Miracle-dz/UniFAR-FTUs huggingface dataset:
Josephson effect at finite temperature along the BCS-BEC crossover

The Josephson current-phase characteristics, that arise when a supercurrent flows across two fermionic superfluids separated by a potential barrier, can be controlled by varying either the inter-particle coupling or the temperature. While the coupling dependence has been addressed in detail both theoretically and experimentally for an attractive Fermi gas undergoing the BCS-BEC crossover, a corresponding study of the temperature dependence of the Josephson characteristics is still lacking in this context. Here, we investigate the combined coupling and temperature dependence of the Josephson characteristics in a systematic way for a wide set of barriers, within ranges of height and width that can be experimentally explored. Our study smoothly connects the two limiting cases, of non-overlapping composite bosons at low temperature described by the Gross-Piatevskii equation, and of strongly overlapping Cooper pairs near the critical temperature described by the Ginzburg-Landau equation. In this way, we are able to explore several interesting effects related to how the current-phase characteristics evolve along the BCS-BEC crossover as a function of temperature and of barrier shape. These effects include the coherence length outside the barrier and the pair penetration length inside the barrier (which is related to the proximity effect), as well as the temperature evolution of the Landau criterion in the limit of a vanishingly small barrier. A comparison is also presented between the available experimental data for the critical current and our theoretical results over a wide range of couplings along the BCS-BEC crossover.
Algodynamics: Teaching Algorithms using Interactive Transition Systems

The importance of algorithms and data structures in computer science curricula has been amply recognized. For many students, however, gaining a good understanding of algorithms remains a challenge. Because of the automated nature of sequential algorithms. there is an inherent tension in directly applying the `learning by doing' approach. This partly explains the limitations of efforts like algorithm animation and code tracing. Algodynamics, the approach we propose and advocate, situates algorithms within the framework of transition systems and their dynamics and offers an attractive approach for teaching algorithms. Algodynamics starts with the premise that the key ideas underlying an algorithm can be identified and packaged into interactive transition systems. The algorithm when `opened up', reveals a transition system, shorn of most control aspects, enriched instead with interaction. The design of an algorithm can be carried out by constructing a series of interactive systems, progressively trading interactivity with automation. These transition systems constitute a family of notional machines. We illustrate the algodynamics approach by considering Bubblesort. A sequence of five interactive transition systems culminate in the classic Bubblesort algorithm. The exercise of constructing the individual systems also pays off when coding Bubblesort: a highly modular implementation whose primitives are borrowed from the transition systems. The transition systems used for Bubblesort have been implemented as interactive experiments. These web based implementations are easy to build. The simplicity and flexibility afforded by the algodynamics framework makes it an attractive option to teach algorithms in an interactive way.
Topic-Aware Abstractive Text Summarization

Automatic text summarization aims at condensing a document to a shorter version while preserving the key information. Different from extractive summarization which simply selects text fragments from the document, abstractive summarization generates the summary in a word-by-word manner. Most current state-of-the-art (SOTA) abstractive summarization methods are based on the Transformer-based encoder-decoder architecture and focus on novel self-supervised objectives in pre-training. While these models well capture the contextual information among words in documents, little attention has been paid to incorporating global semantics to better fine-tune for the downstream abstractive summarization task. In this study, we propose a topic-aware abstractive summarization (TAAS) framework by leveraging the underlying semantic structure of documents represented by their latent topics. Specifically, TAAS seamlessly incorporates a neural topic modeling into an encoder-decoder based sequence generation procedure via attention for summarization. This design is able to learn and preserve global semantics of documents and thus makes summarization effective, which has been proved by our experiments on real-world datasets. As compared to several cutting-edge baseline methods, we show that TAAS outperforms BART, a well-recognized SOTA model, by 2%, 8%, and 12% regarding the F measure of ROUGE-1, ROUGE-2, and ROUGE-L, respectively. TAAS also achieves comparable performance to PEGASUS and ProphetNet, which is difficult to accomplish given that training PEGASUS and ProphetNet requires enormous computing capacity beyond what we used in this study.
Niche Construction, Domestic Animals, and the Spread of Farming in Europe

