Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-objective Integer Programming Approaches for Solving Optimal Feature Selection Problem: A New Perspective on Multi-objective Optimization Problems in SBSE

The optimal feature selection problem in software product line is typically addressed by the approaches based on Indicator-based Evolutionary Algorithm (IBEA). In this study, we frst expose the mathematical nature of this problem — multi-objective binary integer linear programming. Then, we implement/propose three mathematical programming approaches to solve this problem at di? erent scales. For small-scale problems (roughly, less than 100 features), we implement two established approaches to fnd all exact solutions. For medium-to-large problems (roughly, more than 100 features), we propose one efcient approach that can generate a representation of the entire Pareto front in linear time complexity. The empirical results show that our proposed method can fnd signifcantly more non-dominated solutions in similar or less execution time, in comparison with IBEA and its recent enhancement (i.e., IBED that combines IBEA and Di? erential Evolution).
Ecology of Freedom: Competitive Tests of the Role of Pathogens, Climate, and Natural Disasters in the Development of Socio-Political Freedom

Many countries around the world embrace freedom and democracy as part of their political culture. However, culture is at least in part a human response to the ecological challenges that a society faces; hence, it should not be surprising that the degree to which societies regulate the level of individual freedom is related to environmental circumstances. Previous research suggests that levels of societal freedom across countries are systematically related to three types of ecological threats: prevalence of pathogens, climate challenges, and natural disaster threat. Though their incidence overlaps, the literature has not yet provided a competitive test. Drawing upon the ecocultural framework, we tested five rival hypotheses, alternately focused on the above ecological factors and their interactions with economic wealth in explaining country variations in socio-political freedom. Focusing on data from 150 countries, we performed a series of linear mixed-effects regressions predicting freedom in the domains of politics, media, and economy. We found that countries with higher pathogen prevalence were more likely to suppress democracy and media freedom. Economic wealth, however, moderated the effect of pathogen prevalence on economic freedom, with the main effect being only found among wealthy countries, but not among poor countries. In contrast, natural disaster threat predicted political freedom and press freedom only among poor countries, consistent with the idea that disaster threat accompanied by poor resources promote socio-political freedom as a means of increasing collective survival. Throughout our analyses, we found no support for hypotheses based on climatic challenges. In addition, our multilevel approach revealed that country scores for socio-political freedom were highly clustered within world regions, accounting for substantial portions of variance. Overall, the present research offers a nuanced view of the interplay between ecology and wealth in the emergence of socio-political freedom. We discuss new directions in future research considering methodological and theoretical contributions of the present findings.
The impact of different strategies to handle missing data on both precision and bias in a drug safety study: a multidatabase multinational population-based cohort study

Background Missing data are often an issue in electronic medical records (EMRs) research. However, there are many ways that people deal with missing data in drug safety studies. Aim To compare the risk estimates resulting from different strategies for the handling of missing data in the study of venous thromboembolism (VTE) risk associated with antiosteoporotic medications (AOM). Methods New users of AOM (alendronic acid, other bisphosphonates, strontium ranelate, selective estrogen receptor modulators, teriparatide, or denosumab) aged ≥50 years during 1998–2014 were identified in two Spanish (the Base de datos para la Investigación Farmacoepidemiológica en Atención Primaria [BIFAP] and EpiChron cohort) and one UK (Clinical Practice Research Datalink [CPRD]) EMR. Hazard ratios (HRs) according to AOM (with alendronic acid as reference) were calculated adjusting for VTE risk factors, body mass index (that was missing in 61% of patients included in the three databases), and smoking (that was missing in 23% of patients) in the year of AOM therapy initiation. HRs and standard errors obtained using cross-sectional multiple imputation (MI) (reference method) were compared to complete case (CC) analysis – using only patients with complete data – and longitudinal MI – adding to the cross-sectional MI model the body mass index/smoking values as recorded in the year before and after therapy initiation. Results Overall, 422/95,057 (0.4%), 19/12,688 (0.1%), and 2,051/161,202 (1.3%) VTE cases/participants were seen in BIFAP, EpiChron, and CPRD, respectively. HRs moved from 100.00% underestimation to 40.31% overestimation in CC compared with cross-sectional MI, while longitudinal MI methods provided similar risk estimates compared with cross-sectional MI. Precision for HR improved in cross-sectional MI versus CC by up to 160.28%, while longitudinal MI improved precision (compared with cross-sectional) only minimally (up to 0.80%). Conclusion CC may substantially affect relative risk estimation in EMR-based drug safety studies, since missing data are not often completely at random. Little improvement was seen in these data in terms of power with the inclusion of longitudinal MI compared with cross-sectional MI. The strategy for handling missing data in drug safety studies can have a large impact on both risk estimates and precision.
Disruption of Boundary Encoding During Sensorimotor Sequence Learning: An MEG Study

