Miracle-dz/UniFAR-FTUs huggingface dataset:
Identical and Nonidentical Twins: Risk and Factors Involved in Development of Islet Autoimmunity and Type 1 Diabetes

OBJECTIVE There are variable reports of risk of concordance for progression to islet autoantibodies and type 1 diabetes in identical twins after one twin is diagnosed. We examined development of positive autoantibodies and type 1 diabetes and the effects of genetic factors and common environment on autoantibody positivity in identical twins, nonidentical twins, and full siblings. RESEARCH DESIGN AND METHODS Subjects from the TrialNet Pathway to Prevention Study (N = 48,026) were screened from 2004 to 2015 for islet autoantibodies (GAD antibody [GADA], insulinoma-associated antigen 2 [IA-2A], and autoantibodies against insulin [IAA]). Of these subjects, 17,226 (157 identical twins, 283 nonidentical twins, and 16,786 full siblings) were followed for autoantibody positivity or type 1 diabetes for a median of 2.1 years. RESULTS At screening, identical twins were more likely to have positive GADA, IA-2A, and IAA than nonidentical twins or full siblings (all P < 0.0001). Younger age, male sex, and genetic factors were significant factors for expression of IA-2A, IAA, one or more positive autoantibodies, and two or more positive autoantibodies (all P ≤ 0.03). Initially autoantibody-positive identical twins had a 69% risk of diabetes by 3 years compared with 1.5% for initially autoantibody-negative identical twins. In nonidentical twins, type 1 diabetes risk by 3 years was 72% for initially multiple autoantibody–positive, 13% for single autoantibody–positive, and 0% for initially autoantibody-negative nonidentical twins. Full siblings had a 3-year type 1 diabetes risk of 47% for multiple autoantibody–positive, 12% for single autoantibody–positive, and 0.5% for initially autoantibody-negative subjects. CONCLUSIONS Risk of type 1 diabetes at 3 years is high for initially multiple and single autoantibody–positive identical twins and multiple autoantibody–positive nonidentical twins. Genetic predisposition, age, and male sex are significant risk factors for development of positive autoantibodies in twins.
QoS-Aware Load Balancing in Dense Cellular Networks with Dynamic User Traffic

Due to the dense deployment of small cells, the number of mobile users served by each access point is decreasing dramatically, which leads to an increasingly dynamic cell load distribution over time and space. In order to match dynamic traffic load with static infrastructure capacity, a variety of load balancing methods have been proposed. However, the existing load balancing approaches either run self-tuning algorithms to optimize local handover parameters, which may not be optimal for the network-wide performance, or formulate a static optimization problem for a ``snapshot" network, which may require a huge amount of handover signaling in dynamic situations. In this paper, we consider the load balancing problem with dynamic traffic models, in which the average throughput over a certain time period is maximized, while the average packet delay is guaranteed to be below a certain threshold. Simulation results show that our proposed user association and resource allocation algorithm can highly increase the average throughput, compared with the baseline algorithm using the maximum SINR association and equal resource allocation.
Co‐saved, co‐tweeted, and co‐cited networks

Counts of tweets and Mendeley user libraries have been proposed as altmetric alternatives to citation counts for the impact assessment of articles. Although both have been investigated to discover whether they correlate with article citations, it is not known whether users tend to tweet or save (in Mendeley) the same kinds of articles that they cite. In response, this article compares pairs of articles that are tweeted, saved to a Mendeley library, or cited by the same user, but possibly a different user for each source. The study analyzes 1,131,318 articles published in 2012, with minimum tweeted (10), saved to Mendeley (100), and cited (10) thresholds. The results show surprisingly minor overall overlaps between the three phenomena. The importance of journals for Twitter and the presence of many bots at different levels of activity suggest that this site has little value for impact altmetrics. The moderate differences between patterns of saving and citation suggest that Mendeley can be used for some types of impact assessments, but sensitivity is needed for underlying differences.
SmartCheck: Static Analysis of Ethereum Smart Contracts

