Miracle-dz/UniFAR-FTUs huggingface dataset:
UNICON: Unsupervised Intent Discovery via Semantic-level Contrastive Learning

Discovering new intents is crucial for expand-001 ing domains in dialogue systems or natural lan-002 guage understanding (NLU) systems. A typical 003 approach is to leverage unsupervised and semi-004 supervised learning to train a neural encoder to 005 produce representations of utterances that are 006 adequate for clustering then perform clustering 007 on the representations to detect unseen clusters 008 of intents. Recently, instance-level contrastive 009 learning has been proposed to improve repre-010 sentation quality for better clustering. However, 011 the proposed method suffers from semantic dis-012 tortion in text augmentation and even from rep-013 resentation inadequacy due to limitations of 014 using representations of pre-trained language 015 models, typically BERT. Neural encoders can 016 be powerful representation learners, but the ini-017 tial parameters of pre-trained language models 018 do not reliably produce representations that are 019 suitable for capturing semantic distances. To 020 eliminate the necessity of data augmentation 021 and reduce the negative impact of pre-trained 022 language models as encoders, we propose UNI-023 CON, a novel contrastive learning method that 024 utilizes auxiliary external representations to 025 provide powerful guidance for the encoder. The 026 proposed method produces clusters that facili-027 tates intent discovery, achieving state-of-the-art 028 on intent detection benchmarks by a large mar-029 gin in both unsupervised and semi-supervised 030 settings. 031
Waste Resources Waste Landfill Site Selection Model for Bahrain

The municipal solid waste landfill in Bahrain (Askar landfill) reached its end use capacity by 2016; however it's still operating and receiving municipal wastes. This research aims to find out an alternative site for the existing landfill to be used during the current period and for near future. Five landfill alternatives located next to Askar, and eleven decision criteria have been proposed. The study applies two analytical approaches to establish the best landfill. The adopted approaches are Fuzzy Set analysis and Analytical Hierarchal Process analysis; both approaches are based on multi-criteria decision. The results for both methodologies are almost the same as the ranking of the five alternatives for both methodologies are similar with slight difference in landfill positions 3 and 4, where the best alternative is found to be landfill (3), which is located below the existing landfill at Askar.
Get Your Model Puzzled: Introducing Crossword-Solving as a New NLP Benchmark

Solving crossword puzzles requires diverse 001 reasoning capabilities, access to a vast amount 002 of knowledge about language and the world, 003 and the ability to satisfy the constraints im-004 posed by the structure of the puzzle. In this 005 work, we introduce solving crossword puz-006 zles as a new natural language understand-007 ing task. We release a corpus of crossword 008 puzzles collected from the New York Times 009 daily crossword spanning 25 years and con-010 taining a total of 9152 puzzles, with an av-011 erage of 85 clues per puzzle. These puzzles 012 include a diverse set of clues: historic, fac-013 tual, word meaning, synonyms/antonyms, ﬁll-014 in-the-blank, abbreviations, preﬁxes/sufﬁxes, 015 wordplay, and cross-lingual, as well as clues 016 that depend on the answers to other clues. We 017 separately release the clue-answer pairs from 018 these puzzles as an open-domain question an-019 swering dataset containing over half a mil-020 lion unique clue-answer pairs. For the ques-021 tion answering task, our baselines include sev-022 eral sequence-to-sequence and retrieval-based 023 generative models. We also introduce a non-024 parametric constraint satisfaction baseline for 025 solving the entire crossword puzzle. Fi-026 nally, we propose an evaluation framework 027 which consists of several complementary per-028 formance metrics. 029
Combining Paraphrase Pre-trained Model and Controllable Rules for Unsupervised Sentence Simplification