Music performance relies on the ability to learn and execute actions and their associated sounds. The process of learning these auditory-motor contingencies depends on the proper encoding of the serial order of the actions and sounds. Among the different serial positions of a behavioral sequence, the first and last (boundary) elements are particularly relevant. Animal and patient studies have demonstrated a specific neural representation for boundary elements in prefrontal cortical regions and in the basal ganglia, highlighting the relevance of their proper encoding. The neural mechanisms underlying the encoding of sequence boundaries in the general human population remain, however, largely unknown. In this study, we examined how alterations of auditory feedback, introduced at different ordinal positions (boundary or within-sequence element), affect the neural and behavioral responses during sensorimotor sequence learning. Analysing the neuromagnetic signals from 20 participants while they performed short piano sequences under the occasional effect of altered feedback (AF), we found that at around 150–200 ms post-keystroke, the neural activities in the dorsolateral prefrontal cortex (DLPFC) and supplementary motor area (SMA) were dissociated for boundary and within-sequence elements. Furthermore, the behavioral data demonstrated that feedback alterations on boundaries led to greater performance costs, such as more errors in the subsequent keystrokes. These findings jointly support the idea that the proper encoding of boundaries is critical in acquiring sensorimotor sequences. They also provide evidence for the involvement of a distinct neural circuitry in humans including prefrontal and higher-order motor areas during the encoding of the different classes of serial order.
Digital Behaviour Change Interventions to Break and Form Habits

Digital behaviour change interventions, particularly those using pervasive computing technology, hold great promise in supporting users to change their behaviour. However, most interventions fail to take habitual behaviour into account, limiting their potential impact. This failure is partly driven by a plethora of overlapping behaviour change theories and related strategies that do not consider the role of habits. We critically review the main theories and models used in the research to analyse their application to designing effective habitual behaviour change interventions. We highlight the potential for Dual Process Theory, modern habit theory, and Goal Setting Theory, which together model how users form and break habits, to drive effective digital interventions. We synthesise these theories into an explanatory framework, the Habit Alteration Model, and use it to outline the state of the art. We identify the opportunities and challenges of habit-focused interventions.
Failure mode and effects analysis based on D numbers and TOPSIS

Failure mode and effects analysis (FMEA) is a widely used technique for assessing the risk of potential failure modes in designs, products, processes, system, and services. One of the main problems with FMEA is the need to address a variety of assessments given by FMEA team members and the sequence of the failure modes according to the degree of risk factors. Many different methods have been proposed to improve the traditional FMEA, which is impractical when the risk assessments given by multiple experts to one failure mode are imprecise, incomplete, or inconsistent. However, the existing methods cannot adequately handle these types of uncertainties. In this paper, a new risk priority model based on D numbers and technique for the order of preference by similarity to ideal solution (TOPSIS) is proposed to evaluate the risk in FMEA. In the proposed model, the assessments given by the FMEA team members are represented by D numbers, where a new feasible and effective method can effectively represent the uncertain information. The TOPSIS method, a multicriteria decision‐making method is presented to rank the preference of failure modes with respect to risk factors. Finally, an application of the failure modes of the rotor blades of an aircraft turbine is provided to illustrate the efficiency of the proposed method.
Cardiovascular risk and use of conicity index in patients submitted to autologous hematopoietic stem cell transplantation

