Miracle-dz/UniFAR-FTUs huggingface dataset:
Configuring a Software Router by the Erlang- $k$ -Based Packet Latency Prediction

Characterizing a virtual network function performance is one of the most challenging issues in softwarized network function deployment due to different configurations leading to a different performance. Since there has been no clear solution on how to configure a software router that provides the minimum average packet latency, we address this problem by proposing a configuration framework for a software router. First, we advance the performance characterization of a software router by conducting a packet latency prediction model based on the Erlang- $k$ distribution. Motivated by the analysis of the packet latency distribution, the Erlang- $k$ distribution is chosen as a basis of the packet latency prediction model. Our prediction model requires the measurement of only two different configurations, i.e., one and two receiving queues of a network interface card, to predict the average packet latency of all configurations. We cross-verify the accuracy of our prediction model with the measured data. Second, we adopt our prediction model in the configuration selection (CS) algorithm for searching which configuration yields the minimum of average packet latency. The validity of the prediction-based CS algorithm is confirmed by comparing to the measurement-based one. Finally, we present the CS-based measurement system interworking with network function virtualization-management and orchestration for advances in network management.
Antifungal Activities of Volatile Secondary Metabolites of Four Diaporthe Strains Isolated from Catharanthus roseus

Four endophytic fungi were isolated from the medicinal plant, Catharanthus roseus, and were identified as Diaporthe spp. with partial translation elongation factor 1-alpha (TEF1), beta-tubulin (TUB), histone H3 (HIS), calmodulin (CAL) genes, and rDNA internal transcribed spacer (ITS) region (TEF1-TUB-HIS--CAL-ITS) multigene phylogeny suggested for species delimitation in the Diaporthe genus. Each fungus produces a unique mixture of volatile organic compounds (VOCs) with an abundant mixture of terpenoids analyzed by headspace solid-phase microextraction (SPME) fiber-GC/MS. These tentatively-detected terpenes included α-muurolene, β-phellandrene, γ-terpinene, and α-thujene, as well as other minor terpenoids, including caryophyllene, patchoulene, cedrene, 2-carene, and thujone. The volatile metabolites of each isolate showed antifungal properties against a wide range of plant pathogenic test fungi and oomycetes, including Alternaria alternata, Botrytis cinerea, Colletotrichum gloeosporioides, Fusarium graminearum, and Phytophthora cinnamomi. The growth inhibition of the pathogens varied between 10% and 60% within 72 h of exposure. To our knowledge, the endophytic Diaporthe-like strains are first reported from Catharanthus roseus. VOCs produced by each strain of the endophytic Diaporthe fungi were unique components with dominant monoterpenes comparing to known Diaporthe fungal VOCs. A discussion is presented on the inhibitive bioactivities of secondary metabolites among endophytic Diaporthe fungi and this medicinal plant.
Characterizing convolutional neural network workloads on a detailed GPU simulator

Recent frameworks on convolutional neural networks (CNNs) such as Caffe and MXNet have focused primarily on being compatible with CUDA software and hardware application. However, it was designed for GPU architecture of compute capability 3.0 and above. Therefore, it needs verification of function to perform GPGPU-Sim which is implemented as NVIDIA compute capability devices 2.x. We developed a framework which can make inferencing AlexNet on GPGPU-Sim. We also analyze the execution results of the GPGPU-Sim. The number of lines in one set of the L1 data cache is sensitive to influence performance of AlexNet inference.
Comparative Effectiveness of ICA and PCA in Extraction of Fetal ECG From Abdominal Signals: Toward Non-invasive Fetal Monitoring

