Miracle-dz/UniFAR-FTUs huggingface dataset:
ZeroGrad : Mitigating and Explaining Catastrophic Overfitting in FGSM Adversarial Training

Making deep neural networks robust to small adversarial noises has recently been sought in many applications. Adversarial training through iterative projected gradient descent (PGD) has been established as one of the mainstream ideas to achieve this goal. However, PGD is computationally demanding and often prohibitive in case of large datasets and models. For this reason, single-step PGD, also known as FGSM, has recently gained interest in the field. Unfortunately, FGSM-training leads to a phenomenon called ``catastrophic overfitting,"which is a sudden drop in the adversarial accuracy under the PGD attack. In this paper, we support the idea that small input gradients play a key role in this phenomenon, and hence propose to zero the input gradient elements that are small for crafting FGSM attacks. Our proposed idea, while being simple and efficient, achieves competitive adversarial accuracy on various datasets.
The Curious Case of Argon.

In the modern search for life elsewhere in the Universe, we are broadly looking for the following: the planets similar to Earth - physical indicators of habitability, and the manifestation of life - the biological signatures. A biosignature is a measured parameter that has a high probability of being caused by the living organisms, either atmospheric gas species or some surface features. Therefore, the focus of a search is on a product or phenomena produced by the living systems, mostly by microorganisms as these are the most abundant on our planet like, say, methane. However, we may need to distinguish the terms `biosignature' and `bioindicator'. A biosignature is what living organisms produce - a bioproduct, while a bioindicator may be anything necessary for life as we know it, such as water or a rocky planet. Oxygen in this case is a double biomarker; first, it is a byproduct of oxygenic photosynthesis and, second, it is a signature of a complex life, because complex highly organized life requires high levels of oxygen. It is possible that there are other such bioindicators. For example, in the atmospheric compositions of terrestrial planets in our Solar System (including Titan), argon is one of the major constituents, moreover it was recently acknowledged to be a `biologically' active gas, exhibiting organprotective and neuroprotective properties, especially under hypoxic conditions. Here we propose that argon in the atmosphere of a rocky planet is a bioindicator of a highly organized life, provided that the planet is already deemed potentially habitable: with water, atmosphere, and of a certain age allowing for the complex life to evolve. We also delineate its possible detection methods.
PeaceGAN: A GAN-based Multi-Task Learning Method for SAR Target Image Generation with a Pose Estimator and an Auxiliary Classifier

Although generative adversarial networks (GANs) are successfully applied to diverse fields, training GANs on synthetic aperture radar (SAR) data is a challenging task due to speckle noise. On the one hand, in a learning perspective of human perception, it is natural to learn a task by using information from multiple sources. However, in the previous GAN works on SAR image generation, information on target classes has only been used. Due to the backscattering characteristics of SAR signals, the structures of SAR images are strongly dependent on their pose angles. Nevertheless, the pose angle information has not been incorporated into GAN models for SAR images. In this paper, we propose a novel GAN-based multi-task learning (MTL) method for SAR target image generation, called PeaceGAN, that has two additional structures, a pose estimator and an auxiliary classifier, at the side of its discriminator in order to effectively combine the pose and class information via MTL. Extensive experiments showed that the proposed MTL framework can help the PeaceGAN’s generator effectively learn the distributions of SAR images so that it can better generate the SAR target images more faithfully at intended pose angles for desired target classes in comparison with the recent state-of-the-art methods.
Single Object Tracking through a Fast and Effective Single-Multiple Model Convolutional Neural Network

Object tracking becomes critical especially when similar objects are present in the same area. Recent state-of-the-art (SOTA) approaches are proposed based on taking a matching network with a heavy structure to distinguish the target from other objects in the area which indeed drastically downgrades the performance of the tracker in terms of speed. Besides, several candidates are considered and processed to localize the intended object in a region of interest for each frame which is time-consuming. In this article, a special architecture is proposed based on which in contrast to the previous approaches, it is possible to identify the object location in a single shot while taking its template into account to distinguish it from the similar objects in the same area. In brief, first of all, a window containing the object with twice the target size is considered. This window is then fed into a fully convolutional neural network (CNN) to extract a region of interest (RoI) in a form of a matrix for each of the frames. In the beginning, a template of the target is also taken as the input to the CNN. Considering this RoI matrix, the next movement of the tracker is determined based on a simple and fast method. Moreover, this matrix helps to estimate the object size which is crucial when it changes over time. Despite the absence of a matching network, the presented tracker performs comparatively with the SOTA in challenging situations while having a super speed compared to them (up to $120 FPS$ on 1080ti). To investigate this claim, a comparison study is carried out on the GOT-10k dataset. Results reveal the outstanding performance of the proposed method in fulfilling the task.
Understanding the extreme luminosity of DES14X2fna

