Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomalous flux periodicity in proximitised quantum spin Hall constrictions

We theoretically analyse a long constriction between the helical edge states of a two-dimensional topological insulator. The constriction is laterally tunnel-coupled to two superconductors and a magnetic field is applied perpendicularly to the plane of the two-dimensional topological insulator. The Josephson current is calculated analytically up to second order in the tunnel coupling both in the absence and in the presence of a bias (DC and AC Josephson currents). We show that in both cases the current acquires an anomalous 4π-periodicity with respect to the magnetic flux that is absent if the two edges are not tunnel-coupled to each other. The result, that provides at the same time a characterisation of the device and a possible experimental signature of the coupling between the edges, is stable against temperature. The processes responsible for the anomalous 4π-periodicity are the ones where, within the constriction, one of the two electrons forming a Cooper pair tunnels between the two edges.
Evolution of short-period cataclysmic variables: implications from eclipse modeling and stage A superhump method (with New Year's gift)

In 2013, the method of determining mass ratios (q) of dwarf novae using stage A superhumps (growing superhumps) was established. This method is a dynamical one in that it relies only on celestial mechanics. It is not dependent on an experimental calibration. Since then, more than 100 objects have been measured by this method and this method have also been applied to AM CVn stars and a black-hole X-ray binary, but these achievements have often been neglected. In this paper, I provide an updated description of the method. Comparisons with the results of the modern eclipse modeling method, which is considered to be the golden standard, have shown that these two methods agree very well and I have confirmed that the stage A superhump method is as accurate and as reliable as the modern eclipse modeling method. The stage A superhump method has many points advantageous over the eclipse modeling method in that the former does not require large telescopes and can be applicable to non-eclipsing systems. The number of objects determined by the stage A superhump method is now a few times of that by the modern eclipse modeling and they are now indispensable to study the terminal evolution of cataclysmic variables. I also showed that past studies by the other groups assumed incorrect fractional superhump excess ( )-q relations, causing biases in discussing the evolution. In particular, formulae given in Patterson (2001) and Patterson et al. (2005a) should be avoided. I derived a new experimental -q relation of stage B superhumps and showed that the pressure effect during stage B of superhumps has an only weak dependence on q. I derived a refined evolutionary track around the period minimum suggesting that the angular momentum loss is 1.9 times larger than what is expected by gravitational wave radiation. The period minimum on this track occurs at 0.0562 d = 81.0 min. There is a sharp peak in the distribution of the mass of the secondaries around the period minimum. The values rule out the claimed existence of very low-mass white dwarfs (< 0.5M ) among dwarf novae below the period gap. The measurements of stage A superhumps greatly owe to international collaborations with amateurs and professionals. I describe a brief summary of these collaborations and highlight the similarity with the world of birding (or ornithology) in that they play a role in uniting the world via international exchanges of observations. I also describe my thoughts about the similarity and relation between astronomy and ornithology, and give prospects how multidisciplinary works can be made possible between these seemingly distant fields of science.
Simulation of Information Spreading on Twitter Concerning Radiation After the Fukushima Nuclear Power Plant Accident

Information spreading on social media is a crucial issue to build a safe society. In particular, during emergencies, misinformation and uncertain information can lead to social disruption and cause significant damage to our lives. Here we built a retweet network from 24 million radiation-related tweets by 1.3 million accounts in the immediate aftermath of the Fukushima nuclear power plant accident in 2011. Then we simulated the information spreading on the network to explore ways to spread scientifically accurate information. Our simulation replicated the reality in which the number of scientific evidence-based tweets experienced a gradual decline while the number of emotional tweets increased. We also showed that increasing new direct retweets from the influencers could effectively spread scientific evidence-based information in our hypothetical simulations.
Multiphysics Modeling of Thorium-Based Fuel Performance With Cr-Coated SiC/SiC Composite Under Normal and Accident Conditions

Using the finite element multiphysics modeling method, the performance of the thorium-based fuel with Cr-coated SiC/SiC composite cladding under both normal operating and accident conditions was investigated in this work. First, the material properties of SiC/SiC composite and chromium were reviewed. Then, the implemented model was simulated, and the results were compared with those of the FRAPTRAN code to verify the correctness of the model used in this work. Finally, the fuel performance of the Th0.923U0. 077O2 fuel, Th0.923Pu0. 077O2 fuel, and UO2 fuel combined with the Cr-coated SiC/SiC composite cladding and Zircaloy cladding, respectively, was investigated and compared under both normal operating and accident conditions. Compared with the UO2 fuel, the Th0.923U0. 077O2 and Th0.923Pu0. 077O2 fuels were found to increase the fuel centerline temperature under both normal operating and reactivity-initiated accident (RIA) conditions, but decrease the fuel centerline temperature under loss-of-coolant accident (LOCA) condition. Moreover, compared to the UO2 fuel with the Zircaloy cladding, thorium-based fuels with Cr-coated SiC/SiC composite cladding were found to show better mechanical performance such as delaying the failure time by about 3 s of the Cr-coated SiC/SiC composite cladding under LOCA condition, and reducing the plenum pressure by about 0.4 MPa at the peak value in the fuel rod and the hoop strain of the cladding by about 16% under RIA condition.
PVT: Point‐voxel transformer for point cloud learning

