Miracle-dz/UniFAR-FTUs huggingface dataset:
A Highly Scalable Labelling Approach for Exact Distance Queries in Complex Networks

Answering exact shortest path distance queries is a fundamental task in graph theory. Despite a tremendous amount of research on the subject, there is still no satisfactory solution that can scale to billion-scale complex networks. Labelling-based methods are well-known for rendering fast response time to distance queries; however, existing works can only construct labelling on moderately large networks (million-scale) and cannot scale to large networks (billion-scale) due to their prohibitively large space requirements and very long preprocessing time. In this work, we present novel techniques to efficiently construct distance labelling and process exact shortest path distance queries for complex networks with billions of vertices and billions of edges. Our method is based on two ingredients: (i) a scalable labelling algorithm for constructing minimal distance labelling, and (ii) a querying framework that supports fast distance-bounded search on a sparsified graph. Thus, we first develop a novel labelling algorithm that can scale to graphs at the billion-scale. Then, we formalize a querying framework for exact distance queries, which combines our proposed highway cover distance labelling with distance-bounded searches to enable fast distance computation. To speed up the labelling construction process, we further propose a parallel labelling method that can construct labelling simultaneously for multiple landmarks. We evaluated the performance of the proposed methods on 12 real-world networks. The experiments show that the proposed methods can not only handle networks with billions of vertices, but also be up to 70 times faster in constructing labelling and save up to 90\% of labelling space. In particular, our method can answer distance queries on a billion-scale network of around 8B edges in less than 1ms, on average.
Long-peptide vaccination with driver gene mutations in p53 and Kras induces cancer mutation-specific effector as well as regulatory T cell responses

ABSTRACT Mutated proteins arising from somatic mutations in tumors are promising targets for cancer immunotherapy. They represent true tumor-specific antigens (TSAs) as they are exclusively expressed in tumors, reduce the risk of autoimmunity and are more likely to overcome tolerance compared to wild-type (wt) sequences. Hence, we designed a panel of long peptides (LPs, 28–35 aa) comprising driver gene mutations in TP35 and KRAS frequently found in gastrointestinal tumors to test their combined immunotherapeutic potential. We found increased numbers of T cells responsive against respective mutated and wt peptides in colorectal cancer patients that carry the tested mutations in their tumors than patients with other mutations. Further, active immunization of HLA(-A2/DR1)-humanized mice with mixes of the same mutated LPs yielded simultaneous, polyvalent CD8+/CD4+ T cell responses against the majority of peptides. Peptide-specific T cells possessed a multifunctional cytokine profile with CD4+ T cells showing a TH1-like phenotype. Two mutated peptides (Kras[G12V], p53[R248W]) induced significantly higher T cell responses than corresponding wt sequences and comprised HLA-A2/DR1-restricted mutated epitopes. However, vaccination with the same highly immunogenic LPs strongly increased systemic regulatory T cells (Treg) numbers in a syngeneic sarcoma model over-expressing these mutated protein variants and resulted in accelerated tumor outgrowth. In contrast, tumor outgrowth was delayed when vaccination was directed against tumor-intrinsic Kras/Tp53 mutations of lower immunogenicity. Conclusively, we show that LP vaccination targeting multiple mutated TSAs elicits polyvalent, multifunctional, and mutation-specific effector T cells capable of targeting tumors. However, the success of this therapeutic approach can be hampered by vaccination-induced, TSA-specific Tregs.
A Description of the Temporal Pattern of Out-of-Pocket Expenditure Related to Iranian Healthcare Services during 1995–2014

