Miracle-dz/UniFAR-FTUs huggingface dataset:
Fine-Grained Image Classification With Gaussian Mixture Layer

Fine-grained image classification aims at recognizing different subordinates in one basic-level category, for example, distinguishing species of birds. Compared with basic-level classification, it has both low inter-class and high intra-class variances. Therefore, utilization of discriminative parts is crucial for fine-grained classification. In this paper, we propose a Gaussian mixture model, which fuses part features by Gaussian mixture layer. More specifically, it first generates a set of part proposals by selective search. Then, we extract image feature maps from mid-layers of convolutional neural networks. Feature maps and part proposals are used for calculating part features via spatial pyramid pooling. Next, Gaussian mixture layer treats part features as data points and uses several Gaussian components to model their distribution. It finds clusters for input and generates output features based on combination of cluster center. Finally, the output feature can represent the whole image and is used for classification. Training process of the model consists of two loops. The outer loop is the optimization of the whole network, and the inner loop is about the EM algorithm used in Gaussian mixture layer. Experiments demonstrate higher or similar performance on four fine-grained data sets compared with the state-of-the-arts. More discussions on Gaussian mixture layer are also provided.
Loss Allocation in Distribution Networks Based on Aumann–Shapley

This paper outlines a procedure for loss allocation in both radial and meshed distribution networks with distributed generation that could be regulated in various ways. The method is analytically developed based on the theory of electrical circuits combined with game theory based on Aumann–Shapley, which guarantees both the electrical principles and the fair axioms of game theory. The proposed method obtains unitary participation coefficients for each network user based on the currents demanded/injected by each user and the network topology. The proposed allocation method based on Aumann–Shapley has been compared with other traditional allocation methods, is adaptable to distribution networks, and shows great potential and ease of implementation. Moreover, it can be applied to any kind of distribution network (radial or meshed) with distributed energy resources.
Leptospirosis in Ukraine (Lviv Oblast): Clinical and Epidemiological Features

Abstract The article describes the results of a retrospective analysis of medical records of 395 patients with a clinical diagnosis of leptospirosis treated at the Lviv Oblast Infectious Disease Clinical Hospital (Ukraine) between 2002 and 2016. The main risk factors for leptospirosis were contact with rodents or their excrements (26.84%) and bathing in ponds, small lakes, and reservoirs (10.63%). Among 276 patients in whom the anti-leptospira antibodies were detected by the microscopic agglutination test (MAT), the most common serotypes were Leptospira icterohaemorrhagiae (33.33%) and Leptospira grippotyphosa (25.0%). The mortality rate was significantly higher in patients where leptospirosis diagnosis was established based on clinical symptoms without confirmation by MAT (15.13% vs. 5.43%, p < 0.01).
A Clothes Classification Method Based on the gcForest

In this paper, we propose a clothes classification method, an application of the gcForest. The gcForest, a novel decision tree ensemble method proposed by Zhou and Feng, performs excellently in many experiments. Moreover, we come up with some adjustment approaches for gcForest. Using these adjustment approaches, we get a satisfactory result when gcForest is used for clothes classification task. Furthermore, we create a data set for clothes classification which contains 8 categories of clothes including a total of 52000 images. And we preprocess the images in the data set by GrabCut-based clothes extraction algorithm.
Thor: A Scalable Hybrid Switching Architecture for Data Centers

Optical interconnects are emerging as a high bandwidth and low energy alternative to traditional electrical networks. However, most exiting designs put optical switching in the core layer due to issues, including limited scalability, potential bottleneck of the control plane, and high cost of the multi-wavelength switch. To this end, we propose Thor, a hybrid network architecture using optical interconnects as the main load bearing portion. It employs the hypercube topology and multi-hop circuit switching to enable server-level optical access, thus solving the scalability and connectivity limitations. Then, in order to build a faster control plane operating short-lived circuits in a large scale, Thor uses a distributed control system over the electrical network, with a new path setup mechanism capable of establishing multiple optical circuits concurrently. Moreover, Thor employs a novel optical switch design to utilize multiple wavelengths more efficiently. Our evaluation results show that Thor consumes at least 59% and 6% less power than existing electrical and optical designs. For performance, Thor is able to deliver 90% bisection bandwidth of a non-blocking network and reduce the end-to-end latency significantly. Finally, by separately delivering mice and elephant flows, Thor achieves salient improvements in average flow completion times compared with existing approaches.
Genetic testing for high-grade osteosarcoma: a guide for future tailored treatments?