The recently developed pure transformer architectures have attained promising accuracy on point cloud learning benchmarks compared to convolutional neural networks. However, existing point cloud Transformers are computationally expensive because they waste a significant amount of time on structuring irregular data. To solve this shortcoming, we present the Sparse Window Attention module to gather coarse‐grained local features from nonempty voxels. The module not only bypasses the expensive irregular data structuring and invalid empty voxel computation, but also obtains linear computational complexity with respect to voxel resolution. Meanwhile, we leverage two different self‐attention variants to gather fine‐grained features about the global shape according to different scale of point clouds. Finally, we construct our neural architecture called point‐voxel transformer (PVT), which integrates these modules into a joint framework for point cloud learning. Compared with previous transformer‐based and attention‐based models, our method attains a top accuracy of 94.1% on the classification benchmark and 10× $10\times $ inference speedup on average. Extensive experiments also validate the effectiveness of PVT on semantic segmentation benchmarks. Our code and pretrained model are avaliable at https://github.com/HaochengWan/PVT.
Plinko: Eliciting beliefs to build better models of statistical learning and mental model updating

Prior beliefs are central to Bayesian accounts of cognition, but many of these accounts do not directly measure priors. More specifically, initial states of belief heavily influence how new information is assumed to be utilized when updating a particular model. Despite this, prior and posterior beliefs are either inferred from sequential participant actions or elicited through impoverished means. We had participants play a version of the game “Plinko”, to first elicit individual participant priors in a theoretically agnostic manner. Subsequent learning and updating of participant beliefs was then directly measured. We show that participants hold a variety of priors that cluster around prototypical probability distributions that in turn influence learning. In follow-up experiments we show that participant priors are stable over time and that the ability to update beliefs is influenced by a simple environmental manipulation (i.e. a short break). This data reveals the importance of directly measuring participant beliefs rather than assuming or inferring them as has been widely done in the literature to date. The Plinko game provides a flexible and fecund means for examining statistical learning and mental model updating.
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images

Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context, we survey works in the area of automatic report generation from medical images, with emphasis on methods using deep neural networks, with respect to (1) Datasets, (2) Architecture Design, (3) Explainability, and (4) Evaluation Metrics. Our survey identifies interesting developments but also remaining challenges. Among them, the current evaluation of generated reports is especially weak, since it mostly relies on traditional Natural Language Processing (NLP) metrics, which do not accurately capture medical correctness.
Federated Deep Learning in Electricity Forecasting: An MCDM Approach

Large-scale data analysis is growing at an exponential rate as data proliferates in our societies. This abundance of data has the advantage of allowing the decision-maker to implement complex models in scenarios that were prohibitive before. At the same time, such an amount of data requires a distributed thinking approach. In fact, Deep Learning models require plenty of resources, and distributed training is needed. This paper presents a Multicriteria approach for distributed learning. Our approach uses the Weighted Goal Programming approach in its Chebyshev formulation to build an ensemble of decision rules that optimize aprioristically defined performance metrics. Such a formulation is beneficial because it is both model and metric agnostic and provides an interpretable output for the decision-maker. We test our approach by showing a practical application in electricity demand forecasting. Our results suggest that when we allow for dataset split overlapping, the performances of our methodology are consistently above the baseline model trained on the whole dataset.
A note on connectivity preserving splitting operation for matroids representable over $GF(p)$

The splitting operation on a p -matroid does not necessarily preserve connectivity. It is observed that there exists a single element extension of the splitting matroid which is connected. In this paper, we deﬁne the element splitting operation on a p -matroids which is a splitting operation followed by a single element extension. It is proved that the element splitting operation on connected p -matroid yields a connected p -matroid. We give a suﬃcient condition to yield Eulerian p -matroids from Eulerian p -matroids under the element splitting operation. A suﬃcient condition to obtain hamiltonian p -matroid by applying the element splitting operation on p -matroid is also provided.
Combined gene and environmental engineering offers a synergetic strategy to enhance r‐protein production in Chinese hamster ovary cells

