Miracle-dz/UniFAR-FTUs huggingface dataset:
The RigVeda goes “universal”: annotation and analysis of equative constructions in Vedic and beyond

By presenting a case study on Rigvedic equative and similative constructions, this paper demonstrates that treebanks constitute an important support for research in historical linguistics for two main reasons. First, by providing quantitative evidence on linguistic phenomena, they can confirm or dismiss hypotheses formulated on the base of qualitative data. Second, by capturing correlations among linguistic phenomena which could hardly be grasped by linguists’ naked eye, treebank-based analyses allow scholars to formulate new hypotheses. Since an analysis of Rigvedic equative constructions calls for a granular and informative annotation scheme, the Vedic Treebank implements the UD scheme for equative constructions with subrelations; while some such extensions were specifically designed for a study on Rigvedic similes, others might be adopted by every treebank developer interested in representing equative strategies.
Supervised Word Sense Disambiguation on Taiwan Hakka Polysemy with Neural Network Models: A Case Study of BUN, TUNG and LAU

This research aims to explore an optimal model for automatic word sense disambiguation for highly polysemous markers BUN , TUNG and LAU in Taiwan Hakka, a low-resource language. The performance of word sense disambiguation tasks is carried out by examining DNN, BiLSTM and CNN models under different window spans. The results show that the CNN model can achieve the best performance with a multiple sliding window of L2R2+ L3R3 and L5R5.
A reconfigurable two-stage cyclic ADC for low-power applications in 3.3 V 0.35 µm CMOS

ABSTRACT This article presents a reconfigurable pipeline analog-to-digital converter (ADC) using a two-stage cyclic configuration. The ADC consists of two stages with 1.5 effective bit resolution, two reference circuits for voltage and current biasing, and a clock generator and timing circuit. Throughout the development of this ADC, several techniques were combined for reducing the power consumption as well as for preserving the converter linearity. To reduce the power consumption, the circuit has a single operational trans-conductance amplifier shared by both pipeline stages. To keep conversion linearity, circuits such as the bootstrapped complementary metal-oxide semiconductor (CMOS) transmission gates and a robust comparator topology were implemented. The circuit can be configured to perform conversion between 7 and 15 bit resolutions, and it works with the master clock frequency in the range of 1 kHz to 40 MHz. The circuit has been prototyped in a 3.3 V 0.35 µm CMOS process and consumes 14.1 mW at 40 MHz and 8 MSample/s sampling rate. With this resolution and sampling rate, it achieves 60.1 dB SNR, 56.57 dB SINAD and 9.1 bit ENOB at 0.666 MHz input frequency and 53.6 dB SNR, 52.4 dB SINAD and 8.6 bit ENOB at 3.85 MHz input frequency. The technological FOM obtained was 13.2 A s/m2.
UD on Software Requirements: Application and Challenges

Technical documents present distinct challenges when used in natural language processing tasks such as part-of-speech tagging or syntactic parsing. This is mainly due to the nature of their content, which may differ greatly from more studied texts like news articles, encyclopedic extracts or social media entries. This work contributes an English corpus composed of software requirement texts annotated in Universal Dependencies (UD) to study the differences, challenges and issues encountered on these documents when following the UD guidelines. Different structural and linguistic phenomena are studied in the light of their impact on manual and automatic dependency annotation. To better cope with texts of this nature, some modiﬁcations and features are proposed in order to enrich the existing UD guidelines to better cover technical texts. The proposed corpus is compared to other existing corpora to show the structural complexity of the texts as well as the challenge it presents to recent processing methods. This contribution is the ﬁrst software requirement corpus annotated with UD relations.
BINGO: A Dependency Grammar Framework to Understand Hardware Specifications Written in English

Automatic understanding of specifications containing flexible word order and expressiveness close to natural language is a challenging task. We address this challenge by modeling semantic parsing as a game of BINGO with dependency grammar. In this model, the rows in a BINGO chart of a word represent distinct interpretations, and the columns describe the constraints required to complete each of these interpretations. BINGO parsing considers the context of each word in the input specification to ensure high precision in the creation of semantic frames. We encode contextual information of the hardware verification domain in our grammar by adding semantic links to the existing syntactic links of the link grammar. We also define semantic propagation operations as declarative rules that are executed for each dependency edge of the parse tree to create a semantic frame. We used hardware design specifications written in English to evaluate the framework. Our results showed that the system could translate highly expressive specifications. It also demonstrated the ease of creating rules to generate the same semantic frame for specifications with the same meaning but different word order.
How useful are Enhanced Universal Dependencies for semantic interpretation?