Although neural sequence-to-sequence mod-001 els for sentence simplification achieve some 002 progress, they still suffer from the data sparsity 003 problem and are lack of controllability. 004 This paper proposes a two-stage approach for 005 text simplification. First, considering text sim-006 plification is closely related to text summa-007 rization and paraphrase, we fine-tune the pre-008 trained model on the dataset of summarization 009 and paraphrase. Further, in order to achieve 010 interpretation and controllablity, we design con-011 trollable scorers to evaluate the simplified sen-012 tence from three aspects: adequacy, fluency 013 and simplicity, which are applied to sort the 014 generated sentences and output the best one. 015 Experiments show that our approach improves 016 the previous best performance of the unsuper-017 vised model by a considerable margin of 5.53 018 points, achieving a new state-of-the-art result. 019 Our method even performs competitively with 020 supervised models in both automatic metrics 021 and human evaluation. 022
GUSUM: Graph-Based Unsupervised Summarization using Sentence-BERT and Sentence Features

Unsupervised extractive document summariza-001 tion aims to extract salient sentences from a 002 document without requiring a labelled corpus. 003 In existing graph-based methods, vertex and 004 edge weights are mostly created by calculat-005 ing sentence similarities. In this paper, we 006 develop a Graph-Based Unsupervised Summa-007 rization method for extractive text summariza-008 tion. We revive traditional graph ranking algo-009 rithms with recent sentence embedding models 010 and sentence features and modify how sentence 011 centrality is computed. We first use Sentence-012 BERT, a state-of-the-art method for obtaining 013 sentence embeddings to better capture the sen-014 tence meaning. In this way, we define edges 015 of a graph where semantic similarities are rep-016 resented. Then, we create an undirected graph 017 in which the calculated sentence feature scores 018 of each sentence are represented in the vertices. 019 In the last stage, we determine the most impor-020 tant sentences in the document with the ranking 021 method we suggested on the graph created. Ex-022 periments on CNN/Daily Mail and New York 023 Times datasets show our approach achieves 024 high performance on unsupervised graph-based 025 summarization when evaluated both automati-026 cally and by humans. 027
Quantitative characterization of epitaxial graphene for the application of quantum Hall resistance standard

— Monolayer graphene grown epitaxially (epitaxial graphene) on silicon carbide (SiC) is the best graphene material for the application of quantum Hall resistance standards (QHRS). The quality of epitaxial graphene is critical for the performance of QHRS with accuracy of ≈ 1×10 -9 . In this paper, we will characterize the morphology-related defects (multilayer graphene domains and substrate terraces) in epitaxial graphene quantitatively by confocal laser scanning microscopy (CLSM) and Raman spectroscopy. We will correlate the characteristics parameters (size and density of multilayer graphene domain, layer number of graphene, size of terraces) to the performance of graphene QHRS. Understanding the influence of scattering at the morphology-related defects in epitaxial graphene will help further improve the morphology of epitaxial graphene to enhance the performance of graphene QHRS.
Chemical etching induced microporous nickel backbones decorated with metallic Fe@ hydroxide nanocatalysts: An efficient and sustainable OER anode toward industrial alkaline water splitting

The standard reduction electrode potential EFe3+/Fe0 is calculated as follows. Fe2+ + 2e  Fe . (1), E1 = 0.44 V Fe3+ + e  Fe2+. (2), E2 = 0.77 V (E1 and E2 values were taken from the electrochemical series) Adding equation (1) and (2) Fe3+ + 3e  Fe. (3), E3 Let, G1, G2 and G3 are the free energies of the reaction (1), (2) and (3). Then, G1 + G2 = G3. (4) Here, G = -nE0F . (5), where n = number of electron involved in the electrode reaction, E0= standard electrode potential in volt, and F = Faraday constant.
Resilience Factors in Students With Disabilities at a Portuguese State University

Annotation. The study aims to investigate resilience factors identified in students with disabilities at a Portuguese public university. Resilience, self-esteem, family support, subjective well-being, and psychological well-being are protective factors that contribute to a good adaptation and better inclusion in higher education. Accessibility conditions on campus continue to present barriers that hinder mobility and the students’ academic experience.
Plasmid Pro ﬁ ling and Occurrence of β -Lactamase Enzymes in Multidrug-Resistant Uropathogenic Escherichia coli in Kathmandu, Nepal and Drug

