Miracle-dz/UniFAR-FTUs huggingface dataset:
Domino

We present Domino, a descriptive framework for hybrid collaboration and hybrid coupling styles in partially distributed teams. Domino enables researchers to describe, analyze, and understand real-world hybrid collaboration practices, i.e., collaborative practices that involve simultaneous co-located and remote collaboration with phases of both synchronous and asynchronous work that spans multiple groupware applications and devices. It also helps to categorize collaborative activities based on yet undocumented hybrid coupling styles between the members of multiple partially distributed or co-located subgroups. Our Domino framework was derived from initial observations of real-world practice and refined by the detailed analysis of participants' behavior and working styles during a simulation of a complex hybrid collaboration task with six partially distributed teams of four users in our lab. The resulting framework allows researchers to view collaboration through a new analytical lens, use new analytical tools, and also derive implications for the design of collaborative tools.
Fast Mixed Integer Quadratic Programming for Sparse Signal Estimation

It has been recently shown that the $l_{0}$ -norm problem can be reformulated into a mixed integer quadratic programming (MIQP) problem. CPLEX, a commercial optimization software package that can solve integer programming problems, is used to find the global solution to this MIQP problem for sparse signal estimation. However, CPLEX uses an exhaustive approach to search a feasible space to this MIQP problem. Thus, its running time grows exponentially as the problem dimension grows. This means that CPLEX quickly becomes computationally intractable for higher dimension problems. In this paper, we aim to propose a fast first-order-type method for solving this MIQP problem based on the alternating direction method. We conduct extensive simulations to demonstrate that: 1) our method is used to estimate a sparse signal by solving this problem and 2) our method is computationally tractable for problem dimensions up to the order of 1 million.
The Family Burdens and Hopelessness of Turkish Parents of Adolescents With Intellectual Disabilities.

PURPOSE This study was conducted to determine the hopelessness and family burden levels of parents who have children with intellectual disabilities. DESIGN This was a descriptive correlational study. METHODS The study was conducted at a specialized education and rehabilitation center in Fethiye, Turkey, using the Sociodemographic Data Form, the Beck Hopelessness Scale, and the Burden Assessment Scale for Families of Children With Intellectual Disabilities, and the study sample included a total of 95 parents of children with intellectual disabilities. FINDINGS A positive relationship was found between the total scores for hopelessness and the family burden including economic burden, perception of inadequacy, social burden, physical burden, emotional burden, and the required time (p < .05). The study found that the higher the level of family burden on parents, the more hopeless families felt. CONCLUSION This study's findings may help rehabilitation nurses to understand the hopelessness of parents and help them find appropriate resources to cope with their burden. CLINICAL RELEVANCE Nurses should be specialized in the field of rehabilitation and, as rehabilitation nurses, be included in providing integrated care, family education, and counseling services for disabled people in Turkey.
HEXIM1-Tat chimera inhibits HIV-1 replication

Transcription of HIV provirus is a key step of the viral cycle, and depends on the recruitment of the cellular positive transcription elongation factor b (P-TEFb) to the HIV promoter. The viral transactivator Tat can displace P-TEFb from the 7SK small nuclear ribonucleoprotein, where it is bound and inactivated by HEXIM1, and bring it to TAR, which allows the stalled RNA polymerase II to transition to successful transcription elongation. In this study, we designed a chimeric inhibitor of HIV transcription by combining functional domains from HEXIM1 and Tat. The chimera (HT1) potently inhibited gene expression from the HIV promoter, by competing with Tat for TAR and P-TEFb binding, while keeping the latter inactive. HT1 inhibited spreading infection as well as viral reactivation in lymphocyte T cell line models of HIV latency, with little effect on cellular transcription and metabolism. This proof-of-concept study validates an innovative approach to interfering with HIV transcription via peptide mimicry and competition for RNA-protein interactions. HT1 represents a new candidate for HIV therapy, or HIV cure via the proposed block and lock strategy.
Thar's gold in them thar constructs