ABSTRACT Objective To analyze the prevalence of overweight and the use of conicity index for cardiovascular risk assessment in individuals submitted to autologous hematopoietic stem cell transplantation. Methods The sample comprised 91 patients of both sexes, who underwent autologous hematopoietic stem cell transplantation from September 2008 to December 2013, aged 18 years or over. To determine the nutritional profile, we collected anthropometric data on weight, height, waist circumference upon hospital admission. The body mass index and the conicity index were calculated. Results A total of 91 patients diagnosed with multiple myeloma, Hodgkin's lymphoma, non-Hodgkin's lymphoma and other conditions were evaluated. The mean age was 43.5 (14.2) years, 63.7% were male. We verified that according to the body mass index, 63.7% were overweight and, according to waist circumference, 74.7% had a higher risk for cardiovascular diseases. According to the calculation of the conicity index, 92.3% of patients presented increased cardiovascular risk. Patients with multiple myeloma had a higher conicity index when compared to other patients (p<0.01). Conclusion This study revealed a high prevalence of overweight and cardiovascular risk. It should be noted that the conicity index was a good method to evaluate cardiovascular risk and that new studies using this index should be performed.
Experimental and Kinetic Study of the Interaction of a Commercial Soot with NO at High Temperature

An experimental and kinetic study at high temperature was made of the interaction of NO with Printex-U, a commercial carbon black, considered as diesel soot model compound. Foremost, a characterization of this soot was performed by using different techniques such as elemental analysis, BET surface area analysis, SEM, TEM, and XRD. Two series of experiments were carried out. The first one took into account the inlet NO concentration influence (from 200 to 2000 ppm) at a given temperature of 1273 K. In the second one, the temperature influence (from 1173 to 1373 K) at a given NO concentration of 2000 ppm was considered. From the experimental results, a strong effect of the inlet NO concentration and temperature was observed. A kinetic model, the shrinking core model with decreasing size particle and chemical reaction control, was successfully applied, obtaining a fractional reaction order of 0.26 with respect to NO and activation energy of 110 kJ/mol.
Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks

In this paper, we propose Generative Adversarial Network (GAN) architectures that use Capsule Networks for image-synthesis. Based on the principal of positional-equivariance of features, Capsule Network's ability to encode spatial relationships between the features of the image helps it become a more powerful critic in comparison to Convolutional Neural Networks (CNNs) used in current architectures for image synthesis. Our proposed GAN architectures learn the data manifold much faster and therefore, synthesize visually accurate images in significantly lesser number of training samples and training epochs in comparison to GANs and its variants that use CNNs. Apart from analyzing the quantitative results corresponding the images generated by different architectures, we also explore the reasons for the lower coverage and diversity explored by the GAN architectures that use CNN critics.
Representation Learning on Graphs with Jumping Knowledge Networks

Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of "neighboring" nodes that a node's representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture -- jumping knowledge (JK) networks -- that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.
Know What You Don’t Know: Unanswerable Questions for SQuAD

Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. Existing datasets either focus exclusively on answerable questions, or use automatically generated unanswerable questions that are easy to identify. To address these weaknesses, we present SQuADRUn, a new dataset that combines the existing Stanford Question Answering Dataset (SQuAD) with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuADRUn, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. SQuADRUn is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD achieves only 66% F1 on SQuADRUn. We release SQuADRUn to the community as the successor to SQuAD.
Anxiety disorders in children and adolescents: aetiology, diagnosis and treatment

Summary The presentation of anxiety disorders in children and adolescents shares similarities and differences with that in adults, and may vary significantly, depending on the age of the individual. Assessment must differentiate anxiety disorders from developmentally appropriate fears as well as medical conditions and drugs that can mimic anxiety states. Aetiology of anxiety disorders in this group encompasses complex genetic and environmental influences. Additional insight into causation is provided by neuroimaging and research into temperament. Recommended interventions include both cognitive-behavioural therapy and pharmacology. Although childhood anxiety disorders generally remit, there remains an increased risk for anxiety and depressive disorders to emerge in adulthood, most likely through heterotypical continuity.
Adaptive deep learning model selection on embedded systems

The recent ground-breaking advances in deep learning networks (DNNs) make them attractive for embedded systems. However, it can take a long time for DNNs to make an inference on resource-limited embedded devices. Offloading the computation into the cloud is often infeasible due to privacy concerns, high latency, or the lack of connectivity. As such, there is a critical need to find a way to effectively execute the DNN models locally on the devices. This paper presents an adaptive scheme to determine which DNN model to use for a given input, by considering the desired accuracy and inference time. Our approach employs machine learning to develop a predictive model to quickly select a pre-trained DNN to use for a given input and the optimization constraint. We achieve this by first training off-line a predictive model, and then use the learnt model to select a DNN model to use for new, unseen inputs. We apply our approach to the image classification task and evaluate it on a Jetson TX2 embedded deep learning platform using the ImageNet ILSVRC 2012 validation dataset. We consider a range of influential DNN models. Experimental results show that our approach achieves a 7.52% improvement in inference accuracy, and a 1.8x reduction in inference time over the most-capable single DNN model.
Immune checkpoint inhibitors in urothelial cancer: recent updates and future outlook