We discuss the role of enhanced Universal Dependencies (E-UD) in the task of deriving semantic predicate-argument structures from UD treebanks in a universal, non-language-speciﬁc way. We consider the usefulness of three kinds of E-UD annotation (controllers of xcomp s, propagation of outgoing dependencies in coordinations, and coreference in relative clauses) and assess some heuristics for automatically adding such enhancements. We conclude that one large obstacle both for deriving predicate-argument structures from UD treebanks and for the automatic enhancement of basic UD treebanks is the fact that UD does not represent empty elements such as pro-dropped arguments, and we suggest that devoting effort to this would often provide a better return on investment than spending resources on improving or adding E-UD annotations.
Investigating effects of gender identity on interpreters’ styles: A case study of multidimensional analysis

By using the diplomatic discourse interpreting corpus this study investigates gender effects on interpreters’ style in the framework of multidimensional analysis. The results showed that female and male interpreters share similar interpreting style for their interpreted texts and it is characterized by informational production, non-narrative concerns, elaborated reference, overt expression of persuasion, abstract information, and online information elaboration. However, they use different methods to shape such an interpreting style. Independent-samples t-test further showed that 14 factors significantly distinguish female from male interpreters. For example, female interpreters tend to use lengthier words, second person pronoun, "there be" structure, causative adverbial subordinators and connectives whereas male interpreters are more inclined to use the pronoun “it”, which-type attributives and other adverbial clause conjunctions. It is argued that keeping faithfulness to the source language leads to similar interpreter behavior whereas different roles in society and concern for target language practicality attribute to differences in choosing
Talking with the Theorem Prover to Interactively Solve Natural Language Inference

Solving natural language inference (NLI) with formal semantics and automated theorem proving has the merit of high precision and interpretability. However, they suffer from non-logical inference such as lexical inference. To overcome this weakness, we propose a human-in-the-loop mechanism in which the user provides non-logical knowledge to the theorem prover. We focus on the subgoal of a proof. A subgoal is a proposition that remains unproved by the theorem prover. Although the subgoal conveys what knowledge should be supplemented to the proof, it is a logical formula inscrutable to normal users. To make the subgoal understandable to the user, we pro-pose a method to translate the subgoal into a natural language text. The resulting sentence is called a readable subgoal . The reaction to the readable subgoal is communicated to the theorem prover in the form of temporary axiom , a logical formula that conveys the content of the reaction. We experimented with our method by adding an interactive component to an existing NLI system, ccg2lambda. The annotators recruited by Amazon Mechanical Turk used the proposed system to solve SICK, a data set for NLI. Experiments show the interaction improved the recall rate of ccg2lambda from 32 . 1% to 44 . 1% , and the accuracy from 70 . 4% to 75 . 1% .
Theoretical models for designing a 220-GHz folded waveguide backward wave oscillator*

In this paper, the basic equations of beam-wave interaction for designing the 220 GHz folded waveguide (FW) backward wave oscillator (BWO) are described. On the whole, these equations are mainly classified into small signal model (SSM), large signal model (LSM), and simplified small signal model (SSSM). Using these linear and nonlinear one-dimensional (1D) models, the oscillation characteristics of the FW BWO of a given configuration of slow wave structure (SWS) can be calculated by numerical iteration algorithm, which is more time efficient than three-dimensional (3D) particle-in-cell (PIC) simulation. The SSSM expressed by analytical formulas is innovatively derived for determining the initial values of the FW SWS conveniently. The dispersion characteristics of the FW are obtained by equivalent circuit analysis. The space charge effect, the end reflection effect, the lossy wall effect, and the relativistic effect are all considered in our models to offer more accurate results. The design process of the FW BWO tube with output power of watt scale in a frequency range between 215 GHz and 225 GHz based on these 1D models is demonstrated. The 3D PIC method is adopted to verify the theoretical design results, which shows that they are in good agreement with each other.
STATISTICAL ANALYSIS OF THE QUENCH FIELDS IN SCRF CAVITIES

For a given defect , the quench field level in superconducting radiofrequency (SCRF) cavities is strongly decreasing with frequency. That would indicate that lower frequencies are recommended for high fields applications. On the other hand, the lower the frequency, the larger the cavity size. Hence, if defects are randomly distributed, one would assume that larger defects are expected at lower frequencies. That would favor the use of higher frequencies, counteracting the previous statement. Is there an optimal frequency resulting? The aim of this paper is to evaluate the strength of these two opposite effects, calculating the expected average quench field as a function of frequency including both the thermal analysis and the statistical one. That integrated statistical analysis might also help for predicting the percentage of cavities under a given specified field that would be rejected in a production series.
UDWiki: guided creation and exploitation of UD treebanks