Niche construction theory provides a framework to analyze the environmental effects of changing pastoral practices. In Europe, domesticated herd animals facilitated the expansion of farming by extending the spatial framework of early farming communities, produced milk for caloric security of pioneering farmers, and were partners in coevolutionary relationships that resulted in changes in the human genome and the spread of diseases. Domestic herd animal management practices, however, were not uniform during the Neolithic, and variations in livestock management, use, and environment affected the degree of extensive and intensive human niche construction among early farmers. This paper explores the biological and cultural effects of pastoralism for the establishment of new agricultural niches by examining biological and ecological underpinnings of both domesticated animals and their management strategies. It is suggested that strategies varied in their niche outcomes and ecological legacies, highlighting the roles domestic herd animals played as mechanisms of niche construction for the spread of agriculture. These pastoral effects had implications for the environmental legacies experienced by subsequent human generations, and results provide a fresh avenue of investigation for researchers working on the ecologies of agropastoral societies through time and space.
Studies on Correlation Among Sperm Characteristics, Farrowing Rates by AI and Chromatin Structure in Boars

This study was designed to investigate between the semen characteristics and sperm chromatin structure in boar with different farrowing rates and relationship between fertility by AI and results of sperm chromatin structure assay (SCSA). The CASA (computer-aided sperm analysis) and SCSA were performed with liquid semen in boars. The all SCSA parameters based on the farrowing rates by AI were significantly differ (P<0.05). The significant negative correlations (P<0.05) were observed between all SCSA parameters and farrowing rate obtained by AI in the field. In conclusion, these results suggest that the sperm parameters evaluated in these studies may be useful indicators to predict the fertility by AI.
On the compressive nature of turbulence driven by ionizing feedback in the pillars of the Carina Nebula

The ionizing radiation of massive stars sculpts the surrounding neutral gas into pillar-like structures. Direct signatures of star formation through outflows and jets are observed in these structures, typically at their tips. Recent numerical simulations have suggested that this star formation could potentially be triggered by photoionising radiation, driving compressive modes of turbulence in the pillars. In this study we use recent high-resolution ALMA observations of $^{12}\mathrm{CO}$, $^{13}\mathrm{CO}$, and $\mathrm{C}^{18} \mathrm{O}, \; J = 2-1$ emission to test this hypothesis for pillars in the Carina Nebula. We analyse column density and intensity-weighted velocity maps, and subtract any large-scale bulk motions in the plane of the sky to isolate the turbulent motions. We then reconstruct the dominant turbulence driving mode in the pillars, by computing the turbulence driving parameter $b$, characterised by the relation $\sigma_{\rho/\rho_0} = b \mathcal{M}$ between the standard deviation of the density contrast $\sigma_{\rho/\rho_0}$ (with gas density $\rho$ and its average $\rho_0$) and the turbulent Mach number $\mathcal{M}$. We find values of \mbox{$b\sim0.7$--$1.0$} for most of the pillars, suggesting that predominantly compressive modes of turbulence are driven in the pillars by the ionising radiation from nearby massive stars. We find that this range of $b$ values can produce star formation rates in the pillars that are a factor $\sim 3$ greater than with $b \sim 0.5$, a typical average value of $b$ for spiral-arm molecular clouds. Our results provide further evidence for the potential triggering of star formation in pillars through compressive turbulent motions.
Concentration of solutions to random equations with concentration of measure hypotheses

