Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Scheme for Joint Compression and Encryption

Abstract The scheme about compression and encryption is widely used in network traffic to improve system efficiency and security. In this paper, we put forward a scheme to demonstrate an optimal implementation on them. We prove that the hardware accelerators and its corresponding latency can be cut in half. If such do not exist, we further propose a novel and more aggressive software scheme, in which encryption is integrated into the compression algorithm. The integrated optimization scheme achieves an average speedup of 40% compared to the 25% achieved by the parallel software scheme, but it suffers a slight security loss that may not be acceptable for all applications.
A high-speed confocal laser-scanning microscope based on acousto-optic deflectors and a digital micromirror device

Neuronal dendrites are known to possess active computational properties. An ideal method of investigating these properties would be to monitor the complete electrical and chemical behavior of a single cell; however, currently available recording techniques force a tradeoff between spatial and temporal resolution. The objective of this project is to develop a confocal microscope that can make multisite optical recordings of single neuron function in a living brain slice at a frame rate sufficient to measure neuronal events that occur on a 1 ms timescale. Specifically, we would like to measure membrane potential and calcium concentration changes using voltage and ion-sensitive fluorescent indicators at user-selected sites-of-interest (SOIs). To accomplish this, we use acousto-optic deflectors (AODs) as random-access beam positioners in the illumination path, along with a digital micromirror device (DMD) as an addressable spatial filter in the detection path. We have demonstrated that our device is capable of optical sectioning and therefore capable of imaging in light-scattering preparations such as brain slices. Furthermore, our system is capable of an aggregate frame rate of up to 25 kHz.
Current Status of Neurofeedback for Post-traumatic Stress Disorder: A Systematic Review and the Possibility of Decoded Neurofeedback

Background: Post-traumatic stress disorder (PTSD) is a neuropsychiatric affective disorder that can develop after traumatic life-events. Exposure-based therapy is currently one of the most effective treatments for PTSD. However, exposure to traumatic stimuli is so aversive that a significant number of patients drop-out of therapy during the course of treatment. Among various attempts to develop novel therapies that bypass such aversiveness, neurofeedback appears promising. With neurofeedback, patients can unconsciously self-regulate brain activity via real-time monitoring and feedback of the EEG or fMRI signals. With conventional neurofeedback methods, however, it is difficult to induce neural representation related to specific trauma because the feedback is based on the neural signals averaged within specific brain areas. To overcome this difficulty, novel neurofeedback approaches such as Decoded Neurofeedback (DecNef) might prove helpful. Instead of the average BOLD signals, DecNef allows patients to implicitly regulate multivariate voxel patterns of the BOLD signals related with feared stimuli. As such, DecNef effects are postulated to derive either from exposure or counter-conditioning, or some combination of both. Although the exact mechanism is not yet fully understood. DecNef has been successfully applied to reduce fear responses induced either by fear-conditioned or phobic stimuli among non-clinical participants. Methods: Follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic review was conducted to compare DecNef effect with those of conventional EEG/fMRI-based neurofeedback on PTSD amelioration. To elucidate the possible mechanisms of DecNef on fear reduction, we mathematically modeled the effects of exposure-based and counter conditioning separately and applied it to the data obtained from past DecNef studies. Finally, we conducted DecNef on four PTSD patients. Here, we review recent advances in application of neurofeedback to PTSD treatments, including the DecNef. This review is intended to be informative for neuroscientists in general as well as practitioners planning to use neurofeedback as a therapeutic strategy for PTSD. Results: Our mathematical model suggested that exposure is the key component for DecNef effects in the past studies. Following DecNef a significant reduction of PTSD severity was observed. This effect was comparable to those reported for conventional neurofeedback approach. Conclusions: Although a much larger number of participants will be needed in future, DecNef could be a promising therapy that bypasses the unpleasantness of conscious exposure associated with conventional therapies for fear related disorders, including PTSD.
Determination of the Maximal Lactate Steady State by HRV in Overweight and Obese Subjects