Background: Out-of-pocket (OOP) expenditure directly impacts on poverty and household welfare, especially when there is a decline in healthcare financing. This study was aimed to describe the temporal pattern of OOP expenditures related to Iranian healthcare services during 1995–2014. Methods: For describing the trend of OOP spending in Iran, the database of the World Bank was mined for the period under study. Further, the trend analysis has been complemented by an exhaustive and comprehensive review of the extant literature. Results: From 1995 to 2014, out-of-pocket decreased from 53.59% to 47.8% of the total health expenditure, probably because of the different health reforms implemented throughout the years. However, out-of-pocket expenditure in Iran remains higher than the world average (roughly 3 times higher) Conclusion: It is an onus of the Iranian government to make serious attempts in order to reduce out-of-pocket expenditure, as well as to protect particularly poor and vulnerable subjects against catastrophic health expenditure. In order to ensure an equitable and affordable access to the healthcare system, decision- and policy-makers in Iran should implement a review of health care costs, insurance tariffs, and healthcare services packages covered by insurance organizations as well as introduce a progressive tax-based financing scheme as soon as possible.
Delay-dependent robust stability criteria for singular time-delay systems by delay-partitioning approach

ABSTRACT In this paper, the problem of delay-dependent robust stability for singular time-delay systems is investigated. The parametric uncertainties are assumed to be norm bounded. Based on the idea of delay partitioning, A new Lyapunov–Krasovskii functional is proposed to develop new delay-dependent criteria, that ensures the considered system to be regular, impulse free and stable in terms of linear matrix inequalities. Furthermore, the Wirtinger-based integral inequality approach has been employed to derive less conservative results. Finally, some numerical examples are provided to demonstrate the effectiveness of the obtained results and for comparison with previous works.
Low Temporal Stability of Excessive Video Game Use in German Adolescents

The popularity of digital games among adolescents has raised public and scientific concern about players drifting into excessive or pathological gaming patterns. Among the neglected issues in this area of research is the question of whether excessive gaming reflects a transient phenomenon during adolescence or a temporally stable pathological behavior that requires external intervention. In a correlational panel study with two points of measurement, we investigated the temporal stability of excessive gaming in German adolescents (N = 488; aged 12–17 years at Time 1) over a time lag of 1 year. Among the video game players in our sample, 2.8% were classified as excessive gamers at both points of measurement. The correlation of excessive gaming between T1 and T2 was found to be relatively moderate (r = .54). Detailed inspection revealed even lower stability scores for each single component of excessive gaming such as tolerance or conflict, with the most problematic elements (stealing, borrowing money because of gaming problems) displaying the lowest stabilities (r < .30). Thus, the results indicate that – at least for the large majority of adolescent players – excessive gaming is a transient and not a stable condition. Theoretical and policy implications are discussed.
Hierarchical Feature-learning Graph-based Segmentation of Fat-Water MR Images

In this paper, we proposed a deformation-Iregistration-free method for multilabel segmentation of fat-water MR images without need to prior localization or geometry estimation. This method employed a multiresolution (hierarchical) feature- and prior-based Random Walker graph and a hierarchical conditional random field (HCRF). To incorporate both aspatial (intra-patch) and spatial (inter-patch neighborhood) information into the image segmentation, the proposed random walker graph was made of a multiresolution spatial and a multiresolution aspatial (prior-based) sub-graph. Edge weights and prior probabilities of this graph as well as the energy terms of the HCRF were determined by a hierarchical random decision forest classifier. This classifier was trained using multiscale local and contextual features extracted from fat-water (2-channel) magnetic resonance (MR) images. The proposed method was trained and evaluated for simultaneous volumetric segmentation of vertebral bodies and intervertebral discs on fat-water MR images. These evaluations revealed its comparable accuracy to the state-of-the-art while demanding less computations and training data. The proposed method was, however, generic and extendible for segmenting any kind of tissues on other multichannel images.
High Robust and Low Cost Soft Error Hardened Latch Design for Nanoscale CMOS Technology

We propose a high robust and low cost soft error hardened latch design applied to SMIC 65nm technology in this paper. With the method of SET filtering technique and inter-latching structure, the proposed latch can self-recover from SEUs and filter SETs. Simulation results have indicated that the proposed latch design possess high reliability and lowest PDP.
Considerations and beliefs in tooth donation to research in Jordan