Introduction: Extended-spectrum β -lactamases (ESBL) among Gram-negative bacteria, predominantly Escherichia coli ( E. coli ), in Nepal, have been rising. The main objectives of this study were to determine the prevalence of uropathogenic E. coli , antibiotic resistance, ESBLs, ABLs (AmpC type β -lactamases), MBLs (metallo- β -lactamases) and KPCs ( Klebsiella pneumoniae carbapenemases) and their correlation with plasmid pro ﬁ ling patterns among patients with urinary tract infections in a tertiary hospital in Kathmandu, Nepal. Methods: The mid-stream urine samples collected from patients were inoculated in cystine – lactose – electrolyte-de ﬁ cient (CLED) agar plates. E. coli producing ESBLs, ABLs, MBLs/ KPC were identi ﬁ ed phenotypically using standard microbiological methods. Plasmids were extracted by alkaline lysis method from E. coli isolates and pro ﬁ led using agarose gel electrophoresis. Results: Out of the total 2661 urine samples, E. coli were isolated in 64.34% (507/788), among which 170 (33.53%) were multidrug-resistant (MDR) isolates. All MDR isolates were resistant to amoxicillin and third-generation cephalosporins but were highly sensitive to imipenem (94.12%, 160/170), amikacin (92.94%, 158/170) and nitrofurantoin (86.47%, 147/170). Among 170 MDR isolates, 78.2% (133/170) were ESBLs, 46.3% (50/170) were AmpC, 11.2% (19/170) were MBL and 0.6% (1/170) were KPC producers. Coproduction of β -lactamases was detected in 24.12% (41/170) of isolates. E. coli isolates showed one plasmid (>33.5 kb), which was present in all the isolates. Overall, 44 different plasmid pro ﬁ le groups were identi ﬁ ed based on molecular weight and number of plasmids. β Lactamase producers were relatively resistant to the higher number of antibiotics tested ( ≤ 10) than non-producers ( ≤ 8), and the number of plasmids were higher in β -lactamase producers ( ≤ 7) than those in non-producers ( ≤ 5). Conclusion: The higher prevalence of the ESBLs, AmpCs, KPCs and MBLs along with their coproduction in E. coli isolates highlights the importance of routine surveillance of ESBLs, AmpCs, KPCs and MBLs in microbiology laboratories using various phenotypic methods.
Mass reconstruction by gravitational shear and flexion

Aims. Galaxy clusters are considered as excellent probes for cosmology. For that purpose, their mass needs to be measured and their structural properties needs to be understood. Methods. We propose a method for galaxy cluster mass reconstruction which combines information from strong lensing, weak lensing shear and flexion. We extend the weak lensing analysis to the inner parts of the cluster and, in particular, improve the resolution of substructure. Results. We use simulations to show that the method recovers the mass density profiles of the cluster. We find that the weak lensing flexion is sensitive to substructure. After combining the flexion data into the joint weak and strong lensing analysis, we can resolve the cluster properties with substructures.
Sequence-to-Sequence Multilingual Pre-Trained Models: A Hope for Low-Resource Language Translation?

We investigate the capability of mBART, a 001 sequence-to-sequence multilingual pre-trained 002 model in translating low-resource languages 003 under ﬁve factors: the amount of data used 004 in pre-training the original model, the amount 005 of data used in ﬁne-tuning, the noisiness of 006 the data used for ﬁne-tuning, the domain-007 relatedness between the pre-training, ﬁne-008 tuning, and testing datasets, and the language 009 relatedness. When limited parallel corpora 010 are available, ﬁne-tuning mBART can mea-011 surably improve translation performance over 012 training Transformers from scratch. mBART 013 effectively uses even domain-mismatched text, 014 suggesting that mBART can learn meaningful 015 representations when data is scarce. Still, it 016 founders when too-small data in unseen lan-017 guages is provided. 018
NSV 11154 Is a New R Coronae Borealis Star

