Miracle-dz/UniFAR-FTUs huggingface dataset:
Rates and style of Cenozoic deformation around the Gonghe Basin, northeastern Tibetan Plateau

The northeastern Tibetan Plateau constitutes a transitional region between the low-relief physiographic plateau to the south and the high-relief ranges of the Qilian Shan to the north. Cenozoic deformation across this margin of the plateau is associated with localized growth of fault-cored mountain ranges and associated basins. Herein, we combine detailed structural analysis of the geometry of range-bounding faults and deformation of foreland basin strata with geomorphic and exhumational records of erosion in hanging-wall ranges in order to investigate the magnitude, timing, and style of deformation along the two primary fault systems, the Qinghai Nan Shan and the Gonghe Nan Shan. Structural mapping shows that both ranges have developed above imbricate fans of listric thrust faults, which sole into decollements in the middle crust. Restoration of shortening along balanced cross sections suggests a minimum of 0.8–2.2 km and 5.1–6.9 km of shortening, respectively. Growth strata in the associated foreland basin record the onset of deformation on the two fault systems at ca. 6–10 Ma and ca. 7–10 Ma, respectively, and thus our analysis suggests late Cenozoic shortening rates of 0.2 +0.2/–0.1 km/m.y. and 0.7 +0.3/–0.2 km/m.y. along the north and south sides of Gonghe Basin. Along the Qinghai Nan Shan, these rates are similar to late Pleistocene slip rates of ∼0.10 ± 0.04 mm/yr, derived from restoration and dating of a deformed alluvial-fan surface. Collectively, our results imply that deformation along both flanks of the doubly vergent Qilian Shan–Nan Shan initiated by ca. 10 Ma and that subsequent shortening has been relatively steady since that time.
Design and Properties of Hot Rolled Tough Carbide Free Bainitic Steels

Carbide free bainitic microstructures of steels in hot rolled condition have high potential for automotive and structural applications, where both high elongation and toughness at a high strength level are needed. However, achieving a combination of these properties remains a challenge due to difficulties in ensuring a high stability of retained austenite while maintaining industrial processability. Therefore, an attempt has been made in this work to achieve combined high toughness and high elongation in hot rolled carbide free bainitic steels.
KnowComp Submission for WMT23 Word-Level AutoCompletion Task

The NLP community has recently witnessed the success of Large Language Models (LLMs) across various Natural Language Processing (NLP) tasks. However, the potential of LLMs for word-level auto-completion in a multilingual context has not been thoroughly explored yet. To address this gap and benchmark the performance of LLMs, we propose an LLM-based system for the WMT23 Word-Level Auto-Completion (WLAC) task. Our system utilizes ChatGPT to represent LLMs and evaluates its performance in three translation directions: Chinese-English, German-English, and English-German. We also study the task under zero-shot and few-shot settings to assess the potential benefits of incorporating exemplars from the training set in guiding the LLM to perform the task. The results of our experiments show that, on average, our system attains a 29.8% accuracy on the test set. Further analyses reveal that LLMs struggle with WLAC in the zero-shot setting, but performance significantly improves with the help of additional exemplars, though some common errors still appear frequently. These findings have important implications for incorporating LLMs into computer-aided translation systems, as they can potentially enhance the quality of translations. Our codes for evaluation are available at https://github.com/ethanyiwu/WLAC.
Findings of the Word-Level AutoCompletion Shared Task in WMT 2023

This paper presents the overview of the second Word-Level autocompletion (WLAC) shared task for computer-aided translation, which aims to automatically complete a target word given a translation context including a human typed character sequence. We largely adhere to the settings of the previous round of the shared task, but with two main differences: 1) The typed character sequence is obtained from the typing process of human translators to demonstrate system performance under real-world scenarios when preparing some type of testing examples; 2) We conduct a thorough analysis on the results of the submitted systems from three perspectives. From the experimental results, we observe that translation tasks are helpful to improve the performance of WLAC models. Additionally, our further analysis shows that the semantic error accounts for a significant portion of all errors, and thus it would be promising to take this type of errors into account in future.
AlGhafa Evaluation Benchmark for Arabic Language Models

