Miracle-dz/UniFAR-FTUs huggingface dataset:
Allele-Level KIR Genotyping of More Than a Million Samples: Workflow, Algorithm, and Observations

The killer-cell immunoglobulin-like receptor (KIR) genes regulate natural killer cell activity, influencing predisposition to immune mediated disease, and affecting hematopoietic stem cell transplantation (HSCT) outcome. Owing to the complexity of the KIR locus, with extensive gene copy number variation (CNV) and allelic diversity, high-resolution characterization of KIR has so far been applied only to relatively small cohorts. Here, we present a comprehensive high-throughput KIR genotyping approach based on next generation sequencing. Through PCR amplification of specific exons, our approach delivers both copy numbers of the individual genes and allelic information for every KIR gene. Ten-fold replicate analysis of a set of 190 samples revealed a precision of 99.9%. Genotyping of an independent set of 360 samples resulted in an accuracy of more than 99% taking into account consistent copy number prediction. We applied the workflow to genotype 1.8 million stem cell donor registry samples. We report on the observed KIR allele diversity and relative abundance of alleles based on a subset of more than 300,000 samples. Furthermore, we identified more than 2,000 previously unreported KIR variants repeatedly in independent samples, underscoring the large diversity of the KIR region that awaits discovery. This cost-efficient high-resolution KIR genotyping approach is now applied to samples of volunteers registering as potential donors for HSCT. This will facilitate the utilization of KIR as additional selection criterion to improve unrelated donor stem cell transplantation outcome. In addition, the approach may serve studies requiring high-resolution KIR genotyping, like population genetics and disease association studies.
Two Dimensional Image Formation of Interrupted FMCW SAR Through Spectral Analysis

Most of spaceborne Synthetic Aperture Radar (SAR) systems are based on pulsed mode of operation. Such systems are quite expensive and large sized. Frequency Modulated Continuous Wave (FMCW) based SARis a light weight and less expensive solution, but the disadvantage is that it requires two antennas - one for transmit and other for receive. Interrupted-FMCW is a recently proposed idea for spaceborne SAR that uses a single antenna for transmission and reception. The main problem of this approach is discontinuity of data in azimuth. This paper proposes a two-dimensional image formation algorithm that uses spectral analysis methods for processing of interrupted-FMCW SARdata. The algorithm has been tested on simulated point target data as well as on a real FMCW data, and the results have been discussed.
PAPR-Aware Beam Division Multiple Access for mmWave Massive MIMO Systems

Beam division multiple access (BDMA) has recently been proposed for massive multiple-input multiple-output (MIMO) systems by simultaneously transmitting multiple users’ data streams via different beams. Despite its many advantages, BDMA may suffer from a larger peak-to-average-power ratio (PAPR) as the number of transmit antennas increases. In this work, we propose to explicitly incorporate the PAPR constraints into the BDMA design by jointly performing hybrid analog-digital precoding and user-beam scheduling. More specifically, the proposed scheme designs the digital precoder with explicit PAPR constraints. In addition, to facilitate the hybrid precoder design, the proposed scheme opportunistically schedules users who are near orthogonal in the analog beam domain. To efficiently compute the optimal hybrid precoder and user-beam scheduling, we develop a greedy algorithm that first opportunistically selects users before their corresponding hybrid precoders are derived with explicit PAPR constraints. Simulation results confirm the effectiveness of proposed PAPR- aware BDMA scheme.
Influence of DC Winding Configuration on Its Induced Voltage in Wound Field Machines

DC winding induced voltage pulsation in the wound field synchronous machines (WFSMs) will cause dc winding current ripple, challenge the dc power supply, and deteriorate the control performance, especially at high speed. In this paper, the influence of dc winding configuration including the dc coil number and the parallel branch number on its induced voltage pulsation is investigated for WFSMs. Based on the modeling of both open-circuit and on-load dc winding induced voltages, the preferred dc winding configurations for WFSMs having various stator/rotor pole number combinations with double layer or single layer ac windings are obtained and validated by finite element (FE) analysis on four typical partitioned stator WFSMs (PS-WFSMs). A PS-WFSM prototype is built and tested to validate both analytical and FE analyses.
Two-Phase Read Strategy for Low Energy Variation-Tolerant STT-RAM