Non-adaptive signal processing methods have been successfully applied to extract fetal electrocardiograms (fECGs) from maternal abdominal electrocardiograms (aECGs); and initial tests to evaluate the efficacy of these methods have been carried out by using synthetic data. Nevertheless, performance evaluation of such methods using real data is a much more challenging task and has neither been fully undertaken nor reported in the literature. Therefore, in this investigation, we aimed to compare the effectiveness of two popular non-adaptive methods (the ICA and PCA) to explore the non-invasive (NI) extraction (separation) of fECGs, also known as NI-fECGs from aECGs. The performance of these well-known methods was enhanced by an adaptive algorithm, compensating amplitude difference and time shift between the estimated components. We used real signals compiled in 12 recordings (real01–real12). Five of the recordings were from the publicly available database (PhysioNet-Abdominal and Direct Fetal Electrocardiogram Database), which included data recorded by multiple abdominal electrodes. Seven more recordings were acquired by measurements performed at the Institute of Medical Technology and Equipment, Zabrze, Poland. Therefore, in total we used 60 min of data (i.e., around 88,000 R waves) for our experiments. This dataset covers different gestational ages, fetal positions, fetal positions, maternal body mass indices (BMI), etc. Such a unique heterogeneous dataset of sufficient length combining continuous Fetal Scalp Electrode (FSE) acquired and abdominal ECG recordings allows for robust testing of the applied ICA and PCA methods. The performance of these signal separation methods was then comprehensively evaluated by comparing the fetal Heart Rate (fHR) values determined from the extracted fECGs with those calculated from the fECG signals recorded directly by means of a reference FSE. Additionally, we tested the possibility of non-invasive ST analysis (NI-STAN) by determining the T/QRS ratio. Our results demonstrated that even though these advanced signal processing methods are suitable for the non-invasive estimation and monitoring of the fHR information from maternal aECG signals, their utility for further morphological analysis of the extracted fECG signals remains questionable and warrants further work.
T cells establish and maintain CNS viral infection in HIV‐infected humanized mice

&NA; The human brain is an important site of HIV replication and persistence during antiretroviral therapy (ART). Direct evaluation of HIV infection in the brains of otherwise healthy individuals is not feasible; therefore, we performed a large‐scale study of bone marrow/liver/thymus (BLT) humanized mice as an in vivo model to study HIV infection in the brain. Human immune cells, including CD4+ T cells and macrophages, were present throughout the BLT mouse brain. HIV DNA, HIV RNA, and/or p24+ cells were observed in the brains of HIV‐infected animals, regardless of the HIV isolate used. HIV infection resulted in decreased numbers of CD4+ T cells, increased numbers of CD8+ T cells, and a decreased CD4+/CD8+ T cell ratio in the brain. Using humanized T cell‐only mice (ToM), we demonstrated that T cells establish and maintain HIV infection of the brain in the complete absence of human myeloid cells. HIV infection of ToM resulted in CD4+ T cell depletion and a reduced CD4+/CD8+ T cell ratio. ART significantly reduced HIV levels in the BLT mouse brain, and the immune cell populations present were indistinguishable from those of uninfected controls, which demonstrated the effectiveness of ART in controlling HIV replication in the CNS and returning cellular homeostasis to a pre‐HIV state.
Automating Personnel Rostering by Learning Constraints Using Tensors

Many problems in operations research require that constraints be specified in the model. Determining right constraints is a hard and laborsome task. We propose an approach to automate this process using artificial intelligence and machine learning principles. We focus on personnel rostering and scheduling problems in which there are often past schedules available and show that it is possible to automatically learn constraints from such examples. To realize this, we adapted some techniques from the constraint programming community and extended them in order to cope with multidimensional examples. The method uses a tensor representation of the example, which helps in capturing the dimensionality as well as the structure of the example, and applies tensor operations to find the constraints that are satisfied by the example. The algorithm also identifies inherent clusters in the data and uses it as background knowledge to learn more detailed constraints. To evaluate the proposed algorithm, we used constraints from the Nurse Rostering Competition and generated solutions that satisfy these constraints; these solutions were then used as examples to learn constraints. Experiments demonstrate that the proposed algorithm is capable of producing human readable constraints that capture the underlying characteristics of the examples.
Cross Domain Image Generation through Latent Space Exploration with Adversarial Loss