Conceptualization and measurement of constructs provide many opportunities for contributing to information systems research and practice by providing a better understanding and explanation of interesting and important phenomena. To assist researchers in such endeavors, this paper describes four approaches to construct conceptualization and illustrates them with examples from past research. The paper also suggests directions for future research by identifying examples of constructs that stand to benefit from the application of the four approaches.
Phrase-based Unsupervised Machine Translation with Compositional Phrase Embeddings

This paper describes the University of Tartu’s submission to the unsupervised machine translation track of WMT18 news translation shared task. We build several baseline translation systems for both directions of the English-Estonian language pair using monolingual data only; the systems belong to the phrase-based unsupervised machine translation paradigm where we experimented with phrase lengths of up to 3. As a main contribution, we performed a set of standalone experiments with compositional phrase embeddings as a substitute for phrases as individual vocabulary entries. Results show that reasonable n-gram vectors can be obtained by simply summing up individual word vectors which retains or improves the performance of phrase-based unsupervised machine tranlation systems while avoiding limitations of atomic phrase vectors.
Hypoxia Stimulates the Epithelial–to–Mesenchymal Transition in Lung Cancer Cells Through Accumulation of Nuclear β-Catenin

Background/Aim: Recent studies implied a significant role of hypoxia-inducible factor-1α (HIF1α) in cell transformation. This study aimed to assess the effects of HIF1α on the epithelial–to–mesenchymal transition (EMT) and tumorigenesis of lung adenocarcinoma cells. Materials and Methods: Invasion, migration and colony formation assays were used to evaluate cell transformation. Expression of EMT-related markers were analyzed by western blot, reverse-transcription polymerase chain reaction or zymography. A luciferase assay was carried out to access the transcriptional activity of β-catenin. Results: Hypoxia enhanced migration, invasion and transformation of A549 lung adenocarcinoma cells. Hypoxic stimulation promoted the expression of EMT-related markers in lung cancer cells. The expression of HIF1α was found to be involved in hypoxia-mediated modulation of expression of snail family transcriptional repressors 1 (SNAI1) and 2 (SLUG). Hypoxia enhanced nuclear accumulation and transcriptional activity of β-catenin. Conclusion: β-Catenin promotes expression of EMT-related genes and eventually contributes to the metastatic process.
A Semi-Blind Channel Estimation Algorithm for One-bit Massive MIMO Systems

Massive multiple-input-multiple-output (MIMO) uses a large number of antennas and radio frequency (RF) chains which incur a huge cost and power consumption. Since high-resolution analog-to-digital converters (ADCs) consumes a major portion of the RF chain circuit power, so one-bit massive MIMO systems are seen as one of the potential solutions to reduce the power consumption and cost associated with the RF chains. Our paper addresses channel estimation issue which is among one of the crucial needs for the practical realization of one-bit massive MIMO systems. The pilot-aided channel estimator demands additional pilots to improve the estimation accuracy which in turn reduces the spectral efficiency of the system. To overcome this, we propose an iterative semi-blind based channel estimator for one-bit massive MIMO systems. The proposed algorithm consists of two stages: initialization and iteration. The initial channel estimate is obtained from the pilot based initialization stage, which is refined further in the iteration stage with the help of both pilot and few data symbols. So, the semi-blind estimator improves estimation accuracy without the addition of extra pilot symbols into the system. Through simulations, we show that the proposed scheme achieves significant improvement against the existing pilot based estimators in terms of estimation accuracy and bit error rate (BER) at the cost of a nominal increase in the computational complexity. Further, the proposed algorithm attains convergence in almost one iteration for all the considered scenarios of one-bit massive MIMO system. Thus, the semi-blind estimator is spectral and power efficient in comparison to the existing pilot based algorithms.
Tending Unmarked Graves