An adaptive two-phase read strategy is proposed that allows reliable reads under high variability at a substantially lower energy, utilizing the idea of the conditional use of energy-costly variation-tolerance read. The proposed variation-tolerant design technique is developed for dealing with spatial variability while maintaining low read energy by using the conventional read with two-levels of reference voltages and the variation-tolerance read if a bit is not read out correctly by the conventional read. To achieve a two-phase read strategy, a sense amplifier (SA) is designed that performs both the energy-saving conventional read and the energy-costly variation-tolerant read. The energy overhead is significantly reduced by decreasing the use of variation-tolerant read, and the area overhead is minimized by reusing the conventional SA for the variation-tolerant read. The proposed circuit is evaluated using a compact spin-transfer torque magnetic RAM model, targeting an implementation in a 10-nm technology node. Results indicate that the proposed design strategy reduces read energy by 28.6% compared to the best-known variation-tolerant design with 3.7% area overhead assuming that a 1-bit cell is 20 $F^{2}$ and the number of rows is 1024.
Determination of the Influence of Na Accumulation at the Localized Grain Boundaries in CIGS Absorbers

The beginning of the in-line CIGS run is affected by a “start-up” effect - a phenomenon that causes reduced efficiency despite proper process setpoint values for all known parameters. Detailed analysis of Na depth profiles revealed the Na accumulation in the bulk CIGS absorbers, which suggests formation of secondary phases and grain boundaries during the in-line 3-stage growth process. To determine the influence of Na accumulation at the localized grain boundaries on the structural and electronic structure of CIGS absorbers, novel characterization techniques of transmission Kikuchi diffraction in the scanning electron microscope and high-angle annular dark-field scanning transmission electron microscopy on foused ion beam cross-section samples have been evaluated.
Visual Saliency Guided High Dynamic Range Image Compression

Recent years have seen the emergence of the visual saliency-based image and video compression for low dynamic range (LDR) visual content. The high dynamic range (HDR) imaging is yet to follow such an approach for compression as the state-of-the-art visual saliency detection models are mainly concerned with LDR content. Although a few HDR saliency detection models have been proposed in the recent years, they lack the comprehensive validation. Current HDR image compression schemes do not differentiate salient and non-salient regions, which has been proved redundant in terms of the Human Visual System. In this paper, we propose a novel visual saliency guided layered compression scheme for HDR images. The proposed saliency detection model is robust and highly correlates with the ground truth saliency maps obtained from eye tracker. The results show a reduction of bit-rates up to 50% while retaining the same high visual quality in terms of HDR-Visual Difference Predictor (HDR-VDP) and the visual saliency-induced index for perceptual image quality assessment (VSI) metrics in the salient regions.
Dual-SVM tracker via Multiple Support Instance and LEVER Strategy

Visual tracking can be modeled as a binary classification problem, and the classic classifiersupport vector machine (SVM) based methods have been demonstrated encouraging performance in recent object tracking benchmarks. However, the performance of SVM is too sensitive to noisy training data during online update. In this paper, we propose an efficient dual-SVM based tracker to improve classification performance for visual tracking. The tracker proposed consists of two models: the holistic model and the part model. To learn the holistic model, the support instances are derived from the RMI-SVM trained in a deep feature space. As for the part model to highlight local structure of the target, a linear SVM is learned to further encode local details of the target, selecting candidate instances from the support instances by the confidence as input. To fuse the holistic model and the part model, we design a simple but efficient decision strategy (LEVER) to enforce the dual-SVM to focus on the target. The proposed LEVER is updated incrementally to capture changes of the appearance of the target. Extensive experimental results show that the proposed tracker performs favorably against state-of-the-art methods.
Automatic Writer Verification Algorithm for Chinese Characters Using Semi-Global Features and Adaptive Classifier