NSV 11154 has been confirmed as a new member of the rare hydrogen-deficient R Coronae Borealis (RCB) stars based on new photometric and spectroscopic data. Using new photometry, as well as archival plates from the Harvard archive, we have constructed the historical light curve of NSV 11154 from 1896 to the present. The light curve shows the sudden, deep, irregularly spaced declines characteristic of RCB stars. The visible spectrum is typical of a cool (Teff ≲ 5000 K) RCB star, showing no hydrogen lines, strong C2 Swan bands, and no evidence of 13C. In addition, the star shows small pulsations that are typical of an RCB star and an infrared excess due to circumstellar dust, with a temperature of ∼800 K. The distance to NSV 11154 is estimated to be ∼14.5 kpc. RCB stars are very rare in the Galaxy, so each additional star is important to population studies leading to a better understanding the origins of these mysterious stars. Among the known sample of RCB stars, NSV 11154 is unusual in that it lies well above the Galactic plane (5 kpc) and away from the Galactic center, which suggests that its parent population is neither thick disk nor bulge.
Charm production in SIBYLL

SIBYLL 2.1 is an event generator for hadron interactions at the highest energies. It is commonly used to analyze and interpret extensive air shower measurements. In light of the first detection of PeV neutrinos by the IceCube collaboration the inclusive fluxes of muons and neutrinos in the atmosphere have become very important. Predicting these fluxes requires understanding of the hadronic production of charmed particles since these contribute significantly to the fluxes at high energy through their prompt decay. We will present an updated version of SIBYLL that has been tuned to describe LHC data and extended to include the production of charmed hadrons.
Measurements of magnetic fields on T Tauri stars

Abstract Stellar magnetic fields including a strong dipole component are believed to play a critical role in the early evolution of newly formed stars and their circumstellar accretion disks. It is currently believed that the stellar magnetic field truncates the accretion disk several stellar radii above the star. This action forces accreting material to flow along the field lines and accrete onto the star preferentially at high stellar latitudes. It is also thought that the stellar rotation rate becomes locked to the Keplerian velocity near the radius where the disk is truncated. This paper reviews recent efforts to measure the magnetic field properties of low mass pre-main sequence stars, focussing on how the observations compare with the theoretical expectations. A picture is emerging indicating that quite strong fields do indeed cover the majority of the surface on these stars; however, the dipole component of the field appears to be alarmingly small. On the other hand, at least one accretion model which takes into account the non-dipole nature of the magnetic field provides predictions relating various stellar and accretion parameters which are present in the current data.
A Multi-Task and Transfer Learning based Approach for MOS Prediction

Automatic speech quality assessment aims to train a model capable of automatically measuring the performance of synthesis systems. This is a challenging task, especially when the domain of the evaluation data is different to that of the training data. In this paper, we present a multi-task and transfer learning framework for predicting the mean opinion score (MOS) of synthetic speech from different domains. Specifically, the proposed framework consists of a common encoder shared by data from different domains and two domain-specific decoders for in-domain and out-of-domain data, respectively. A wav2vec2 fine-tuned for phone recognition task is utilized as an initialization of the shared encoder to make full use of its learned knowledge from large number of unlabeled data and task-related labeled data. The experiments are conducted on the VoiceMOS Challenge dataset. The results show that the proposed system outperforms the baseline solutions for both in-domain and out-of-domain MOS prediction scenarios. Further, we show that the wav2vec2 encoder fine-tuned for phone recognition can be transferred to boost the performance of the MOS prediction.
Accelerated Boron Ions

State Key Laboratory of Nuclear Physics and Technology, and Key Laboratory of HEDP of the Ministry of Education, CAPT, Peking University, Beijing 100871, China Hebei Key Laboratory of Compact Fusion, Langfang 065001, China ENN Science and Technology Development Co., Ltd., Langfang 065001, China Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China School of Nuclear Science and Technology, University of South China, Hengyang 421001, China Beijing Laser Acceleration Innovation Center, Huairou, Beijing 101400, China Institute of Guangdong Laser Plasma Technology, Baiyun, Guangzhou 510540, China
Resource Bounded Kolmogorov Complexity, A Link between Computational Complexity & Information Theory