ABSTRACT Introduction: Genetic characterization of osteosarcoma has evolved during the last decade, thanks to the integrated application of conventional and new candidate-driven and genome-wide technologies. Areas covered: This review provides an overview of the state of art in genetic testing applied to osteosarcoma, with particular regard to novel candidate genetic biomarkers that can be analyzed in tumor tissue and blood samples, which might be used to predict toxicity and prognosis, detect disease relapse, and improve patients’ selection criteria for tailoring treatment. Expert commentary: Genetic testing based on modern technologies is expected to indicate new osteosarcoma-related prognostic markers and driver genes, which may highlight novel therapeutic targets and patients stratification biomarkers. The definition of tailored or targeted treatment approaches may improve outcome of patients with localized tumors and, even more, of those with metastatic disease, for whom progress in cure probability is highly warranted.
APOE4 Genotype Exerts Greater Benefit in Lowering Plasma Cholesterol and Apolipoprotein B than Wild Type (E3/E3), after Replacement of Dietary Saturated Fats with Low Glycaemic Index Carbohydrates

We examined the impact of APOE genotype on plasma lipids and glucose in a secondary analysis of data from a five-arm, randomised controlled, parallel dietary intervention trial (‘RISCK’ study), to investigate the impact of replacing saturated fatty acids (SFA) with either monounsaturated fat (MUFA) or carbohydrate of high or low glycaemic index (GI) on CVD risk factors and insulin sensitivity. We tested the impact of APOE genotype (carriage of E2 and E4 alleles versus E3/E3), determined retrospectively, on plasma lipids, lipoproteins and glucose homeostasis at baseline (n = 469), and on the change in these variables after 24 weeks of dietary intervention (n = 389). At baseline, carriers of E2 (n = 70), E4 (n = 125) and E3/E3 (n = 274) expressed marked differences in total plasma cholesterol (TC, p = 0.001), low density lipoprotein cholesterol (LDL-C, p < 0.0001), apolipoprotein B (apo B, p < 0.0001) and total to high density lipoprotein cholesterol ratio (TC:HDL-C, p = 0.002), with plasma concentrations decreasing in the order E4 > E3/E3 > E2. Following intervention, there was evidence of a significant diet x genotype interaction with significantly greater decreases in TC (p = 0.02) and apo B (p = 0.006) among carriers of E4 when SFA was replaced with low GI carbohydrate on a lower fat diet (TC −0.28 mmol/L p = 0.03; apo B −0.1 g/L p = 0.02), and a relative increase in TC (in comparison to E3/E3) when SFA was replaced with MUFA and high GI carbohydrates (TC 0.3 mmol/L, p = 0.03). Among carriers of E2 (compared with E3/E3) there was an increase in triacylglycerol (TAG) when SFA was replaced with MUFA and low GI carbohydrates 0.46 mmol/L p = 0.001). There were no significant interactions between APOE genotype and diet for changes in indices of glucose homeostasis. In conclusion, variations in APOE genotype led to differential effects on the lipid response to the replacement of SFA with MUFA and low GI carbohydrates.
A Zebrafish Acromegaly Model Elevates DNA Damage and Impairs DNA Repair Pathways

Acromegaly is a pathological condition due to excess growth hormone (GH) secretion. Acromegaly patients exhibit a deterioration of health and many associated complications, such as cardiovascular issues, arthritis, kidney diseases, muscular weakness, and colon cancer. Since these complications are generalized throughout the body, we investigated the effect of GH excess on cellular integrity. Here, we established stable acromegaly model zebrafish lines that overexpress tilapia GH and the red fluorescence protein (RFP) reporter gene for tracking GH gene expression throughout generations, and performed RNA-Seq data analysis from different organs. Intriguingly, heatmap and Expression2Kinases (X2K) analysis revealed the enrichment of DNA damage markers in various organs. Moreover, H2A.X immunostaining analysis in acromegaly zebrafish larvae and the adult acromegaly model brain and muscle showed a robust increase in the number of DNA-damaged cells. Using Gene Set Enrichment Analysis (GSEA), we found that the acromegaly zebrafish model had impaired DNA repair pathways in the liver, such as double-strand break (DSB), homologous recombination repair (HRR), non-homologous end joining (NHEJ), nucleotide excision repair (NER), and translesion synthesis (TLS). Interestingly, the impairment of DNA repair was even more prominent in acromegaly model than in aged zebrafish (three years old). Thus, our study demonstrates that affection of cellular integrity is characteristic of acromegaly.
Mechanisms underlying contrast-dependent orientation selectivity in mouse V1