Background Research that involves dental pulp stem cells (DPSCs) is growing rapidly. DPSCs can be used for the treatment of craniofacial bone abnormalities and tooth repair. The procedure requires a donation of sound teeth, which might be associated with ethical and moral issues. The purpose of this study was to understand the attitudes and awareness of patients with respect to the donation of their teeth to research. Patients and methods This study involved 500 patients recruited from Dental Care and Dental Teaching Center in Irbid during May 2017–July 2017. Results A well-structured questionnaire was administered and prepared using Google forms and filled out using a tablet device. The majority of patients (62.8%) were willing to donate their teeth to research with significant association with educational level. Half of the patients considered that the donated tooth belongs to them even after extraction, whereas 19% believed that the researcher owns it after donation. Almost half (53.6%) of the participants wished to be informed about the type of scientific research that will be carried out on their teeth. The majority (66.5%) preferred to sign a consent document on tooth donation to research during the consultation visit before extraction. Finally, about 61% were worried that their tooth might be extracted for research purposes rather than medical purposes. Conclusion A good fraction of Jordanian is willing to donate their teeth to research. Educational programs are demanded to enhance the awareness and attitudes of patients on the ownership of extracted teeth, consent process, and donation of teeth.
The dynamic response of a tactile sensor

Tests were performed on a thumb-shaped tactile sensor containing an 8 by 20 array of tactile elements. Each element measures the strain of the rubber finger material at a point below the surface of the finger. To characterize the finger behavior, a precision force application device was designed. Static tests determined the steady-state linearity of the elements with respect to force magnitude. The frequency response was determined in the range of 0.10 to 20 Hz. Permanent deformation due to stress was seen to be predictable. The Maxwell-Kelvin model for viscoelasticity was fit to the stress-strain data obtained by probing the finger and recording the applied force and the strain response of a single element. Results showed it to be better than a spring model. A second-order model was also fit to the stress-strain data. The model was inverted to predict the magnitude of the force of a point contact given the strain of one tactile element. The inverse model predicted the fore better than the spring model. <<ETX>>
Regional Detection of Traffic Congestion Using in a Large-Scale Surveillance System via Deep Residual TrafficNet

Despite the huge amount of traffic surveillance videos and images have been accumulated in the daily monitoring, deep learning approaches have been underutilized in the application of traffic intelligent management and control. In this paper, traffic images, including various illumination, weather conditions, and vast scenarios, are extracted from the current surveillance system using in Shaanxi Province and preprocessed to set up a proper training dataset. In order to detect traffic congestion, a network structure is proposed based on residual learning to be pre-trained and fine-tuned. The network is then transferred to the traffic application and re-trained with self-established training dataset to generate the TrafficNet. The accuracy of TrafficNet to classify congested and uncongested road states reaches 99% for the validation dataset and 95% for the testing dataset. The proposed TrafficNet are verified by a regional detection of traffic congestion on a large-scale surveillance system currently using in China. The effectiveness and efficiencies are magnificently demonstrated with quick detection in the high accuracy in the case study. The experimental trial could extend its successful application to traffic surveillance system and has potential enhancement for intelligent transport system in future.
Comparison Study of Surge Current Capability of Body Diode of SiC MOSFET and SiC Schottky Diode

The superior performance of the SiC MOSFETs operating in synchronous mode converter without external antiparallel SiC Schottky diodes have been demonstrated recently. However, there are few studies of the surge current capability of the SiC MOSFET's body diode, leading severe concern for its ruggedness in practical power converter applications. The purpose of this paper is to experimentally compare the non-repetitive surge current capability of the SiC MOSFET's intrinsic body diode and SiC Schottky diode, and analyze the physical mechanisms of their degradation after surge current stress. Their surge current capability and electrical characteristics before and after surge current stress are measured and analyzed. Experimental study shows that the non-repetitive peak surge current of the SiC MOSFET's body diode is slightly larger than that of the SiC Schottky diodes. The degradation of the SiC Schottky diode after surge current stress is accompanied with the increase of drain leakage current, while the degradation of the SiC MOSFET after the body diode's surge current stress is accompanied with the variation of the threshold voltage and input capacitance of the SiC MOSFET. The analysis shows that the degradation of the SiC MOSFET after the surge current stress may be correlated with the interface traps of SiC/SiO2 interface.
A Coordinated Compensation Strategy for Module Mismatch of CHB-PV Systems Based on Improved LS-PWM and Reactive Power Injection

