Miracle-dz/UniFAR-FTUs huggingface dataset:
Layerwise Perturbation-Based Adversarial Training for Hard Drive Health Degree Prediction

With the development of cloud computing and big data, the reliability of data storage systems becomes increasingly important. Previous researchers have shown that machine learning algorithms based on SMART attributes are effective methods to predict hard drive failures. In this paper, we use SMART attributes to predict hard drive health degrees which are helpful for taking different fault tolerant actions in advance. Given the highly imbalanced SMART datasets, it is a nontrivial work to predict the health degree precisely. The proposed model would encounter overfitting and biased fitting problems if it is trained by the traditional methods. In order to resolve this problem, we propose two strategies to better utilize imbalanced data and improve performance. Firstly, we design a layerwise perturbation-based adversarial training method which can add perturbations to any layers of a neural network to improve the generalization of the network. Secondly, we extend the training method to the semi-supervised settings. Then, it is possible to utilize unlabeled data that have a potential of failure to further improve the performance of the model. Our extensive experiments on two real-world hard drive datasets demonstrate the superiority of the proposed schemes for both supervised and semi-supervised classification. The model trained by the proposed method can correctly predict the hard drive health status 5 and 15 days in advance.
Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms

Gradient boosting decision trees (GBDTs) have seen widespread adoption in academia, industry and competitive data science due to their state-of-the-art performance in many machine learning tasks. One relative downside to these models is the large number of hyper-parameters that they expose to the end-user. To maximize the predictive power of GBDT models, one must either manually tune the hyper-parameters, or utilize automated techniques such as those based on Bayesian optimization. Both of these approaches are time-consuming since they involve repeatably training the model for different sets of hyper-parameters. A number of software GBDT packages have started to offer GPU acceleration which can help to alleviate this problem. In this paper, we consider three such packages: XGBoost, LightGBM and Catboost. Firstly, we evaluate the performance of the GPU acceleration provided by these packages using large-scale datasets with varying shapes, sparsities and learning tasks. Then, we compare the packages in the context of hyper-parameter optimization, both in terms of how quickly each package converges to a good validation score, and in terms of generalization performance.
Experience-related reductions of myelin and axon diameter in adulthood

The production of new myelin has been highlighted as an underappreciated mechanism of brain plasticity, but whether plastic decreases in myelin also happen in the adult brain has been largely unexplored. Recently, Sinclair et al. (Sinclair JS, Fischl MJ, Alexandrova O, Heß M, Grothe B, Leibold C, and Kopp-Scheinpflug C. J Neurosci 37: 8239–8255, 2017) have shown that auditory deprivation can lead to decrease in myelination and axon caliber even in healthy adulthood. These findings show that activity-regulated myelination is more complex than previously thought and expand our knowledge of how adult brain plasticity could operate on a cellular level.
Tensor-Based Big Data Management Scheme for Dimensionality Reduction Problem in Smart Grid Systems: SDN Perspective

Smart grid (SG) is an integration of traditional power grid with advanced information and communication infrastructure for bidirectional energy flow between grid and end users. A huge amount of data is being generated by various smart devices deployed in SG systems. Such a massive data generation from various smart devices in SG systems may lead to various challenges for the networking infrastructure deployed between users and the grid. Hence, an efficient data transmission technique is required for providing desired QoS to the end users in this environment. Generally, the data generated by smart devices in SG has high dimensions in the form of multiple heterogeneous attributes, values of which are changed with time. The high dimensions of data may affect the performance of most of the designed solutions in this environment. Most of the existing schemes reported in the literature have complex operations for the data dimensionality reduction problem which may deteriorate the performance of any implemented solution for this problem. To address these challenges, in this paper, a tensor-based big data management scheme is proposed for dimensionality reduction problem of big data generated from various smart devices. In the proposed scheme, first the Frobenius norm is applied on high-order tensors (used for data representation) to minimize the reconstruction error of the reduced tensors. Then, an empirical probability-based control algorithm is designed to estimate an optimal path to forward the reduced data using software-defined networks for minimization of the network load and effective bandwidth utilization. The proposed scheme minimizes the transmission delay incurred during the movement of the dimensionally reduced data between different nodes. The efficacy of the proposed scheme has been evaluated using extensive simulations carried out on the data traces using ‘R’ programming and Matlab. The big data traces considered for evaluation consist of more than two million entries (2,075,259) collected at one minute sampling rate having hetrogenous features such as–voltage, energy, frequency, electric signals, etc. Moreover, a comparative study for different data traces and a real SG testbed is also presented to prove the efficacy of the proposed scheme. The results obtained depict the effectiveness of the proposed scheme with respect to the parameters such as- network delay, accuracy, and throughput.
Diuretics, Ca-Antagonists, and Angiotensin-Converting Enzyme Inhibitors Affect Zinc Status in Hypertensive Patients on Monotherapy: A Randomized Trial

