Miracle-dz/UniFAR-FTUs huggingface dataset:
Passing Parser Uncertainty to the Transformer. Labeled Dependency Distributions for Neural Machine Translation.

Existing syntax-enriched neural machine translation (NMT) models work either with the single most-likely unlabeled parse or the set of n-best unlabeled parses coming out of an external parser. Passing a single or n-best parses to the NMT model risks propagating parse errors. Furthermore, unlabeled parses represent only syntactic groupings without their linguistically relevant categories. In this paper we explore the question: Does passing both parser uncertainty and labeled syntactic knowledge to the Transformer improve its translation performance? This paper contributes a novel method for infusing the whole labeled dependency distributions (LDD) of the source sentence’s dependency forest into the self-attention mechanism of the encoder of the Transformer. A range of experimental results on three language pairs demonstrate that the proposed approach outperforms both the vanilla Transformer as well as the single best-parse Transformer model across several evaluation metrics.
T405, a New Penem, Exhibits In Vivo Efficacy against M. abscessus and Synergy with β-Lactams Imipenem and Cefditoren

Mycobacteroides abscessus (Mab) is an emerging environmental microbe that causes chronic lung disease in patients with compromised lung function such as cystic fibrosis and bronchiectasis. It is intrinsically resistant to most antibiotics, therefore there are only few antibiotics that can be repurposed to treat Mab disease. ABSTRACT Mycobacteroides abscessus (Mab) is an emerging environmental microbe that causes chronic lung disease in patients with compromised lung function such as cystic fibrosis and bronchiectasis. It is intrinsically resistant to most antibiotics, therefore there are only few antibiotics that can be repurposed to treat Mab disease. Although current recommendations require daily intake of multiple antibiotics for more than a year, cure rate is low and often associated with significant adverse events. Here, we describe in vivo efficacy of T405, a recently discovered β-lactam antibiotic of the penem subclass, in a mouse model of pulmonary Mab infection. Imipenem, one of the standard-of-care drugs to treat Mab disease, and also a β-lactam antibiotic from a chemical class similar to T405, was included as a comparator. Probenecid was included with both T405 and imipenem to reduce the rate of their renal clearance. T405 exhibited bactericidal activity against Mab from the onset of treatment and reduced Mab lung burden at a rate similar to that exhibited by imipenem. The MIC of T405 against Mab was unaltered after 4 weeks of exposure to T405 in the lungs of mice. Using an in vitro assay, we also demonstrate that T405 in combination with imipenem, cefditoren or avibactam exhibits synergism against Mab. Additionally, we describe a scheme for synthesis and purification of T405 on an industrial scale. These attributes make T405 a promising candidate for further preclinical assessment to treat Mab disease.
DeepSPIN: Deep Structured Prediction for Natural Language Processing

DeepSPIN is a research project funded by the European Research Council (ERC) whose goal is to develop new neural structured prediction methods, models, and algorithms for improving the quality, interpretability, and data-efficiency of natural language processing (NLP) systems, with special emphasis on machine translation and quality estimation. We describe in this paper the latest findings from this project.
Economic literacy among beginning higher education economics students in Japan and Germany and its socio-demographic influencing factors

In view of cross-national student mobility and increasing internationalization of the labor market, a common understanding of economic concepts as well as awareness for country-specific factors influencing economics education is essential. Therefore, the development of instruments that allow for comparable investigation across countries is crucial. The present study describes economic education in Japan and Germany and explores the specific conditions for access to higher education in each country. On this basis, we examine the level of economic literacy (using TEL-IV) at the beginning of higher education among 232 German and 198 Japanese students of Business & Economics and the impact of personal influencing factors thereon. Overall, comparable entry levels can be observed, whereby each student group shows different response patterns. Predicting economic literacy, primarily gender and interest reach significance. A final outlook regarding the significance of cross-national studies is given, taking limitations and implications of this study into account.
MINING ONLINE REVIEWS TO SUPPORT CUSTOMERS’ DECISION-MAKING PROCESS IN E-COMMERCE PLATFORMS: A NARRATIVE LITERATURE REVIEW

ABSTRACT By dint of the massive daily production of user-generated content (textual reviews) in E-commerce platforms, the need to automatically process it and extract different types of knowledge from it becomes a necessity. In this work, an attempt has been made to summarize some studies that aim to propose systems, which automatically mine textual reviews expressed in natural languages for the purpose of supporting customers’ decision-making process in E-commerce (buying, renting, and booking). The given review is the first work of this type and it includes 44 studies (30 aspect/feature-based summarizers and 14 reputation systems) published from 2004 to 2021. First, it investigates aspect and feature-based summarizers that aim to help customers in making an informed decision toward online entities (products, movies, hotels, services …). Second, it introduces reputation generation systems that seek to provide valuable information about online items. Finally, it provides recommendations for future research directions and open problems.
Receiving a gift from God in times of trouble: links between gratitude to God, the affective circumplex, and perceived closeness to God