User-generated content is central to social computing scholarship. However, researchers and practitioners often presume that these users are alive. Failing to account for mortality is problematic in social media where an increasing number of profiles represent those who have died. Identifying mortality can empower designers to better manage content and support the bereaved, as well as promote high-quality data science. Based on a computational linguistic analysis of post-mortem social media profiles and content, we report on classifiers developed to detect mortality and show that mortality can be determined after the first few occurrences of post-mortem content. Applying our classifiers to content from two other platforms also provided good results. Finally, we discuss trade-offs between models that emphasize pre- vs. post-mortem precision in this sensitive context. These results mark a first step toward identifying mortality at scale, and show how designers and scientists can attend to mortality in their work.
Dynamic Speculation And Synchronization Of Data Dependence

Data dependence speculation is used in instruction-level parallel (ILP) processors to allow early execution of an instruction before a logically preceding instruction on which it may be data dependent. If the instruction is independent, data dependence speculation succeeds; if not, it fails, and the two instructions must be synchronized. The modern dynamically scheduled processors that use data dependence speculation do so blindly (i.e., every load instruction with unresolved dependences is speculated). In this paper, we demonstrate that as dynamic instruction windows get larger, significant performance benefits can result when intelligent decisions about data dependence speculation are made. We propose dynamic data dependence speculation techniques: (i) to predict if the execution of an instruction is likely to result in a data dependence mis-specalation, and (ii) to provide the synchronization needed to avoid a mis-speculation. Experimental results evaluating the effectiveness of the proposed techniques are presented within the context of a Multiscalar processor.
Robust Iterative Find-Next-Hit Ray Traversal

We present two different methods for improving the performance and robustness of ray tracing algorithms that require iterating through multiple successive intersections along the same ray. Our methods do so without ever missing intersections, even in cases of co-planar or numerically close surfaces, and without having to rely on a dedicated all-hit traversal kernel. Furthermore, our evaluation shows that, in many cases, our methods consistently outperform existing all-hit kernels.
Highly Efficient In Vivo Targeting of the Pulmonary Endothelium Using Novel Modifications of Polyethylenimine: An Importance of Charge

Pulmonary vascular disease encompasses a wide range of serious afflictions with important clinical implications. There is critical need for the development of efficient, nonviral gene therapy delivery systems. Here, a promising avenue to overcome critical issues in efficient cell targeting within the lung via a uniquely designed nanosystem is reported. Polyplexes are created by functionalizing hyperbranched polyethylenimine (PEI) with biological fatty acids and carboxylate‐terminated poly(ethylene glycol) (PEG) through a one‐pot 1‐ethyl‐3‐(3‐dimethylaminopropyl)carbodiimide hydrochloride/N‐hydroxysuccinimide reaction. Following intravenous injection, polyplexes show an exceptionally high specificity to the pulmonary microvascular endothelium, allowing for the successful delivery of stabilized enhanced green fluorescent protein (eGFP) expressing messenger ribonucleic acid (mRNA). It is further shown, quantitatively, that positive surface charge is the main mechanism behind such high targeting efficiency for these polyplexes. Live in vivo imaging, flow cytometry of single cell suspensions, and confocal microscopy are used to demonstrate that positive polyplexes are enriched in the lung tissue and disseminated in 85–90% of the alveolar capillary endothelium, whilst being sparse in large vessels. Charge modification, achieved through poly(acrylic acid) or heparin coating, drives a highly significant reduction in both targeting percentage and targeting strength, highlighting the importance of specific surface charge, derived from chemical formulation, for efficient targeting of the pulmonary microvascular endothelium.
The Exchange in StackExchange

