Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing Rapid Releases in a Large Banking Company: A Case Study

The increased competition in today’s business environment has pushed a large number of organizations to shift towards a rapid development model, i.e. a software development model with a short release cycle of a few days or weeks [1]. As the trend of rapid releases (RRs) is increasingly being adopted in open-source and commercial software [2], it is vital to understand how RRs work and how they affect the quality of the released software. Recent research on RRs addresses the impact of short release cycles on several aspects of software quality. However, a characterization of rapid releases is missing. By exploring the characteristics of RRs in industry, we can obtain valuable insights in what the urgent problems in the field are, and what data and techniques are needed to address them. Such knowledge can provide researchers with promising research directions that, in our opinion, can help industry today. This paper presents a case study of rapid releases at ING, a large Netherlands-based internationally operating bank that develops software solutions in-house. Since 2011 the bank has gradually shifted to a rapid development model, which resulted in a mixed environment of rapid and traditional teams. We conducted a mixed-methods study, consisting of a survey with 461 engineers, and a statistical analysis of 2 years of code quality data for 611 teams.
Macrophage Polarization: Different Gene Signatures in M1(LPS+) vs. Classically and M2(LPS–) vs. Alternatively Activated Macrophages

Macrophages are found in tissues, body cavities, and mucosal surfaces. Most tissue macrophages are seeded in the early embryo before definitive hematopoiesis is established. Others are derived from blood monocytes. The macrophage lineage diversification and plasticity are key aspects of their functionality. Macrophages can also be generated from monocytes in vitro and undergo classical (LPS+IFN-γ) or alternative (IL-4) activation. In vivo, macrophages with different polarization and different activation markers coexist in tissues. Certain mouse strains preferentially promote T-helper-1 (Th1) responses and others Th2 responses. Their macrophages preferentially induce iNOS or arginase and have been called M1 and M2, respectively. In many publications, M1 and classically activated and M2 and alternatively activated are used interchangeably. We tested whether this is justified by comparing the gene lists positively [M1(=LPS+)] or negatively [M2(=LPS–)] correlated with the ratio of IL-12 and arginase 1 in transcriptomes of LPS-treated peritoneal macrophages with in vitro classically (LPS, IFN-γ) vs. alternatively activated (IL-4) bone marrow derived macrophages, both from published datasets. Although there is some overlap between in vivo M1(=LPS+) and in vitro classically activated (LPS+IFN-γ) and in vivo M2(=LPS–) and in vitro alternatively activated macrophages, many more genes are regulated in opposite or unrelated ways. Thus, M1(=LPS+) macrophages are not equivalent to classically activated, and M2(=LPS–) macrophages are not equivalent to alternatively activated macrophages. This fundamental discrepancy explains why most surface markers identified on in vitro generated macrophages do not translate to the in vivo situation. Valid in vivo M1/M2 surface markers remain to be discovered.
CAR T Cells Targeting the Tumor MUC1 Glycoprotein Reduce Triple-Negative Breast Cancer Growth

Antibody-derived chimeric antigen receptor (CAR) T cell therapy has achieved gratifying breakthrough in hematologic malignancies but has shown limited success in solid tumor immunotherapy. Monoclonal antibody, TAB004, specifically recognizes the aberrantly glycosylated tumor form of MUC1 (tMUC1) in all subtypes of breast cancer including 95% of triple-negative breast cancer (TNBC) while sparing recognition of normal tissue MUC1. We transduced human T cells with MUC28z, a chimeric antigen receptor comprising of the scFv of TAB004 coupled to CD28 and CD3ζ. MUC28z was well-expressed on the surface of engineered activated human T cells. MUC28z CAR T cells demonstrated significant target-specific cytotoxicity against a panel of human TNBC cells. Upon recognition of tMUC1 on TNBC cells, MUC28z CAR T cells increased production of Granzyme B, IFN-γ and other Th1 type cytokines and chemokines. A single dose of MUC28z CAR T cells significantly reduced TNBC tumor growth in a xenograft model. Thus, MUC28z CAR T cells have high therapeutic potential against tMUC1-positive TNBC tumors with minimal damage to normal breast epithelial cells.
Mining Extension Point Patterns in Scala