Conditional domain generation is a good way to interactively control sample generation process of deep generative models. However, once a conditional generative model has been created, it is often expensive to allow it to adapt to new conditional controls, especially the network structure is relatively deep. We propose a conditioned latent domain transfer framework across latent spaces of unconditional variational autoencoders(VAE). With this framework, we can allow unconditionally trained VAEs to generate images in its domain with conditionals provided by a latent representation of another domain. This framework does not assume commonalities between two domains. We demonstrate effectiveness and robustness of our model under widely used image datasets.
SeDuCe: Toward a testbed for research on thermal and power management in datacenters

With the advent of Cloud Computing, the size of datacenters is ever increasing and the management of servers and their power consumption and heat production have become challenges. The management of the heat produced by servers has been experimentally less explored than the management of their power consumption. It can be partly explained by the lack of a public testbed that provides reliable access to both thermal and power metrics of server rooms. In this paper we propose SeDuCe, a testbed that targets research on power and thermal management of servers, by providing public access to precise data about the power consumption and the thermal dissipation of 48 servers integrated in Grid'5000 as the new ecotype cluster. We present the chosen software and hardware architecture for the first version of the SeDuCe testbed, highlighting its current limitation and proposing some improvements that will increase its relevance.
Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review

Complexity science has provided new perspectives and opportunities for understanding a variety of complex natural or social phenomena, including brain dysfunctions like epilepsy. By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged. These discoveries have revealed that complexity is a fundamental aspect of physiological processes. The inherent nonlinearity and non-stationarity of physiological processes limits the methods based on simpler underlying assumptions to point out the pathway to a more comprehensive understanding of their behavior and relation with certain diseases. The perspective of complexity may benefit both the research and clinical practice through providing novel data analytics tools devoted for the understanding of and the intervention about epilepsies. This review aims to provide a sketchy overview of the methods derived from different disciplines lucubrating to the complexity of bio-signals in the field of epilepsy monitoring. Although the complexity of bio-signals is still not fully understood, bundles of new insights have been already obtained. Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications. Multidisciplinary collaborations and more high-quality data accessible to the whole community are needed for reproducible research and the development of such applications.
L 4 oprof : A System-Wide Profiler Using Hardware PMU in L 4 Environment

The recent advance of L4 microkernel technology enables building a secure embedded system with comparable performance to a traditional monolithic kernel-based system. According to the different system software architecture, the execution behavior of an application in microkernel environment differs greatly from that in traditional monolithic environment. Therefore, we need a performance profiler to improve performance of the application in microkernel environment. Currently, L4’s profiling tools provides only program-level information such as the number of function calls, IPCs, context switches, etc. In this paper, we present L4oprof, a system-wide statistical profiler in L4 microkernel environment. L4oprof leverages the hardware performance counters of PMU on a CPU to enable profiling of a wide variety of hardware events such as clock cycles and cache and TLB misses. Our evaluation shows that L4oprof incurs 0~3% higher overhead than Linux OProfile. Moreover, the main cause of performance loss in L4Linux applications is shown compared with Linux applications.
Parent healthful eating attitudes and motivation are prospectively associated with dietary quality among youth with type 1 diabetes