Ethereum is a major blockchain-based platform for smart contracts - Turing complete programs that are executed in a decentralized network and usually manipulate digital units of value. Solidity is the most mature high-level smart contract language. Ethereum is a hostile execution environment, where anonymous attackers exploit bugs for immediate financial gain. Developers have a very limited ability to patch deployed contracts. Hackers steal up to tens of millions of dollars from flawed contracts, a well-known example being "The DAO", broken in June 2016. Advice on secure Ethereum programming practices is spread out across blogs, papers, and tutorials. Many sources are outdated due to a rapid pace of development in this field. Automated vulnerability detection tools, which help detect potentially problematic language constructs, are still underdeveloped in this area. We provide a comprehensive classification of code issues in Solidity and implement SmartCheck -- an extensible static analysis tool that detects them. The source code is available at https://github.com/smartdec/smartcheck. SmartCheck translates Solidity source code into an XML-based intermediate representation and checks it against XPath patterns. We evaluated our tool on a big dataset of real-world contracts and compared the results with manual audit on three contracts. Our tool reflects the current state of knowledge on Solidity vulnerabilities and shows significant improvements over alternatives. SmartCheck has its limitations, as detection of some bugs requires more sophisticated techniques such as taint analysis or even manual audit. We believe though that a static analyzer should be an essential part of contract developers' toolbox, letting them fix simple bugs fast and allocate more effort to complex issues.
Transcriptome Analysis Reveals Multiple Hormones, Wounding and Sugar Signaling Pathways Mediate Adventitious Root Formation in Apple Rootstock

Adventitious roots (AR) play an important role in the vegetative propagation of apple rootstocks. The potential role of hormone, wounding, and sugar signalling pathways in mediating AR formation has not been adequately explored and the whole co-expression network in AR formation has not been well established in apple. In order to identify the molecular mechanisms underlying AR formation in ‘T337’ apple rootstocks, transcriptomic changes that occur during four stages of AR formation (0, 3, 9 and 16 days) were analyzed using high-throughput sequencing. A total of 4294 differentially expressed genes were identified. Approximately 446 genes related to hormones, wounding, sugar signaling, root development, and cell cycle induction pathways were subsequently selected based on their potential to be involved in AR formation. RT-qPCR validation of 47 genes with known functions exhibited a strong positive correlation with the RNA-seq data. Interestingly, most of the candidate genes involved in AR formation that were identified by transcriptomic sequencing showed auxin-responsive expression patterns in an exogenous Indole-3-butyric acid (IBA)-treatment assay: Indicating that endogenous and exogenous auxin plays key roles in regulating AR formation via similar signalling pathways to some extent. In general, AR formation in apple rootstocks is a complex biological process which is mainly influenced by the auxin signaling pathway. In addition, multiple hormones-, wounding- and sugar-signaling pathways interact with the auxin signaling pathway and mediate AR formation in apple rootstocks.
The Beginning of a Cognitive Software Engineering Era with Self-Managing Applications

The recent explosion of data and the resurgence of AI, Machine Learning and Deep Learning, and the emergence of unbounded cloud computing resources are stretching current software engineering practices to meet business application development, deployment and management requirements. As consumers demand communication, collaboration and commerce almost at the speed of light without interruption, businesses are looking for information technologies that keep up the pace in delivering faster time to market and real-time data processing to meet rapid fluctuations in both workload demands and available computing resources. While the performance of server, network and storage resources have dramatically improved by orders of magnitude in the past decade, software engineering practices and IT operations are evolving at a slow pace. This paper explores a new approach that will provide a path to self-managing software systems with fluctuation tolerance to both workload demands and available resource pools. The infusion of a cognitive control overlay enables an advanced management of application workloads in a distributed multi-cloud computing infrastructure. Resulting architecture provides a uniform framework for managing workload non-functional requirements such as availability, performance, security, data compliance and cost independent of the execution venue for functional requirement workflows.
Multimodal Alignment for Affective Content

Humans routinely extract important information from images and videos, relying on their gaze. In contrast, computational systems still have difﬁculty annotating important visual information in a human-like manner, in part because human gaze is often not included in the modeling process. Human input is also particularly relevant for processing and interpreting affective visual information. To address this challenge, we captured human gaze, spoken language, and facial expressions simultaneously in an experiment with visual stimuli characterized by subjective and affective content. Observers described the content of complex emotional images and videos depicting positive and negative scenarios and also their feelings about the imagery being viewed. We explore patterns of these modalities, for example by comparing the affective nature of participant-elicited linguistic tokens with image valence. Ad-ditionally, we expand a framework for generating automatic alignments between the gaze and spoken language modalities for visual annotation of images. Multimodal alignment is challenging due to their varying temporal offset. We explore alignment robustness when images have affective content and whether image valence inﬂuences alignment results. We also study if word frequency-based ﬁltering impacts results, with both the unﬁltered and ﬁltered scenarios performing better than baseline comparisons, and with ﬁltering resulting in a substantial decrease in alignment error rate. We provide visualizations of the resulting annotations from multimodal alignment. This work has implications for areas such as image understanding, media accessibility, and multimodal data fusion.
Automatic Detection of Code-switching Style from Acoustics