Writer verification is to identify whether the script was written by a person himself. However, although there are many advanced machine learning methods now, automatic writer verification is still a very challenging work since the training data in the false case (forged case) is usually very hard to acquire. In practice, to avoid being convicted, a criminal may write scripts that are fully different from what he wrote on the forged document. In this manuscript, we adopt global and semi-global information, including log-Gabor features, advanced moments features, and co-occurrence matrix features for writer verification. In addition, a more flexible classifier is proposed. Though the convolutional neural network is popular, its performance is limited when the training data is not enough. Therefore, another classifier based on the weighted squared Euclidean distance is adopted. Simulations show that the proposed algorithm outperforms other methods and will be very helpful for identifying forged scripts.
Osiris: Hunting for Integer Bugs in Ethereum Smart Contracts

The capability of executing so-called smart contracts in a decentralised manner is one of the compelling features of modern blockchains. Smart contracts are fully fledged programs which cannot be changed once deployed to the blockchain. They typically implement the business logic of distributed apps and carry billions of dollars worth of coins. In that respect, it is imperative that smart contracts are correct and have no vulnerabilities or bugs. However, research has identified different classes of vulnerabilities in smart contracts, some of which led to prominent multi-million dollar fraud cases. In this paper we focus on vulnerabilities related to integer bugs, a class of bugs that is particularly difficult to avoid due to some characteristics of the Ethereum Virtual Machine and the Solidity programming language. In this paper we introduce Osiris -- a framework that combines symbolic execution and taint analysis, in order to accurately find integer bugs in Ethereum smart contracts. Osiris detects a greater range of bugs than existing tools, while providing a better specificity of its detection. We have evaluated its performance on a large experimental dataset containing more than 1.2 million smart contracts. We found that 42,108 contracts contain integer bugs. Besides being able to identify several vulnerabilities that have been reported in the past few months, we were also able to identify a yet unknown critical vulnerability in a couple of smart contracts that are currently deployed on the Ethereum blockchain.
The antioxidant activity of steamed ginger and its protective effects on obesity induced by high-fat diet in C57BL/6J mice

BACKGROUND/OBJECTIVES Ginger, a root vegetable, is known to have antioxidant and antiobesity effects. Preparation, such as by steaming, can affect the chemical composition of prepared root vegetables or herbs and can change their functional activities. In the present study, we investigated the protective effects of steamed ginger against oxidative stress and steatosis in C57BL/6J mice fed a high-fat diet. MATERIALS/METHODS The levels of polyphenols and flavonoids in two different extracts of steamed ginger, i.e., water extract (SGW) and ethanolic extract (SGE); as well, their antioxidant activities were examined. Forty male C57BL/6J mice were fed a normal diet (ND, n = 10), high-fat diet (HFD, 60% fat, w/w, n = 10), HFD supplemented with 200 mg/kg of SGE or garcinia (GAR) by weight (SGED or GARD, respectively, n = 10) for 12 weeks. Serum chemistry was examined, and the expressions of genes involved in lipid metabolism were determined in the liver. Histological analysis was performed to identify lipid accumulations in epididymal fat pads and liver. RESULTS The SGE had higher contents of polyphenols and flavonoids and higher DPPH and ABTS+ free radical scavenging activities compared to those of SGW. Treatment with SGE or GAR significantly decreased the HFD-induced weight gain. Both SGE and GAR significantly reduced the high serum total cholesterol (TC), triglyceride (TG) and low-density lipoprotein levels induced by HFD. Compared to ND, HFD significantly increased hepatic TC and TG levels. SGE or GAR supplementation significantly decreased the increase of hepatic lipids by HFD. Interestingly, SGE had a more significant effect in reducing hepatic TC and TG levels than GAR. Furthermore, hepatic genes involved in lipogenesis and lipolysis were altered in both the SGED and GARD groups. CONCLUSIONS The present study indicates that steamed ginger supplementation can decrease plasma TC and TG and can inhibit liver steatosis by regulating the expressions of hepatic genes.
Centralized and Decentralized Cache-Aided Interference Management in Heterogeneous Parallel Channels