We propose here to study the concentration of random objects that are implicitly formulated as fixed points to equations Y = f (X) where f is a random mapping. Starting from an hypothesis taken from the concentration of the measure theory, we are able to express precisely the concentration of such solutions, under some contractivity hypothesis on f. This statement has important implication to random matrix theory, and is at the basis of the study of some optimization procedures like the logistic regression for instance. In those last cases, we give precise estimations to the first statistics of the solution Y which allows us predict the performances of the algorithm.
SOrT-ing VQA Models : Contrastive Gradient Learning for Improved Consistency

Recent research in Visual Question Answering (VQA) has revealed state-of-the-art models to be inconsistent in their understanding of the world - they answer seemingly difficult questions requiring reasoning correctly but get simpler associated sub-questions wrong. These sub-questions pertain to lower level visual concepts in the image that models ideally should understand to be able to answer the reasoning question correctly. To address this, we first present a gradient-based interpretability approach to determine the questions most strongly correlated with the reasoning question on an image, and use this to evaluate VQA models on their ability to identify the relevant sub-questions needed to answer a reasoning question. Next, we propose a contrastive gradient learning based approach called Sub-question Oriented Tuning (SOrT) which encourages models to rank relevant sub-questions higher than irrelevant questions for an pair. We show that SOrT improves model consistency by up to 6.5% points over existing approaches, while also improving visual grounding and robustness to rephrasings of questions.
Blind Federated Edge Learning

We study federated edge learning (FEEL), where wireless edge devices, each with its own dataset, learn a global model collaboratively with the help of a wireless access point acting as the parameter server (PS). At each iteration, wireless devices perform local updates using their local data and the most recent global model received from the PS, and send their local updates to the PS over a wireless fading multiple access channel (MAC). The PS then updates the global model according to the signal received over the wireless MAC, and shares it with the devices. Motivated by the additive nature of the wireless MAC, we propose an analog ‘over-the-air’ aggregation scheme, in which the devices transmit their local updates in an uncoded fashion. However, unlike recent literature on over-the-air FEEL, here we assume that the devices do not have channel state information (CSI), while the PS has imperfect CSI. On the other hand, the PS is equipped with multiple antennas to alleviate the destructive effect of the channel, exacerbated due to the lack of perfect CSI. We design a receive beamforming scheme at the PS, and show that it can compensate for the lack of perfect CSI when the PS has a sufficient number of antennas. We also derive the convergence rate of the proposed algorithm highlighting the impact of the lack of perfect CSI, as well as the number of PS antennas. Both the experimental results and the convergence analysis illustrate the performance improvement of the proposed algorithm with the number of PS antennas, where the wireless fading MAC becomes deterministic despite the lack of perfect CSI when the PS has a sufficiently large number of antennas.
Individual corpora predict fast memory retrieval during reading

The corpus, from which a predictive language model is trained, can be considered the experience of a semantic system. We recorded everyday reading of two participants for two months on a tablet, generating individual corpus samples of 300/500K tokens. Then we trained word2vec models from individual corpora and a 70 million-sentence newspaper corpus to obtain individual and norm-based long-term memory structure. To test whether individual corpora can make better predictions for a cognitive task of long-term memory retrieval, we generated stimulus materials consisting of 134 sentences with uncorrelated individual and norm-based word probabilities. For the subsequent eye tracking study 1-2 months later, our regression analyses revealed that individual, but not norm-corpus-based word probabilities can account for first-fixation duration and first-pass gaze duration. Word length additionally affected gaze duration and total viewing duration. The results suggest that corpora representative for an individual’s long-term memory structure can better explain reading performance than a norm corpus, and that recently acquired information is lexically accessed rapidly.
Interactions between nanoparticles and plants: phytotoxicity and defense mechanisms