Significance Recently, sophisticated optogenetic tools for mouse have enabled many detailed studies of the neuronal circuits of its primary visual cortex (V1), providing much more specific information than is available for cat or monkey. Among various other differences, they show a striking contrast dependency in orientation selectivity in mouse V1 rather than the well-known contrast invariance for cat and monkey. Constrained by the existing experiment data, we develop a comprehensive large-scale model of an effective input layer of mouse V1 that successfully reproduces the contrast-dependent phenomena and many other response properties. The model helps to probe different mechanisms based on excitation–inhibition balance that underlie both contrast dependencies and invariance, and it provides implications for future studies on these circuits. Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties—one of which is that contrast invariance in the orientation selectivity (OS) of the neurons’ firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties—for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance—providing insights for future studies on contrast dependence (invariance).
Local Dynamic Fusion for 24-Hour Load Pattern Prediction in Power System

The paper presents local dynamic approach to fusion of results of many predictors, forecasting the 24-hour load pattern in electric power system. The prediction of 24-elements vector for the next day power need is done here using one member of an ensemble, which was the best in the learning stage for the input vector, closest to the actually applied input data. This way we get the highest level of statistical forecasting accuracy, since each task is performed by the predictor the best suited to it. The numerical experiments aimed on forecasting the 24-element vector of hourly load of the power system in Poland have confirmed the superiority of the presented approach. The quality measures of forecast have been significantly improved.
Outage Probability Region and Optimal Power Allocation for Uplink SCMA Systems

As a promising non-orthogonal multiple access scheme, sparse code multiple access (SCMA) technology has attracted much attention. Because inter-user interference is present in code domain and multi-user iterative detection is required, user capacity and outage probability analysis for uplink SCMA systems are challenging and have not been presented in the literature. In this paper, the capacity region for uplink SCMA systems is analyzed, based on which the common and individual outage probability regions are calculated. Optimizing the outage probability within the outage probability region can be casted as an Lagrangian duality problem and solved by an iterative descent algorithm, which however imposes high complexity since the expectation operation is required in each iteration. To reduce the computational complexity of solving this Lagrangian duality problem, an adaptive algorithm is developed, which is capable of providing the optimal outage probability and adaptively updating it. Furthermore, a power allocation policy is naturally obtained to achieve the optimized outage probability in the outage probability region.
Experimental social rejection increases dyspnoea perception and neural processing of respiratory sensations in healthy subjects

Patients suffering from dyspnoea frequently report social rejection as a severe limitation of their lives [1]. While the effects of different social challenges on the respiratory system are already established in animal models [2, 3], little is known about potential consequences of social rejection on the perception and neural processing of dyspnoea in humans. Detrimental effects of social rejection are well documented for other aversive somatic symptoms [4, 5]. The current study examined whether social rejection would increase perceived dyspnoea and whether this would be paralleled by increased neural processing of respiratory sensations as measured with respiratory-related evoked potentials (RREPs) in the electroencephalogram (EEG). Social rejection is associated with increased dyspnoea perception and neural processing of respiratory sensations http://ow.ly/p5hA30mcZMS
AutismKB 2.0: a knowledgebase for the genetic evidence of autism spectrum disorder

Abstract Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder with strong genetic contributions. To provide a comprehensive resource for the genetic evidence of ASD, we have updated the Autism KnowledgeBase (AutismKB) to version 2.0. AutismKB 2.0 integrates multiscale genetic data on 1379 genes, 5420 copy number variations and structural variations, 11 669 single-nucleotide variations or small insertions/deletions (SNVs/indels) and 172 linkage regions. In particular, AutismKB 2.0 highlights 5669 de novo SNVs/indels due to their significant contribution to ASD genetics and includes 789 mosaic variants due to their recently discovered contributions to ASD pathogenesis. The genes and variants are annotated extensively with genetic evidence and clinical evidence. To help users fully understand the functional consequences of SNVs and small indels, we provided comprehensive predictions of pathogenicity with iFish, SIFT, Polyphen etc. To improve user experiences, the new version incorporates multiple query methods, including simple query, advanced query and batch query. It also functionally integrates two analytical tools to help users perform downstream analyses, including a gene ranking tool and an enrichment analysis tool, KOBAS. AutismKB 2.0 is freely available and can be a valuable resource for researchers.
A Model Predictive Controller for Minimum Time Cornering