The resource bounded Kolmogorov complexity classes identify finite strings (and by extension infinite strings) according to how much we can compress and then recover the strings in a space or time bounded environment. We view the resource bounded Kolmogorov complexity classes as a link between computational complexity and information theory. Firstly, we study these classes with respect to information theory (or unbounded Kolmogorov complexity classes). Most of what we know about information theory can also be shown in a space bounded environment. Whether it also stands in a time bounded environment is an interesting problem and parallels open questions about the power of nondeterminism. Then, we investigate the structure of the classes and show the analogies and differences with the structure of computational complexity classes. For example, we build hierarchies paralleling the time and space hierarchies. We show that the exponential hierarchy collapses if and only if our parallel hierarchy collapses. Lastly, we analyse the statistical properties of random strings. Martin-Lof has shown that all strings with no short description possess all the computable statistical properties of random strings. We show that this result carries over to the space bounded Kolmogorov random strings, which pass all statistical tests using less space than the space bound. Also, allowing a little more space, we can design a test to detect all those strings. These results are further applied to the theory of pseudo-random number generators. Among other consequences, it is shown that there are some properties that no space bounded random number generator can possess. The notion of statistical tests is compared to the notion of a good pseudo-random number generator, as defined by Andrew Yao.
Bifurcation in Rotating Bodies

This monograph is a mathematical study of steady-state motions of a spinning hyperelastic incompressible, homogenous and isotropic cylinder. So far there was very little quantitative information on this subject and no mathematical analysis. The authors developed mathematical tools that are not standard in this kind of bifurcation problems. Qualitative results independent of the stress-strain relation are obtained that are consistent with physical experiments. These phenomena are shown to be related to mathematical bifurcation. The spectral analysis is accomplished by a new method that combines techniques of the modern theory of partial differential equations and abstract functional analysis with Fourier decomposition and sharper results on Bessel functions. These techniques represent in their own right a contribution that will find application not only in the study of rotating bodies, but in other physical situations that can be characterized by non-compact bifurcation.
Variations of cosmic large-scale structure covariance matrices across parameter space

The likelihood function for cosmological parameters, given by e.g. weak lensing shear measurements, depends on contributions to the covariance induced by the nonlinear evolution of the cosmic web. As nonlinear clustering to date has only been described by numerical $N$-body simulations in a reliable and sufficiently precise way, the necessary computational costs for estimating those covariances at different points in parameter space are tremendous. In this work we describe the change of the matter covariance and of the weak lensing covariance matrix as a function of cosmological parameters by constructing a suitable basis, where we model the contribution to the covariance from nonlinear structure formation using Eulerian perturbation theory at third order. We show that our formalism is capable of dealing with large matrices and reproduces expected degeneracies and scaling with cosmological parameters in a reliable way. Comparing our analytical results to numerical simulations we find that the method describes the variation of the covariance matrix found in the SUNGLASS weak lensing simulation pipeline within the errors at one-loop and tree-level for the spectrum and the trispectrum, respectively, for multipoles up to $\ell\leq 1300$. We show that it is possible to optimize the sampling of parameter space where numerical simulations should be carried out by minimising interpolation errors and propose a corresponding method to distribute points in parameter space in an economical way.
The Fermi-LAT view of young radio sources

Compact Symmetric Objects (CSO) are considered to be the young version of Fanaroff-Riley type I and type II radio galaxies, with typical sizes smaller than 1 kpc and ages of the order of a few thousand years. Before the launch of the Fermi satellite, young radio sources were predicted to emerge as a possible new y-ray emitting population detectable by the Large Area Telescope (LAT). After more than 6 years of Fermi operation, the question of young radio sources as y-ray emitting objects still remains open. In this contribution, we discuss candidate y-ray emitting CSO and future perspective for detecting young radio sources with Fermi -LAT. (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)