The cascaded H-bridge (CHB) converter has become a promising candidate topology for utility-scale photovoltaic systems thanks to merits like modular structure, distributed maximum power point tracking (MPPT), and direct distribution grid access without medium-voltage transformers. However, module mismatches arising from nonideal elements like partial shading and parameter variations pose a technical challenge for such systems. If not dealt with properly, module mismatches can lead to adverse effects like unbalanced dc-link voltages of the modules, distortion of grid current, and reduced power generation. Conventional methods, such as reactive power compensation and level-shifted pulsewidth modulation (LS-PWM) based compensation, can alleviate this issue, but their performances are still limited by the allowable modulation range of power converters. In this paper, a compensation strategy combining reactive power compensation with a novel modulation method is proposed to extend the operating range in terms of module mismatch. Experimental results on a 2.4 kW/208 V single-phase setup are presented and have demonstrated that the proposed method can not only ride through a larger range of module mismatches but also improve solar power utilization and system efficiency owing to reduced switching events, noncompromised MPPT, and less required reactive power.
Development of Simulink Based Modeling Platform for 3.3kV/400A SiC MOSFET Power Module

The main objective of this paper is to develop a Simulink based modeling platform for a commercial 3.3 kV/400 A SiC MOSEFT power module. The implemented equivalent circuit of 3.3 kV SiC MOSFET in Simulink platform is based on the original well-established single-chip Mc Nutt/Hefner model. The developed model has been validated with the experimental data both for static and dynamic tests at several temperatures. The dynamic simulations have further been verified with various gate resistances that accurately describes the transient operation during turn-on and turn off of the pulse sequence. The influence of various parasitic elements (i.e., stray inductances) have been studied. Finally, the operation of a Buck converter, whose switch consists of two modules in series, has been verified, studying the converter efficiency under various circuit parameters.
A Preliminary Study on the Relationship Among Software Metrics and Specific Vulnerability Types

Several studies have highlighted the ability of software metrics to predict vulnerabilities. However, limited attention has been given on the capacity of software metrics to discriminate between different types of vulnerabilities, while the existence of potential interdependencies among different vulnerability types has not been studied yet. For this purpose, an empirical study was conducted based on 100 widely-used Java libraries. A wide range of software metrics were calculated for each project of the code base, along with the densities of a carefully selected set of vulnerability categories, which were quantified through static analysis. Correlation analysis was employed in order to find statistically significant relationships. The preliminary results suggest that: (i) software metrics may not be sufficient indicators of specific vulnerability types, (ii) software metrics are more capable of discriminating between security-specific and quality-specific weaknesses, than between specific vulnerability types, (iii) previously uninvestigated metrics may be good indicators of security issues, and (iv) important interdependencies may exist among security-specific issues. To the best of our knowledge, this is the largest study in terms of code base size, while it is the first attempt for finding interdependencies among different vulnerability types.
Glucose intake hampers PKA-regulated HSP90 chaperone activity