ABSTRACT With the rapid development of nanotechnology, the potential releases of nanoparticles (NPs) have drawn considerable attention. Plants are essential fundamental components of all ecosystems, and the interaction between NPs and plants is an indispensable aspect of the risk assessment. Originally, this review focuses on NP phytotoxicity, which is an important precondition to promote the application of nanotechnology and to avoid the potential ecological risks. Both enhancive and inhibitive effects of various NPs on different plants’ growth have been documented. In this paper, the influence factors of nanotoxicity and the mechanisms of these toxic effects are also summarized. Subsequently, the defense mechanisms are presented as well. Eventually, this review puts forward the prospects of research direction of the environmental behavior and the biological toxicity of NPs, hoping to bring new ideas to the further research on NP phytotoxicity. Abbreviations: 1O2: singlet oxygen; AA: ascorbate; ABA: abscisic acid; APX: ascorbate peroxidase; B-Ag NPs: biosynthesized silver nanoparticles; Bt: Bacillus thuringiensis; CAT: catalase; CDs: carbon dots; Cu/Zn-SOD: copper/Zinc SOD; DHA: dehydroascorbate; DHAR: dehydroascorbate reductase; GA: gibberellic acid: GA-Ag NPs: coated silver nanoparticles; GPOX: guaiacol peroxidase; GPX: glutathione peroxidase; GR: glutathione reductase; GSH: glutathione; GSSG: oxidized glutathione; GST: glutathione S-transferases; •OH: hydroxyl radical; HA: high amylose; IAA: indole-3-acetic acid; LA: low amylose; MA: medium amylose; MDA: malondialdehyde; MDHAR: monodehydroascorbate reductase; NPs: nanoparticles; : superoxide radical; POD: promoting peroxidase; ROS: reactive oxygen species; SOD: superoxide dismutase; t-ZR: trans-zeatin-riboside
The Detection of Thoracic Abnormalities ChestX-Det10 Challenge Results

The detection of thoracic abnormalities challenge is organized by the Deepwise AI Lab. The challenge is divided into two rounds. In this paper, we present the results of 6 teams which reach the second round. The challenge adopts the ChestX-Det10 dateset proposed by the Deepwise AI Lab. ChestX-Det10 is the first chest X-Ray dataset with instance-level annotations, including 10 categories of disease/abnormality of 3,543 images. The annotations are located at https://github.com/Deepwise-AILab/ChestX-Det10-Dataset. In the challenge, we randomly split all data into 3001 images for training and 542 images for testing.
Hysteresis and Hydrodynamic Collapse in Leidenfrost Vapor Layers

During the Leidenfrost effect, a thin insulating vapor layer separates an evaporating liquid from a hot solid. Here we show that Leidenfrost vapor layers can be sustained at much lower temperatures than those required for formation. We use an electrical technique with microsecond resolution to measure the thickness of the vapor and find that explosive failure occurs at temperatures and thicknesses that do not depend on material and fluid properties, such as thermal conductivity or salinity, for smooth, metallic surfaces. Numerical simulations confirm that the hydrodynamic flow of the vapor determines the lowest possible temperature for maintaining stable Leidenfrost films.
Morphology of Active Deformable 3D Droplets

We numerically investigate the morphology and disclination line dynamics of active nematic droplets in three dimensions. Although our model only incorporates the simplest possible form of achiral active stress, active nematic droplets display an unprecedented range of complex morphologies. For extensile activity finger-like protrusions grow at points where disclination lines intersect the droplet surface. For contractile activity, however, the activity field drives cup-shaped droplet invagination, run-and-tumble motion or the formation of surface wrinkles. This diversity of behaviour is explained in terms of an interplay between active anchoring, active flows and the dynamics of the motile dislocation lines. We discuss our findings in the light of biological processes such as morphogenesis, collective cancer invasion and the shape control of biomembranes, suggesting that some biological systems may share the same underlying mechanisms as active nematic droplets.
Uniform Approximation of Continuous Functions by Nontrivial Simple Functions

