups. Recognizing if offender groups are geographically dispersed or geographically concentrated can help law enforcement and intelligence agencies to prioritize their preventative deployments and proactive investigations in combating crime. For the experimental evaluation, we use a real-world crime dataset comprising crime incidents in the time period 2001–2006 in the regions of British Columbia, Canada policed by the RCMP.
Differential profiles of soluble and cellular toll like receptor (TLR)-2 and 4 in chronic periodontitis

Chronic periodontitis is a common inflammatory disease initiated by a complex microbial biofilm and mediated by the host response causing destruction of the supporting tissues of the teeth. Host recognition of pathogens is mediated by toll-like receptors (TLRs) that bind conserved molecular patterns shared by groups of microorganisms. The oral epithelial cells respond to most periodontopathic bacteria via TLR-2 and TLR-4. Many studies have previously reported the presence of elevated numbers of viable exfoliated epithelial cells (SEC) in the saliva of patients with chronic periodontitis. In addition to the membrane-associated receptors, soluble forms of TLR-2 (sTLR-2) and TLR-4 (sTLR-4) have been identified and are thought to play a regulatory role by binding microbial ligands. sTLR-2 has been shown to arise from ectodomain shedding of the extracellular domain of the membrane receptor and sTLR-4 is thought to be an alternate spliced form. The objective of this study was to investigate the potential value of salivary sTLR-2/4 and the paired epithelial cell-associated TLR-2/4 mRNA as diagnostic markers for chronic periodontitis. Unstimulated whole saliva was collected after obtaining informed consent from 40 individuals in either periodontitis or gingivitis cohorts. The levels of sTLR-2/4 were measured by enzyme-linked immunosorbent assay (ELISA). SEC TLR-2/4 transcripts were quantitated by real time polymerase chain reaction. While levels of sTLR-2 exhibited an inverse correlation, sTLR-4 positively correlated with clinical parameters in the gingivitis cohort. Interestingly, both correlations were lost in the periodontitis cohort indicating a dysregulated host response. On the other hand, while sTLR-2 and the paired SEC associated TLR-2 mRNA exhibited a direct correlation (r2=0.62), that of sTLR4 and SEC TLR-4 mRNA exhibited an inverse correlation (r2=0.53) in the periodontitis cohort. Collectively, assessments of salivary sTLR2 and sTLR4 together with the respective transcripts in SECs could provide clinically relevant markers of disease progression from gingivitis to periodontitis.
The association between olive oil consumption and primary prevention of cardiovascular diseases

Introduction: American Heart Association and many other recommend Mediterranean diet for patients with cardiovascular risk. This systematic review and meta-analysis are conducted to review the effects of Mediterranean diet on the cardiovascular events as reported in randomized controlled trials (RCTs). Methods: A systematic research is conducted on MEDLINE via Ovid, Embase, PubMed, Google Scholar, Web of Science, and Informit. Databases of studies conducted between 2000 and 2017 were included in the analysis. All the collected studies were screened, and at the end, seven RCTs met the inclusion criteria. All the characteristics of trails such as study design, interventions, follow-up duration, and primary and secondary outcomes were recorded. RevMan was used to evaluate risk reduction in each trial individual using forest plot and fixed effects. Results: Four studies were included in the review, having a total of 25,195 participants. The effects of Mediterranean diet were found in cardiovascular events (627), coronary events (251), and all-cause death (887). The analysis revealed that there is a statistically significant relationship between Mediterranean diet and reduction in cardiovascular events at P = 0.02. However, other parameters did not show any statistically significant results that need further investigation. Conclusion: The individual RCT provides evidence of protective effects of the Mediterranean diet on cardiovascular events. However, the quality and quantity of data available in those trails are not reliable and limited. Therefore, the results of those trails must be cautiously interpreted.
The Bogert effect revisited: Salamander regulatory behaviors are differently constrained by time and space

