Miracle-dz/UniFAR-FTUs huggingface dataset:
Normal-Metal–Superconductor Near-Field Thermal Diodes and Transistors

In recent years there has been a number of proposals of thermal devices operating in the near-field regime that make use of phase-transition materials. Here, we present a theoretical study of near-field thermal diodes and transistors that combine superconducting materials with normal (non-superconducting) metals. To be precise, we show that a system formed by two parallel plates made of Nb and Au can exhibit unprecedented rectification ratios very close to unity at temperatures around Nb superconducting critical temperature and for a wide range of gap size values within the near-field regime. Moreover, we also show that a superconducting Nb layer placed between Au plates can operate as a near-field thermal transistor where the amplification factor can be greatly tuned by varying different parameters such as the temperature and thickness of the Nb layer or the distance between the Nb layer and the Au plates. Overall, our work shows the potential of the use of superconductors for the realization of near-field thermal devices.
Multi-Task Adversarial Attack

Deep neural networks have achieved impressive performance in various areas, but they are shown to be vulnerable to adversarial attacks. Previous works on adversarial attacks mainly focused on the single-task setting. However, in real applications, it is often desirable to attack several models for different tasks simultaneously. To this end, we propose Multi-Task adversarial Attack (MTA), a unified framework that can craft adversarial examples for multiple tasks efficiently by leveraging shared knowledge among tasks, which helps enable large-scale applications of adversarial attacks on real-world systems. More specifically, MTA uses a generator for adversarial perturbations which consists of a shared encoder for all tasks and multiple task-specific decoders. Thanks to the shared encoder, MTA reduces the storage cost and speeds up the inference when attacking multiple tasks simultaneously. Moreover, the proposed framework can be used to generate per-instance and universal perturbations for targeted and non-targeted attacks. Experimental results on the Office-31 and NYUv2 datasets demonstrate that MTA can improve the quality of attacks when compared with its single-task counterpart.
Temporal Surrogate Back-propagation for Spiking Neural Networks

Spiking neural networks (SNN) are usually more energy-efficient as compared to Artificial neural networks (ANN), and the way they work has a great similarity with our brain. Back-propagation (BP) has shown its strong power in training ANN in recent years. However, since spike behavior is non-differentiable, BP cannot be applied to SNN directly. Although prior works demonstrated several ways to approximate the BP-gradient in both spatial and temporal directions either through surrogate gradient or randomness, they omitted the temporal dependency introduced by the reset mechanism between each step. In this article, we target on theoretical completion and investigate the effect of the missing term thoroughly. By adding the temporal dependency of the reset mechanism, the new algorithm is more robust to learning-rate adjustments on a toy dataset but does not show much improvement on larger learning tasks like CIFAR-10. Empirically speaking, the benefits of the missing term are not worth the additional computational overhead. In many cases, the missing term can be ignored.
Kernel Phase and Coronagraphy with Automatic Differentiation

The accumulation of aberrations along the optical path in a telescope produces distortions and speckles in the resulting images, limiting the performance of cameras at high angular resolution. It is important to achieve the highest possible sensitivity to faint sources, using both hardware and data analysis software. While analytic methods are efficient, real systems are better modeled numerically, but numerical models of complicated optical systems with many parameters can be hard to understand, optimize, and apply. Automatic differentiation or “backpropagation” software developed for machine-learning applications now makes calculating derivatives with respect to aberrations in arbitrary planes straightforward for any optical system. We apply this powerful new tool to the problem of high-angular-resolution astronomical imaging. Self-calibrating observables such as the “closure phase” or “bispectrum” have been widely used in optical and radio astronomy to mitigate optical aberrations and achieve high-fidelity imagery. Kernel phases are a generalization of closure phases valid in the limit of small phase errors. Using automatic differentiation, we reproduce existing kernel phase theory within this framework and demonstrate an extension to the case of a Lyot coronagraph, which is found to have self-calibrating combinations of speckles. which are resistant to phase noise, but only in the very high-wave-front-quality regime. As an illustrative example, we reanalyze Palomar adaptive optics observations of the binary α Ophiuchi, finding consistency between the new pipeline and the existing standard. We present a new Python package morphine that incorporates these ideas, with an interface similar to the popular package poppy, for optical simulation with automatic differentiation. These methods may be useful for designing improved astronomical optical systems by gradient descent.
Geography-Aware Self-Supervised Learning