Recent advances in the space of Arabic large language models have opened up a wealth of potential practical applications. From optimal training strategies, large scale data acquisition and continuously increasing NLP resources, the Arabic LLM landscape has improved in a very short span of time, despite being plagued by training data scarcity and limited evaluation resources compared to English. In line with contributing towards this ever-growing field, we introduce AlGhafa, a new multiple-choice evaluation benchmark for Arabic LLMs. For showcasing purposes, we train a new suite of models, including a 14 billion parameter model, the largest monolingual Arabic decoder-only model to date. We use a collection of publicly available datasets, as well as a newly introduced HandMade dataset consisting of 8 billion tokens. Finally, we explore the quantitative and qualitative toxicity of several Arabic models, comparing our models to existing public Arabic LLMs.
Analyzing Multi-Sentence Aggregation in Abstractive Summarization via the Shapley Value

Abstractive summarization systems aim to write concise summaries capturing the most essential information of the input document in their own words. One of the ways to achieve this is to gather and combine multiple pieces of information from the source document, a process we call aggregation. Despite its importance, the extent to which both reference summaries in benchmark datasets and system-generated summaries require aggregation is yet unknown. In this work, we propose AggSHAP, a measure of the degree of aggregation in a summary sentence. We show that AggSHAP distinguishes multi-sentence aggregation from single-sentence extraction or paraphrasing through automatic and human evaluations. We find that few reference or model-generated summary sentences have a high degree of aggregation measured by the proposed metric. We also demonstrate negative correlations between AggSHAP and other quality scores of system summaries. These findings suggest the need to develop new tasks and datasets to encourage multi-sentence aggregation in summarization.
Bridging the Gap between Position-Based and Content-Based Self-Attention for Neural Machine Translation

Position-based token-mixing approaches, such as FNet and MLPMixer, have shown to be exciting attention alternatives for computer vision and natural language understanding. The motivation is usually to remove redundant operations for higher efficiency on consumer GPUs while maintaining Transformer quality. On the hardware side, research on memristive crossbar arrays shows the possibility of efficiency gains up to two orders of magnitude by performing in-memory computation with weights stored on device. While it is impossible to store dynamic attention weights based on token-token interactions on device, position-based weights represent a concrete alternative if they only lead to minimal degradation. In this paper, we propose position-based attention as a variant of multi-head attention where the attention weights are computed from position representations. A naive replacement of token vectors with position vectors in self-attention results in a significant loss in translation quality, which can be recovered by using relative position representations and a gating mechanism. We show analytically that this gating mechanism introduces some form of word dependency and validate its effectiveness experimentally under various conditions. The resulting network, rPosNet, outperforms previous position-based approaches and matches the quality of the Transformer with relative position embedding while requiring 20% less attention parameters after training.
Low-Resource Text Style Transfer for Bangla: Data & Models

Text style transfer (TST) involves modifying the linguistic style of a given text while retaining its core content. This paper addresses the challenging task of text style transfer in the Bangla language, which is low-resourced in this area. We present a novel Bangla dataset that facilitates text sentiment transfer, a subtask of TST, enabling the transformation of positive sentiment sentences to negative and vice versa. To establish a high-quality base for further research, we refined and corrected an existing English dataset of 1,000 sentences for sentiment transfer based on Yelp reviews, and we introduce a new human-translated Bangla dataset that parallels its English counterpart. Furthermore, we offer multiple benchmark models that serve as a validation of the dataset and baseline for further research.
The Double-Edged Sword Effect of the Presence of a Moral Star: Promotion Versus Inhibition of Nonstars' Prosocial Behavior

Abstract Although a growing body of literature on star employees has focused on top performers, the influence of moral stars has been neglected, an unfortunate situation given that employees’ moral behavior has prolonged impacts on organizations and society as a whole. In this case, we propose the concept of the moral star, defined as the employee (not the team leader) who exhibits disproportionately high and prolonged morality relative to others and has a reputation of being moral on his or her team. We further draw upon self-categorization theory and investigate the double-edged sword effect of the presence of a moral star on the prosocial behavior of other team members. Specifically, we propose that for nonstar employees who have high levels of moral identity, the presence of a moral star is positively related to their felt moral responsibility and prosocial behavior. In contrast, for nonstar employees with low levels of moral identity, the presence of a moral star is negatively related to their felt moral responsibility and prosocial behavior. We found support for our hypotheses across an experiment and a multi-wave and multi-source field study. Taken together, our findings call for closer attention to the recognition of moral stars, as well as their potential unintended negative impact on teams and organizations.
Performance Capital

Assuming responsibility and developing the tools needed for self- and political construction are hallmarks of the liberal imagination. A newly emerging subfield of performance studies—call it “performance capital studies”—teaches us what those tools are and how they may be put to use. “Performance” now comes to be seen as an exchange of cultural, legal, and identity capital retailing different forms of knowledge and power in the constitution and regulation of governance.
Fear of Labor Rigidities: The Role of Expectations on Employment Growth in Peru