Multilingual speakers switch between languages in an non-trivial fashion displaying inter sentential, intra sentential, and congruent lexicalization based transitions. While monolingual ASR systems may be capable of recognizing a few words from a foreign language, they are usually not robust enough to handle these varied styles of code-switching. There is also a lack of large code-switched speech corpora capturing all these styles making it difficult to build code-switched speech recognition systems. We hypothesize that it may be useful for an ASR system to be able to first detect the switching style of a particular utterance from acoustics, and then use specialized language models or other adaptation techniques for decoding the speech. In this paper, we look at the first problem of detecting code-switching style from acoustics. We classify code-switched Spanish-English and Hindi-English corpora using two metrics and show that features extracted from acoustics alone can distinguish between different kinds of code-switching in these language pairs.
Intrusion Detection in the Era of IoT: Building Trust via Traffic Filtering and Sampling

In the Internet of Things (IoT) era, the number of connected devices and subnets of devices is rapidly increasing. Yet, it remains a challenge for intrusion detection mechanisms to build a trust map among various IoT devices because of the devices’ large quantity and dynamic nature. Through a case study, the author highlights the importance of traffic filtration and sampling in evaluating trustworthiness among IoT devices.
Metabolite Profiles, Bioactivity, and HPLC Fingerprint of Different Varieties of Eucommia ulmoides Oliv.: Towards the Utilization of Medicinal and Commercial Chinese Endemic Tree

Eucommia ulmoides Oliv. is widely regarded in China as a precious medicinal and commercial endemic tree. Due to cross-breeding or natural variation of E. ulmoides, the metabolite composition may vary significantly, making control of the medical quality difficult. In order to improve the rational development and utilization, the quality of seven varieties of E. ulmoides were evaluated based on metabolite profiles (total phenolic, total flavonoid, gutta-percha, aucubin, geniposidic acid, chlorogenic acid, geniposide, pinoresinol diglucoside, rutin, hyperoside, and astragalin), bioactivities (in vitro, in vivo antioxidant activities, and antibacterial activities) and HPLC fingerprint combined with chemometrics analysis. On this basis, the differences of medicinal parts (leaf and bark) were further carried out. For the traditional use of bark, Purple-leaf E. ulmoides was the most suitable. For the use of leaf, Qinzhong 1 and Purple-leaf E. ulmoides were appropriate. HPLC fingerprint analysis showed that significant differences in metabolite profiles exist among seven varieties of E. ulmoides. Combined with chemometrics analysis, seven varieties of E. ulmoides were divided into three groups from the use of leaf and bark. The analysis not only evaluated quality of seven varieties of E. ulmoides, but also could distinguish different varieties and different regions of origin. The results can provide theoretical basis for E. ulmoides resources utilization and cultivation of fine varieties.
Remote regions: a simple abstraction for remote memory

We propose an intuitive abstraction for a process to export its memory to remote hosts, and to access the memory exported by others. This abstraction provides a simpler interface to RDMA and other remote memory technologies compared to the existing verbs interface. The key idea is that a process can export parts of its memory as files, called remote regions, that can be accessed through the usual file system operations (read, write, memory map, etc). We built this abstraction in the Linux kernel, and evaluated it. We show that remote regions are easy to use and perform close to RDMA. We demonstrate it via micro-benchmarks and by adapting two in-memory single-host applications to use remote memory: R and Metis. With R, using remote regions requires no changes to the code and allows R to run with remote memory that exceeds the physical memory of a host. With Metis, the modifications amount to ≈100 lines of code and they allow Metis to scale its performance across 8 hosts.
Extreme-Strike Comparisons and Structural Bounds for SPX and VIX Options

This article explores the relationship between the SPX and VIX options markets. High-strike VIX call options are used to hedge tail risk in the SPX, which means that SPX options are a reflection of the extreme-strike asymptotics of VIX options, and vice versa. This relationship can be quantified using moment formulas in a model-free way. Comparisons are made between VIX and SPX implied volatilities along with various examples of stochastic volatility models.
SuperNMT: Neural Machine Translation with Semantic Supersenses and Syntactic Supertags

In this paper we incorporate semantic supersensetags and syntactic supertag features into EN–FR and EN–DE factored NMT systems. In experiments on various test sets, we observe that such features (and particularly when combined) help the NMT model training to converge faster and improve the model quality according to the BLEU scores.
Identification of a biologically active fragment of ALK and LTK-Ligand 2 (augmentor-α)