StackExchange is a network of Question & Answer (Q&A) sites that support collaborative knowledge exchange on a variety of topics. Prior research found a significant imbalance between those who contribute content to Q&A sites (predominantly people from Western countries) and those who passively use the site (the so-called "lurkers"). One possible explanation for such participation differences between countries could be a mismatch between culturally related preferences of some users and the values ingrained in the design of the site. To examine this hypothesis, we conducted a value-sensitive analysis of the design of the StackExchange site Stack Overflow and contrasted our findings with those of participants from societies with varying cultural backgrounds using a series of focus groups and interviews. Our results reveal tensions between collectivist values, such as the openness for social interactions, and the performance-oriented, individualist values embedded in Stack Overflow's design and community guidelines. This finding confirms that socio-technical sites like Stack Overflow reflect the inherent values of their designers, knowledge that can be leveraged to foster participation equity.
Logistical and structural challenges are the major obstacles for family medicine physicians’ ability to administer adult vaccines

ABSTRACT Purpose: This study was conducted to characterize the vaccination practices and challenges of family medicine physicians in Los Angeles County, California. Methods: The Los Angeles Academy of Family Physicians (LA AFP) sent out electronic surveys to all of their active members (N = 1121) between December 2017 and January 2018, and asked them to answer questions about themselves, their practice, their patient population, and their immunization practices and challenges. We then analyzed the results through basic statistical calculations and Pearson’s chi-squared tests. Results: Seventy-four people (6.6%) responded to the survey, and 75% of responders stated that they administer all Advisory Committee on Immunization Practices (ACIP) recommended vaccines. The lowest vaccine administration rates were for the high-dose influenza vaccine, which 66.2% (n = 49) of respondents reported to administer, followed by the meningococcal B vaccine (68.9%; n = 51). The respondents who belonged to practices with more than 11 providers, were part of a large hospital or healthcare system, had electronic medical records (EMRs), and used the California Immunization Registry (CAIR) were more likely to report to vaccinate. The number one responding physician-reported challenge to vaccination was limited time and resources to address patient resistance followed by vaccine cost and lack of infrastructure to store vaccines. Conclusions: In this pilot study, structural and logistical challenges appeared to make the biggest impact on adult vaccination for the responding family medicine physicians. Solutions addressing these challenges will help improve the adult immunization rates.
OPT: online person tracking system for context-awareness in wireless personal network

Context aware systems are the order of the day. With the advent of low cost sensor networks and systems ambient intelligence is becoming more and more sought after feature. In this work, we present an Online Person Tracking (OPT) system for indoor environment which is aimed to provide location information to the context aware applications in Personal Network. We present our extensive measurements and experimental study of the popular Tmote-Sky devices which use IEEE 802.15. 4 standard. We use our experimental results to design a prototype for a real-time person tracking system with minimal number of anchor motes. A Weighted Minimum Mean Square Error (W-MMSE) localization algorithm is used in our system. This paper thoroughly records the efforts involved in designing and prototyping such a system.
Target Meaning-Making of Workplace Incivility Based on Perceived Personality Similarity with Perpetrators

Abstract Target meaning-making of incivility (i.e., the ways targets assess uncivil events) has received attention as an important mediating mechanism in explaining the varied outcomes of incivility. The present study draws on person-environment (PE) fit theory, as well as the similarity-attraction paradigm and repulsion hypothesis, to uncover how perceived personality (dis)similarity affects both cognitive (i.e., attribution, negative appraisal) and emotional reactions (i.e., hostility) to incivility. Specifically, we examine whether incongruence between target personality and perceived perpetrator personality worsens target meaning-making (vice versa for personality congruence). Study 1 (N = 479 employees) addressed (dis)similarity in agreeableness, while Study 2 (N = 296 working adults) addressed (dis)similarity in neuroticism. Based on polynomial regressions with response surface modeling, the results generally supported the hypotheses. Along the line of dissimilarity, targets often assessed (i.e., attributed intent to, appraised, reacted emotionally to) uncivil events more negatively when they perceived personality dissimilarity with perpetrators. However, similarity in personality did not always have the opposite effect by buffering against negative assessments; meaning-making was worse when both parties scored high on neuroticism. Implications for workplace incivility and PE fit literatures are discussed, along with practical implications that highlight information elaboration and perspective taking.
Explainable Autonomy: A Study of Explanation Styles for Building Clear Mental Models