Abstract The impact of employment protection legislation has been thoroughly analyzed in varied contexts. Most studies highlight the potential harm of the legislation on labor outcomes, although evidence remains inconclusive. However, the literature has focused primarily on ex post impacts, analyzing the regulation’s effect after implementation. This article departs from that analysis to focus on anticipated or ex ante effects of labor regulation. More specifically, we study the role of firms’ expectations in future stricter labor legislation related to employment and income in Peru’s formal and informal labor market. To account for expectations, we used the number of news items related to the approval of a proposed law—the General Labor Law—to increase labor rigidities in Lima’s most important business newspaper. Using the Peruvian labor survey, we find a negative but decreasing relationship between firms’ expectations of a future stricter labor market and employment and average income. We also collect evidence that bigger news items and ones closer to the front page have a negative relationship with formal employment and income.
Web-Based Digital Tool: A Way Forward for Synchronous and Asynchronous Computer-Mediated Communicative Language Learning

During the advent of the COVID-19 pandemic in 2020, institutions of higher learning were forced to shut down and physical classes were prohibited to curb the spread of the virus. It initiated lecturers and instructors all over the world to start learning and using web-based digital tools to teach their students online. This phenomenon still continues in this COVID-19 endemic period whereby lecturers, including language instructors are beginning to blend their physical classes with online ones via web-based digital tools such as Padlet. Padlet is a digital notice board that is accessible online with features that enable its user to post texts, images, links, audios, videos, documents and comments, as well as interact on a shared “wall” at the same time and at different times. In order words, it affords both synchronous and asynchronous computer-mediated communication (SAS CMC). This paper therefore aims to report a group of undergraduate students’ feedback of their experience using Padlet for SAS computer-mediated communicative English language learning. Three themes which emerged from analysis of their written responses are usability, experience and usefulness in which majority of them gave positive comments with reference to the themes. Hence, it can be concluded that web-based digital tools such as Padlet has the potential to enable SAS CMC, increase language learners’ interest and motivation to learn the target language and eventually improve their competency in the language.
AEWS-Net: An Auditor Early-Warning Indicator Set Construction Network Based on Contrastive Learning

Through the construction of early warning index set of electric power management environment audit, it can effectively support electric power enterprises to optimize audit process and improve audit efficiency. However, the traditional way of building audit early warning index set is mainly formulated by experts, which cannot be flexibly changed according to the complex and changeable business environment. In addition, audit data is often derived from different areas or sectors, which presents challenges for the effective processing of multiple sources of data. Therefore, we use deep learning technology to propose an end-to-end multi-source data processing network AEWS-Net (Auditor Early-Warning indicator Set construction Network) based on contrast learning and self-attention mechanism to efficiently screen and construct the set of audit early warning indicators. The model uses convolution view and self-attention view contrast learning methods to learn sequence information of multi-source audit data to obtain richer feature representations, and uses multi-source feature attention mechanism to learn the importance of different audit data sources to the early warning indicator set. Through the performance and ablation experiments on the power audit data, the superiority of the proposed method in the task of index set screening and construction and the rationality of the model architecture are proved.
Reliability Criteria for News Websites

Misinformation poses a threat to democracy and to people’s health. Reliability criteria for news websites can help people identify misinformation. But despite their importance, there has been no empirically substantiated list of criteria for distinguishing reliable from unreliable news websites. We identify reliability criteria, describe how they are applied in practice, and compare them to prior work. Based on our analysis, we distinguish between manipulable and less manipulable criteria and compare politically diverse laypeople as end users and journalists as expert users. We discuss 11 widely recognized criteria, including the following 6 criteria that are difficult to manipulate: content, political alignment, authors, professional standards, what sources are used, and a website’s reputation. Finally, we describe how technology may be able to support people in applying these criteria in practice to assess the reliability of websites.
Assessing the Influence of Social Media Platforms on Academic Performance: A Case Study