Contrastive learning methods have significantly narrowed the gap between supervised and unsupervised learning on computer vision tasks. In this paper, we explore their application to geo-located datasets, e.g. remote sensing, where unlabeled data is often abundant but labeled data is scarce. We first show that due to their different characteristics, a non-trivial gap persists between contrastive and supervised learning on standard benchmarks. To close the gap, we propose novel training methods that exploit the spatio-temporal structure of remote sensing data. We leverage spatially aligned images over time to construct temporal positive pairs in contrastive learning and geo-location to design pre-text tasks. Our experiments show that our proposed method closes the gap between contrastive and supervised learning on image classification, object detection and semantic segmentation for remote sensing. Moreover, we demonstrate that the proposed method can also be applied to geo-tagged ImageNet images, improving downstream performance on various tasks.
Fatty acid and lipid contents differentiation in cuts of rabbit meat

ABSTRACT The study aimed at quantifying fatty acids (FA), especially the health-promoting ones, in cuts of rabbit meat. Loin, containing mostly glycolytic muscles, had less lipids than cuts containing oxidative (hind leg) or intermediate muscles (foreleg and brisket). Brisket had the highest polyunsaturated FA (PUFA), odd- and branched-chain FAs (OBCFA) and palmitoleic acid C16:1 n-7 contents, better n-6/n-3 proportion, advantageous nutritional indexes (AI, TI, H/H) than other cuts. Hind leg contained less atherogenic saturated FAs (LCSFA) than other cuts. Foreleg was rich in two important n-3 LC-PUFAs: C20:5 and C22:5. The results suggest that apart from the preferred cuts (loin and hind leg) used in gastronomy, brisket and foreleg should be included for being rich in nutritionally valuable PUFA, especially from the n-3 family. Daily portion (250 g) of foreleg would cover 16% of the requirement for EPA and DHA, and the same amount of brisket or hind leg—11%.
A Sequence-to-Sequence Approach to Dialogue State Tracking

This paper is concerned with dialogue state tracking (DST) in a task-oriented dialogue system. Building a DST module that is highly effective is still a challenging issue, although significant progresses have been made recently. This paper proposes a new approach to dialogue state tracking, referred to as Seq2Seq-DU, which formalizes DST as a sequence-to-sequence problem. Seq2Seq-DU employs two BERT-based encoders to respectively encode the utterances in the dialogue and the descriptions of schemas, an attender to calculate attentions between the utterance embeddings and the schema embeddings, and a decoder to generate pointers to represent the current state of dialogue. Seq2Seq-DU has the following advantages. It can jointly model intents, slots, and slot values; it can leverage the rich representations of utterances and schemas based on BERT; it can effectively deal with categorical and non-categorical slots, and unseen schemas. In addition, Seq2Seq-DU can also be used in the NLU (natural language understanding) module of a dialogue system. Experimental results on benchmark datasets in different settings (SGD, MultiWOZ2.2, MultiWOZ2.1, WOZ2.0, DSTC2, M2M, SNIPS, and ATIS) show that Seq2Seq-DU outperforms the existing methods.
The Pearson Bayes factor: An analytic formula for computing evidential value from minimal summary statistics