—To use a framework, developers often need to hook into and customize some of its functionality. These customizations are often made by instantiating a type provided by the framework, or by extending or implementing a framework type and instantiating this subtype, and providing the resulting object to other framework objects. Recommending extension patterns that frequently occur at such extension points can help developers to adopt a new framework correctly and to exploit it fully. In this paper we transpose an existing technique for mining extension patterns in Java projects to the Scala context. Our goal is to evaluate whether the unique features of the Scala language have an impact on the mined extension patterns. To this aim, we propose SCALA - XP - MINER , a tool for mining extension point patterns in Scala projects. We preliminary evaluate SCALA - XP - MINER on a corpus of 9 projects using the S WING framework. Our ﬁrst results reveal that extension points are not very diffused in Scala projects using S WING and that only one type of extension points is adopted by developers.
Towards a language independent Twitter bot detector

This article describes our work in developing an application that recognizes automatically generated tweets. The objective of this machine learning application is to increase data accuracy in sociolin-guistic studies that utilize Twitter by reducing skewed sampling and inaccuracies in linguistic data. Most previous machine learning attempts to exclude bot material have been language dependent since they make use of monolingual Twitter text in their training phase. In this paper, we present a language independent approach which classiﬁes each single tweet to be either autogenerated (AGT) or human-generated (HGT). We deﬁne an AGT as a tweet where all or parts of the natural language content is generated automatically by a bot or other type of program. In other words, while AGT/HGT refer to an individual message, the term bot refers to non-personal and automated accounts that post content to online social networks. Our approach classiﬁes a tweet using only metadata that comes with every tweet, and we utilize those metadata parameters that are both language and country independent. The empirical part shows good success rates. Using a bilingual training set of Finnish and Swedish tweets, we correctly classiﬁed about 98.2% of all tweets in a test set using a third language (English).
Therapeutic Potential of Exogenous Ketone Supplement Induced Ketosis in the Treatment of Psychiatric Disorders: Review of Current Literature

Globally, psychiatric disorders, such as anxiety disorder, bipolar disorder, schizophrenia, depression, autism spectrum disorder, and attention-deficit/hyperactivity disorder (ADHD) are becoming more prevalent. Although the exact pathological alterations are not yet clear, recent studies have demonstrated that widespread changes of very complex metabolic pathways may partially underlie the pathophysiology of many psychiatric diseases. Thus, more attention should be directed to metabolic-based therapeutic interventions in the treatment of psychiatric disorders. Emerging evidence from numerous studies suggests that administration of exogenous ketone supplements, such as ketone salts or ketone esters, generates rapid and sustained nutritional ketosis and metabolic changes, which may evoke potential therapeutic effects in cases of central nervous system (CNS) disorders, including psychiatric diseases. Therefore, the aim of this review is to summarize the current information on ketone supplementation as a potential therapeutic tool for psychiatric disorders. Ketone supplementation elevates blood levels of the ketone bodies: D-β-hydroxybutyrate (βHB), acetoacetate (AcAc), and acetone. These compounds, either directly or indirectly, beneficially affect the mitochondria, glycolysis, neurotransmitter levels, activity of free fatty acid receptor 3 (FFAR3), hydroxycarboxylic acid receptor 2 (HCAR2), and histone deacetylase, as well as functioning of NOD-like receptor pyrin domain 3 (NLRP3) inflammasome and mitochondrial uncoupling protein (UCP) expression. The result of downstream cellular and molecular changes is a reduction in the pathophysiology associated with various psychiatric disorders. We conclude that supplement-induced nutritional ketosis leads to metabolic changes and improvements, for example, in mitochondrial function and inflammatory processes, and suggest that development of specific adjunctive ketogenic protocols for psychiatric diseases should be actively pursued.
Characterization of 0.18{\mu}m CMOS Ring Oscillator at Liquid Helium Temperature

