Miracle-dz/UniFAR-FTUs huggingface dataset:
On Numerosity of Deep Neural Networks

Recently, a provocative claim was published that number sense spontaneously emerges in a deep neural network trained merely for visual object recognition. This has, if true, far reaching significance to the fields of machine learning and cognitive science alike. In this paper, we prove the above claim to be unfortunately incorrect. The statistical analysis to support the claim is flawed in that the sample set used to identify number-aware neurons is too small, compared to the huge number of neurons in the object recognition network. By this flawed analysis one could mistakenly identify number-sensing neurons in any randomly initialized deep neural networks that are not trained at all. With the above critique we ask the question what if a deep convolutional neural network is carefully trained for numerosity? Our findings are mixed. Even after being trained with number-depicting images, the deep learning approach still has difficulties to acquire the abstract concept of numbers, a cognitive task that preschoolers perform with ease. But on the other hand, we do find some encouraging evidences suggesting that deep neural networks are more robust to distribution shift for small numbers than for large numbers.
Thermodynamic signatures of the polaron-molecule transition in a Fermi gas

We consider the highly spin-imbalanced limit of a two-component Fermi gas, where there is a small density of $\downarrow$ impurities attractively interacting with a sea of $\uparrow$ fermions. In the single-impurity limit at zero temperature, there exists the so-called polaron-molecule transition, where the impurity sharply changes its character by binding a $\uparrow$ fermion at sufficiently strong attraction. Using a recently developed variational approach, we calculate the thermodynamic properties of the impurity, and we show that the transition becomes a smooth crossover at finite temperature due to the thermal occupation of excited states in the impurity spectral function. However, remnants of the single-impurity transition are apparent in the momentum-resolved spectral function, which can in principle be probed with Raman spectroscopy. We furthermore show that the Tan contact exhibits a characteristic non-monotonic dependence on temperature that provides a signature of the zero-temperature polaron-molecule transition. For a finite impurity density, we argue that descriptions purely based on the behavior of the Fermi polaron are invalid near the polaron-molecule transition, since correlations between impurities cannot be ignored. In particular, we show that the spin-imbalanced system undergoes phase separation at low temperatures due to the strong attraction between $\uparrow\downarrow$ molecules induced by the Fermi sea. Thus, we find that the impurity spectrum and the induced impurity-impurity interactions are key to understanding the phase diagram of the spin-imbalanced Fermi gas.
Fredholm property and essential spectrum of 3-D Dirac operators with regular and singular potentials

ABSTRACT We consider the 3-D Dirac operator with variable regular magnetic and electrostatic potentials, and singular potentials (1) where (2) is the singular potential with being a matrix and is the delta-function with support on a surface which divides on two open domains with the common boundary Σ, is a vector-function on with values in are the standard Dirac matrices. We associate with the formal Dirac operator an unbounded operator in generated by with domain in consisting of functions satisfying transmission conditions on Σ. We consider the self-adjointness of operator , its Fredholm properties, and the essential spectrum in the case if Σ is either a closed -surface or an unbounded -hypersurface with a regular behaviour at infinity. As application we consider the electrostatic and Lorentz scalar δ-shell interactions.
Identification of state functions by physically-guided neural networks with physically-meaningful internal layers

Substitution of well-grounded theoretical models by data-driven predictions is not as simple in engineering and sciences as it is in social and economic fields. Scientific problems suffer most times from paucity of data, while they may involve a large number of variables and parameters that interact in complex and non-stationary ways, obeying certain physical laws. Moreover, a physically-based model is not only useful for making predictions, but to gain knowledge by the interpretation of its structure, parameters, and mathematical properties. The solution to these shortcomings seems to be the seamless blending of the tremendous predictive power of the data-driven approach with the scientific consistency and interpretability of physically-based models. We use here the concept of physically-constrained neural networks (PCNN) to predict the input-output relation in a physical system, while, at the same time fulfilling the physical constraints. With this goal, the internal hidden state variables of the system are associated with a set of internal neuron layers, whose values are constrained by known physical relations, as well as any additional knowledge on the system. Furthermore, when having enough data, it is possible to infer knowledge about the internal structure of the system and, if parameterized, to predict the state parameters for a particular input-output relation. We show that this approach, besides getting physically-based predictions, accelerates the training process, reduces the amount of data required to get similar accuracy, filters partly the intrinsic noise in the experimental data and provides improved extrapolation capacity.
Exploring Self-Attention for Visual Odometry