ABSTRACT A family-based behavioral nutrition intervention grounded in social cognitive and self-determination theories showed an increased intake of whole plant foods. This study examined (1) whether the intervention changed parent diet-related attitudes/beliefs, (2) whether these attitudes/beliefs were associated with youth diet quality, and (3) the moderating roles of youth age and parent nutritional knowledge. Youth with type 1 diabetes and their parents (n = 136, mean ± SD youth age = 12.6 ± 2.8 years) participated in an 18-month trial targeting intake of whole plant foods. Parents reported attitudes/beliefs (self-efficacy, outcome expectations, perceived barriers, autonomous and controlled motivation) for providing healthy food to their families, and type 1 diabetes-specific nutrition knowledge at 6, 12, and 18 months. Whole Plant Food Density (WPFD; cup or ounce equivalents per 1000 kcal of whole grains, fruit, vegetables, legumes, nuts,and seeds) was calculated from 3-day youth food records. Linear mixed models estimated the intervention effect on parent attitudes/beliefs, associations of parent attitudes/beliefs with youth WPFD, and the moderating roles of parent nutrition knowledge and youth age. There was no effect of the intervention on parent attitudes/beliefs. Across groups, higher parent self-efficacy and autonomous motivation were positively associated with youth WPF. Parent-perceived barriers and negative outcome expectations were inversely associated with youth WPFD, especially when parents had higher nutrition knowledge. Youth age did not modify any associations. Parent diet-related attitudes/beliefs were associated with youth diet quality, highlighting the importance of parent psychosocial factors across this age range. Despite a positive effect on youth diet, the intervention did not affect parent attitudes/beliefs, suggesting it worked through a different mechanism.
Electroacupuncture therapy in inflammation regulation: current perspectives

Although acupuncture therapy is increasingly used to treat diverse symptoms and disorders in humans, its underlying mechanism is not known well. Only recently have experimental studies begun to provide insights into how acupuncture stimulation generates and relates to pathophysiological responsiveness. Acupuncture intervention is frequently used to control pathologic symptoms in several visceral organs, and a growing number of studies using experimental animal models suggest that acupuncture stimulation may be involved in inducing anti-inflammatory responses. The vagus nerve, a principal parasympathetic nerve connecting neurons in the central nervous system to cardiovascular systems and a majority of visceral organs, is known to modulate neuroimmune communication and anti-inflammatory responses in target organs. Here, we review a broad range of experimental studies demonstrating anti-inflammatory effects of electroacupuncture in pathologic animal models of cardiovascular and visceral organs and also ischemic brains. Then, we provide recent progress on the role of autonomic nerve activity in anti-inflammation mediated by electroacupuncture. We also discuss a perspective on the role of sensory signals generated by acupuncture stimulation, which may induce a neural code unique to acupuncture in the central nervous system.
Management of Patellar Tendon Rupture after Total Knee Arthroplasty using Hamstring graft: A Case Report

Introduction: The disruption of extensor mechanism during or after total knee arthroplasty is a rare but disabling complication for the patients. The results of various methods of managing patellar ligament rupture are not predictable and favorable. Case Report: We report a case of patellar ligament rupture which occurred 6 months after the primary total knee replacement. It was managed satisfactorily using hamstring graft and minimal implant. Conclusion: Simple and easily reproducible technique for patellar tendon reconstruction that can be performed by surgeons not routinely performing ligament reconstruction gives favorable outcome.
New Fault-Tolerant Datacenter Network Topologies

 — Data Center Network (DCN) topologies, being integral parts of cloud computing infrastructure, are vulnerable to failures due to the presence of huge number of servers, switches and links. One of the biggest challenges in the faulttolerant DCN design is to provide a graceful degradation in performance in the event of a link or server failure. This paper proposes two new fault-tolerant DCN topologies, called DCellStar and DCell-Ring. The proposed topologies are cost-effective, scalable, fault-tolerant, and enhance overall performance (throughput and latency) of the standard DCell topology. Performance evaluation of the proposed topologies is done through a simulation study, and the results are compared with the standard DCell topology. The comparison is done in terms of various metrics such as throughput, latency, and diameter. The simulation results show that the proposed topologies outperform the standard DCell topology due to the availability of multiple alternate shortest paths between pairs of servers, resulting in an improvement of about 5% in throughput even for a small-size network.
Villani at SemEval-2018 Task 8: Semantic Extraction from Cybersecurity Reports using Representation Learning