Bladder cancer is the sixth most common cancer in the US and most tumors have urothelial (transitional cell) histology. Platinum-based chemotherapy has long been the standard of care in advanced disease, but long-term outcomes have largely remained poor. Since the peak incidence of bladder cancer is in the eighth decade of life and beyond, medical comorbidities may often limit the use of chemotherapy. Immune checkpoint inhibitors with their favorable toxicity profiles and notable antitumor activity have ushered in a new era in the treatment of advanced urothelial cancer (UC) with five agents targeting the PD-1/PD-L1 pathway being recently approved by the US Food and Drug administration. A plethora of clinical trials are ongoing in diverse disease settings, employing agents targeting PD-1/PD-L1 and related immune checkpoint pathways. While reactivating anti-tumor immunity, these agents may lead to a unique constellation of immune-related adverse events, which may warrant discontinuation of therapy and potential use of immunosuppression. Novel combinations with various treatment modalities and optimal sequencing of active therapies are being investigated in prospective clinical trials and retrospective registries. At the era of precision molecular medicine, and since patients do not respond uniformly to these agents, there is a growing need for identification and validation of biomarkers that can accurately predict treatment response and assist in patient selection. This review discusses current updates and future directions of immunotherapy in advanced UC.
Desh: deep learning for system health prediction of lead times to failure in HPC

Today's large-scale supercomputers encounter faults on a daily basis. Exascale systems are likely to experience even higher fault rates due to increased component count and density. Triggering resilience-mitigating techniques remains a challenge due to the absence of well defined failure indicators. System logs consist of unstructured text that obscures essential system health information contained within. In this context, efficient failure prediction via log mining can enable proactive recovery mechanisms to increase reliability. This work aims to predict node failures that occur in supercomputing systems via long short-term memory (LSTM) networks that exploit recurrent neural networks (RNNs). Our framework, Desh1 (Deep Learning for System Health), diagnoses and predicts failures with short lead times. Desh identifies failure indicators with enhanced training and classification for generic applicability to logs from operating systems and software components without the need to modify any of them. Desh uses a novel three-phase deep learning approach to (1) train to recognize chains of log events leading to a failure, (2) re-train chain recognition of events augmented with expected lead times to failure, and (3) predict lead times during testing/inference deployment to predict which specific node fails in how many minutes. Desh obtains as high as 3 minutes average lead time with no less than 85% recall and 83% accuracy to take proactive actions on the failing nodes, which could be used to migrate computation to healthy nodes.
Ring x2: Designing Gestures for Smart Rings using Temporal Calculus

We introduce "Ring×2," a design space for gesture input with two smart rings. Wearing two rings at once opens new input opportunities, unexplored by the community so far, such as bimanual ring gestures or shifting input from one smart ring to the other to effectively manage situational impairments, such as encumbrance. To enable such developments and explorations, we present a formal description of designing two-ring gestures from the perspective of temporal calculus, a logic-based framework for reasoning about events and actions occurring in time, which we exemplify on a dataset of 83 bimanual gestures performed by 24 participants with two rings. We hope that our exploration of two-ring gestures and our design approach rooted in temporal calculus will be provocative and inspiring for the community, leading to new designs of input techniques for smart rings.
Explainable Recommendation via Multi-Task Learning in Opinionated Text Data

Explaining automatically generated recommendations allows users to make more informed and accurate decisions about which results to utilize, and therefore improves their satisfaction. In this work, we develop a multi-task learning solution for explainable recommendation. Two companion learning tasks of user preference modeling for recommendation and opinionated content modeling for explanation are integrated via a joint tensor factorization. As a result, the algorithm predicts not only a user's preference over a list of items, i.e., recommendation, but also how the user would appreciate a particular item at the feature level, i.e., opinionated textual explanation. Extensive experiments on two large collections of Amazon and Yelp reviews confirmed the effectiveness of our solution in both recommendation and explanation tasks, compared with several existing recommendation algorithms. And our extensive user study clearly demonstrates the practical value of the explainable recommendations generated by our algorithm.