Visual odometry networks commonly use pretrained optical flow networks in order to derive the ego-motion between consecutive frames. The features extracted by these networks represent the motion of all the pixels between frames. However, due to the existence of dynamic objects and texture-less surfaces in the scene, the motion information for every image region might not be reliable for inferring odometry due to the ineffectiveness of dynamic objects in derivation of the incremental changes in position. Recent works in this area lack attention mechanisms in their structures to facilitate dynamic reweighing of the feature maps for extracting more refined egomotion information. In this paper, we explore the effectiveness of self-attention in visual odometry. We report qualitative and quantitative results against the SOTA methods. Furthermore, saliency-based studies alongside specially designed experiments are utilized to investigate the effect of self-attention on VO. Our experiments show that using self-attention allows for the extraction of better features while achieving a better odometry performance compared to networks that lack such structures.
Deep Active Surface Models

Active Surface Models have a long history of being useful to model complex 3D surfaces. But only Active Contours have been used in conjunction with deep networks, and then only to produce the data term as well as meta-parameter maps controlling them. In this paper, we advocate a much tighter integration. We introduce layers that implement them that can be integrated seamlessly into Graph Convolutional Networks to enforce sophisticated smoothness priors at an acceptable computational cost.We will show that the resulting Deep Active Surface Models outperform equivalent architectures that use traditional regularization loss terms to impose smoothness priors for 3D surface reconstruction from 2D images and for 3D volume segmentation.
Minimal Zeeman field requirement for a topological transition in superconductors

Platforms for creating Majorana quasiparticles rely on superconductivity and breaking of time-reversal symmetry. By studying continuous deformations to known trivial states, we find that the relationship between superconducting pairing and time reversal breaking imposes rigorous bounds on the topology of the system. Applying these bounds to s-wave systems with a Zeeman field, we conclude that a topological phase transition requires that the Zeeman energy at least locally exceed the superconducting pairing by the energy gap of the full Hamiltonian. Our results are independent of the geometry and dimensionality of the system.
Close Category Generalization for Out-of-Distribution Classification

Out-of-distribution generalization is a core challenge in machine learning. We introduce and propose a solution to a new type of out-of-distribution evaluation, which we call close category generalization. This task specifies how a classifier should extrapolate to unseen classes by considering a bi-criteria objective: (i) on in-distribution examples, output the correct label, and (ii) on out-of-distribution examples, output the label of the nearest neighbor in the training set. In addition to formalizing this problem, we present a new training algorithm to improve the close category generalization of neural networks. We compare to many baselines, including robust algorithms and out-of-distribution detection methods, and we show that our method has better or comparable close category generalization. Then, we investigate a related representation learning task, and we find that performing well on close category generalization correlates with learning a good representation of an unseen class and with finding a good initialization for few-shot learning. The code is available at https://github.com/yangarbiter/close-category-generalization
A Review of Generalized Zero-Shot Learning Methods

Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples under the condition that some output classes are unknown during supervised learning. To address this challenging task, GZSL leverages semantic information of the seen (source) and unseen (target) classes to bridge the gap between both seen and unseen classes. Since its introduction, many GZSL models have been formulated. In this review paper, we present a comprehensive review on GZSL. First, we provide an overview of GZSL including the problems and challenges. Then, we introduce a hierarchical categorization for the GZSL methods and discuss the representative methods in each category. In addition, we discuss the available benchmark data sets and applications of GZSL, along with a discussion on the research gaps and directions for future investigations.
Characterization of the Chemical and Electrical Properties of Defects at the Niobium-Silicon Interface