Background: Antihypertensive drugs affect mineral metabolism, inflammation, and the oxidative state. The aim of this study was to evaluate the effects of antihypertensive monopharmacotherapy with diuretics, β-blockers, calcium antagonists (Ca-antagonists), angiotensin-converting enzyme inhibitors (ACE-I), and angiotensin II receptor antagonists (ARBs) on zinc (Zn), iron (Fe), and copper (Cu) status, parameters of oxidative and inflammatory states, and glucose and lipid metabolism in patients with newly diagnosed primary arterial hypertension (AH). Methods: Ninety-eight hypertensive subjects received diuretics, β-blockers, Ca-antagonists, ACE-I, or ARB for three months. Zn, Fe, and Cu concentrations were determined in blood, urine, and hair. Results: A decrease in zinc serum and erythrocyte concentration and an increase in zinc urine concentration were registered after diuretic administration. Ca-antagonists led to a decrease in erythrocyte zinc concentration. A decrease in serum zinc concentration was observed after ACE-I. A decrease in triglyceride serum concentration was noted after ACE-I therapy, and a decrease in tumor necrosis factor-α serum concentration was seen following administration of Ca-antagonists. Hypotensive drugs led to decreases in catalase and superoxide dismutase serum concentrations. Conclusions: Three-months of monotherapy with diuretics, Ca-antagonists, or ACE-I impairs zinc status in patients with newly diagnosed primary AH. Antihypertensive monopharmacotherapy and zinc metabolism alterations affect lipid metabolism, the oxidative state, and the inflammatory state.
Discovering Low-Precision Networks Close to Full-Precision Networks for Efficient Embedded Inference

To realize the promise of ubiquitous embedded deep network inference, it is essential to seek limits of energy and area efficiency. To this end, low-precision networks offer tremendous promise because both energy and area scale down quadratically with the reduction in precision. Here we demonstrate ResNet-18, -34, -50, -152, Inception-v3, Densenet-161, and VGG-16bn networks on the ImageNet classification benchmark that, at 8-bit precision exceed the accuracy of the full-precision baseline networks after one epoch of finetuning, thereby leveraging the availability of pretrained models. We also demonstrate ResNet-18, -34, -50, -152, Densenet-161, and VGG-16bn 4-bit models that match the accuracy of the full-precision baseline networks -- the highest scores to date. Surprisingly, the weights of the low-precision networks are very close (in cosine similarity) to the weights of the corresponding baseline networks, making training from scratch unnecessary. We find that gradient noise due to quantization during training increases with reduced precision, and seek ways to overcome this noise. The number of iterations required by SGD to achieve a given training error is related to the square of (a) the distance of the initial solution from the final plus (b) the maximum variance of the gradient estimates. Therefore, we (a) reduce solution distance by starting with pretrained fp32 precision baseline networks and fine-tuning, and (b) combat gradient noise introduced by quantization by training longer and reducing learning rates. Sensitivity analysis indicates that these simple techniques, coupled with proper activation function range calibration to take full advantage of the limited precision, are sufficient to discover low-precision networks, if they exist, close to fp32 precision baseline networks. The results herein provide evidence that 4-bits suffice for classification.
Analysis of Shapelet Transform Usage in Traffic Event Detection