Abstract The study assessed if the maximal lactate steady state (MLSS) may be determined by HRV in overweight and obese individuals. Fourteen obese (OB) and 14 overweight (OW) participants performed an incremental exercise test and several constant-load tests on a bicycle ergometer to determine the MLSS. HRV was analysed by using time domain and non-linear parameters of the Poincaré plot. Various HRV thresholds (HRVt) were detected and compared with the MLSS. Overall, Bland-Altman plots demonstrated moderate to strong agreements between the power at the MLSS and the power at HRVt, with all HRVt overestimating the MLSS (range: − 14.6 to−19.8 W). All HRVt were detected at higher intensities (69.2–78.8%Pmax) compared to the MLSS (62.6–66.8%Pmax). The primarily vagally modulated parameter HRVtSD1 revealed higher correlations (r=0.66–0.76) and lower differences (16.8–19.9%) compared to the parameter HRVtSD2 (r=0.56–r=0.66; 22.4–22.9%). The data suggest a delayed vagal withdrawal during incremental exercise in obese and overweight individuals. For this population, the use of HRV to determine the MLSS seems questionable.
Detection of network buffer overflow attacks: A case study

This paper presents an automated detection method based on classification of network traffic using predefined set of network metrics. We proposed the set of metrics with focus on behavior of buffer overflow attacks and their sufficient description without the need of deep packet inspection. In this paper we describe two laboratory experiments of automated detection of buffer overflow attacks on vulnerable network services and their description by proposed set of network metrics. We present the principles of several chosen network metrics and their application on experimental attacks according to their nature in comparison to valid communication.
Service Discovery Architecture Applied in Cloud Computing Environments

This paper proposed a service discovery architecture (SDA) that can be applied in cloud computing environments. This architecture supports common service discovery capabilities to achieve cloud service discovery function in cloud computing environments. The main idea is that, cloud services can be divided into different cloud service domains (CSD) according to the characteristics of the functions of cloud services, each CSD has a sub-service directory, and there is also a root service directory above all sub-service directories. Setting of two-level service directories can achieve the most suitable massive cloud services quickly. Finally, through mathematical modeling, theoretical analyses, and numerical simulations, the performance analyses of the architecture are studied. The results show the validity and advantages of the SDA.
New Zealand's Food System Is Unsustainable: A Survey of the Divergent Attitudes of Agriculture, Environment, and Health Sector Professionals Towards Eating Guidelines

Background: The Food and Agriculture Organisation has called for sustainable diets, which align with SDG 2, Zero Hunger, and SDG 12, Sustainable Consumption and Production. The inclusion of sustainability characteristics in New Zealand's (NZ) eating and activity guidelines (EAGs) may lead to achieving sustainable diets. This study aimed to evaluate the agreement among sectoral professionals of including sustainability characteristics within the guidelines. Methods: Agriculture, environment, and health sector professionals were invited to complete an online survey to establish agreement with sustainability characteristics and sustainability statements. Opinion and attitude questions were completed using a 5-item Likert scale. One-way ANOVA analyses were conducted to compare the level of agreement and differences in means of the sector levels of agreement whilst controlling for covariates. Post-hoc tests were used to determine sectoral differences. Results: Overall, 298 (65% female) respondents completed the survey from the agriculture (37%), environment (22%), and health (41%) sectors. Two-thirds (66%) of respondents were over 35 years and 90% had a tertiary education. Two-thirds (63%) of respondents disagreed that NZ's current food system is sustainable; health (77%) and environment (78%) sector respondents had greater disagreement than those from agriculture (35%; P = 0.00). Overall, 77% of respondents agreed that sustainability characteristics should be included in guidelines; health (90%) and environment (84%) sector respondents had greater agreement than from agriculture (58%; P = 0.00). Five sustainability characteristics received high levels of agreement (>90%) for inclusion: dietary diversity, sustainable seafood, limit processed foods, reduced food waste, and sustainable lifestyle behaviours. Agreement for eight sustainability characteristics was highest among the health and environment sectors vs. the agricultural sector (P < 0.05). A relatively low level of agreement was received from all three sectors, particularly the environmental sector (68.7%), towards the characteristic “to consume recommended serves of dairy products. ” Only 38.5% of all respondents agreed with the inclusion of “organic food produce. ” Negative associations were observed between respondents' opinions regarding the sustainability of NZ's current food system and familiarity with the EAGs. Conclusion: Professionals from the agriculture, environment, and health sectors largely support the inclusion of sustainability characteristics in NZ's EAGs. A multi-sectoral approach will be required to address areas of divergence.
Accurate Classification of Differential Expression Patterns in a Bayesian Framework With Robust Normalization for Multi-Group RNA-Seq Count Data