As unmanned vehicles become more autonomous, it is important to maintain a high level of transparency regarding their behaviour and how they operate. This is particularly important in remote locations where they cannot be directly observed. Here, we describe a method for generating explanations in natural language of autonomous system behaviour and reasoning. Our method involves deriving an interpretable model of autonomy through having an expert ‘speak aloud’ and providing various levels of detail based on this model. Through an online evaluation study with operators, we show it is best to generate explanations with multiple possible reasons but tersely worded. This work has implications for designing interfaces for autonomy as well as for explainable AI and operator training.
Mesh-TensorFlow: Deep Learning for Supercomputers

Batch-splitting (data-parallelism) is the dominant distributed Deep Neural Network (DNN) training strategy, due to its universal applicability and its amenability to Single-Program-Multiple-Data (SPMD) programming. However, batch-splitting suffers from problems including the inability to train very large models (due to memory constraints), high latency, and inefficiency at small batch sizes. All of these can be solved by more general distribution strategies (model-parallelism). Unfortunately, efficient model-parallel algorithms tend to be complicated to discover, describe, and to implement, particularly on large clusters. We introduce Mesh-TensorFlow, a language for specifying a general class of distributed tensor computations. Where data-parallelism can be viewed as splitting tensors and operations along the "batch" dimension, in Mesh-TensorFlow, the user can specify any tensor-dimensions to be split across any dimensions of a multi-dimensional mesh of processors. A Mesh-TensorFlow graph compiles into a SPMD program consisting of parallel operations coupled with collective communication primitives such as Allreduce. We use Mesh-TensorFlow to implement an efficient data-parallel, model-parallel version of the Transformer sequence-to-sequence model. Using TPU meshes of up to 512 cores, we train Transformer models with up to 5 billion parameters, surpassing state of the art results on WMT'14 English-to-French translation task and the one-billion-word language modeling benchmark. Mesh-Tensorflow is available at this https URL .
"What do you recommend a complete beginner like me to practice?"

CSCW scholarship has previously addressed how professionals use digital technologies for learning and communication, but limited attention has been paid to professional self-disclosure on social media. Acts of self-disclosure-intentionally revealing personal information to others-are often considered beneficial for communication and formation of relationships, and describing the role of disclosure in professional communication is important to advance CSCW research that focuses on occupations or organizational settings. In this paper, we present a mixed-methods study of professional self-disclosure in an online community focused on user experience design (UX), documenting how acts of self-disclosure may support professional development. We found that self-disclosure was frequently used as an effective rhetorical and content-focused strategy to provoke discussions and request assistance with the goal of developing or maintaining professional competence. Through the identification of these self-disclosure strategies, we discuss professional self-disclosure in relation to professional identity development in online communities.
The continued risks of unsecured public Wi-Fi and why users keep using it: Evidence from Japan

Many people today use public Wi-Fi networks but they harbor security and privacy risks. We investigated the extent of these risk today, and what factors influenced users to use the networks, adapting the design of a previous UK study, this time in Japan. We first set up an experimental open public Wi-Fi network at ll locations in downtown Nara and captured Internet traffic. From approximately 7.7 million packets captured from 196 unique mobile devices during a 150-hour experiment, we found private photos, emails, documents, and login credentials being transmitted without encryption - showing that many people use unsecured public Wi-Fi networks, and many applications do not encrypt data they send. We then examined why people use public Wi-Fi in a range of scenarios through a survey with 103 participants. We found that the desire to conserve mobile data allowance was linked to a risk-taking attitude, and use of unsecured public Wi-Fi, especially among participants with a low monthly data allowance. Gender and education also played a role; female participants and those with high school education were more likely to use public Wi-Fi.