Summary In Bayesian hypothesis testing, evidence for a statistical model is quantified by the Bayes factor, which represents the relative likelihood of observed data under that model compared to another competing model. In general, computing Bayes factors is difficult, as computing the marginal likelihood of data under a given model requires integrating over a prior distribution of model parameters. In this paper, I capitalize on a particular choice of prior distribution that allows the Bayes factor to be expressed without integral representation, and I develop a simple formula – the Pearson Bayes factor – that requires only minimal summary statistics as commonly reported in scientific papers, such as the t or F score and the degrees of freedom. In addition to presenting this new result, I provide several examples of its use and report a simulation study validating its performance. Importantly, the Pearson Bayes factor gives applied researchers the ability to compute exact Bayes factors from minimal summary data, and thus easily assess the evidential value of any data for which these summary statistics are provided, even when the original data is not available.
Data-Driven Robust Optimization using Unsupervised Deep Learning

Robust optimization has been established as a leading methodology to approach decision problems under uncertainty. To derive a robust optimization model, a central ingredient is to identify a suitable model for uncertainty, which is called the uncertainty set, containing all scenarios against which we wish to protect. An ongoing challenge in the recent literature is to derive uncertainty sets from given historical data. In this paper we use an unsupervised deep learning method to construct non-convex uncertainty sets from data, which have a more complex structure than the typically considered sets. We show that the trained neural networks can be integrated into a robust optimization model by formulating the adversarial problem as a convex quadratic mixed-integer program. This allows us to derive robust solutions through an iterative scenario generation process. In extensive computational experiments, we compare this approach to a current state-of-the-art approach, which derives uncertainty sets by kernel-based support vector clustering. We find that uncertainty sets derived by the unsupervised deep learning method can give a better description of data, leading to robust solutions that considerably outperform the comparison method both with respect to objective value and feasibility.
The Robot Household Marathon Experiment

In this paper, we present an experiment, designed to investigate and evaluate the scalability and the robustness aspects of mobile manipulation. The experiment involves performing variations of mobile pick and place actions and opening/closing environment containers in a human household. The robot is expected to act completely autonomously for extended periods of time. We discuss the scientific challenges raised by the experiment as well as present our robotic system that can address these challenges and successfully perform all the tasks of the experiment. We present empirical results and the lessons learned as well as discuss where we hit limitations.
A geometric representation unveils learning dynamics in primate neurons

Primates can quickly and advantageously adopt complex rule-based behaviors. We studied acquisition of a classification task while recording single neurons in the dorsal-anterior-cingulate-cortex (dACC) and the Striatum. Monkeys performed trial-by-trial classification on a rich set of multicue patterns, allowing de-novo learning with varying conceptual complexities every few days. To examine neural dynamics during the learning itself, we represent each rule with a spanning set of the space formed by the stimulus features. Because neural preference can be expressed by feature combinations, we can track neural dynamics in geometrical terms in this space, allowing a compact universal description of neural trajectories by observing changes in either vector-magnitude and/or angle-to-rule. We find that a large fraction of cells in both regions follow the behavior during learning. Neurons in the dACC mainly rotate towards the policy, suggesting an increase in selectivity that approximates it; whereas in the Putamen we additionally find a prominent magnitude increase, suggesting strengthening of confidence. Moreover, magnitude increases in the striatum followed rotation in the dACC, and finally, the neural policy at the end of the session predicted next-day behavior. Using this novel framework enables tracking of neural dynamics during learning and suggests differential roles of confidence and policy for the different brain regions.
Frege in Space: A Program of Compositional Distributional Semantics

The lexicon of any natural language encodes a huge number of distinct word meanings. Just to understand this article, you will need to know what thousands of words mean. The space of possible sentential meanings is infinite: In this article alone, you will encounter many sentences that express ideas you have never heard before, we hope. Statistical semantics has addressed the issue of the vastness of word meaning by proposing methods to harvest meaning automatically from large collections of text (corpora). Formal semantics in the Fregean tradition has developed methods to account for the infinity of sentential meaning based on the crucial insight of compositionality, the idea that meaning of sentences is built incrementally by combining the meanings of their constituents. This article sketches a new approach to semantics that brings together ideas from statistical and formal semantics to account, in parallel, for the richness of lexical meaning and the combinatorial power of sentential semantics. We adopt, in particular, the idea that word meaning can be approximated by the patterns of co-occurrence of words in corpora from statistical semantics, and the idea that compositionality can be captured in terms of a syntax-driven calculus of function application from formal semantics.
Understanding Causal Coherence Relations