We present DES14X2fna, a high-luminosity, fast-declining Type IIb supernova (SN IIb) at redshift z = 0.0453, detected by the Dark Energy Survey (DES). DES14X2fna is an unusual member of its class, with a light curve showing a broad, luminous peak reaching Mr ≃ −19.3 mag 20 d after explosion. This object does not show a linear decline tail in the light curve until ≃60 d after explosion, after which it declines very rapidly (4.30 ± 0.10 mag 100 d−1 in the r band). By fitting semi-analytic models to the photometry of DES14X2fna, we find that its light curve cannot be explained by a standard 56Ni decay model as this is unable to fit the peak and fast tail decline observed. Inclusion of either interaction with surrounding circumstellar material or a rapidly-rotating neutron star (magnetar) significantly increases the quality of the model fit. We also investigate the possibility for an object similar to DES14X2fna to act as a contaminant in photometric samples of SNe Ia for cosmology, finding that a similar simulated object is misclassified by a recurrent neural network (RNN)-based photometric classifier as an SN Ia in ∼1.1–2.4 per cent of cases in DES, depending on the probability threshold used for a positive classification.
RobustNet: Improving Domain Generalization in Urban-Scene Segmentation via Instance Selective Whitening

Enhancing the generalization capability of deep neural networks to unseen domains is crucial for safety-critical applications in the real world such as autonomous driving. To address this issue, this paper proposes a novel instance selective whitening loss to improve the robustness of the segmentation networks for unseen domains. Our approach disentangles the domain-specific style and domain-invariant content encoded in higher-order statistics (i.e., feature covariance) of the feature representations and selectively removes only the style information causing domain shift. As shown in Fig. 1, our method provides reasonable predictions for (a) low-illuminated, (b) rainy, and (c) unseen structures. These types of images are not included in the training dataset, where the baseline shows a significant performance drop, contrary to ours. Being simple yet effective, our approach improves the robustness of various backbone networks without additional computational cost. We conduct extensive experiments in urban-scene segmentation and show the superiority of our approach to existing work. Our code is available at this link1.
Near surface defects: Cause of deficit between internal and external open‐circuit voltage in solar cells

Interface recombination in a complex multilayered thin‐film solar structure causes a disparity between the internal open‐circuit voltage (VOC,in), measured by photoluminescence, and the external open‐circuit voltage (VOC,ex), that is, a VOC deficit. Aspirations to reach higher VOC,ex values require a comprehensive knowledge of the connection between VOC deficit and interface recombination. Here, a near‐surface defect model is developed for copper indium di‐selenide solar cells grown under Cu‐excess conditions. These cell show the typical signatures of interface recombination: a strong disparity between VOC,in and VOC,ex, and extrapolation of the temperature dependent q·VOC,ex to a value below the bandgap energy. Yet, these cells do not suffer from reduced interface bandgap or from Fermi‐level pinning. The model presented is based on experimental analysis of admittance and deep‐level transient spectroscopy, which show the signature of an acceptor defect. Numerical simulations using the near‐surface defects model show the signatures of interface recombination without the need for a reduced interface bandgap or Fermi‐level pinning. These findings demonstrate that the VOC,in measurements alone can be inconclusive and might conceal the information on interface recombination pathways, establishing the need for complementary techniques like temperature dependent current–voltage measurements to identify the cause of interface recombination in the devices.
Libri-adhoc40: A dataset collected from synchronized ad-hoc microphone arrays

Recently, there is a research trend on ad-hoc mi-crophone arrays. However, most research was conducted on simulated data. Although some datasets were collected with a small number of distributed devices, they were not synchronized which hinders the fundamental theoretical research on ad-hoc microphone arrays. To address this issue, this paper presents a synchronized speech corpus, named Libri-adhoc40, which collects the replayed Librispeech data from loudspeakers by ad-hoc microphone arrays of 40 strongly synchronized distributed nodes in a real office environment. Besides, to provide the evaluation target for speech frontend processing and other applications, we also recorded the replayed speech in an anechoic chamber. We trained several multi-device speech recognition systems on both the Libri-adhoc40 dataset and a simulated dataset. Experimental results demonstrate the validity of the proposed corpus which can be used as a benchmark to reflect the trend and difference of the models with different ad-hoc microphone arrays. The dataset is online available at https://github.com/ISmallFish/Libri-adhoc40.
A Survey on Ethical Hacking: Issues and Challenges