Empirical Bayes is a choice framework for differential expression (DE) analysis for multi-group RNA-seq count data. Its characteristic ability to compute posterior probabilities for predefined expression patterns allows users to assign the pattern with the highest value to the gene under consideration. However, current Bayesian methods such as baySeq and EBSeq can be improved, especially with respect to normalization. Two R packages (baySeq and EBSeq) with their default normalization settings and with other normalization methods (MRN and TCC) were compared using three-group simulation data and real count data. Our findings were as follows: (1) the Bayesian methods coupled with TCC normalization performed comparably or better than those with the default normalization settings under various simulation scenarios, (2) default DE pipelines provided in TCC that implements a generalized linear model framework was still superior to the Bayesian methods with TCC normalization when overall degree of DE was evaluated, and (3) baySeq with TCC was robust against different choices of possible expression patterns. In practice, we recommend using the default DE pipeline provided in TCC for obtaining overall gene ranking and then using the baySeq with TCC normalization for assigning the most plausible expression patterns to individual genes.
A multi-resolution multi-size-windows disparity estimation approach

This paper describes an algorithm for estimating the disparity between 2 images of a stereo pair. The disparity is related to the depth of the objects in the scene. Being able to obtain the depth of the objects in the scene is useful in many applications such as virtual reality, 3D user interfaces, background-foreground segmentation, or depth-image-based synthesis. This last application has motivated the proposed algorithm as part of a system that estimates disparities from a stereo pair and synthesizes new views. Synthesizing virtual views enables the post-processing of 3D content to adapt to user preferences or viewing conditions, as well as enabling the interface with multi-view auto-stereoscopic displays. The proposed algorithm has been designed to fulfill the following constraints: (a) low memory requirements, (b) local and parallelizable processing, and (c) adaptability to a sudden reduction in processing resources. Our solution uses a multi-resolution multi-size-windows approach, implemented as a line-independent process, well-suited for GPU implementation. The multi-resolution approach provides adaptability to sudden reduction in processing capabilities, besides computational advantages; the windows-based image processing algorithm guarantees low-memory requirements and local processing.
The Ontogeny of Monocyte Subsets

Classical and non-classical monocytes, and the macrophages and monocyte-derived dendritic cells they produce, play key roles in host defense against pathogens, immune regulation, tissue repair and many other processes throughout the body. Recent studies have revealed previously unappreciated heterogeneity among monocytes that may explain this functional diversity, but our understanding of mechanisms controlling the functional programming of distinct monocyte subsets remains incomplete. Resolving monocyte heterogeneity and understanding how their functional identity is determined holds great promise for therapeutic immune modulation. In this review, we examine how monocyte origins and developmental influences shape the phenotypic and functional characteristics of monocyte subsets during homeostasis and in the context of infection, inflammation, and cancer. We consider how extrinsic signals and transcriptional regulators impact monocyte production and functional programming, as well as the influence of epigenetic and metabolic mechanisms. We also examine the evidence that functionally distinct monocyte subsets are produced via different developmental pathways during homeostasis and that inflammatory stimuli differentially target progenitors during an emergency response. We highlight the need for a more comprehensive understanding of the relationship between monocyte ontogeny and heterogeneity, including multiparametric single-cell profiling and functional analyses. Studies defining mechanisms of monocyte subset production and maintenance of unique monocyte identities have the potential to facilitate the design of therapeutic interventions to target specific monocyte subsets in a variety of disease contexts, including infectious and inflammatory diseases, cancer, and aging.
High-power industrial drives