The nature and concentration of defects near niobium-silicon interfaces prepared with different silicon surface treatments were characterized using current-voltage (I-V), deep level transient spectroscopy (DLTS), and secondary ion mass spectroscopy (SIMS). All samples have H, C, O, F, and Cl chemical contamination in the Si within 50 nm of the interface and electrically active defects with activation energies of 0.147, 0.247, 0.339, and 0.556 eV above the valence band maximum (E$_{vbm}$). In all cases, the deep level defect concentration is dominated by the hole trap at E$_{vbm}$ + 0.556eV, which we assign to a Nb point defect in Si, presumably Nb$_\textrm{Si}$. This defect is present with concentrations ranging from $7\times10^{13}$ to $5\times10^{14}$ cm$^{-3}$ and depends on the final surface clean process. The optimum surface treatment used in this study is an HF etch followed by an in-situ 100 eV Ar-gas ion milling process. Higher energy ion milling is found to increase the electrically active Nb defect concentration in the Si, and increase the concentration of defects. The HF etch alone removes O from the interface, but results in significant H and F contamination, electrically-active point defect concentrations, and levels of Shockley-Reed-Hall recombination (i.e. Nb/Si Schottky diodes with an ideality factor, n, of $\approx$ 1.6). The RCA clean increases the depth and concentration of H, F, C, and Nb contamination.
Jet-like structures from PSR J1135–6055

Pulsar wind nebulae (PWNe) produced from supersonic runaway pulsars can render extended X-ray structures in the form of tails and prominent jets. In this Letter, we report on the analysis of ∼130 ks observations of the PWN around PSR J1135–6055 that were obtained with the Chandra satellite. The system displays bipolar jet-like structures of uncertain origin, a compact nebula around the pulsar likely formed by the bow shock ahead of it, and a trailing tail produced by the pulsar fast proper motion. The spectral and morphological properties of these structures reveal strong similarities with the PWNe in other runaway pulsars, such as PSR J1509–5850 and Geminga. We discuss their physical origin considering both canonical PWN and jet formation models as well as alternative scenarios that can also yield extended jet-like features following the escape of high-energy particles into the ambient magnetic field.
Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks

Spiking Neural Networks (SNNs) have attracted enormous research interest due to temporal information processing capability, low power consumption, and high biological plausibility. However, the formulation of efficient and high-performance learning algorithms for SNNs is still challenging. Most existing learning methods learn weights only, and require manual tuning of the membrane-related parameters that determine the dynamics of a single spiking neuron. These parameters are typically chosen to be the same for all neurons, which limits the diversity of neurons and thus the expressiveness of the resulting SNNs. In this paper, we take inspiration from the observation that membrane-related parameters are different across brain regions, and propose a training algorithm that is capable of learning not only the synaptic weights but also the membrane time constants of SNNs. We show that incorporating learnable membrane time constants can make the network less sensitive to initial values and can speed up learning. In addition, we reevaluate the pooling methods in SNNs and find that max-pooling will not lead to significant information loss and have the advantage of low computation cost and binary compatibility. We evaluate the proposed method for image classification tasks on both traditional static MNIST, Fashion-MNIST, CIFAR-10 datasets, and neuromorphic N-MNIST, CIFAR10-DVS, DVS128 Gesture datasets. The experiment results show that the proposed method outperforms the state-of-the-art accuracy on nearly all datasets, using fewer time-steps. Our codes are available at https://github.com/fangwei123456/Parametric-Leaky-Integrate-and-Fire-Spiking-Neuron.
Preventive Efficacy of Tenofovir/Emtricitabine Against Severe Acute Respiratory Syndrome Coronavirus 2 Among Pre-Exposure Prophylaxis Users