Security attacks are growing in an exponential manner and their impact on existing systems is seriously high and can lead to dangerous consequences. However, in order to reduce the effect of these attacks, penetration tests are highly required, and can be considered as a suitable solution for this task. Therefore, the main focus of this paper is to explain the technical and non-technical steps of penetration tests. The objective of penetration tests is to make existing systems and their corresponding data more secure, efficient and resilient. In other terms, pen testing is a simulated attack with the goal of identifying any exploitable vulnerability or/and a security gap. In fact, any identified exploitable vulnerability will be used to conduct attacks on systems, devices, or personnel. This growing problem should be solved and mitigated to reach better resistance against these attacks. Moreover, the advantages and limitations of penetration tests are also listed. The main issue of penetration tests that it is efficient to detect known vulnerabilities. Therefore, in order to resist unknown vulnerabilities, a new kind of modern penetration tests is required, in addition to reinforcing the use of shadows honeypots. This can also be done by reinforcing the anomaly detection of intrusion detection/prevention system. In fact, security is increased by designing an efficient cooperation between the different security elements and penetration tests.
Colloquium:
 Nonthermal pathways to ultrafast control in quantum materials

We review recent progress in utilizing ultrafast light-matter interaction to control the macroscopic properties of quantum materials. Particular emphasis is placed on photoinduced phenomena that do not result from ultrafast heating effects but rather emerge from microscopic processes that are inherently nonthermal in nature. Many of these processes can be described as transient modifications to the free-energy landscape resulting from the redistribution of quasiparticle populations, the dynamical modification of coupling strengths and the resonant driving of the crystal lattice. Other pathways result from the coherent dressing of a material's quantum states by the light field. We discuss a selection of recently discovered effects leveraging these mechanisms, as well as the technological advances that led to their discovery. A road map for how the field can harness these nonthermal pathways to create new functionalities is presented.
BrainPainter v2: Mouse Brain Visualization Software

BrainPainter is a software for the 3D visualization of human brain structures; it generates colored brain images using user-defined biomarker data for each brain region. However, BrainPainter is only able to generate human brain images. In this paper, we present updates to the existing BrainPainter software which enables the generation of mouse brain images. We load meshes for each mouse brain region, based on the Allen Mouse Brain Atlas, into Blender, a powerful 3D computer graphics engine. We then use Blender to color each region and generate images of subcortical, outer-cortical, inner-cortical, top and bottom view renders. In addition to those views, we add new render angles and separate visualization settings for the left and right hemispheres. While BrainPainter traditionally ran from the browser ( https://brainpainter.csail.mit.edu ), we also created a graphical user interface that launches image-generation requests in a user-friendly way, by connecting to the Blender backend via a Docker API. We illustrate a use case of BrainPainter for modeling the progression of tau protein accumulation in a mouse study. Our contributions can help neuroscientists visualize brains in mouse studies and show disease progression. In addition, integration into Blender can subsequently enable the generation of complex animations using a moving camera, generation of complex mesh deformations that simulate tumors and other pathologies, as well as visualization of toxic proteins using Blender's particle system.
Spatial Characterization of Electromagnetic Random Channels

The majority of stochastic channel models rely on the electromagnetic far-field assumption, which allows to decompose the channel in terms of plane waves. The far-field assumption breaks down in future applications that push towards the electromagnetic near-field region, such as those where the use of electromagnetically large antenna arrays is envisioned. Motivated by this consideration, we show how physical principles can be used to derive a plane-wave scalar channel model that is also valid in the reactive near-field region. Precisely, we show that narrowband wave propagation through a three-dimensional scattered medium can be generally modeled as a linear and space-variant system. We first review the physics principles that lead to a closed-form deterministic plane-wave representation of the channel impulse response. This serves as a basis for deriving a stochastic representation of the channel in terms of statistically independent Gaussian random coefficients for spatially stationary random propagation environments. The very desirable property of spatial stationarity can always be retained in the radiative near-field region by excluding reactive propagation mechanisms confined in close proximity to the source. Remarkably, the provided stochastic representation is directly connected to the Fourier spectral representation of a general stationary spatial random field.
HDR Video Reconstruction: A Coarse-to-fine Network and A Real-world Benchmark Dataset

High dynamic range (HDR) video reconstruction from sequences captured with alternating exposures is a very challenging problem. Existing methods often align low dynamic range (LDR) input sequence in the image space using optical flow, and then merge the aligned images to produce HDR output. However, accurate alignment and fusion in the image space are difficult due to the missing details in the over-exposed regions and noise in the under-exposed regions, resulting in unpleasing ghosting artifacts. To enable more accurate alignment and HDR fusion, we introduce a coarse-to-fine deep learning framework for HDR video reconstruction. Firstly, we perform coarse alignment and pixel blending in the image space to estimate the coarse HDR video. Secondly, we conduct more sophisticated alignment and temporal fusion in the feature space of the coarse HDR video to produce better reconstruction. Considering the fact that there is no publicly available dataset for quantitative and comprehensive evaluation of HDR video reconstruction methods, we collect such a benchmark dataset, which contains 97 sequences of static scenes and 184 testing pairs of dynamic scenes. Extensive experiments show that our method outperforms previous state-of-the-art methods. Our code and dataset can be found at https://guanyingc.github. io/DeepHDRVideo.
High‐Pressure Phase Transformations in Hexagonal and Amorphous Selenium