After the classification and a short review of the evolution, the different drive systems which became important in industrial applications are described. It is shown how motor, converter, and controls form a drive system. The specific characteristics of each system are emphasized in order to show which system is best suited for which kind of applications. Based on the development potential the paper also gives a projection for the future. The systems discussed include synchronous motors fed by externally commutated current source converters, induction motors fed by current source converters, cycloconverter fed synchronous motors, voltage source inverters, and slip power controlled drives. >
Do sheep really recognize faces? Evidence from previous studies. Response to ‘Sheep recognize familiar and unfamiliar human faces from two-dimensional images’

& 2019 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. Do sheep really recognize faces? Evidence from previous studies. Response to ‘Sheep recognize familiar and unfamiliar human faces from two-dimensional images’
Altered Neurometabolic Profile in Early Parkinson's Disease: A Study With Short Echo-Time Whole Brain MR Spectroscopic Imaging

Objective: To estimate alterations in neurometabolic profile of patients with early stage Parkinson's disease (PD) by using a short echo-time whole brain magnetic resonance spectroscopic imaging (wbMRSI) as possible biomarker for early diagnosis and monitoring of PD. Methods: 20 PD patients in early stage (H&Y ≤ 2) without evidence of severe other diseases and 20 age and sex matched healthy controls underwent wbMRSI. In each subject brain regional concentrations of metabolites N-acetyl-aspartate (NAA), choline (Cho), total creatine (tCr), glutamine (Gln), glutamate (Glu), and myo-inositol (mIns) were obtained in atlas-defined lobar structures including subcortical basal ganglia structures (the left and right frontal lobes, temporal lobes, parietal lobes, occipital lobes, and the cerebellum) and compared between patients and matched healthy controls. Clinical characteristics of the PD patients were correlated with spectroscopic findings. Results: In comparison to controls the PD patients revealed altered lobar metabolite levels in all brain lobes contralateral to dominantly affected body side, i.e., decreases of temporal NAA, Cho, and tCr, parietal NAA and tCr, and frontal as well as occipital NAA. The frontal NAA correlated negatively with the MDS-UPDRS II (R = 22120.585, p = 0.008), MDS-UPDRS IV (R = −0.458, p = 0.048) and total MDS-UPDRS scores (R = −0.679, p = 0.001). Conclusion: In early PD stages metabolic alterations are evident in all contralateral brain lobes demonstrating that the neurodegenerative process affects not only local areas by dopaminergic denervation, but also the functional network within different brain regions. The wbMRSI-detectable brain metabolic alterations reveal the potential to serve as biomarkers for early PD.
Risk alleles for tuberculosis infection associate with reduced immune reactivity in a wild mammalian host

Integrating biological processes across scales remains a central challenge in disease ecology. Genetic variation drives differences in host immune responses, which, along with environmental factors, generates temporal and spatial infection patterns in natural populations that epidemiologists seek to predict and control. However, genetics and immunology are typically studied in model systems, whereas population-level patterns of infection status and susceptibility are uniquely observable in nature. Despite obvious causal connections, organizational scales from genes to host outcomes to population patterns are rarely linked explicitly. Here we identify two loci near genes involved in macrophage (phagocyte) activation and pathogen degradation that additively increase risk of bovine tuberculosis infection by up to ninefold in wild African buffalo. Furthermore, we observe genotype-specific variation in IL-12 production indicative of variation in macrophage activation. Here, we provide measurable differences in infection resistance at multiple scales by characterizing the genetic and inflammatory variation driving patterns of infection in a wild mammal.
Lancet: A self-correcting Latency Measuring Tool