Environmental growth‐inhibition conditions (GICs) have been used extensively for increasing cell‐specific productivity (qP) of Chinese hamster ovary (CHO) cells, with the most common being temperature downshift and sodium butyrate (NaBu) treatment. B lymphocyte‐induced maturation protein‐1 (BLIMP1) overexpression in CHO cells can also inhibit cell growth and increase product titers and qP. Given the similar responses, this study evaluated the individual and combined effects of BLIMP1 expression, low temperature, and NaBu treatment on culture performance, cell metabolism, and recombinant protein production of CHO cells. As expected, all three interventions decreased cell growth, arrested cells in G1/G0 cell cycle phase, and increased qP. However, CHO cells presented different responses when considering cell viability, recombinant gene expression, and cell metabolism that indicated differences in the molecular loci by which BLIMP1 and GICs generated higher productivities. Combinations of BLIMP1 expression and GICs acted synergistically to inhibit cell growth and maximize r‐protein production, with the BLIMP1/NaBu condition leading to the most significant improvements in product titers and qP. This latter condition also proved to substantially increase product yields (up to 9.8 g immunoglobulin G1 [IgG1]/L and 2.2 g erythropoietin‐Fc [EPO‐Fc]/L) and qP (up to 179 pg/cell/day [pcd] for IgG1 and 30 pcd for EPO‐Fc) in high‐density perfusion cultures. These findings offered mechanistic insights about the productivity‐enhancing effects of BLIMP1 and GICs, as well as their complementarity for generating highly productive processes.
An Antisense RNA Fine-Tunes Gene Expression of the Type II MazEF Toxin-Antitoxin System

Next-generation RNA sequencing of numerous organisms has revealed that transcription is widespread across the genome, termed pervasive transcription, and does not adhere to annotated gene boundaries. The function of pervasive transcription is enigmatic and has generated considerable controversy as to whether it is transcriptional noise or biologically relevant. ABSTRACT Despite their ubiquitous nature, few antisense RNAs have been functionally characterized, and this class of RNAs is considered by some to be transcriptional noise. Here, we report that an antisense RNA (asRNA), aMEF (antisense mazEF), functions as a dual regulator for the type II toxin-antitoxin (TA) system mazEF. Unlike type I TA systems and many other regulatory asRNAs, aMEF stimulates the synthesis and translation of mazEF rather than inhibition and degradation. Our data indicate that a double-stranded RNA intermediate and RNase III are not necessary for aMEF-dependent regulation of mazEF expression. The lack of conservation of asRNA promoters has been used to support the hypothesis that asRNAs are spurious transcriptional noise and nonfunctional. We demonstrate that the aMEF promoter is active and functional in Escherichia coli despite poor sequence conservation, indicating that the lack of promoter sequence conservation should not be correlated with functionality. IMPORTANCE Next-generation RNA sequencing of numerous organisms has revealed that transcription is widespread across the genome, termed pervasive transcription, and does not adhere to annotated gene boundaries. The function of pervasive transcription is enigmatic and has generated considerable controversy as to whether it is transcriptional noise or biologically relevant. Antisense transcription is one class of pervasive transcription that occurs from the DNA strand opposite an annotated gene. Relatively few pervasively transcribed asRNAs have been functionally characterized, and their regulatory roles or lack thereof remains unknown. It is important to study examples of these asRNAs and determine if they are functional regulators. In this study, we elucidate the function of an asRNA (aMEF) demonstrating that pervasive transcripts can be functional.
Unknown Intent Detection using Multi-Objective Optimization on Deep Learning Classifiers

Modelling and understanding dialogues in a conversation depends on identifying the user intent from the given text. Unknown or new intent detection has became an important task, as in a realistic scenario a user intent may frequently change over time and divert even to an intent previously not encountered. This task of separating the unknown intent samples from known intents, is challenging as the unknown user intent can range from intents similar to the known ones or to something completely different. Prior research for intent discovery often considers this problem as a clas-siﬁcation task where an unknown intent can belong to a ﬁnite or predeﬁned set of known intent classes, thus limiting the scope of such research. In this paper, we tackle the problem of detecting a completely unknown intent without any prior hints about the kind of classes belonging to unknown intents. We propose an effective post-processing method using multi-objective optimization to tune an existing neural network based intent classiﬁer making it capable of detecting unknown in-tents. Thus our method can be plugged into existing deep-learning based classiﬁers and ﬁne-tuned for the purpose of unknown intent detection. We perform experiments using existing state-of-the-art intent classiﬁers and use our method on top of them for unknown intent detection. Our experiments across different domains and real-world datasets show that our method yields signiﬁcant improvement compared with the state-of-the-art methods for unknown intent detection.
Knowledge Grounded Multimodal Dialog Generation in Task-oriented Settings