This paper presents a model predictive control approach to drive the vehicle up to its tires adhesion limits. The main focus is optimality of the closed-loop control to follow a certain track in minimum time. A Linear Time Varying Model Predictive Controller (LTV-MPC) is developed to be able to adapt with high non-linearities of the model, but also having low computational complexity in order to work in real-time. In order to have the best reference trajectory and apply it to the LTV-MPC, the problem is first solved in a nonlinear optimal control framework. This solution represents a benchmark as well, to which the LTV-MPC results are compared. The proposed controller is tested in simulation showing promising results.
Visualized identification of the maximal surgical delay effect in a rat flap model

Currently, experimental evidence suggests that the surgical delay can increase flap survival area, but its effect may decrease if the optimal delay period is missed. The aim of this study is to establish a sensitive and objective modality based on the visualized and individualized infrared thermography for identifying the maximal surgical delay effect. A rectangular three‐angiosome flap was designed on the unilateral dorsum of the rat. Ninety‐six rats were randomly divided into six groups according to the various delay time. Both the relative temperature and the relative temperature ratio were measured by the infrared thermography. Arterial density, number of vessels >0.1 mm in diameter, microvessel density, VEGF concentration, and flap viability were measured. Receiving operating characteristic curve with the highest Youden‐Index was used to detect and identify an optimal cutoff point of the relative temperature ratio in the maximal surgical delay effect. The criteria for identifying the flap maximum delay effect based on the infrared thermography included the surface of the postdelayed flaps presented white color (higher temperature) instead of the red and white pattern of the normal skin and the optimal cutoff point of the relative temperature ratio was ≥1.17 with a sensitivity of 84.6% and a specificity of 77.3%. Instead, the sensitivity and specificity of the conventional method based on the delay time were 38.5 and 90.9%, respectively. Infrared thermal imaging can accurately identify the maximum delay effect when combined with the relative temperature ratio.
Particle-in-Cell Simulation of Anomalous Transport in a Penning Discharge

Results are presented from two-dimensional (radial-azimuthal), electrostatic Particle-In-Cell simulations of a Penning discharge, where an electron beam is injected along the axis, parallel to the magnetic field, into a cylindrical plasma radially bounded by grounded metal. In preliminary, one-dimensional (radial) simulations, the anomalous electron transport is solely due to the instability of the pure lower-hybrid mode. With also the azimuthal direction numerically resolved, the Simon-Hoh mode becomes active and the lower-hybrid mode is destabilized by density-gradient effects. In the more realistic, twodimensional simulations, the lower-hybrid and Simon-Hoh modes both contribute to the anomalous transport. These latter simulations demonstrate the excitation of an azimuthally asymmetric, spoke-like structure rotating in the direction of ExB and electron diamagnetic drifts and persisting throughout the remainder of this simulation. The spokes are characterized by an increase in plasma density and are shown to channel most of the radial leakage current. The spoke appears similar to the one seen in experiments and it exhibits the same scaling with ion mass. The Hall parameter in the simulations is also in good agreement with experiment.
Enhanced-Online-Random-Forest Model for Static Voltage Stability Assessment Using Wide Area Measurements

Application of data mining based methods in online voltage stability assessment has attracted vast attentions in recent years. To account for significant system changes, most of the data mining based methods reconstruct an entire model based on the updated training database. Instead of entirely rebuilding a model in offline mode, this paper presents a novel online learning framework for monitoring the voltage stability of a transmission grid using wide area measurements. A new enhanced online random forest model based on the drift detection and online bagging techniques is proposed. It enables to online update the trees involving tree growth and/or tree replacement. The trees in the forest are then combined via a weighted majority voting, which makes the decision model better adapted to system changes. The framework was first demonstrated on the IEEE 57-bus system, and then applied to a practical power system, the Taiwan power (Taipower) system composed of 1821 buses. In addition to accuracy-based measures, robustness and speed of the proposed framework were also validated. Extensive studies demonstrate that the proposed framework is able to provide reliable and accurate online voltage stability assessment.