This paper presents low power dissipation, low phase noise ring oscillators (ROs) based on Semiconductor Manufacturing International Corporation (SMIC) 0.18{\mu}m CMOS technology at liquid helium temperature (LHT). First, the characterization and modelling of CMOS at LHT are presented. The temperature-dependent device parameters are revised and the model then shows good agreement with the measurement results. The ring oscillator is then designed with energy efficiency optimization by application of forward body biasing (FBB). FBB is proposed to compensate for the threshold voltage (VTH) shift to preserve the benefits of the enhancement of the carrier mobility at 4.2K. The delay per stage ({\tau}p), the static current (ISTAT), the dynamic current (IDYN), the power dissipation (P) and the phase noise (L(foff)) are analyzed at both 298 K and 4.2 K, with and without FBB. The performance of the designed RO in terms of speed ({\tau}p=179ps), static current (23.55nA/stage), power dissipation (2.13{\mu}W) and phase noise (-177.57dBc/Hz@1MHz) can be achieved at 4.2K with the supply voltage (VDD) reduced to 0.9V.
On generalized Piterbarg-Berman function

This paper aims to evaluate the Piterbarg-Berman function given by $$\mathcal{P\! B}_\alpha^h(x, E) = \int_\mathbb{R}e^z\mathbb{P} \left\{{\int_E \mathbb{I}\left(\sqrt2B_\alpha(t) - |t|^\alpha - h(t) - z>0 \right) {\text{d}} t > x} \right\} {\text{d}} z,\quad x\in[0, {mes}(E)],$$ with $h$ a drift function and $B_\alpha$ a fractional Brownian motion (fBm) with Hurst index $\alpha/2\in(0,1]$, i.e., a mean zero Gaussian process with continuous sample paths and covariance function \begin{align*} {\mathrm{Cov}}(B_\alpha(s), B_\alpha(t)) = \frac12 (|s|^\alpha + |t|^\alpha - |s-t|^\alpha). \end{align*} This note specifies its explicit expression for the fBms with $\alpha=1$ and $2$ when the drift function $h(t)=ct^\alpha, c>0$ and $E=\mathbb{R}_+\cup\{0\}$. For the Gaussian distribution $B_2$, we investigate $\mathcal{P\! B}_2^h(x, E)$ with general drift functions $h(t)$ such that $h(t)+t^2$ being convex or concave, and finite interval $E=[a,b]$. Typical examples of $\mathcal{P\! B}_2^h(x, E)$ with $h(t)=c|t|^\lambda-t^2$ and several bounds of $\mathcal{P\! B}_\alpha^h(x, E)$ are discussed. Numerical studies are carried out to illustrate all the findings. Keywords: Piterbarg-Berman function; sojourn time; fractional Brownian motion; drift function
Editorial: Concepts and Experiences in Framing, Integration and Evaluation of One Health and EcoHealth

1 Section of Epidemiology, Vetsuisse Faculty, University of Zurich, Zurich, Switzerland, Health Services Management, Faculty of Health Sciences, Msida, Malta, Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD), Montpellier, France, Centre National de la Recherche Scientifique (CNRS), Paris, France, Department of Virology, Parasitology, and Immunology, Faculty of Veterinary Medicine, Ghent University, Ghent, Belgium, 6 Belgian Biodiversity Platform Research Institute Nature & Forest (INBO), Brussels, Belgium, Department of Primary and Interdisciplinary Care Antwerp, Faculty of Medicine and Health Sciences, University of Antwerp, Wilrijk, Belgium
Perceived Gaze Direction Differentially Affects Discrimination of Facial Emotion, Attention, and Gender – An ERP Study

The perception of eye-gaze is thought to be a key component of our everyday social interactions. While the neural correlates of direct and averted gaze processing have been investigated, there is little consensus about how these gaze directions may be processed differently as a function of the task being performed. In a within-subject design, we examined how perception of direct and averted gaze affected performance on tasks requiring participants to use directly available facial cues to infer the individuals’ emotional state (emotion discrimination), direction of attention (attention discrimination) and gender (gender discrimination). Neural activity was recorded throughout the three tasks using EEG, and ERPs time-locked to face onset were analyzed. Participants were most accurate at discriminating emotions with direct gaze faces, but most accurate at discriminating attention with averted gaze faces, while gender discrimination was not affected by gaze direction. At the neural level, direct and averted gaze elicited different patterns of activation depending on the task over frontal sites, from approximately 220–290 ms. More positive amplitudes were seen for direct than averted gaze in the emotion discrimination task. In contrast, more positive amplitudes were seen for averted gaze than for direct gaze in the gender discrimination task. These findings are among the first direct evidence that perceived gaze direction modulates neural activity differently depending on task demands, and that at the behavioral level, specific gaze directions functionally overlap with emotion and attention discrimination, precursors to more elaborated theory of mind processes.
Optimum Low-Complexity Decoder for Spatial Modulation