We consider the problem of cache-aided interference management in a network consisting of <inline-formula> <tex-math notation="LaTeX">$K_{\mathrm {T}}$ </tex-math></inline-formula> single-antenna transmitters and <inline-formula> <tex-math notation="LaTeX">$K_{\mathrm {R}}$ </tex-math></inline-formula> single-antenna receivers, where each node is equipped with a cache memory. Transmitters communicate with receivers over two heterogenous parallel subchannels: the P-subchannel for which transmitters have perfect instantaneous knowledge of the channel state, and the N-subchannel for which the transmitters have no knowledge of the instantaneous channel state. Under the assumptions of uncoded placement and separable one-shot linear delivery over the two subchannels, we characterize the optimal degrees-of-freedom (DoF) to within a constant multiplicative factor of 2. We extend the result to a decentralized setting in which no coordination is required for content placement at the receivers. In this case, we characterize the optimal one-shot linear DoF to within a factor of 3.
Modelling and Simulation of Fog and Edge Computing Environments using iFogSim Toolkit

Resource management in Fog computing is very complicated as it engages significant number of diverse and resource constraint Fog nodes to meet computational demand of IoT-enabled systems in distributed manner. Its integration with Cloud triggers further difficulties in combined resource management. Different sensing frequency of IoT devices, distributed application structure and their coordination also influence resource management in Fog computing environment. For advancement of Fog and its resource management, the necessity of extensive research in beyond question. To develop and evaluate different ideas and resource management policies, empirical analysis on Fog environment is the key. Since Fog computing environment incorporates IoT devices, Fog nodes and Cloud datacenters along with huge amount of IoT-data and distributed applications, real-world implementation of Fog environment for research will be very costly. Moreover, modification of any entity in real-world Fog environment will be tedious. In this circumstance, simulation of Fog computing environment can be very helpful. Simulation toolkits not only provide frameworks to design customized experiment environment but also assist in repeatable evaluation. In this chapter we focus on delivering a tutorial on iFogSim toolkit for modelling and simulating Fog computing environments. We believe this chapter will offer researchers a simplified way to apply iFogSim in their research works.
Transcripts and tumors: regulatory and metabolic programming during biotrophic phytopathogenesis

Biotrophic fungal pathogens of plants must sense and adapt to the host environment to complete their life cycles. Recent transcriptome studies of the infection of maize by the biotrophic pathogen Ustilago maydis are providing molecular insights into an ordered program of changes in gene expression and the deployment of effectors as well as key features of nutrient acquisition. In particular, the transcriptome data provide a deeper appreciation of the complexity of the transcription factor network that controls the biotrophic program of invasion, proliferation, and sporulation. Additionally, transcriptome analysis during tumor formation, a key late stage in the life cycle, revealed features of the remodeling of host and pathogen metabolism that may support the formation of tremendous numbers of spores. Transcriptome studies are also appearing for other smut species during interactions with their hosts, thereby providing opportunities for comparative approaches to understand biotrophic adaptation.
A System for Automated Image Editing from Natural Language Commands

This work presents the task of modifying images in an image editing program using natural language written commands. We utilize a corpus of over 6000 image edit text requests to alter real world images collected via crowdsourcing. A novel framework composed of actions and entities to map a user's natural language request to executable commands in an image editing program is described. We resolve previously labeled annotator disagreement through a voting process and complete annotation of the corpus. We experimented with different machine learning models and found that the LSTM, the SVM, and the bidirectional LSTM-CRF joint models are the best to detect image editing actions and associated entities in a given utterance.
Sublethal Effects of Ionic and Nanogold on the Nematode Caenorhabditis elegans

