Miracle-dz/UniFAR-FTUs huggingface dataset:
Lexical preference in second dialect acquisition in a second language

Aims and objectives/purpose/research questions: How does second dialect acquisition in a second language compare to that in a first language in terms of rates and predictors of second dialect vocabulary use? Design/methodology/approach: A lexical preference task was completed by four groups of participants residing in Australia: first language speakers of Australian (L1D1) and American (L1D2) English, and first language speakers of Russian who acquired Australian (L2D1) and American (L2D2) English first. The participants named objects which are denoted by different words in American and Australian English (e.g. bell pepper vs capsicum). Data and analysis: The response was coded as either American or Australian, and percentage of use of Australian items was calculated for each group. Findings/conclusions: L1D1 used Australian words the most and L1D2 the least. L2D1 and L2D2 fell between the two L1 groups. L1D2 rate of use was predicted by proportion of life spent in Australia. L2D1 were more likely to choose Australian words if they had lived in Australia longer and had positive attitudes toward Australia. L2D2 were less likely to use Australian words the longer they had lived in the USA. Similar, but not identical, factors predict second dialect acquisition in the first and second languages. Originality: The research is innovative in considering second dialect acquisition in second language speakers and creates a bridge between second language and second dialect acquisition research. Significance/implications: The finding that second language speakers may be more flexible in second dialect acquisition than first language speakers has important implications for our understanding of cognitive and social constraints on acquisition.
Dominant, passive, and recessive feminism: a postfeminist reading of Taiwanese cinema

ABSTRACT This paper aims to add to the underrepresented scholarship on feminist critique in Taiwanese cinema by investigating the ways in which female power has been represented in the female-centred Taiwanese film, The Bold, the Corrupt and the Beautiful (BCB). Through a postfeminist reading, this study attempts to challenge the dichotomy of Asian versus Western values in order to engage Taiwanese films into the global debate of feminism. By revealing the problematization of representations of the seemingly powerful female characters in BCB produced by male filmmakers, we unveil the hidden contradiction of feminism and anti-feminism in order to gain a profound understanding of women’s power relations and the socio-cultural perception of them in the Taiwanese context. We categorise the three leading female characters into significations of “dominant feminism”, “passive feminism”, and “recessive feminism”, and show how their power is exercised through scheming, seduction and detainment, respectively. Therefore, we argue that these seemingly powerful female images in BCB are all represented as stigmatic which attempts to equalize female power to negativity thereby essentially disavowing feminism.
The Role of Mistrust in the Modelling of Opinion Adoption

Societies tend to partition into factions based on shared beliefs, leading to sectarian conflict in society. This paper investigatesmistrust as a cause for this partitioningby extending an establishedopiniondynamics model with Bayesian updating that specifies mistrust as the underlying mechanism for disagreement and, ultimately, polarisation. We demonstrate that mistrust is at the foundation of polarisation. Detailed analysis and the results of rigorous simulation studies provide new insight into the potential role ofmistrust in polarisation. We show that consensus results whenmistrust levels are low, but introducing extreme agentsmakes consensus significantly harder to reach and highly fragmented and dispersed. These results also suggest amethod to verify the model using real-world experimental or observational data empirically.
Gender Diversity in Computer Science at a Large Public R1 Research University: Reporting on a Self-study

With the number of jobs in computer occupations on the rise, there is a greater need for computer science (CS) graduates than ever. At the same time, most CS departments across the country are only seeing 25–30% of women students in their classes, meaning that we are failing to draw interest from a large portion of the population. In this work, we explore the gender gap in CS at Rutgers University–New Brunswick, a large public R1 research university, using three data sets that span thousands of students across six academic years. Specifically, we combine these data sets to study the gender gaps in four core CS courses and explore the correlation of several factors with retention and the impact of these factors on changes to the gender gap as students proceed through the CS courses toward completing the CS major. For example, we find that a significant percentage of women students taking the introductory CS1 course for majors do not intend to major in CS, which may be a contributing factor to a large increase in the gender gap immediately after CS1. This finding implies that part of the retention task is attracting these women students to further explore the major. Results from our study include both novel findings and findings that are consistent with known challenges for increasing gender diversity in CS. In both cases, we provide extensive quantitative data in support of the findings.
Current role of systemic therapy in transarterial chemotherapy refractory hepatocellular carcinoma patients

Transarterial chemotherapy (TACE) is the standard treatment for patients with intermediate-stage hepatocellular carcinoma (HCC), defined as large, unresectable, or multinodular HCC in patients with good functional performance. The definition of TACE refractoriness is not well established. Generally, TACE refractoriness is defined as an insufficient response after two or more consecutive TACE. An increase in the number of liver lesions, continuously elevated tumor markers, vascular invasion, and extrahepatic spread also suggest TACE refractoriness. Timely switching to systemic therapy for TACE refractoriness should be considered to improve the outcome. Although data are sparse, the combination of anti-angiogenic and immune checkpoint inhibitor therapies shows promise for TACE-refractory patients. In this article, we review the role of systemic therapy in TACE-refractory patients with HCC.
An Energy Consumption Model for Electrical Vehicle Networks via Extended Federated-learning