Abstract The use of behavior to buffer extreme environmental variation is expected to enable species to (a) extend the breadth of environments they inhabit beyond that predicted from climatic data and (b) diminish the negative effects of broad scale and chronic disturbances such as climate change. The term Bogert effect refers to behavioral compensation entailing microhabitat selection to maintain performance across a gradient of environmental conditions resulting in evolutionary inertia of physiological traits. Here, we compare microhabitats used by plethodontid salamanders distributed along an elevational gradient to determine whether there is behavioral compensation that buffers them from deleterious temperatures and moisture levels. Overall, salamanders preferred cooler and more mesic environments and occupied microhabitats that maintained constant moisture conditions at both high‐ and low‐elevation sites. Our results suggest that salamanders use microhabitats to regulate temperature and moisture levels, which is consistent with the Bogert effect. Maintenance of more moist conditions may help buffer these species from rising temperatures but only in suitable high‐elevation environments that are likely to disappear over the next century. We conclude that behavioral regulation of temperature and moisture is a potential mechanism for the Bogert effect in plethodontid salamanders.
Differing patterns of plant spinescence affect blue duiker (Bovidae: Philantomba monticola) browsing behavior and intake rates

Abstract The ways in which spines and thorns on plants affect browsing behavior and instantaneous intake rate (IIR) have been investigated for several medium and large ungulates, with most authors concluding that spines either affect the ability to obtain a full bite, or prevent the removal of twig material. We investigated how a very small ruminant, the blue duiker (Philantomba monticola; mass 5 kg), altered its feeding strategy when confronted with intact or despined branches of three species of woody plant that differed in leaf and spine size, density, and arrangement, viz. Dichrostachys cinerea africana, Vachellia (Acacia) karroo and Ziziphus mucronata. Increasing spine length and density reduced IIR (g/min), while bite size was directly related to leaf area. Bite rate and the lag time to taking the first bite did not differ among treatments. In all treatments, blue duikers cropped leaves in preference to pruning shoots. High spine density forced duikers to crop leaves at the ends of branches where spines were softer. At low spine density and on despined treatments, leaves midway along branches were preferred. Single bites (using incisors) were used preferentially in the presence of spines, with a shift to cheek bites on despined branches. We conclude that, as found with larger browsers, spines coupled with small leaf size provide the best defense against defoliation.
Divergence in female damselfly sensory structures is consistent with a species recognition function but shows no evidence of reproductive character displacement

Abstract Males and females transmit and receive signals prior to mating that convey information such as sex, species identity, or individual condition. In some animals, tactile signals relayed during physical contact between males and females before and during mating appear to be important for mate choice or reproductive isolation. This is common among odonates, when a male grasps a female's thorax with his terminal appendages prior to copulation, and the female subsequently controls whether copulation occurs by bending her abdomen to complete intromission. It has been hypothesized that mechanosensory sensilla on the female thoracic plates mediate mating decisions, but is has been difficult to test this idea. Here, we use North American damselflies in the genus Enallagma (Odonata: Coenagrionidae) to test the hypothesis that variation in female sensilla traits is important for species recognition. Enallagma anna and E. carunculatum hybridize in nature, but experience strong reproductive isolation as a consequence of divergence in male terminal appendage morphology. We quantified several mechanosensory sensilla phenotypes on the female thorax among multiple populations of both species and compared divergence in these traits in sympatry versus allopatry. Although these species differed in features of sensilla distribution within the thoracic plates, we found no strong evidence of reproductive character displacement among the sensilla traits we measured in regions of sympatry. Our results suggest that species‐specific placement of female mechanoreceptors may be sufficient for species recognition, although other female sensory phenotypes might have diverged in sympatry to reduce interspecific hybridization.
Novel oral-targeted therapies for mucosal healing in ulcerative colitis

Ulcerative colitis (UC), a chronic, relapsing, remitting disease of the colon and rectum, is characterized by inflammatory ulceration of the mucosa. Current UC therapy relies on controlling acute episodes and preventing relapse. To predict modifications in the natural course of UC, mucosal healing (MH) has emerged as a major treatment goal. Endoscopic evaluation is considered the gold standard for assessing MH, which can be achieved by conventional drugs and biologics in many, but not all, patients. Consequently, interest is focusing on the development of new substances for UC therapy, and new oral agents are in the pipeline. This review will focus on the ability of newly developed oral drugs to induce and maintain MH in UC patients.