Abstract Background The preventive effect that tenofovir/emtricitabine (FTC) could have against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in human immunodeficiency virus-negative people is unknown. The objective of this study was to analyze the seroprevalence and clinical manifestations of COVID-19 among users of pre-exposure prophylaxis (PrEP), disoproxil fumarate/FTC (TDF/FTC), or tenofovir alafenamide (TAF)/FTC and to compare it to that of a control group. Methods An observational descriptive study of the seroprevalence of antibodies for SARS-CoV-2 among men who have sex with men and transgender women without use of PrEP (Group 1; n = 250) and PrEP users with TDF/FTC (n = 409) or TAF/FTC (n = 91) (Group 2; n = 500) was conducted from May11, 2020 to June 27, 2020. All participants were provided with a structured questionnaire that collected information on the variables to be analyzed, and testing for immunoglobulin G antibodies to SARS-CoV-2 (chemiluminescent microparticle immunoassay) was then carried out. Results The seroprevalence of SARS-CoV-2 was 9.2% (95% confidence interval [CI], 5.9–13.5) in the group without PrEP and 15.0% (95% CI, 12.0–18.4) in the group with PrEP (P = .026). Among users of TDF/FTC it was 14.7% (95% CI, 11.4–18.5), and in users of TAF/FTC it was 16.5% (95% CI, 9.5–25.7) (P = .661). In those who tested positive for SARS-CoV-2 and receiving PrEP, 57.4% manifested symptoms, compared with 78.3% in the control group (P = .070). In users of TDF/FTC the figure was 53.3% and in users of TAF/FTC the figure was 73.3% (P = .100). The duration of symptoms was 11.5 days in the control group, 9.0 days in PrEP users (P = .116), 7.0 days in users of TDF/FTC, and 13.0 days in users of TAF/FTC (P = .100). Conclusions Users of PrEP, TDF/FTC, or TAF/FTC presented a higher seroprevalence to SARS-CoV-2 than the control group. No statistically significant differences were found in relation to clinical manifestations. The PrEP users should use the same prevention measures as those indicated for the general population.
Feasibility of Mini-Scale Injection Molding for Resource-Efficient Screening of PP-Based Cable Insulation Nanocomposites

This study presents a critical evaluation of the effect of two different sample manufacturing techniques on the morphological and dielectric properties of polypropylene (PP)-based nanocomposites, namely mini-scale injection molding (IM) vs. pilot-scale cast film extrusion. Polarized light microscopy revealed that the IM specimen morphology exhibited a layered “skin-core” type morphology, largely differing from the spherulitic morphology of the corresponding extruded cast films. Higher degree of crystallinity in the IM specimens was evidenced by calorimetric and X-ray diffraction methods. The processing-dependent morphological differences were found to affect the isothermal charging current (ICC) and thermally stimulated depolarization current (TSDC) characteristics due to differences in charge mobility and trapping, thus making direct comparison of IM and cast film specimens non-straightforward. Nevertheless, mini-scale injection molding can be seen as a resource-efficient sample manufacturing method for facilitating early-stage screening of the best-performing material candidates, given that the morphological features are carefully taken into account.
Survival and end-of-life aspects among subjects on long-term noninvasive ventilation

ABSTRACT Background The need for noninvasive ventilation (NIV) is commonly considered a predictor of poor survival, but life expectancy may vary depending on the underlying disease. We studied the factors associated with decreased survival and end-of-life characteristics in an unselected population of subjects starting NIV. Methods We conducted a retrospective study including 205 subjects initiating NIV from 1/1/2012-31/12/2015 who were followed up until 31/12/2017. Results The median survival time was shorter in subjects needing help with activities of daily living than in independent subjects (hazard ratio (HR) for death 1.7, 95% CI 1.2–2.6, P = 0.008) and was also shorter in subjects on long-term oxygen therapy (LTOT) than in those not on LTOT (HR for death 2.8, 95% CI 1.9–4.3, P < 0.001). There was marked difference in survival according to the disease necessitating NIV, and subjects with amyotrophic lateral sclerosis or interstitial lung disease seemed to have the shortest survival. The two most common diseases resulting in the need for NIV were chronic obstructive pulmonary disease (COPD) and obesity hypoventilation syndrome (OHS). The median survival time was 4.4 years in COPD subjects, but the median survival time was not reached in subjects with OHS (HR for death COPD vs. OHS: 3.2, 95% CI 1.9–5.5, P < 0.001). Most of the deceased subjects (55.6%) died in the hospital, while only 20.0% died at home. The last hospitalization admission leading to death occurred through the emergency room in 44.4% of the subjects. Conclusions Survival among subjects starting NIV in this real-life study varied greatly depending on the disease and degree of functional impairment. Subjects frequently died in the hospital after admission through the emergency department. A comprehensive treatment approach with timely advance care planning is therefore needed, especially for those needing help with activities of daily living and those with both NIV and LTOT.
Patogenicidade de nematóides entomopatogênicos a cochonilha-da-raiz-do-cafeeiro Dysmicoccus texensis (Tinsley) (Hemiptera: Pseudococcidae) em laboratório