Electrical vehicle (EV) raises to promote an eco-sustainable society. Nevertheless, the “range anxiety” of EV hinders its wider acceptance among customers. This paper proposes a novel solution to range anxiety based on a federated-learning model, which is capable of estimating battery consumption and providing energy-efficient route planning for vehicle networks. Specifically, the new approach extends the federated-learning structure with two components: anomaly detection and sharing policy. The first component identifies preventing factors in model learning, while the second component offers guidelines for information sharing amongst vehicle networks when the sharing is necessary to preserve learning efficiency. The two components collaborate to enhance learning robustness against data heterogeneities in networks. Numerical experiments are conducted, and the results show that compared with considered solutions, the proposed approach could provide higher accuracy of battery-consumption estimation for vehicles under heterogeneous data distributions, without increasing the time complexity or transmitting raw data among vehicle networks.
Overdominant and partially dominant mutations drive short-term adaptation in diploid yeast

Identification of adaptive targets in experimental evolution typically relies on extensive replication and allele reconstructions. An alternative approach is to directly assay all mutations in an evolved clone by generating pools of segregants that contain random combinations of the evolved mutations. Here, we apply this method to 6 clones isolated from 4 diploid populations that were clonally evolved for 2,000 generations in rich glucose medium. Each clone contains ∼20-25 mutations relative to the ancestor. We derived intermediate genotypes between the founder and the evolved clones by bulk mating sporulated cultures of each evolved clone to a barcoded haploid version of the founder. We competed the barcoded segregants en masse and quantified the fitness of each barcode. We estimated average fitness effects of evolved mutations using barcode fitness and whole genome sequencing for a subset of segregants or time-course whole population whole genome sequencing. In contrast to our previous work in haploid populations, we find that diploids carry fewer evolved mutations with a detectable fitness effect (6%), contributing a modest fitness advantage (up to 5.4%). In agreement with theoretical expectations, reconstruction experiments show that all adaptive mutations manifest some degree of dominance over the ancestral allele, and most are overdominant. Competition assays under conditions that deviated from the evolutionary environment show that adaptive mutations are often pleiotropic.
A Survey: FPGA‐Based Dynamic Scheduling of Hardware Tasks

To meet the increasing computing needs of various application fields, Field programmable gate array (FPGA) has been widely deployed. In FPGA-based processing, hardware tasks can be better accelerated by allocating appropriate computing resources. Therefore, FPGA-based hardware task scheduling has become one of the mainstream research directions in academia and industry. However, the optimization objectives of existing FPGA-based hardware task scheduling methods are relatively scattered. In this regard, this paper summarizes the research status of hardware task dynamic scheduling from the three essential elements of FPGA processing:time, resources, and power consumption. This paper analyzes, sorts out, categorizes the ideas and implementations of various scheduling methods and analyzes and evaluates optimization effects of various scheduling methods from multiple dimensions. Then, the shortcomings of the existing methods are summarized and some practical applications are introduced. Finally, the research direction of task scheduling based on FPGA is prospected and summarized.
Earthquake Swarm Analysis around Mt. Salak, West Java, Indonesia, Using BMKG Data from August 10 to November 24, 2019

Earthquake swarms commonly come approximately active tectonic and volcanic area. Interestingly, the swarm events occurred ~23 km southwest from Mt. Salak-Bogor, West Java, Indonesia, from August 10 to November 24, 2019, and were recorded by local/regional network of the Indonesian Agency for Meteorology, Climatology, and Geophysics (BMKG). Our previous study showed that in this area a destructive ML 4.6 earthquake with thrust faulting occurred on September 8, 2012. The double-difference method was applied to update the hypocenter locations from the BMKG data. In the time period of ~3.5 months, we relocated 79 swarm events with ~9.4 km depth average for local magnitude (ML) 2.2 to 4.2. The source mechanism result for selected events shows a strike-slip faulting. Our interpretation is that these swarm events are probably related to stress change due to volcano-tectonic activity.
Deep Reinforcement Learning Based Optimization Algorithm for Permutation Flow-Shop Scheduling