Knowledge-grounded dialogue generation is the process of formulating an informed response based on both the conversation context and external knowledge. Multi-modality in dialogue systems has paved the way for more robust conversational bots. Any multi-modal system seeks to bridge the gap between language and vision by combining information from image, audio, video, and text, all of which are often complementary. In every task-oriented dialog system, product or service attributes are crucial for satisfying the user’s needs. We propose the task of knowledge grounded response generation in a multi-modal task-oriented dialog setting. We employ a multi-modal knowledge grounded incremental transformer network to generate responses that combine information from context, visuals, and external knowledge. We prepare a Knowledge Grounded Multi-Modal Dialog (KGMMD) dataset to help users in more efﬁcient decision making. Dialog utterances, accompanying images, and knowledge in the form of hotel reviews from the hotel domain are all included in the dataset. The newly formed KGMMD dataset has been subjected to both quantitative and qualitative analysis. Evaluation results on the KGMMD datasets show that the proposed methodology outperforms the baseline models for knowledge grounded multimodal response generation.
Study of Similarity Measures as Features in Classification for Answer Sentence Selection Task in Hindi Question Answering: Language-Specific v/s Other Measures

Answer sentence selection is an important sub-task in Question Answering (QA) that determines the correct answer sentence from a passage. This task can naturally be reduced to the semantic text similarity problem between question and answer candidate. In this work, we investigate the signiﬁcance of various similarity measures for the answer sentence selection task in Hindi an Indo-Aryan language. Karaka relations is the core of dependency annotation scheme used for Hindi and are crucial to syntactico-semantic analysis of the sentence. We investigate this, and compare them to other, hitherto known measures. To investigate and compare the utility of various measures, we develop a test-bench over a benchmark Hindi and English multilingual QA corpus for comparison, making two tool-chains and designing empirical experiments across combinations of similarity measures, sentence embedding schemes, and supervised machine learning models for classiﬁcation. Combining Karaka relations with different similarity measures shows signiﬁcant performance improvement for sentence selection task, suggesting them as potentially a semantic similarity measure. Moreover, our results give us conﬁdence that reﬁnement of Karaka relations extraction to optimal quality will reduce the need for availability of large pre-trained language models.
Automata Approach to Inexact Tree Pattern Matching Using 1-degree Edit Distance

We compare labeled ordered trees based on unit cost 1-degree edit distance that uses operations vertex relabeling, leaf insertion, and leaf deletion. Given an input tree T and a tree pattern P , we ﬁnd all subtrees in T that match P with up to k errors. We show that this problem can be solved by ﬁnite automaton when T and P are represented in linear, preﬁx bar, notation. First, we solve this problem by a pushdown automaton. Then, we show that it can be transformed into a nondeterministic ﬁnite automaton due to its restricted use of the pushdown store. We also show a simulation of the nondeterministic ﬁnite automaton by dynamic programming.
Corpus-based language universals analysis using Universal Dependencies

This paper presents experiments aiming at verifying Greenberg’s universals based on Universal Dependencies (UD) corpora (de Marneffe et al., 2021). We adopt a corpus-based approach that allows us to highlight inconsistencies between corpora of the same language and to explore the causes of these inconsistencies. In addition to intra-language inconsistency, our analysis on 141 corpora, i
Towards Building a Modern Written Tamil Treebank

In this paper, we describe the creation of a morphosyntactically annotated treebank for modern written Tamil following the Universal Dependencies (UD) framework to support the implementation and evaluation of Tamil dependency parsers. At present, this treebank consists of 534 sentences. This paper discusses unique constructions found in Tamil and explains sub-relations and language-speciﬁc relations introduced, apart from outlining the methodology. This carefully annotated treebank can also serve as the benchmark dataset to evaluate Tamil Natural Language Processing (NLP) tools. The treebank will be extended further to cover more complex constructions in Tamil, and annotations will be enriched by incorporating the Enhanced Universal Dependencies scheme.
ParaCotta: Synthetic Multilingual Paraphrase Corpora from the Most Diverse Translation Sample Pair

We release our synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Romanian, Russian, Swedish, Vietnamese, and Chinese. Our method relies only on monolingual data and a neural machine translation system to generate paraphrases, hence simple to apply. We generate multiple translation samples using beam search and choose the most lexically diverse pair according to their sentence BLEU. We compare our generated corpus with the \texttt{ParaBank2}. According to our evaluation, our synthetic paraphrase pairs are semantically similar and lexically diverse.