Automatic traffic incident detection from sensors data is a long studied topic that has been advancing with the introduction of new algorithms and recently from machine learning. While the traffic incidents detection problem can be treated as a time series classification task, there are not many attempts in this area and further investigations should be conducted. Recently, the Shapelet Transform algorithm has been proposed as a promising solution for time series classification. In this paper, we study the usage of Shapelet Transform in the field of traffic event detection. We first prove the applicability of the algorithm for automatic incident detection where it provides comparable performance to other techniques. In addition, we show how the Shapelet Transform algorithm can help in improving the detection by guiding the expert input in a cognitive approach. We test our approach using a real data set produced from road sensors of the M25 London Circular road. Results show an improvement comparing to using Shapelet Transform solely.
Intraindividual Variability in Neuropsychological Performance Predicts Cognitive Decline and Death in HIV

Objective: HIV-associated neurocognitive disorder (HAND) occurs in a significant percentage of HIV-infected (HIV+) adults. Increased intraindividual variability (IIV) in cognitive function may be an early marker of emerging neurocognitive disorder, which suggests that IIV may be a sensitive measure of neurologic compromise in HIV. In the current study, we hypothesize that increased IIV may predict impending morbidity, including future cognitive decline and death. Method: In 708 HIV+ participants followed longitudinally for up to 14 years, we assessed the role of dispersion in forecasting death and cognitive decline. Incident neurocognitive impairment was predicted in a mixed-effects ordinal logistic regression model using age, gender, baseline mean cognitive functioning, CD4+, time followed, years of education, and dispersion at the previous visit. Death before the next visit was predicted in a binomial mixed-effects regression model using age, gender, baseline mean cognitive functioning, CD4+, time followed, years of education, and dispersion. Results: Point-in-time dispersion and change in dispersion between visits predict future cognitive decline and death in HIV+ individuals. Individuals with greater dispersion at a visit or who had larger changes in dispersion between visits were more likely to demonstrate greater neurocognitive impairment at the subsequent visit. Greater IIV was also associated with an increased risk of death prior to the subsequent visit, even after controlling for HAND severity and global cognitive functioning. Conclusions: We conclude that the IIV in cognitive functioning may be more predictive of future disease consequence than mean level of cognitive functioning.
BenchBox: A User-Driven Benchmarking Framework for Fat-Client Storage Systems

In many online storage services, end-users mainly interact with the system via “fat” storage clients that integrate complex functionality. This means that to obtain a complete performance evaluation of one of such systems we may need to generate workloads on the client side that reproduce the behavior of real users. Unfortunately, this remains as an open research challenge today. We present <monospace>BenchBox</monospace>: A distributed performance evaluation framework for fat-client storage systems. On the one hand, <monospace>BenchBox</monospace> can generate workloads directly in storage clients that mimic users exhibiting a certain behavior, namely, <italic>user stereotypes</italic>. To this end, the framework enables to plug-in <italic>workload models</italic> and feed them with compact recipes that capture the behavior of user stereotypes (e.g., storage activity, type of file contents, data sharing links). On the other hand, <monospace>BenchBox</monospace> provides researchers with <italic>management and monitoring</italic> facilities to deploy experiments and analyze the performance of groups of storage clients. To demonstrate our framework, we equipped <monospace>BenchBox</monospace> with a <italic>2-layer workload model</italic> that reproduces both the <italic>activity</italic>—e.g., types of operations, frequency— and <italic>data </italic>—e.g., file sizes, data types—of users in a Personal Cloud. We used this model to generate workloads based on user stereotypes that we identified in real traces (UbuntuOne). Our experiments with public providers show how distinct types of users impact on the performance and efficiency of Personal Clouds, which may guide their optimization.
The roles and mechanisms of G3BP1 in tumour promotion