As a new analogy paradigm of human learning process, reinforcement learning (RL) has become an emerging topic in computational intelligence (CI). The synergy between the RL and CI is an emerging way to develop efficient solution algorithms for solving complex combinatorial optimization (CO) problems like machine scheduling problem. In this paper, we proposed an efficient optimization algorithm based on Deep RL for solving permutation flow-shop scheduling problem (PFSP) to minimize the maximum completion time. Firstly, a new deep neural network (PFSPNet) is designed for the PFSP to achieve the end-to-end output without limitation of problem sizes. Secondly, an actor-critic method of RL is used to train the PFSPNet without depending on the collection of high-quality labelled data. Thirdly, an improvement strategy is designed to refine the solution provided by the PFSPNet. Simulation results and statistical comparison show that the proposed optimization algorithm based on deep RL can obtain better results than the existing heuristics in similar computational time for solving the PFSP.
The reliability of a smartphone application in measuring the foot structure of cyclists during sitting and standing

Abstract Previous studies have focussed on the relation between foot structure and function, revealing that 3D scanning is the measurement tool with higher precision and accuracy. However, to date only non-studies have investigated the test–retest reliability and potential of a smartphone 3D application for measuring foot morphology. The purpose of the present study was to assess the intra-rater reliability of the Avatar3D application in analysing the foot structure of cyclists. Thirty-five female road cyclists assessed her own foot through the Avatar3D application on two occasions separated by 10 minutes. Instep girth, foot length, ball girth, and ball width were registered in sitting and standing positions during the test and retest. The reliability of the measurements was evaluated using intraclass correlation coefficient (ICC), coefficient of variation (CV’s) and Bland–Altman plots. All dependent variables showed ICC range values from moderate to very high with low CV’s (<6%), evidencing no significant changes in any variable. This thesis was reinforced by Bland–Altman plots and one sample t-tests (p > 0.05). The Avatar3D application is a reliable and easy-to-use tool for assessing the foot structure in female cyclists in sitting and standing positions.
Toward Computational Accuracy in Realistic Systems to Aid Understanding of Field-Level Water Quality Issues

Contemplating what will unfold in this new decade and those after, it is not difficult to imagine the increasing importance of conservation and protection of clean water supplies. A worrying but predictable offshoot of humanity’s technological advances is the seemingly ever-increasing chemical load burdening our waterways. In this perspective are presented a few modest areas where computational chemistry modelling could provide benefit to these efforts by harnessing the continually improving computational power available to the field. In the acute event of a chemical spill incident, true quantum-chemistry-based predictions of physicochemical properties and surface-binding behaviors can be used to help decision making in remediating the spill threat. The chronic burdens of microplastics and perfluorinated “forever chemicals” can also be addressed with computational modelling to fill the gap between feasible laboratory experiment timescales and the much-longer material lifetimes. For all of these systems, field-level accuracy models will avail themselves as the model computational systems are able to incorporate more realistic features that are relevant to water quality issues.
Risk Assessment Algorithm for Power Transformer Fleets Based on Condition and Strategic Importance

In every electric power system, power transformers (PT) play a critical role. Under ideal circumstances, PT should receive the utmost care to maintain the highest operative condition during their lifetime. Through the years, different approaches have been developed to assess the condition and the inherent risk during the operation of PT. However, most proposed methodologies tend to analyze PT as individuals and not as a fleet. A fleet assessment helps the asset manager make sound decisions regarding the maintenance scheduling for groups of PT with similar conditions. This paper proposes a new methodology to assess the risk of PT fleets, considering the technical condition and the strategic importance of the units. First, the state of the units was evaluated using a health index (HI) with a fuzzy logic algorithm. Then, the strategic importance of each unit was assessed using a weighting technique to obtain the importance index (II). Finally, the analyzed units with similar HI and II were arranged into a set of clusters using the k-means clustering technique. A fleet of 19 PTs was used to validate the proposed method. The obtained results are also provided to demonstrate the viability and feasibility of the assessment model.
CASTLE: Compression Architecture for Secure Low Latency, Low Energy, High Endurance NVMs

CASTLE is a Compression-based main memory Architecture realizing a read-decrypt-free (i.e., write-only) Secure solution for low laTency, Low Energy, high endurance non-volatile memories (NVMs). CASTLE integrates pattern-based data compression and incomplete data mapping (i.e., expansion coding) to improve NVM energy, latency, and lifetime without impacting the security guarantees of the underlying security architecture. System-level simulations of a TLC RRAM architecture show that CASTLE reduces memory energy by 19% and write latency by 38.7%, and improves lifetime by 1.8 × over state-of-the-art solutions for NVM security.
Intrinsic and Extrinsic Control of Hepatocellular Carcinoma by TAM Receptors

