Miracle-dz/UniFAR-FTUs huggingface dataset:
Research Progress and Challenges on Application-Driven Adversarial Examples: A Survey

Great progress has been made in deep learning over the past few years, which drives the deployment of deep learning–based applications into cyber-physical systems. But the lack of interpretability for deep learning models has led to potential security holes. Recent research has found that deep neural networks are vulnerable to well-designed input examples, called adversarial examples. Such examples are often too small to detect, but they completely fool deep learning models. In practice, adversarial attacks pose a serious threat to the success of deep learning. With the continuous development of deep learning applications, adversarial examples for different fields have also received attention. In this article, we summarize the methods of generating adversarial examples in computer vision, speech recognition, and natural language processing and study the applications of adversarial examples. We also explore emerging research and open problems.
On an Axisymmetric Direct Simulation Monte Carlo Method

This study proposes an application of the variable time step (VTS) method to solve the non-conservation of fluxes encountered in the conventional direct simulation Monte Carlo (DSMC) method in axisymmetric simulations. A legacy parallel DSMC code with an unstructured cell using C++language (PDSC++) was employed by adjusting the reference scale of the original VTS to fulfill the requirements of axisymmetric simulation. Three test cases were simulated to verify the proposed axisymmetric DSMC method. First, the hypersonic flow past a cylinder was simulated using different wall boundary conditions. Second, the hypersonic flow past a 1.6-m sphere was simulated considering complicated air chemistry. Third, the flow field of the hypersonic flow past a double-cone blunt body was simulated and compared with the experimental data. The results showed that the newly proposed axisymmetric DSMC method can meet the simulation accuracy without an inconvenience through particle cloning and destruction.
Plasma Sheath Modelling for Computational Aerothermodynamics and Magnetohydrodynamics

ABSTRACT To date, plasma sheath effects have not been incorporated into most CFD simulations of magnetohydrodynamics (MHD) or aerothermodynamics due to the high computational costs involved. The accurate modeling of both collisional and collisionless plasma sheaths for hypersonic applications is here presented. This is followed by the outline of recent numerical methods that enable the sheaths to be integrated with large time steps and to be incorporated within CFD codes. For the first time, the electron energy transport in non-equilibrium is here integrated in coupled form with the plasma sheath within a MHD process. Some example test problems are then presented including MHD in a scramjet combustor and electron transpiration cooling in a hypersonic flow. Such show that the plasma sheath effects can be added within CFD simulations with little loss in computational efficiency. The inclusion of plasma sheath models is seen to be critical in predicting accurately such problems.
ON OPTIMAL THRESHOLDS FOR PAIRS TRADING IN A ONE-DIMENSIONAL DIFFUSION MODEL

Abstract We study the static maximization of long-term averaged profit, when optimal preset thresholds are determined to describe a pairs trading strategy in a general one-dimensional ergodic diffusion model of a stochastic spread process. An explicit formula for the expected value of a certain first passage time is given, which is used to derive a simple equation for determining the optimal thresholds. Asymptotic arbitrage in the long run of the threshold strategy is observed.
Gender-Aware Estimation of Depression Severity Level in a Multimodal Setting

Depression is a severe psychological disorder that is experienced by a significant number of individuals across the globe. It greatly changes the way one thinks, triggering a constant decline in mood. Studies have shown that gender can act as a good indicator of depression. In this paper, we analyse the effects of gender information in the estimation of depression. We have carried out different experiments on the benchmark data set named Distress Analysis Interview Corpus - a Wizard of Oz (DAIC-WOZ). Concretely, we discovered that a) gender information substantially improves the performance of depression severity estimation, and b) adversarially learning to predict the depression score distributed by gender improves the performance of depression severity estimation.
A Deep Learning-Based Modeling of a 270 V-to-28 V DC-DC Converter Used in More Electric Aircrafts

This article presents a novel approach for black-box modeling of 270 V-to-28 V dc–dc step-down converters used in more electric aircrafts. These converters normally feed constant power loads. The proposed deep learning approach, uses offline experimental data of the converter to find an accurate model that reproduces its behavior. It covers a broad range of loading conditions to build a model that replicates the whole behavior of the converter. This article compares the performance of the proposed method, which requires a very low computational burden once the model is trained, with that of a conventional recurrent neural network topology. Results presented in this article show the ability of the obtained solution to accurately emulate the behavior of the real step-down converter when the internal structure is unknown, with no knowledge of the internal parameters, thus preventing disclosure of manufacturer's confidential data. The modeling strategy presented in this article is validated with experimental data by using a step-down converter used in aircrafts. The approach is compared to existing modeling techniques to test its accuracy. This approach can also be applied to many power devices, including diverse types of power converters, power supplies, or filters among others.
Hybrid-Three-Level Current-Fed Series-Resonant Isolated DC-DC Converter and its Optimization Modulation Strategy