ABSTRACT We investigated how gratitude to God (GTG) functions within the context of negative events. Specifically, we sought to locate the position of GTG within the affective circumplex and to test whether GTG relates to higher levels of perceived closeness to God (PCTG). In a cross-sectional study, participants (N = 142 U.S. undergraduate believers in God) completed self-report measures pertaining to a negative event (and positive event, for comparison purposes). Supporting preregistered hypotheses, (a) GTG was located in the pleasant half of the affective circumplex (in the activated quadrant for the negative event and the deactivated quadrant for the positive event), and (b) GTG associated with higher levels of PCTG uniquely from affect, general gratitude, and the tendency to draw close to God when stressed (for both events). These results contribute to nascent knowledge about GTG and underscore the potential importance of GTG in the process of growing spiritually through adversity.
Multimodality in translation: a look into EFL and JSL classrooms

Abstract There has been an increased interest in multimodality in the field of both language pedagogy and translation studies. To our knowledge, however, only a few empirical studies have investigated students’ perceptions and learning through translation classrooms focusing on multimodality. This paper reports on multimodal translation tasks implemented in two different classrooms: a Korean-to-English translation classroom in Korea (English as a foreign language (EFL) classroom) and a Japanese-English translation classroom in Japan (Japanese as a second language (JSL) classroom). In both classrooms, the students were assigned a task translating comics. In addition to the text itself, students also discussed other semiotics that appear in the comics, including images, font size, and colour, and made translation decisions accordingly. Based on the analysis of students’ learning journals, this paper discusses a number of pedagogical benefits and challenges derived from a multimodal translation classroom. In doing so, it highlights the importance of raising students’ awareness of different semiotics that construct text.
Points of Concordance, Points of Discordance: A Qualitative Examination of Telemedicine Implementation

Introduction: Health systems undertook a rapid transition to increase the use of telemedicine in the wake of the COVID-19 pandemic. A continued need for telemedicine services in the coming years is likely. This article examines telemedicine from multiple stakeholders' perspectives considering reach, effectiveness, adoption, implementation, and maintenance (RE-AIM) outcomes. Methods: Semistructured interviews were conducted with primary care practice team members and patients. Rapid qualitative analysis was used to identify themes in experiences and perceptions related to telemedicine implementation. The RE-AIM implementation framework was applied to thematic findings to understand influences on implementation outcomes. Results: Twenty-four practice members and 17 patients across 5 clinics participated. All stakeholder groups reported that technological capabilities influenced patients' access to telemedicine and that certain patients and reasons for visits were not appropriate for telemedicine. All groups felt that telemedicine was a good option for some patients some of the time but not all patients all of the time. Discussion: Telemedicine works well if it is used for the appropriate visits and patient types and with needed technological elements. Older age may limit the feasibility of telehealth for some patients. Added administrative work and associated costs support systematic screening to determine visit appropriateness for telemedicine.
DPPFit: Developing and Testing a Technology-Based Adaptation of the Diabetes Prevention Program (DPP) to Address Prediabetes in a Primary Care Setting

Objective: The objective of this study was to adapt the National Diabetes Prevention Program (N-DPP) into a pragmatic tool for primary care settings by using daily text messaging to deliver all N-DPP content, supplemented by Fitbit technology to provide behavioral strategies typically delivered by personnel in traditional programs. Test the mobile health (mHealth), technology-based N-DPP adaptation (DPPFit) in primary care patients with prediabetes using a remote intervention based on the traditional 16 core sessions of the DPP. Methods: A pilot study with pre/post survey analysis of aggregate data were used to determine changes in weight, physical activity, sedentary behavior, and associated diabetes risk outcomes among study participants (n = 33). In this study, participants were issued Fitbit devices and provided the remote intervention over 16 weeks via automated text messaging technology, which followed the content of the DPP core education sessions. Results: Data analysis from baseline to 6-month follow-up demonstrate mean weight loss of 3.3 kg (95% CI: -6.2 to -0.5; P = .026), reduction in body mass index by 1.25 points (95% CI: -2.1 to -0.4; P = .005), a significant average increase of 2 days in self-reported physical activity per week (95% CI: 0.4 to 3.6; P = .015) and an average 10% decrease in sedentary time (P = .007). Conclusions: The remote DPPFit intervention demonstrates a promising and practical approach to the management of prediabetes in a primary care setting. The results support the use of the DPPFit program and application to achieve meaningful outcomes in a population with prediabetes. A randomized controlled trial with a larger sample is warranted.
ASAP: architecture support for asynchronous persistence