Simple Summary Tyro3, Axl, and MerTK are receptor tyrosine kinases of the TAM family, which are activated by their ligands Gas6 and Protein S. TAM receptors have large physiological implications, including the removal of dead cells, activation of immune cells, and prevention of bleeding. In the last decade, TAM receptors have been suggested to play a relevant role in liver fibrogenesis and the development of hepatocellular carcinoma. The understanding of TAM receptor functions in tumor cells and their cellular microenvironment is of utmost importance to advances in novel therapeutic strategies that conquer chronic liver disease including hepatocellular carcinoma. Abstract Hepatocellular carcinoma (HCC) is the major subtype of liver cancer, showing high mortality of patients due to limited therapeutic options at advanced stages of disease. The receptor tyrosine kinases Tyro3, Axl and MerTK—belonging to the TAM family—exert a large impact on various aspects of cancer biology. Binding of the ligands Gas6 or Protein S activates TAM receptors causing homophilic dimerization and heterophilic interactions with other receptors to modulate effector functions. In this context, TAM receptors are major regulators of anti-inflammatory responses and vessel integrity, including platelet aggregation as well as resistance to chemotherapy. In this review, we discuss the relevance of TAM receptors in the intrinsic control of HCC progression by modulating epithelial cell plasticity and by promoting metastatic traits of neoplastic hepatocytes. Depending on different etiologies of HCC, we further describe the overt role of TAM receptors in the extrinsic control of HCC progression by focusing on immune cell infiltration and fibrogenesis. Additionally, we assess TAM receptor functions in the chemoresistance against clinically used tyrosine kinase inhibitors and immune checkpoint blockade in HCC progression. We finally address the question of whether inhibition of TAM receptors can be envisaged for novel therapeutic strategies in HCC.
Screening for Adult Attention-Deficit Hyperactivity Disorder in the Korean Community Sample: Prevalence and Association with Mood and Sleep Symptoms

Objective: Attention-deficit hyperactivity disorder (ADHD) symptoms start in early childhood and continue into adulthood, and are frequently associated with comorbid symptoms of depression, bipolar, insomnia, and daytime sleepiness. The aim of this study was to screen adult ADHD symptoms in a community sample and investigate the association of adult ADHD symptoms with mood and sleep symptoms. Methods: Respondents (n = 1500) from the general adult population, aged between 19 and 49 years, were recruited. The sample consisted of a population in Busan, Korea that was representative in terms of sex, age, and residential area. ADHD symptoms and mood/sleep symptoms were assessed using validated questionnaires with cut-off threshold. Results: The screening for adult ADHD documented ADHD symptoms in 3% (45 out of 1500) of the participants. The ADHD screen-positive group was more likely to have depression symptoms (OR = 14.97, 95% CI: 7.94-28.25, P < .001) and daytime sleepiness symptoms (OR = 3.18, 95% CI: 1.55-6.52, P < .001). Conclusion: Present result indicates high prevalence of adult ADHD symptoms in a community sample in Korea. The findings of this study also suggest significant association between adult ADHD symptoms and depression symptoms and daytime sleepiness. Optimal management of adult ADHD requires the consideration of not only ADHD symptoms but also the assessment and treatment of symptoms associated with comorbid conditions.
Trajectory-Based Failure Prediction for Autonomous Driving

In autonomous driving, complex traffic scenarios can cause situations that require human supervision to resolve safely. Instead of only reacting to such events, it is desirable to predict them early in advance. While predicting the future is challenging, there is a source of information about the future readily available in autonomous driving: the planned trajectory the car intends to drive. In this paper, we propose to analyze the trajectories planned by the vehicle to predict failures early on. We consider sequences of trajectories and use machine learning to detect patterns that indicate impending failures. Since no public data of disengagements of autonomous vehicles is available, we use data provided by development vehicles of the BMW Group. From over six months of test drives, we obtain more than 2600 disengagements of the automated system. We train a Long Short-Term Memory classifier with sequences of planned trajectories that either resulted in successful driving or disengagements. The proposed approach outperforms existing state-of-the-art failure prediction with low-dimensional data by more than 3 % in a Receiver Operating Characteristic analysis. Since our approach makes no assumptions on the underlying system, it can be applied to predict failures in other safety-critical areas of robotics as well.
The ab initio model potential method: Third-series transition metal elements

In this paper we present nonrelativistic and relativistic core ab initio model potentials (AIMPs) and valence basis sets for La and the third-series transition metal elements. The relativistic AIMPs are derived from atomic Cowan–Griffin calculations; they are made of a spin-free part and a one-electron spin-orbit operator according to Wood and Boring. The core potentials correspond to the 62-electron core [Cd,4f]. The valence basis sets are optimized and spin-orbit corrected. We present monitoring spin-free calculations on the atoms, singly ionized ions and monohydrides of the ten elements, which show a good performance overall. A spin-free-state-shifted spin-orbit-configuration interaction calculation on Pt, which uses empirical spin-free data and which is expected to be essentially free from spin-free deficiencies, points out that the quality of the spin-orbit operators is very good.