In this paper, a novel low-complexity detection algorithm for spatial modulation (SM), referred to as the minimum-distance of maximum-length (m-M) algorithm, is proposed and analyzed. The proposed m-M algorithm is a smart searching method that is applied for the SM tree-search decoders. The behavior of the m-M algorithm is studied for three different scenarios: 1) perfect channel state information at the receiver side (CSIR); 2) imperfect CSIR of a fixed channel estimation error variance; and 3) imperfect CSIR of a variable channel estimation error variance. Moreover, the complexity of the m-M algorithm is considered as a random variable, which is carefully analyzed for all scenarios, using probabilistic tools. Based on a combination of the sphere decoder (SD) and ordering concepts, the m-M algorithm guarantees to find the maximum-likelihood (ML) solution with a significant reduction in the decoding complexity compared with SM-ML and existing SM-SD algorithms; it can reduce the complexity up to 94% and 85% in the perfect CSIR and the worst scenario of imperfect CSIR, respectively, compared with the SM-ML decoder. The Monte Carlo simulation results are provided to support our findings as well as the derived analytical complexity reduction expressions.
Secure Genome Processing in Public Cloud and HPC Environments

Aligning next generation sequencing data requires significant compute resources. HPC and cloud systems can provide sufficient compute capacity, but do not offer the required data security guarantees. HPC environments are typically designed for many groups of trusted users and often only include minimal security enforcement, while Cloud environments are mostly under the control of untrusted entities and companies. In this work we present a scalable pipeline approach that enables the use of public Cloud and HPC environments, while improving the patients’ privacy. The applied techniques include adding noisy data, cryptography, and a MapReduce program for the parallel processing of data.
An analytical framework for path reliabilities in mobile ad hoc networks

An ad hoc network is a collection of mobile nodes connected through multi-hop wireless links without the required intervention of any centralized access point or existing infrastructure. In this work, we have developed an analytical framework for computing the reliabilities of multi-hop paths. We have also investigated the effect of multi-path characteristics on path reliabilities. Among the main parameters considered here are, average link clustering, number of multiple paths, path complexity factor, and hop difference factor. Our approach is unified and general in the sense that it is able to incorporate single as well a multiple paths between a given source and destination pair.
A Perceptual Weighting Filter Loss for DNN Training In Speech Enhancement

Single-channel speech enhancement with deep neural networks (DNNs) has shown promising performance and is thus intensively being studied. In this paper, instead of applying the mean squared error (MSE) as the loss function during DNN training for speech enhancement, we design a perceptual weighting filter loss motivated by the weighting filter as it is employed in analysis-by-synthesis speech coding, e.g., in code-excited linear prediction (CELP). The experimental results show that the proposed simple loss function improves the speech enhancement performance compared to a reference DNN with MSE loss in terms of perceptual quality and noise attenuation. The proposed loss function can be advantageously applied to an existing DNN-based speech enhancement system, without modification of the DNN topology for speech enhancement.
Efficient variational approach to dynamics of a spatially extended bosonic Kondo model

We develop an efficient variational approach to studying dynamics of a localized quantum spin coupled to a bath of mobile spinful bosons. We use parity symmetry to decouple the impurity spin from the environment via a canonical transformation and reduce the problem to a model of the interacting bosonic bath. We describe coherent time evolution of the latter using bosonic Gaussian states as a variational ansatz. We provide full analytical expressions for equations describing variational time evolution that can be applied to study in- and out-of-equilibrium phenomena in a wide class of quantum impurity problems. In the accompanying paper [Y. Ashida {\it et al.}, Phys. Rev. Lett. 123, 183001 (2019)], we present a concrete application of this general formalism to the analysis of the Rydberg Central Spin Model, in which the spin-1/2 Rydberg impurity undergoes spin-changing collisions in a dense cloud of two-component ultracold bosons. To illustrate new features arising from orbital motion of the bath atoms, we compare our results to the Monte Carlo study of the model with spatially localized bosons in the bath, in which random positions of the atoms give rise to random couplings of the standard central spin model.
Some limitations of norm based generalization bounds in deep neural networks