We prove that every nonnegative continuous real-valued function on a given compact metric space is the uniform limit of some increasing sequence of nonnegative simple functions being linear combinations of indicators of open sets; here the nontriviality is relative to the standard choice(s) of approximating simple functions for measurable functions, where one loses control over the indicated measurable sets. Thus the standard uniform approximation of bounded nonnegative measurable real-valued functions by increasing nonnegative simple functions may be improved for nonnegative continuous real-valued functions on compact metric spaces. There are also some interesting consequences regarding semi-continuous functions and smooth functions.
Asteroseismic Analysis of $\delta$ Scuti Components of Binary systems: The Case of KIC 8504570

The present work concerns the Asteroseismology of the Kepler detached eclipsing binary KIC 8504570. Particularly, it focuses on the pulsational behaviour of the oscillating component of this system and the estimation of its physical parameters in order to enrich the so far poor sample of this kind of systems. Using spectroscopic observations, the spectral type of the primary component was determined and used to create accurate light curves models and estimate its absolute parameters. The light curve residuals were subsequently analysed using Fourier transformation techniques to obtain the pulsation models. Theoretical models of $\delta$ Scuti stars were employed to identify the oscillation modes of the six detected independent frequencies of the pulsator. In addition, more than 385 combination frequencies were also detected. The absolute and the pulsational properties of the $\delta$ Scuti star of this system are discussed and compared with all the currently known similar cases. Moreover, using a recent (empirical) luminosity-pulsation period relationship for $\delta$ Scuti stars, the distance of the system was estimated.
Could preoperative sonographic criteria predict the difficulty of laparoscopic cholecystectomy?

Background: Although laparoscopic cholecystectomy (LC) is the gold standard approach for gallbladder diseases, this sometimes may face difficulties and require conversion to open surgery. The preoperative ultrasonographic study may provide information about the probability of difficult LC, but the data in this term are uncertain. We assessed the value of preoperative ultrasonographic findings for the prediction of LC's difficulty. Materials and Methods: The current prospective clinical trial was conducted on 150 patients who were candidates for LC due to symptomatic gallstone. All of the patients underwent ultrasonography study preoperatively, and then, LC was performed. The surgeon completed a checklist regarding the easy or difficult surgical criteria. Finally, the values of ultrasonographic findings for the prediction of LC difficulty were evaluated. Results: Among the 150 included patients, 80 had easy LC and 70 had difficult LC. Statistically significant differences were found between the two groups of easy and difficult LC regarding gallbladder wall thickness (P = 0.008), stone impaction (P = 0.009), and gallbladder flow (P = 0.04). The area under the curve (standard error [SE]) for the thickness of the gallbladder wall, flow in the gallbladder wall, and stone impaction was 0.598 ± 0.048, 0.543 ± 0.047, and 0.554 ± 0.047, respectively (P < 0.05). The highest specificity was for gallbladder wall flow (100%). Binary logistic regression showed that stone impaction had predictive value for determining difficult LC (odds ratio = 3.10; 95% confidence interval: 1.03–9.30; P = 0.04). Conclusion: Although a significant difference was observed between two groups in terms of impacted stone, flow in the gallbladder wall, and thickness of the gallbladder wall, only stone impaction had predictive value for determining difficult LC.
ColloQL: Robust Text-to-SQL Over Search Queries

Translating natural language utterances to executable queries is a helpful technique in making the vast amount of data stored in relational databases accessible to a wider range of non-tech-savvy end users. Prior work in this area has largely focused on textual input that is linguistically correct and semantically unambiguous. However, real-world user queries are often succinct, colloquial, and noisy, resembling the input of a search engine. In this work, we introduce data augmentation techniques and a sampling-based content-aware BERT model (ColloQL) to achieve robust text-to-SQL modeling over natural language search (NLS) questions. Due to the lack of evaluation data, we curate a new dataset of NLS questions and demonstrate the efficacy of our approach. ColloQL’s superior performance extends to well-formed text, achieving an 84.9% (logical) and 90.7% (execution) accuracy on the WikiSQL dataset, making it, to the best of our knowledge, the highest performing model that does not use execution guided decoding.