Significance The limited availability of the two ligands of the receptor tyrosine kinases ALK and LTK precluded the elucidation of their physiological roles and mechanism of action. In this report we describe two new approaches to produce full-length ALKAL2/AUG-α and an active variant lacking the N-terminal variable region. Detailed characterization of the primary structure and disulfide bridges using mass spectrometry demonstrates that the N-terminal variable region plays an important role in AUG-α dimerization and provides insight into the structural organization of a previously unknown augmentor fold. A variety of cellular experiments show that both AUG-α and its deletion mutant induce similar activation of ALK and LTK and stimulation neuronal differentiation of human neuroblastoma NB1 and rat pheochromocytoma PC12 cells. Elucidating the physiological roles and modes of action of the recently discovered ligands (designated ALKAL1,2 or AUG-α,β) of the receptor tyrosine kinases Anaplastic Lymphoma Kinase (ALK) and Leukocyte Tyrosine Kinase (LTK) has been limited by difficulties in producing sufficient amounts of the two ligands and their poor stability. Here we describe procedures for expression and purification of AUG-α and a deletion mutant lacking the N-terminal variable region. Detailed biochemical characterization of AUG-α by mass spectrometry shows that the four conserved cysteines located in the augmentor domain (AD) form two intramolecular disulfide bridges while a fifth, primate-specific cysteine located in the N-terminal variable region mediates dimerization through formation of a disulfide bridge between two AUG-α molecules. In contrast to AUG-α, the capacity of AUG-α AD to undergo dimerization is strongly compromised. However, full-length AUG-α and the AUG-α AD deletion mutant stimulate similar tyrosine phosphorylation of cells expressing either ALK or LTK. Both AUG-α and AUG-α AD also stimulate a similar profile of MAP kinase response in L6 cells and colony formation in soft agar by autocrine stimulation of NIH 3T3 cells expressing ALK. Moreover, both AUG-α and AUG-α AD stimulate neuronal differentiation of human neuroblastoma NB1 and PC12 cells in a similar dose-dependent manner. Taken together, these experiments show that deletion of the N-terminal variable region minimally affects the activity of AUG-α toward LTK or ALK stimulation in cultured cells. Reduced dimerization might be compensated by high local concentration of AUG-α AD bound to ALK at the cell membrane and by potential ligand-induced receptor–receptor interactions.
Padrões de evolução na prática de desenvolvimento distribuído de software em ambientes de internal offshoring: um modelo de capacidade

The Software Engineering (SE) community has witnessed a significant change in the way software projects have been developed in the last years. Software project teams have become geographically distributed and the term Distributed Software Development (DSD) is frequently used in industry. To embark on a DSD journey, a company usually defines a strategy based on a DSD business model. The two main models in a global development context include offshore outsourcing (contracting services with an external company) and internal offshoring (contracting with a wholly owned subsidiary). The goal of this research was to understand and to identify patterns in the evolution of practices of organizations involved in internal offshoring, in order to further knowledge about DSD practices. This understanding is also useful for companies starting DSD operations. These companies can benefit from this knowledge which describes practices adopted by other organizations in the past. The result of this research is a capability model that describes patterns of evolution in the practice of distributed software development activities within and among several subsidiaries owned by an organization in the internal offshoring model. This model is organized in three dimensions: capability levels, capability areas (people, project, portfolio and subsidiary) and capability attributes. To develop this model, we have used a qualitative retrospective study, based on Yin’s Case Study approach [154], with qualitative and quantitative data collection instruments.
Celer: a Fast Solver for the Lasso with Dual Extrapolation

Convex sparsity-inducing regularizations are ubiquitous in high-dimensional machine learning, but solving the resulting optimization problems can be slow. To accelerate solvers, state-of-the-art approaches consist in reducing the size of the optimization problem at hand. In the context of regression, this can be achieved either by discarding irrelevant features (screening techniques) or by prioritizing features likely to be included in the support of the solution (working set techniques). Duality comes into play at several steps in these techniques. Here, we propose an extrapolation technique starting from a sequence of iterates in the dual that leads to the construction of improved dual points. This enables a tighter control of optimality as used in stopping criterion, as well as better screening performance of Gap Safe rules. Finally, we propose a working set strategy based on an aggressive use of Gap Safe screening rules. Thanks to our new dual point construction, we show significant computational speedups on multiple real-world problems.
Software-Defined Vehicular Networks with Caching and Computing for Delay-Tolerant Data Traffic

With the explosion in the number of connected devices and Internet of Things (IoT) services in smart city, the challenges to meet the demands from both data traffic delivery and information processing are increasingly prominent. Meanwhile, the connected vehicle networks have become an essential part in smart city, bringing massive data traffic as well as significant networking, caching and computing resources. In this paper, we propose a novel vehicle network architecture, mitigating the network congestion with the joint optimization of networking, caching and computing. Cloud computing at the data centers as well as mobile edge computing (MEC) at the evolved node Bs (eNodeBs) and on-board units (OBUs) are taken as the paradigms to provide caching and computing resources. The programmable control principle originated from software-defined networking (SDN) paradigm has been introduced to facilitate the system architecture and resource integration. With the careful modeling of the services, the vehicle mobility and the system state, a joint resource management scheme is proposed and formulated as a partially observable Markov decision process (POMDP) to minimize system cost, which consists of both network overhead and execution time of computing tasks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.