Abstract Ras-GTPase-activating protein SH3 domain-binding protein 1 (G3BP1) is a SH3 domain-binding protein that is overexpressed in a variety of tumour tissues and cancers, such as head and neck cancer, lung cancer, prostate cancer, colon cancer and breast cancer. G3BP1 promotes tumour cell proliferation and metastasis and inhibits apoptosis by regulating the Ras, TGF-β/Smad, Src/FAK and p53 signalling pathways. At present, polypeptides targeting G3BP1 have shown anti-tumour activity and G3BP1 also involved in anti-cancer effects of some polyphenolic compounds (resveratrol and EGCG). Therefore G3BP1 may be a potential target for tumour treatment.
Topological order, emergent gauge fields, and Fermi surface reconstruction

This review describes how topological order associated with the presence of emergent gauge fields can reconstruct Fermi surfaces of metals, even in the absence of translational symmetry breaking. We begin with an introduction to topological order using Wegner’s quantum gauge theory on the square lattice: the topological state is characterized by the expulsion of defects, carrying magnetic flux. The interplay between topological order and the breaking of global symmetry is described by the non-zero temperature statistical mechanics of classical XY models in dimension D = 3; such models also describe the zero temperature quantum phases of bosons with short-range interactions on the square lattice at integer filling. The topological state is again characterized by the expulsion of certain defects, in a state with fluctuating symmetry-breaking order, along with the presence of emergent gauge fields. The phase diagrams of the gauge theory and the XY models are obtained by embedding them in U(1) gauge theories, and by studying their Higgs and confining phases. These ideas are then applied to the single-band Hubbard model on the square lattice. A SU(2) gauge theory describes the fluctuations of spin-density-wave order, and its phase diagram is presented by analogy to the XY models. We obtain a class of zero temperature metallic states with fluctuating spin-density wave order, topological order associated with defect expulsion, deconfined emergent gauge fields, reconstructed Fermi surfaces (with ‘chargon’ or electron-like quasiparticles), but no broken symmetry. We conclude with the application of such metallic states to the pseudogap phase of the cuprates, and note the recent comparison with numerical studies of the Hubbard model and photoemission observations of the electron-doped cuprates. In a detour, we also discuss the influence of Berry phases, and how they can lead to deconfined quantum critical points: this applies to bosons on the square lattice at half-integer filling, and to quantum dimer models.
Pain Beliefs Mediate Relations Between General Resilience and Dysfunction from Chronic Back Pain

Purpose/Objective: A substantial percentage of people affected by chronic back pain maintain a high quality of life despite ongoing discomfort. Presumably, more resilient persons view pain and their capacities to manage it in a manner that mitigates pain-related dysfunction. Research Method/Design: To test this premise, 307 mainland Chinese adults with chronic back pain (189 women, 118 men) completed self-report measures of psychological resilience, pain beliefs (challenge appraisals of pain, pain self-efficacy, pain catastrophizing) and pain-related dysfunction (i.e., pain intensity, disability, affective distress, depression) within a cross-sectional research design. Results: Structural equation modeling indicated elevations in general psychological resilience were related to more frequent appraisals of pain as a challenge, higher pain self-efficacy levels, and lower pain catastrophizing levels. In turn, resilience, pain self-efficacy, and pain catastrophizing were linked to pain-related dysfunction while challenge appraisals were not. Conclusions/Implications: Together, results underscored specific pain beliefs underlying the pathway between resilience and reduced dysfunction from chronic back pain. Aside from evaluating dysfunctional expectancies (e.g., pain catastrophizing) and outcomes, practitioners and researchers should consider beliefs about personal effectiveness in managing pain within assessment and treatment protocols of chronic back pain.
Molecular Pathways Associated With Methylmercury-Induced Nrf2 Modulation