Aging is an intricate phenomenon associated with the gradual loss of physiological functions, and both nutrient sensing and proteostasis control lifespan. Although multiple approaches have facilitated the identification of candidate genes that govern longevity, the molecular mechanisms that link aging pathways are still elusive. Here, we conducted a quantitative mass spectrometry screen and identified all phosphorylation/dephosphorylation sites on yeast proteins that significantly responded to calorie restriction, a well-established approach to extend lifespan. Functional screening of 135 potential regulators uncovered that Ids2 is activated by PP2C under CR and inactivated by PKA under glucose intake. ids2Δ or ids2 phosphomimetic cells displayed heat sensitivity and lifespan shortening. Ids2 serves as a co-chaperone to form a complex with Hsc82 or the redundant Hsp82, and phosphorylation of Ids2 impedes its association with chaperone HSP90. Thus, PP2C and PKA orchestrate glucose sensing and protein folding to enable cells to maintain protein quality for sustained longevity.
Cloud-based Real-time Network Intrusion Detection Using Deep Learning

Deep learning has increased in popularity with researchers and developers investigating and using it for various use cases and applications. This research work focuses on realtime network intrusion detection by making use of deep learning. A cloud-based prototype system was developed to investigate the capability of deep learning based binomial classification and multinomial models to detect network intrusions in real-time. An evaluation study was carried out using the benchmark NSL-KDD dataset to compare deep learning models built using H2O and DeepLearning4J libraries, with other commonly used machine learning models such as Support Vector Machines, Random Forest, Logistic Regression and Naive Bayes. The results showed that the choice of the deep learning library is an important factor to consider for real-time applications. The H2O deep learning based binomial and multinomial models generally outperformed the other models, achieving over 99.5% accuracy using cross-validation on the NSL-KDD training dataset and over 83% accuracy on the test dataset.
A Geodesic Flow Particle Filter For Nonthresholded Radar Tracking

Homotopy methods are an emerging approach to particle filtering that avoid numerical deficiencies of standard particle methods using a particle flow. This correspondence develops a new filter with nonthresholded measurements (i.e., a track-before-detect log-homotopy particle filter). We show the performance by simulating a rotating pulsed radar forming nonthresholded Range/Doppler maps.
Securidaca–saponins are natural inhibitors of AKT, MCL-1, and BCL2L1 in cervical cancer cells

Introduction Scientific research is beginning to prove the connection between claims by African traditional medicine and the natural chemical specifics contained in medicinal plant Securidaca longipedunculata. Our previous studies showed that two natural saponin fractions (4A3 and 4A4) identified in the plant as triterpenoid glycosides are capable of activating apoptosis on cervical tumor cell lines. Considering this and some critical roles of human papillomavirus (HPV) E6 oncogene on cervical cells, by promoting carcinogenesis and cell survival, it became necessary to investigate the possible pathways for apoptosis transmission. Methods Tests conducted on relevant cervical tumor cell lines such as Caski and Bu25TK included the following: MTT assay; scratch assay (to determine cell migration/invasion); fluorescence microscopy with Annexin V–fluorescein isothiocyanate, muscle progenitor cell) and propidium iodide staining; and finally reverse transcriptase quantitative PCR (RT-qPCR) for gene analysis. Results Reduced cell proliferation was observed due to activities of 4A3 and 4A4 fractions, with half-maximal inhibitory concentration (IC50) of 7.03 and 16.39 μg/mL, respectively, on Caski cell line. A significant reduction in cell migration occurred within 48 and 72 hours, respectively, for Caski and Bu25TK cell lines. Late apoptosis was activated by 4A3, staining both Annexin V and PI, in contrast to 4A4’s early apoptosis. RT-qPCR data revealed a fold change (FC) inhibition of antiapoptotic proteins such as MCL-1 and BCL2L1, with diminished level of AKT-3, VEGFA, MALAT1, etc. The expression of p53, proapoptotic BAD, and caspase-8 was nonsignificant. Conclusion The low expression of AKT-3 and antiapoptotic proteins (MCL-1 and BCL2L1), as well as VEGFA, could simply be an indication for possible suppression of cell survival mechanisms via multiple channels. We therefore conclude that 4A3 and 4A4 fractions mediate activity via the inhibition of phosphatidylinositol-3-OH kinase (PI3K)-AKT/mTOR/NF-kB-dependent antiapoptotic stimuli. Further studies are ongoing to reveal the chemical structures and compositions of these two fractions.