Social media has become a necessity that everyone, including university students, cannot avoid. As one of the most significant social media users, several factors encourage the use of social media among students, including: as a communication tool, an information channel, and an entertainment platform. Unfortunately, the use of social media is often questioned whether social media can be used as an effective learning tool that can support academic learning and more. This research attempts to identify the influence of social media platforms on academic performance using a logistic regression approach. The reason for using logistic regression is that this method can accommodate all types of independent variables, both metric and non-metric, and does not require the assumption of multivariate normality. A case study was analyzed at Diponegoro University, Indonesia. Based on the data obtained from two hundred respondents, it is discovered that of the twenty-seven independent variables, only five variables were significant statistically, i.e., students' perceptions of the effect of social media use on sleep time, students take more time for social media when studying, students know how to use social media as an effective learning tool, time to sleep. Students feel they lose useful information if they do not use social media. Researchers suspect other factors influence academic performance apart from the variables that have been used because there may still be many variables that have not been studied but instead affect on academic performance.
Spatial Variability of Soil Properties in the Obudu Mountain Region of Southeastern Nigeria

This study examined the spatial variability of soils in the Obudu Mountain regions of southeastern-Nigeria. Data for this study were obtained from direct field measurements, topographic maps and laboratory analysis of the area. Twelve soil samples were collected from six topographic gradients (2.5, 27.5, 22.5, 17.5, 12.5 and 7.5 per cent) located within the study area. The soil samples were collected with a soil auger at depths of 0-15 and 1530cm, parceled, labeled and taken to the laboratory for analysis of selected physico-chemical properties. The different topographic gradients were deduced from the topographic map of the area and ground-truthed with an abney level. Soil texture ranged from sandy loam to loamy sand with per cent sand having means of 63 and 66 respectively. The soil in the area is dominated with sand fraction. Soil reaction is slightly acidic, with moderate distribution of organic carbon and low total nitrogen contents (mean values for 0-15 and 15-30cm depths; 6.20 and 5.90, 0.80 and0.70, and 0.10). Available phosphorus is also low being less than 8 ppm in all the slope gradients. Percentage base saturation was high (>50% across the sample points) across the sampled slope positions. The coefficient of variation indicated that chemical properties were more variable than the physical properties with exchangeable calcium being the most variable (57.1 per cent) for surface layers and exchangeable sodium (88.9 per cent) for subsurface layers. The result revealed variations in soil properties among the landscape segments which were probably due to the topo-sequence characteristics in soils. However, there was no consistence in the sequence of distribution of particle size fractions from the crest to the valley floor. Slope position and gradients influence the distribution of soils in the area but its spread was not consistent from the crest to the valley floor. Hence, it is recommended that farming activities on the slope should be reduced to minimize the risk of environmental hazards.
Usability Evaluation of Educational Robotics

The study attempts to identify the joy and satisfaction of children through their interaction with educational robotics, as well as the ease of use and learning of robotics. It focuses on the challenge of new experiences, the sense of achieving new goals, enhancing outcomes, innovation, the joy of creation, and teamwork. An educational robotics program is implemented and offered to 120 children aged between 9 and 15, who constitute the sample of the research. Following usability evaluation techniques, descriptive statistics were used to analyze the results. The main finding of the research is that the majority of participants were satisfied with their involvement in the program, felt comfortable, and found the programming and construction processes to be straightforward. Additionally, they had no issues working in teams to complete the tasks and would wholeheartedly recommend it to others. The analysis of additional statistical results in relation to previous research findings and the general theory serves as its conclusion.
Study Of Species Changes Under Impact Of TheChange In An Ecological Factor. Case Study:The Flood Spreading Project In Zanjan, Iran

More than 90% of Iran is located in an arid and semi-arid area and water is one of the most important factors that can prevent sustainable development in all agricultural areas. Thus, water plays the important role in ecological condition changes, especially for plants. In this research, it was attempted to survey the water harvesting and its spreading impact on plant composition changes in a flood spreader. The study area is located in the part of the Zanjan plain, in the north-west of Zanjan city and between two rivers; Sorhain and Qarecharian, which includes rain fed and released lands that have been occupied by quaternary deposition. For this purpose, the amount of rainfall and diverted floodwater to the station was monitored and measured during the flooding period. In order to evaluate the plant composition changes in the three sites in spreading area and one in the control site, a total of 11 permanent transects (9 in spreading area and 2 as control) were stabilized using the Line Intercept Method, the canopy cover percentage of all species were noted and all species were distinguished. This study showed that some species under impact of floodwater spreading were omitted, some increased or decreased, and some new species were observed for the first time. The increase of vegetation cover and appearance of palatable plants in the last year showed that floodwater spreading can improve foliage production. Considering the life form of the observed species, although perennial forbs increased, floodwater spreading in the case of establishment of perennial grasses did not operate successfully. Therefore, the shortage of the studying period and changes in the amount of harvested floodwater make the correctness of these obtained results rather less valid.