UDWiki is an online environment designed to make creating new UD treebanks easier. It helps in setting up all the necessary data needed for a new treebank up in a GUI, where the interface takes care of guiding you through all the descriptive ﬁles needed, adding new texts to your corpus, and helping in annotating the texts. The system is built on top of the TEITOK corpus environment, using an XML based version of UD annotation, where dependencies can be combined with various other types of annotations. UDWiki can run all the necessary or helpful scripts (tag-gers, parsers, validators) via the interface. It also makes treebanks under development directly searchable, and can be used to maintain or search existing UD treebanks
The role of cognitive and social leisure activities in dementia risk: assessing longitudinal associations of modifiable and on-modifiable risk factors

Abstract Aims With the projected surge in global dementia cases and no curative treatment available, research is increasingly focusing on lifestyle factors as preventive measures. Social and cognitive leisure activities are promising targets, but it is unclear which types of activities are more beneficial. This study investigated the individual and joint contribution of cognitive and social leisure activities to dementia risk and whether they modify the risks associated with other potentially modifiable and non-modifiable risk factors. Methods We used data from the English Longitudinal Study of Ageing (ELSA) from 7917 participants, followed up from 2008/2009 (Wave 4) until 2018/2019 (Wave 9) for incident dementia. Self-reported baseline cognitive activities (e.g. ‘reading the newspaper’), the number of social memberships (e.g. being a member of a social club) and social participation (e.g. ‘going to the cinema’) were clustered into high and low based on a median split. Subsequently, their individual and joint contribution to dementia risk, as well as their interaction with other dementia risk factors, were assessed with Cox regression models, adjusting for age, sex, level of education, wealth and a composite score of 11 lifestyle-related dementia risk factors. Results After a median follow-up period of 9.8 years, the dementia incidence rate was 54.5 cases per 10.000 person-years (95% CI 49.0–60.8). Adjusting for demographic and other lifestyle-related risk factors, higher engagement in cognitive activities (HR = 0.58; 95% CI 0.40–0.84), a greater number of social memberships (HR = 0.65; 95% CI 0.51–0.84) and more social participation (HR = 0.71; 95% CI 0.54–0.95) were associated with lower dementia risk. In a joint model, only engagement in cognitive activities (HR = 0.60; 95% CI 0.40–0.91) and social memberships (HR = 0.75; 95% CI 0.56–0.99) independently explained dementia risk. We did not find any interaction with other modifiable and non-modifiable risk factors. Conclusions Engagement in cognitive and social leisure activities may be beneficial for overall dementia risk, independent of each other and other risk factors. Both types of activities may be potential targets for dementia prevention measures and health advice initiatives.
Adaptive Speed Control of a Robot Vehicle by Neural Networks

This paper proposes an adaptive speed control method for a robot vehicle of which dynamics greatly varies by the steering angle. The proposed system consists of the PID controller and two neural networks, which tune not only a set of PID gain parameters but also a feed-forward compensation. These PID gain tuner and feed-forward compensator generate the adequate PID gains and offset according to steering angles and target speeds.A computer simulation of vehicle motion is carried out to. show the effectiveness of a proposed control method.
Tensor Robust PCA with Nonconvex and Nonlocal Regularization

Tensor robust principal component analysis (TRPCA) is a classical way for low-rank tensor recovery, which minimizes the convex surrogate of tensor rank by shrinking each tensor singular value equally. However, for real-world visual data, large singular values represent more significant information than small singular values. In this paper, we propose a nonconvex TRPCA (N-TRPCA) model based on the tensor adjustable logarithmic norm. Unlike TRPCA, our N-TRPCA can adaptively shrink small singular values more and shrink large singular values less. In addition, TRPCA assumes that the whole data tensor is of low rank. This assumption is hardly satisfied in practice for natural visual data, restricting the capability of TRPCA to recover the edges and texture details from noisy images and videos. To this end, we integrate nonlocal self-similarity into N-TRPCA, and further develop a nonconvex and nonlocal TRPCA (NN-TRPCA) model. Specifically, similar nonlocal patches are grouped as a tensor and then each group tensor is recovered by our N-TRPCA. Since the patches in one group are highly correlated, all group tensors have strong low-rank property, leading to an improvement of recovery performance. Experimental results demonstrate that the proposed NN-TRPCA outperforms existing TRPCA methods in visual data recovery. The demo code is available at https://github.com/qguo2010/NN-TRPCA.
Embodied hands

Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and hands separately. Here we formulate a model of hands and bodies interacting together and fit it to full-body 4D sequences. When scanning or capturing the full body in 3D, hands are small and often partially occluded, making their shape and pose hard to recover. To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations). MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses. The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand. MANO provides a compact mapping from hand poses to pose blend shape corrections and a linear manifold of pose synergies. We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting complex, natural, activities of subjects captured with a 4D scanner. The fitting is fully automatic and results in full body models that move naturally with detailed hand motions and a realism not seen before in full body performance capture. The models and data are freely available for research purposes at http://mano.is. tue.mpg. de.
Accuracy and Timeliness of Knowledge Dissemination on COVID-19 Among People in Rural and Remote Regions of China at the Early Stage of Outbreak

Coronavirus disease 2019 (COVID-19) spread throughout China in January 2020. To contain the virus outbreak, the Chinese government took extraordinary measures in terms of public policy, wherein accurate and timely dissemination of information plays a crucial role. Despite all of the efforts toward studying this health emergency, little is known about the effectiveness of public policies that support health communication during such a crisis to disseminate knowledge for self-protection. Particularly, we focus on the accuracy and timeliness of knowledge dissemination on COVID-19 among people in remote regions—a topic largely omitted in existing research. In February 2020, at the early-stages of the COVID-19 outbreak, a questionnaire survey was carried out. In total, 8,520 participants from seven less economically developed provinces situated in the borderlands of China with large ethnic minority groups responded. We analyzed the data through poisson regression and logistic regression analyses. We found that (1) people in remote regions of China obtained accurate information on COVID-19. Further, they were able to take appropriate measures to protect themselves. (2) Result from both descriptive analysis and multivariable regression analysis revealed that there is no large difference in the accuracy of information among groups. (3) Older, less educated, and rural respondents received information with a significant delay, whereas highly educated, younger, urban residents and those who obtained information through online media were more likely to have received the news of the outbreak sooner and to be up to date on the information. This research provides evidence that disadvantage people in remote regions obtained accurate and essential information required to act in an appropriate manner in responses to the COVID-19 outbreak. However, they obtained knowledge on COVID-19 at a slower pace than other people; thus, further improvement in the timely dissemination of information among disadvantage people in remote regions is warranted.
Landscape Evolution as a Diversification Driver in Freshwater Fishes

The exceptional concentration of vertebrate diversity in continental freshwaters has been termed the “freshwater fish paradox,” with > 15,000 fish species representing more than 20% of all vertebrate species compressed into tiny fractions of the Earth’s land surface area (<0.5%) or total aquatic habitat volume (<0.001%). This study asks if the fish species richness of the world’s river basins is explainable in terms of river captures using topographic metrics as proxies. The River Capture Hypothesis posits that drainage-network rearrangements have accelerated biotic diversification through their combined effects on dispersal, speciation, and extinction. Yet rates of river capture are poorly constrained at the basin scale worldwide. Here we assess correlations between fish species density (data for 14,953 obligate freshwater fish species) and basin-wide metrics of landscape evolution (data for 3,119 river basins), including: topography (elevation, average relief, slope, drainage area) and climate (average rainfall and air temperature). We assess the results in the context of both static landscapes (e.g., species-area and habitat heterogeneity relationships) and transient landscapes (e.g., river capture, tectonic activity, landscape disequilibrium). We also relax assumptions of functional neutrality of basins (tropical vs. extratropical, tectonically stable vs. active terrains). We found a disproportionate number of freshwater species in large, lowland river basins of tropical South America, Africa, and Southeast Asia, under predictable conditions of large geographic area, tropical climate, low topographic relief, and high habitat volume (i.e., high rainfall rates). However, our results show that these conditions are only necessary, but not fully sufficient, to explain the basins with the highest diversity. Basins with highest diversity are all located on tectonically stable regions, places where river capture is predicted to be most conducive to the formation of high fish species richness over evolutionary timescales. Our results are consistent with predictions of several landscape evolution models, including the River Capture Hypothesis, Mega Capture Hypothesis, and Intermediate Capture Rate Hypothesis, and support conclusions of numerical modeling studies indicating landscape transience as a mechanistic driver of net diversification in riverine and riparian organisms with widespread continental distributions.