The research reported in this dissertation focuses on the cognitive processes and representations involved in understanding causal coherence relations in text. Coherence relations are the meaning relations between the information units in the text, such as Cause-Consequence. These relations can be made explicit by means of causal connectives, such as because, or other linguistic indicators of coherence relations. The main assumption underlying this thesis is that causal coherence relations are part of the meaning representation readers construct on the basis of the text. In a series of experiments, the cognitive status of this central assumption is investigated further. The experimental results corroborate the central claim that causal coherence relations are part of the meaning representation of the text, but at the same time allow for a more detailed account of the processes and representation involved in understanding causal coherence relations. To be more precise, the research allows for the conclusion that understanding causal coherence relations is a cognitive process in which readers construct a situational representation of the relation. Three major results contribute to this conclusion. First, evidence is obtained that shows that the presence of explicit indicators (e.g. connectives) influence an inferential process that consists of encoding the general knowledge underlying the causal connection. For instance, when readers interpret the sequence 'John's body was covered with bruises. He had fallen of his bike', the knowledge that falling of a bike may cause bruises is incorporated in the meaning representation. Second, in a recognition experiment evidence was only obtained for a situational representation of the causal coherence relation. Third, the experimental results indicate that readers can make immediate use of knowledge of text structure during the interpretation of causal coherence relations. This knowledge allows them to generate expectations about how the incoming text will be related to the previous context. These results indicate how the understanding of causal relations can be characterized as a process in which the reader integrates explicit text and background knowledge (both general and text structural) to form a situational representation of the information in the text.
Hydration kinetics of compacted low-density bentonite

The hydration kinetics of a compacted low-density bentonite is investigated from both experimental and numerical points of view. A water retention curve and water permeability under saturated conditions were determined to characterize the hydraulic behaviour of the compacted material. The evolution of swelling strains developed during a soaking test performed under oedometer conditions is interpreted as a measure of the hydration kinetics. While soaking was immediate, the vertical strains appeared to develop very slowly during the first two hours of testing. Numerical simulations of the soaking test were performed to achieve a better understanding of the hydration mechanisms. It is shown that the numerical results overestimate the hydration rate of the sample. Two explanations for the very slow hydration are proposed and discussed in the paper, namely the formation of clay gel and an initial drying of the sample.
Protein pattern analysis in tolerant and susceptible wheat cultivars under salinity stress conditions

To investigate proteome pattern of wheat cultivars, young leaves were collected from tillering stage of seedlings two weeks after development of the salinity stress. The extraction of proteins from leaf tissue was done and two dimensional electrophoresis using IPG strips and SDS-PAGE in the control and salinity treatments were performed. In total, 198 and 203 protein spots were identified in tolerant (‘Moghan3’) and susceptible (‘Pishtaz’) cultivars respectively. Also, among these, spots number 21 and 22 were detected with significant IF in ‘Moghan3’ and ‘Pishtaz’ respectively. Two-stage mass spectrometry (MS/MS) was used to identify protein spots. Common identified proteins, including proteins involved in removal of oxidants, Calvin cycle proteins, proteins involved in light reaction of photosynthesis and proton transfer, and heat shock protein were identified on basis of the functional groups and their frequency. In total, ‘Moghan3’ maintained the stability of the structure and performance of carbon metabolism under stress better than susceptible cultivar. In addition, defense against oxidative stress induced by salinity stress was performed by 2-cys peroxiredoxin BAS1 and Cu-Zn SOD proteins that tolerant cultivar defended against oxidative stress better than the susceptible cultivar. The greatest strength of ‘Moghan3’ and major weakness in ‘Pishtaz’ are relying on the unique proteins formed under salinity stress for the removal of oxidants and to maintain the activity of the photosynthetic light reactions, respectively.
From beat tracking to beat expectation: Cognitive-based beat tracking for capturing pulse clarity through time