The nematode Caenorhabditis elegans is used as an ecotoxicological model species in both aqueous medium and solid substrates. It is easy and of low cost to maintain in the laboratory and it produces hundreds of offspring within a short period of time. It also has a small body size (1 mm), making it possible for in vivo assays to be conducted in 12-well plates. Engineered nanomaterials (ENMs) are a class of emerging pollutants. Nanogold (nAu) is used in many consumer products and in vivo drug delivery. These materials can be released into the aquatic environment during production or discarding of consumer products. As nAu is insoluble in water, the sediment would become the final depository for the materials. It has become increasingly important to use sediment dwelling organisms to screen for possible toxicity of these ENMs. In this study C. elegans was exposed to a range of concentrations of nAu and ionic gold in M9-media, acting as a substitute for pore water. After 96-hour growth, fertility and reproduction were determined. Internal structure damage and internalisation of particles in C. elegans were determined by using SEM and CytoViva® Darkfield Imaging. From these images the nanomaterials are distributed around the oocytes in the reproductive organs, as well as the pharynx. Results obtained indicate that nAu affects reproduction more than growth due to internal gonad damage, albeit at very high exposure concentrations, indicating no toxicity at environmentally relevant concentrations. Ionic Au is more toxic than nAu and effects fertility and reproduction due to ion release. These results give more information regarding the toxicity and in vivo uptake of nAu and form part of an environmental risk assessment of ENMs.
Improving semantic similarity retrieval with word embeddings

Word similarity matchmaking is one of the core research areas of information retrieval. The existing methods based on a synonym dictionary would lead to the problem of semantic gap, which could be caused by the absence of synonyms. To address this problem, we improve semantic similarity retrieval by incorporating word embeddings. Especially, word embeddings are trained by Word2Vec and then use them to depict the semantic similarity between words. Experiments are conducted on two different datasets, ie, one is a public long text dataset (ie, Reuters‐21578), and the other is a short text dataset (ie, 120ask) collected from a healthcare community. The experimental results on the two datasets show that the proposed method further improves the accuracy of the similarity retrieval.
Pathways to Improving Mental Health in Compassion-Focused Therapy: Self-Reassurance, Self-Criticism and Affect as Mediators of Change

Objective: The working mechanisms of compassion-focused therapy (CFT) remain understudied. Drawing on the theoretical model underlying CFT, we examined four putative working mechanisms – self-reassurance, self-criticism, positive/negative affect – in relation to changes in well-being and psychological distress. Methods: Data of a waitlist randomised controlled trial (N = 242) investigating the effectiveness of a self-help CFT-intervention in a non-clinical sample were analysed. Using single and multiple mediation models, we assessed if changes in self-reassurance, self-criticism and positive/negative affect during the intervention (3-month interval) mediated changes in well-being and depressive/anxiety symptoms from baseline to follow-up (6-month interval) compared to the waitlist condition. Results: For each outcome, single analyses revealed that the effects of CFT were significantly mediated by self-reassurance and self-criticism. The mediating role of affect differed across outcomes. In combined models, self-reassurance emerged as a significant mediator for well-being and anxiety symptoms. Additionally, positive and negative affect were found significant mediators of the effects on depressive and anxiety symptoms, respectively. Conclusion: This study provides preliminary empirical evidence that CFT operates through cultivating self-reassurance, reducing self-criticism and regulating positive and negative affect in a non-clinical sample. To advance the development of CFT, further exploration of therapeutic change processes and their interplay is needed.
Inferring Point Clouds from Single Monocular Images by Depth Intermediation

In this paper, we propose a framework for generating 3D point cloud of an object from a single-view RGB image. Most previous work predict the 3D point coordinates from single RGB images directly. We decompose this problem into depth estimation from single images and point completion from partial point clouds. Our method sequentially predicts the depth maps and then infers the complete 3D object point clouds based on the predicted partial point clouds. We explicitly impose the camera model geometrical constraint in our pipeline and enforce the alignment of the generated point clouds and estimated depth maps. Experimental results for the single image 3D object reconstruction task show that the proposed method outperforms state-of-the-art methods. Both the qualitative and quantitative results demonstrate the generality and suitability of our method.