In this paper, we describe our proposal for the task of Semantic Extraction from Cybersecurity Reports. The goal is to explore if natural language processing methods can provide relevant and actionable knowledge to contribute to better understand malicious behavior. Our method consists of an attention-based Bi-LSTM which achieved competitive performance of 0.57 for the Subtask 1. In the due process we also present ablation studies across multiple embeddings and their level of representation and also report the strategies we used to mitigate the extreme imbalance between classes.
Acute Hyperbaric Oxygenation, Contrary to Intermittent Hyperbaric Oxygenation, Adversely Affects Vasorelaxation in Healthy Sprague-Dawley Rats due to Increased Oxidative Stress

The present study was aimed at assessing endothelium-dependent vasorelaxation, at measuring superoxide production in the aorta and femoral artery, and at determining antioxidative enzyme expression and activity in aortas of male Sprague-Dawley rats (N = 135), randomized to an A-HBO2 group exposed to a single hyperbaric oxygenation session (120′ of 100% O2 at 2.0 bars), a 24H-HBO2 group (single session, examined 24 h after exposure), a 4D-HBO2 group (4 consecutive days of single sessions), and a CTRL group (untreated group). Vasorelaxation of aortic rings in response to acetylcholine (AChIR) and to reduced pO2 (HIR) was tested in vitro in the absence/presence of NOS inhibitor L-NAME and superoxide scavenger TEMPOL. eNOS, iNOS, antioxidative enzyme, and NADPH oxidase mRNA expression was assessed by qPCR. Serum oxidative stress markers and enzyme activity were assessed by spectrometry, and superoxide production was determined by DHE fluorescence. Impaired AChIR and HIR in the A-HBO2 group were restored by TEMPOL. L-NAME inhibited AChIR in all groups. Serum oxidative stress and superoxide production were increased in the A-HBO2 group compared to all other groups. The mRNA expression of iNOS was decreased in the A-HBO2 and 24H-HBO2 groups while SOD1 and 3 and NADPH oxidase were increased in the 4D-HBO2 group. The expression and activity of catalase and glutathione peroxidase were increased in the 4D-HBO2 group as well. AChIR was NO dependent. Acute HBO2 transiently impaired vasorelaxation due to increased oxidative stress. Vasorelaxation was restored and oxidative stress was normalized 24 h after the treatment.
Monitoring pigment‐driven vegetation changes in a low‐Arctic tundra ecosystem using digital cameras

Arctic vegetation phenology is a sensitive indicator of a changing climate and rapid assessment of vegetation status is necessary to more comprehensively understand the impacts on foliar condition and photosynthetic activity. Airborne and space-borne optical remote sensing have been successfully used to monitor vegetation phenology in Arctic ecosystems by exploiting the biophysical and biochemical changes associated with vegetation growth and senescence. However, persistent cloud cover and low sun angles in the region make the acquisition of high quality temporal optical data within one growing season challenging. In the following study, we examine the capability of “near-field” remote sensing technologies, in this case digital, true colour, cameras to produce surrogate in-situ spectral data to characterize changes in vegetation driven by seasonal pigment dynamics. Simple linear regression was used to investigate relationships between common pigment-driven spectral indices calculated from field-based spectrometry and red, green, and blue (RGB) indices from corresponding digital photographs in three dominant vegetation communities across three major seasons at Toolik Lake, North Slope, Alaska. We chose the strongest and most consistent RGB index across all communities to represent each spectral index. Next, linear regressions were used to relate RGB indices and extracted leaf-level pigment content with a simple additive error propagation of the root mean square error (RMSE). Results indicate that the green-based RGB indices had the strongest relationship with chlorophyll a and total chlorophyll, while a red-based RGB index showed moderate relationships with the chlorophyll to carotenoid ratio. The results suggest that vegetation colour contributes strongly to the response of pigment-driven spectral indices and RGB data can act as a surrogate to track seasonal vegetation change associated with pigment development and degradation. Overall, we find that low-cost, easy-to-use digital cameras can monitor vegetation status and changes related to seasonal foliar condition and photosynthetic activity in three dominant, low-Arctic vegetation communities.