Pulse is the base timing to which western music is commonly notated, generally expressed by a listener by performing periodic taps with their hand or foot. This cognitive construction helps organize the perception of timed events in music and is the most basic expectation in rhythms. The analysis of expectations, and more specifically the strength with which the beat is felt—the pulse clarity—has been used to analyze affect in music. Most computational models of pulse clarity, and rhythmic expectation in general, analyze the input as a whole, without exhibiting changes through a rhythmic passage. We present Tactus Hypothesis Tracker (THT), a model of pulse clarity over time intended for symbolic rhythmic stimuli. The model was developed based on ideas of beat tracking models that extract beat times from musical stimuli. Our model also produces possible beat interpretations for the rhythm, a fitness score for each interpretation and how these evolve in time. We evaluated the model’s pulse clarity by contrasting against tapping variability of human annotators achieving results comparable to a state-of-the-art pulse clarity model. We also analyzed the clarity metric dynamics on synthetic data that introduced changes in the beat, showing that our model presented doubt in the pulse estimation process and adapted accordingly to beat changes. Finally, we assessed if the beat tracking generated by the model was correct regarding listeners tapping data. We compared our beat tracking results with previous beat tracking models. The THT model beat tracking output showed generally correct estimations in phase but exhibits a bias towards a musically correct subdivision of the beat.
Clinical Characterization of Ulcerative Colitis in Patients with Primary Sclerosing Cholangitis

Objectives The clinical/colonoscopic features of ulcerative colitis (UC) associated with primary sclerosing cholangitis (PSC), the prognostic impact of UC, and the utility of UC screening in PSC patients are unknown. We characterized UC associated with PSC and assessed UC's impact on the prognosis of PSC and the importance of colonoscopic UC screening in PSC patients. Methods We retrospectively analyzed the cases of 77 patients treated for PSC at a single center (April 2000–July 2019). We reviewed the clinical/colonoscopic profiles of the concurrent UC patients and compared the clinical profiles, survival, and primary causes of death between the patients with/without UC (n = 35/n = 42). The details of all patients' colonoscopies were reviewed. Results The concurrent UC group: 17 men, 18 women, diagnosed with PSC at the mean (SD) age of 36 (17) years; 21 patients (60%) had no UC symptoms. Colonoscopy revealed pancolitis in all patients, predominantly affecting the right-sided colon in 30 patients (86%). Lesions were scattered. Backwash ileitis (n = 13, 37%) and rectal sparing (n = 18, 51%) were observed. Most patients had mild UC; some had moderate or more severe UC (median Ulcerative Colitis Endoscopic Index of Severity (UCEIS) score 2; range, 1–5). Ludwig's stage determined by liver biopsy did not correlate with the Mayo endoscopic score for UC. The patients with UC were diagnosed with PSC at a significantly younger age than those without UC (mean (SD), 36 [17] years vs. 55 [19] years, p < 0.0001) and had a significantly higher 5-year survival rate (97.1% vs. 70.5%, p = 0.0028). UC was detected in 19 of 34 asymptomatic patients (56%) who underwent colonoscopy screening. Conclusions Our cohort's clinical/colonoscopic features of UC associated with PSC are more moderate or severe UC than previous cases. The coexistence of UC might affect the prognosis of PSC. In this regard, colonoscopy in PSC patients is an important examination for determining prognosis. There is also asymptomatic UC in patients with PSC. In this regard, screening for colonoscopy in PSC patients is essential. When a diagnosis of PSC is made, immediate colonoscopy is a priority with UC complications in mind.