Methylmercury (MeHg) is a potent neurotoxin that affects particularly the developing brain. Since MeHg is a potent electrophilic agent, a wide range of intracellular effects occur in response to its exposure. Yet, the molecular mechanisms associated with MeHg-induced cell toxicity have yet to be fully understood. Activation of cell defense mechanisms in response to metal exposure, including the up-regulation of Nrf2- (nuclear factor erythroid 2-related factor 2)-related genes has been previously shown. Nrf2 is a key regulator of cellular defenses against oxidative, electrophilic and environmental stress, regulating the expression of antioxidant proteins, phase-II xenobiotic detoxifying enzymes as well phase-III xenobiotic transporters. Analogous to other electrophiles, MeHg activates Nrf2 through modification of its repressor Keap1 (Kelch-like ECH-associated protein 1). However, recent findings have also revealed that Keap1-independent signal pathways might contribute to MeHg-induced Nrf2 activation and cytoprotective responses against MeHg exposure. These include, Akt phosphorylation (Akt/GSK-3β/Fyn-mediated Nrf2 activation pathway), activation of the PTEN/Akt/CREB pathway and MAPK-induced autophagy and p62 expression. In this review, we summarize the state-of-the-art knowledge regarding Nrf2 up-regulation in response to MeHg exposure, highlighting the modulation of signaling pathways related to Nrf2 activation. The study of these mechanisms is important in evaluating MeHg toxicity in humans, and can contribute to the identification of the molecular mechanisms associated with MeHg exposure.
Clinical stage and histological type of the most common carcinomas diagnosed in young adults in a reference cancer hospital

OBJECTIVES: Cancer in young adults represents a great challenge, both biologically and socially, and understanding the unique characteristics of neoplasms in this age group is important to improving care. We aimed to evaluate the most common carcinomas and their characteristics, such as histological type and clinical stage, in young adults in the largest cancer hospital in Latin America. METHODS: The hospital registry was consulted for the period between 2008 and 2014. Young adults were defined as individuals aged 18 to 39 years, and older adults were defined as individuals aged 40 years and older. Differences between age groups were assessed through chi-square tests. RESULTS: Of the 39,389 patients included, 3,821 (9.7%) were young adults. Among the young adults, the most frequent cancer types were the following: breast, lymph node, colorectal, thyroid, testicle, hematopoietic and reticuloendothelial, uterine cervix, brain, soft tissue and stomach; these sites accounted for 74.5% of the observed tumors. Breast, colorectal and stomach cancers were more frequently diagnosed at advanced stages in young adults than in older adults (p<0.001). The most common histological types were infiltrating ductal carcinoma (86.12%) for breast cancer, adenocarcinomas not otherwise specified (45.35%) for colorectal cancer, squamous cell carcinoma not otherwise specified (65.26%) for uterine cervix cancer, signet ring cell adenocarcinomas (49.32%) for stomach cancer and adenocarcinomas not otherwise specified (50.79%) for lung cancer. CONCLUSION: Young adults are diagnosed with cancer at more advanced stages, indicating that health professionals should be aware of cancer incidence in this age group. It is necessary to develop a better understanding of cancer in young adults and to implement dedicated health care strategies for these patients.
Decoupled Heterogeneous Networks With Millimeter Wave Small Cells

Deploying sub-6-GHz network together with millimeter wave (mm-wave) is a promising solution to simultaneously achieve sufficient coverage and high data rate. In heterogeneous networks, the traditional coupled access, i.e., users are constrained to be associated with the same base station in both downlink and uplink, is no longer optimal, and the concept of downlink and uplink decoupling (DUDe) has recently been proposed. In this paper, we analyze the coverage probability and area throughput for both the downlink and uplink of sub-6-GHz/mm-wave cellular networks with decoupled access. Compared with the existing works, we take uplink power control and mm-wave interference into account. Using the tools from stochastic geometry, the expressions of signal-to-interference-plus-noise ratio coverage probability, user-perceived rate coverage probability and the area throughput are derived. The impact of decoupled access and different small cells (SCells) is investigated. In particular, analytical results reveal that with decoupled access, UEs are more likely to be associated with SCells in uplink when the network is sparse, and the uplink traffic will be offloaded from sub-6-GHz SCells to mm-wave SCells when the network is dense. Moreover, the dense deployment of mm-wave SCells rather than sub-6-GHz SCells is more reasonable, and the DUDe is a key factor in improving the performance of dense cellular networks with multi-band.