Supporting atomic durability of updates for persistent memories is typically achieved with Write-Ahead Logging (WAL). WAL flushes log entries to persistent memory before making the actual data persistent to ensure that a consistent state can be recovered if a crash occurs. Performing WAL in hardware is attractive because it makes most aspects of log management transparent to software, and it completes log persist operations (LPOs) and data persist operations (DPOs) in the background, overlapping them with the execution of other instructions. Prior hardware logging solutions commit atomic regions synchronously. That is, once the end of a region is reached, all outstanding persist operations required for the region to commit must complete before instruction execution may proceed. For undo logging, LPOs and DPOs are both performed synchronously to ensure that the region commits synchronously. For redo logging, DPOs can be performed asynchronously, but LPOs are performed synchronously to ensure that the region commits synchronously. In both cases, waiting for synchronous persist operations (LPO or DPO) at the end of an atomic region causes atomic regions to incur high latency. To tackle this limitation, we propose ASAP, a hardware logging solution that allows atomic regions to commit asynchronously. That is, once the end of an atomic region is reached, instruction execution may proceed without waiting for outstanding persist operations to complete. As such, both LPOs and DPOs can be performed asynchronously. The challenge with allowing atomic regions to commit asynchronously is that it can lead to control and data dependence violations in the commit order of the atomic regions, leaving data in an unrecoverable state in case of a crash. To address this issue, ASAP tracks and enforces control and data dependencies between atomic regions in hardware to ensure that the regions commit in the proper order. Our evaluation shows that ASAP outperforms the state-of-the-art hardware undo and redo logging techniques by 1.41X and 1.53X, respectively, while achieving 0.96X the ideal performance when no persistence is enforced, at a small hardware cost (< 3%). ASAP also reduces memory traffic to persistent memory by 38% and 48%, compared with the state-of-the-art hardware undo and redo logging techniques, respectively. ASAP is robust against increasing persistent memory latency, making it suitable for both fast and slow persistent memory technologies.
Cascading structured pruning: enabling high data reuse for sparse DNN accelerators

Performance and efficiency of running modern Deep Neural Networks (DNNs) are heavily bounded by data movement. To mitigate the data movement bottlenecks, recent DNN inference accelerator designs widely adopt aggressive compression techniques and sparse-skipping mechanisms. These mechanisms avoid transferring or computing with zero-valued weights or activations to save time and energy. However, such sparse-skipping logic involves large input buffers and irregular data access patterns, thus precluding many energy-efficient data reuse opportunities and dataflows. In this work, we propose Cascading Structured Pruning (CSP), a technique that preserves significantly more data reuse opportunities for higher energy efficiency while maintaining comparable performance relative to recent sparse architectures such as SparTen. CSP includes the following two components: At algorithm level, CSP-A induces a predictable sparsity pattern that allows for low-overhead compression of weight data and sequential access to both activation and weight data. At architecture level, CSP-H leverages CSP-A's induced sparsity pattern with a novel dataflow to access unique activation data only once, thus removing the demand for large input buffers. Each CSP-H processing element (PE) employs a novel accumulation buffer design and a counter-based sparse-skipping mechanism to support the dataflow with minimum controller overhead. We verify our approach on several representative models. Our simulated results show that CSP achieves on average 15× energy efficiency improvement over SparTen with comparable or superior speedup under most evaluations.
Does Size Matter for Enhancing Company Resilience and Performance of Indonesian Trucking Company during COVID-19 Pandemic?

It is not clear whether or not the size of a company affects its survival during the COVID-19 pandemic. This research will address the issue of size and its contribution to company resilience and performance by looking at factors affecting resilience such as financial resources management, innovation and digitalisation technology, resilient leadership, risk and business continuity management. We focus specifically on the trucking sector. Both factors and its indicator were determined through an in-depth literature review and then transformed into questionnaires, distributed to Indonesian trucking companies and then validated through statistical analysis using PLS-SEM. This study found that different resilience factors significantly contributed to company resilience, and the size does matter in terms of achieving resilience in facing COVID-19 disruption.
Laser Range Scanners for Enabling Zero-overhead WiFi-based Indoor Localization System