Deep convolutional neural networks have been shown to be able to fit a labeling over random data while still being able to generalize well on normal datasets. Describing deep convolutional neural network capacity through the measure of spectral complexity has been recently proposed to tackle this apparent paradox. Spectral complexity correlates with GE and can distinguish networks trained on normal and random labels. We propose the first GE bound based on spectral complexity for deep convolutional neural networks and provide tighter bounds by orders of magnitude from the previous estimate. We then investigate theoretically and empirically the insensitivity of spectral complexity to invariances of modern deep convolutional neural networks, and show several limitations of spectral complexity that occur as a result.
Causal Discovery with Cascade Nonlinear Additive Noise Models

Identification of causal direction between a causal-effect pair from observed data has recently attracted much attention. Various methods based on functional causal models have been proposed to solve this problem, by assuming the causal process satisfies some (structural) constraints and showing that the reverse direction violates such constraints. The nonlinear additive noise model has been demonstrated to be effective for this purpose, but the model class is not transitive--even if each direct causal relation follows this model, indirect causal influences, which result from omitted intermediate causal variables and are frequently encountered in practice, do not necessarily follow the model constraints; as a consequence, the nonlinear additive noise model may fail to correctly discover causal direction. In this work, we propose a cascade nonlinear additive noise model to represent such causal influences--each direct causal relation follows the nonlinear additive noise model but we observe only the initial cause and final effect. We further propose a method to estimate the model, including the unmeasured intermediate variables, from data, under the variational auto-encoder framework. Our theoretical results show that with our model, causal direction is identifiable under suitable technical conditions on the data generation process. Simulation results illustrate the power of the proposed method in identifying indirect causal relations across various settings, and experimental results on real data suggest that the proposed model and method greatly extend the applicability of causal discovery based on functional causal models in nonlinear cases.
A fast digital phase locked loop based on model predictive controller

This paper presents a new approach for design of a digital phase locked loop. By employing a digitally controlled oscillator (DCO) and a time-to-digital converter (TDC), the loop filter of a DPLL becomes all-digital. Instead of designing the loop filter by digitizing a continuous-time loop response as has been commonly done, more sophisticated control schemes can be employed. In this paper, we proposed to design feedback loop filter by first modeling the DCO and TDC as appropriate model in transfer function form. Then by employing model predictive control (MPC) approach for design of loop filter instead of existing conventional loop filter generate optimal signal that accurately account for the transport delay in the digital feedback system. Using the proposed approach loop filter can overcome latency problem that digital feedback loops suffer and imposes limitation on loop bandwidth and even can make instability on loop. Simulation results by MATLAB confirm ability of the proposed design and shows it is suitable for digital phase locked loop.
Kaleido: An Efficient Out-of-core Graph Mining System on A Single Machine

Graph mining is one of the most important categories of graph algorithms. However, exploring the subgraphs of an input graph produces a huge amount of intermediate data. The "think like a vertex" programming paradigm, pioneered by Pregel, cannot readily formulate mining problems, which is designed to produce graph computation problems like PageRank. Existing mining systems like Arabesque and RStream need large amounts of computing and memory resources.In this paper, we present Kaleido, an efficient single machine, out-of-core graph mining system which treats disks as an extension of memory. Kaleido treats intermediate data in graph mining tasks as a tensor and adopts a succinct data structure for the intermediate data. Kaleido implements half-memory-half-disk storage for storing large intermediate data, which treats the disk as an extension of the memory. Kaleido adopts a lightweight isomorphism checking strategy which uses an eigenvalue-based algorithm for small graphs and solves tree isomorphism for the other graphs. Comparing with two state-of-the-art mining systems, Arabesque and RStream, Kaleido outperforms them by a GeoMean 13.2× and 64.8× respectively.