We present LANCET, a self-correcting tool designed to measure the open-loop tail latency of μs-scale datacenter applications with high fan-in connection patterns. LANCET is selfcorrecting as it relies on online statistical tests to determine situations in which tail latency cannot be accurately measured from a statistical perspective. The workload configuration, the client infrastructure, or the application itself could, under circumstances, prevent accurate measurement. Because of its design, LANCET is also extremely easy to use. In fact, the user is only responsible for (i) configuring the workload parameters, i.e., the mix of requests and the size of the client connection pool, and (ii) setting the desired confidence interval for a particular tail latency percentile. All other parameters, including the length of the warmup phase, the measurement duration, and the sampling rate, are dynamically determined by the LANCET experiment coordinator. When available, LANCET leverages NIC-based hardware timestamping to measure RPC end-to-end latency. Otherwise, it uses an asymmetric setup with a latency-agent that leverages busy-polling system calls to reduce the client bias. Our evaluation shows that LANCET automatically identifies situations in which tail latency cannot be determined and accurately reports the latency distribution of workloads with single-digit μs service time. For the workloads studied, LANCET can successfully report, with 95% confidence, the 99th percentile tail latency within an interval of ≤ 10μs. In comparison with state-of-the-art tools such as Mutilate and Treadmill, LANCET reports a latency cumulative distribution that is ∼20μs lower when the NIC timestamping capability is available and ∼10μs lower when it is not.
Cryptanalysis and an Improvement of New Remote Mutual Authentication Scheme using Smart Cards

Abstract In this paper, the security of a remote user mutual authentication scheme using smart cards proposed by Rajaram et al., [Rajaram R, Amutha Prabakar M, New Remote Mutual Authentication Scheme using Smart Cards, Transactions on Data Privacy, 2(2009): 141--152] is analysed. We show that Rajaram et al., scheme is still vulnerable to user impersonation attacks, off-line password guessing attacks and time synchronization problem. Moreover, the scheme does not provide an option to change or update the password and efficient login, and lacks proper mutual authentication. We also propose a password-based remote user mutual authentication scheme which provides the remedy for the vulnerabilities of Rajaram et al., scheme. Furthermore, performance analysis shows that compared with Rajaram et al., authentication scheme, our proposed scheme is simple and secure.
Influences of Vitamin A on Vaccine Immunogenicity and Efficacy

Vitamin A deficiencies and insufficiencies are widespread in developing countries, and may be gaining prevalence in industrialized nations. To combat vitamin A deficiency (VAD), the World Health Organization (WHO) recommends high-dose vitamin A supplementation (VAS) in children 6–59 months of age in locations where VAD is endemic. This practice has significantly reduced all-cause death and diarrhea-related mortalities in children, and may have in some cases improved immune responses toward pediatric vaccines. However, VAS studies have yielded conflicting results, perhaps due to influences of baseline vitamin A levels on VAS efficacy, and due to cross-regulation between vitamin A and related nuclear hormones. Here we provide a brief review of previous pre-clinical and clinical data, showing how VAD and VAS affect immune responses, vaccines, and infectious diseases. We additionally present new results from a VAD mouse model. We found that when VAS was administered to VAD mice at the time of vaccination with a pneumococcal vaccine (Prevnar-13), pneumococcus (T4)-specific antibodies were significantly improved. Preliminary data further showed that after challenge with Streptococcus pneumoniae, all mice that had received VAS at the time of vaccination survived. This was a significant improvement compared to vaccination without VAS. Data encourage renewed attention to vitamin A levels, both in developed and developing countries, to assist interpretation of data from vaccine research and to improve the success of vaccine programs.