Robust and accurate indoor localization has been the goal of several research efforts over the past decade. Toward achieving this goal, WiFi fingerprinting-based indoor localization systems have been proposed. However, fingerprinting involves significant effort—especially when done at high density—and needs to be repeated with any change in the deployment area. While a number of recent systems have been introduced to reduce the calibration effort, these still trade overhead with accuracy. This article presents LiPhi++, an accurate system for enabling fingerprinting-based indoor localization systems without the associated data collection overhead. This is achieved by leveraging the sensing capability of transportable laser range scanners to automatically label WiFi scans, which can subsequently be used to build (and maintain) a fingerprint database. As part of its design, LiPhi++ leverages this database to train a deep long short-term memory network utilizing the signal strength history from the detected access points. LiPhi++ also has provisions for handling practical deployment issues, including the noisy wireless environment, heterogeneous devices, among others. Evaluation of LiPhi++ using Android phones in two realistic testbeds shows that it can match the performance of manual fingerprinting techniques under the same deployment conditions without the overhead associated with the traditional fingerprinting process. In addition, LiPhi++ improves upon the median localization accuracy obtained from crowdsourcing-based and fingerprinting-based systems by 284% and 418%, respectively, when tested with data collected a few months later.
Achieving superior SME performance: Overarching role of marketing, innovation, and learning capabilities

The purpose of this study, the impact of innovation capabilities, Marketing and learning in small and medium companies achieve superior performance is. To do so, the community of small and medium companies randomly selected sample of 200 questionnaires were distributed among them that of the 107 companies responded to the questionnaire and refused to cooperate. This comment the purpose of the application, and the method of data collection for survey and the study of relationships between variables correlation. To collect data from standardized questionnaire Soka (2013) the validity of the content validity and reliability were verified through Cronbach's alpha was used. The results of hypothesis testing using software SPSS and Lisrel Suggests that innovation capabilities, Marketing and learning in small and medium companies achieve superior performance with its effective and positive relationship.
ACT: designing sustainable computer systems with an architectural carbon modeling tool

Given the performance and efficiency optimizations realized by the computer systems and architecture community over the last decades, the dominating source of computing's carbon footprint is shifting from operational emissions to embodied emissions. These embodied emissions owe to hardware manufacturing and infrastructure-related activities. Despite the rising embodied emissions, there is a distinct lack of architectural modeling tools to quantify and optimize the end-to-end carbon footprint of computing. This work proposes ACT, an architectural carbon footprint modeling framework, to enable carbon characterization and sustainability-driven early design space exploration. Using ACT we demonstrate optimizing hardware for carbon yields distinct solutions compared to optimizing for performance and efficiency. We construct use cases, based on the three tenets of sustainable design---Reduce, Reuse, Recycle---to highlight future methods that enable strong performance and efficiency scaling in an environmentally sustainable manner.
Heterogeneity of Treatment Effects of a Video Recommendation System for Algebra

Previous research has shown that providing video recommendations to students in virtual learning environments implemented at scale positively affects student achievement. However, it is also critical to evaluate whether the treatment effects are heterogeneous, and whether they depend on contextual variables such as disadvantaged student status and characteristics of the school settings. The current study extends the evaluation of a novel video recommendation system by performing an exploratory search for sources of heterogeneity of treatment effects. This study's design is a multi-site randomized controlled trial with an assignment at the student level across three large and diverse school districts in the southeast United States. The study occurred in Spring 2021, when some students were in regular classrooms and others in online classrooms. The results of the current study replicate positive effects found in a previous field experiment that occurred in Spring 2020, at the onset of the COVID-19 pandemic. Then, causal forests were used to investigate the heterogeneity of treatment effects. This study contributes to the literature on content sequencing systems and recommendation systems by showing how these systems can disproportionally benefit the groups of students who had higher levels of previous algebra ability, followed more recommendations, learned remotely, were Hispanic, and received free or reduced-price lunch, which has implications for the fairness of implementation of educational technology solutions.
Exploring the Relationships between Social and Teaching Presence in Video-based Informal Learning Using Network Analysis

In this study, we explored the relationships between the categories of social presence and teaching presence within theory-oriented and application-oriented sessions of an online course on databases in Bilibili, a popular Chinese video-based social media platform. Teaching presence in video-based learning was analyzed based on the video content, and students' social presence was analyzed based on 3,456 'Danmaku', the synchronized on-screen comments posted by users while watching the teaching video in each episode. Significant differences were observed in the frequencies of co-occurrence among pairs of teaching and social presence categories when comparing theory- and application-oriented sessions, as confirmed through chi-square testing. This study contributes to the theoretical development of understanding the interactions between teaching presence and social presence in video-based online learning, and provides insights for the design of effective video-based online learning resources.