Hexagonal selenium transforms to a new phase at 140 ± 10 kbar. The c/a ratio of 1.42 at the transformation corresponds to the same c/a ratio for tellurium at its transformation at 40 kbar. It has not been possible to decide unambiguously a unit cell from the diffraction pattern of the new phase. The high‐pressure phase is irreversible being retained upon releasing the pressure. Amorphous Se slowly transforms to the crystalline state under pressure and eventually develops the same unknown high‐pressure structure as the hexagonal form. The pressure‐cycled amorphous material retains its crystalline structure upon releasing the pressure. This structure appears to be identical to the irreversible hexagonal phase. It is thought that the high‐pressure structure is superconducting.
Lower Bounds on the Generalization Error of Nonlinear Learning Models

We study in this paper lower bounds for the generalization error of models derived from multi-layer neural networks, in the regime where the size of the layers is commensurate with the number of samples in the training data. We derive explicit generalization lower bounds for general biased estimators, in the cases of two-layered networks. For linear activation function, the bound is asymptotically tight. In the nonlinear case, we provide a comparison of our bounds with an empirical study of the stochastic gradient descent algorithm. In addition, we derive bounds for unbiased estimators, which show that the latter have unacceptable performance for truly nonlinear networks. The analysis uses elements from the theory of large random matrices.
Lie triple maps on generalized matrix algebras

Abstract. In this article, we introduce the notion of Lie triple centralizer as follows. Let A be an algebra, and φ : A → A be a linear mapping. We say that φ is a Lie triple centralizer whenever φ([[a, b], c]) = [[φ(a), b], c] for all a, b, c ∈ A. Then we characterize the general form of Lie triple centralizers on a generalized matrix algebra U and under some mild conditions on U we present the necessary and sufficient conditions for a Lie triple centralizer to be proper. As an application of our results, we characterize generalized Lie triple derivations on generalized matrix algebras.
Nanocomposite of superparamagnetic maghemite nanoparticles and ferroelectric liquid crystal

In this study we present a novel type of hybrid nanocomposite prepared by mixing maghemite nanoparticles and a chiral liquid crystalline compound. The hybrid system exhibits ferroelectric as well superparamagnetic properties. The impact of nanoparticles and the effect of an applied magnetic field on the ferroelectric liquid crystalline properties were established. The ferroelectric properties are conserved up to a concentration of 5.6% of maghemite. The magnetic behavior of the composites is typical for a system of superparamagnetic nanoparticles with inter-particle dipolar interactions and surface spin disorder.
The Pregravid Vascular Risk Factor Profile of Low-Risk Women Who Develop Pregnancy Outcomes That Predict Future Cardiovascular Disease

Background: Women with a history of certain adverse outcomes in pregnancy (preterm birth, delivery of a small-for-gestational age [SGA] infant, preeclampsia, and gestational diabetes mellitus [GDM]) have an elevated lifetime prevalence of metabolic syndrome (MetS) and cardiovascular disease, compared with their peers. However, it is not known if MetS precedes the index pregnancy in young, nulliparous women who experience these antepartum outcomes. Thus, we sought to evaluate the relationship between pregravid cardiovascular risk factor profile and these pregnancy outcomes in low-risk women. Methods: In this prospective preconception cohort study, 1183 newly married women underwent systematic assessment of cardiovascular risk factors (anthropometry, blood pressure, lipids, glucose) at median 24.7 weeks before pregnancy, whereupon they were followed for the outcomes of preterm birth, SGA delivery, preeclampsia, and GDM. Results: Women who had pregravid MetS (harmonized definition) (n = 49) were more likely to have a Caesarean delivery than their peers (61.4% vs. 38.6%, p = 0.003). However, they did not have a higher incidence of preterm delivery, SGA, preeclampsia, or GDM. Similarly, women who had at least one of these adverse pregnancy outcomes (n = 141) did not have a higher prevalence of MetS or any of its component disorders before pregnancy. Indeed, before pregnancy, there were no significant differences between these women and their peers in waist circumference, body mass index, blood pressure, fasting glucose, triglycerides, low-density-lipoprotein, or high-density-lipoprotein cholesterol. Conclusions: The adverse cardiovascular risk factor profile that is seen in women with a history of preterm birth, SGA, preeclampsia, or GDM does not necessarily manifest before their pregnancy.