A cochonilha-da-raiz-do-cafeeiro Dysmicoccus texensis (Tinsley) ataca as raizes desta planta, podendo causar serios danos as plantacoes e consequentes perdas na producao. Os nematoides entomopatogenicos (NEPs) sao agentes eficientes no controle de varias pragas encontradas no solo, o que sugere provavel eficiencia no controle da cochonilha-da-raiz-do-cafeeiro. Dessa forma, este trabalho teve por objetivo avaliar a patogenicidade de alguns isolados de NEPs a cochonilhada-raiz-do-cafeeiro atraves de testes de selecao de isolados, determinacao da concentracao letal maxima (CLM99) e patogenicidade a criptas coletadas no campo. Os isolados Heterorhabditis sp. CCA, Heterorhabditis bacteriophora, Heterorhabditis sp. JPM3.1 e Heterorhabditis sp. JPM3 foram os que apresentaram maior virulencia, alcancando valores maximos de mortalidade de 100, 94, 94 e 82%, respectivamente, na maior concentracao testada (100 juvenis infectantes (JI)/inseto). A CL99 determinada para Heterorhabditis sp. CCA foi estimada em 530 JI/placa. Valor semelhante foi encontrado para Heterorhabditis sp. JPM3 que teve a CL99 igual a 560 JI/placa. Estimando a concentracao por area, o valor obtido para Heterorhabditis sp. CCA e Heterorhabditis sp. JPM3 foi de 28 e 29 JI/cm2 , respectivamente. No teste realizado com criptas, ambos os isolados de NEPs foram patogenicos aos insetos.
Ultra-wideband Optical Receiver Using Electrical Spectrum Decomposition Technique

We propose an electrical spectrum decomposition technique using an ultra-broadband electrical bandwidth divider consisting of newly developed AMUX-based frequency shifters and an adder-subtractor based on in-house InP-HBT technology. Ultra-wideband optical $2\times 56$-GBaud QPSK signal was successfully received and decomposed into narrowband signals.
FEF25-75% Values in Patients with Normal Lung Function Can Predict the Development of Chronic Obstructive Pulmonary Disease

Purpose The forced mid-expiratory flow (FEF25-75%) value is a potentially sensitive marker of obstructive peripheral airflow. We aimed to assess whether FEF25-75% can be an early predictor of chronic obstructive pulmonary disease (COPD). Patients and Methods Between July 1, 2007 and June 31, 2009, we identified 3624 patients who underwent a pulmonary function test (PFT) in Gangnam Severance Hospital. We selected 307 patients aged over 40 years without COPD who had normal PFT results at baseline and who had follow-up PFT records more than 1 year later. A FEF25-75% z-score less than −0.8435 was considered low. We defined COPD as a forced expiratory volume in one second/forced vital capacity value of less than 0.7 before July 31, 2019. Results Among 307 patients, 91 (29.6%) had low FEF25-75% at baseline. After 10 years, the incidence rate of COPD in the low FEF25-75% group was significantly higher than that in the normal FEF25-75% group (41.8% vs 7.4%; P-value<0.001). The Cox proportional hazard model showed that age (hazard ratio [HR] 1.09; P-value<0.001), smoking status (occasional smoker HR, 4.59; P-value<0.001 and long-term smoker HR, 2.18; P-value=0.023), and low FEF25-75% (HR, 3.31; P-value<0.001) were predictive factors for the development of COPD. Conclusion The FEF25-75% value in patients with normal lung function is a useful predictor for the development of COPD. We should carefully monitor patients who present with low FEF25-75% values, even if they have normal lung function.