In this article, a hybrid three-level current-fed series-resonant isolated bidirectional dc–dc converter is proposed, containing an interleaved parallel bidirectional buck/boost converter at the low-voltage side and a half-bridge three-level converter at the high-voltage side. The operation principles and characteristics of the proposed converter are thoroughly analyzed with the duty-cycle plus dual phase-shift control. The relationship between the general case of three independent control variables and the transfer power is derived. Then, a modulation strategy optimization algorithm by taking the minimum root mean square (rms) value of the resonant current as the objective function is presented. Under the optimal operation point, the converter operates efficiently with the minimum rms value of the resonant current and accordingly increases the efficiency. The detailed experimental results verify the correctness and feasibility of the proposed converter and modulation strategy.
Angular Triplet Loss-based Camera Network for ReID

Person re-identification (ReID) is a challenging cross-camera retrieval task to identify pedestrians. Many complex network structures are proposed recently and many of them concentrate on multi-branch features to achieve high performance. However, they are too heavy-weight to deploy in realworld applications. Additionally, pedestrian images are often captured by different surveillance cameras, so the varied lights, perspectives and resolutions result in inevitable multi-camera domain gaps for ReID. To address these issues, this paper proposes ATCN, a simple but effective angular triplet loss-based camera network, which is able to achieve compelling performance with only global features. In ATCN, a novel angular distance is introduced to learn a more discriminative feature representation in the embedding space. Meanwhile, a lightweight camera network is designed to transfer global features to more discriminative features. ATCN is designed to be simple and flexible so it can be easily deployed in practice. The experiment results on various benchmark datasets show that ATCN outperforms many SOTA approaches.
Feature Importance Analysis for Customer Management of Insurance Products

Optimizing customer contact strategies is important to improving customer experience, increasing sales and improving business profitability. This study focuses on finding an optimal time to contact customers, using demographic features provided by a private insurance broker and area characteristics from national census data. We train machine learning models and interpret the results using SHAP to analyze how each feature explains customer contactability. Among all the interesting results, we find that for older people, the best time to contact is during late evenings and nights.
Deep Recurrent Interpolation Networks for Anomalous Sound Detection

An anomalous sound detection (ASD) system detects substantial deviations from the norm and reports the degree of abnormality through an anomaly score. An important application scenario is the detection of malfunctions in factory machinery. Recent approaches train autoencoders on small segments of the sound's time-frequency representation and use the reconstruction error as a measure of abnormality. However, it was recently shown that this approach leads to consistently higher reconstruction errors for the edge frames of the segments. To alleviate this problem, the Interpolation Deep Neural Network (IDNN) predicts the center frame from the remaining context frames. In this work, we propose DRINK - Deep Recurrent INterpolation NetworKs, an extension of the aforementioned IDNN that enables a variable amount of center and context frames. Moreover, we use a Long-Short Term Memory network to explicitly account for the sequential nature of sound as opposed to simple feed-forward neural networks in the original work. We show that under the right setting of context and center frames, our method is able to outperform the IDNN and autoencoder baselines on a dataset of recordings from factory machinery in 13 out of 16 cases.
Multiplicative Attention Mechanism for Multi-horizon Time Series Forecasting

Multi-horizon time series forecasting plays an important role in many industrial and business decision processes. To grasp complex and various patterns across different time series is the crucial step in achieving promising performance. However, most deep learning-based forecasting approaches simply take series-specific static (i.e. time-invariant) covariates as input features, which can fail to capture the complex pattern variation for each possible time series. In this paper, we propose a novel multiplicative attention-based architecture to tackle such forecasting problem. Our modification to multi-head attention layers leverages the series-specific covariates to build flexible attention functions for each possible time series. This improvement contributes to greater representation capacity to grasp different patterns across related time series. Experiment results demonstrate that our approach achieves state-of-the-art performance on a variety of real-world datasets.
Compact Modeling of the Switching Dynamics and Temperature Dependencies in TiOₓ Memristors—Part II: Physics-Based Model

In the second part of this series, we propose a physics-based model for describing the temperature dependence of TiO<sub><italic>x</italic></sub>-based memristors, both switching and static. We show that the current–voltage (<inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula>) characteristics of memristor in the nonswitching regime, indicating a Schottky emission mechanism, can be described by minor modifications to the Schottky current equation. This leads to a physics-based static <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> compact model. Simultaneously, we show that the temperature dependence of the switching dynamics model parameters naturally emerges as a mere scaling factor from the static <inline-formula> <tex-math notation="LaTeX">${I}$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">${V}$ </tex-math></inline-formula> model. This is a computationally efficient approach, which does not require any additional parameters to extend the switching dynamics model for incorporating thermal dependence.
Progressive Data Augmentation Method for Remote Sensing Ship Image Classification Based on Imaging Simulation System and Neural Style Transfer

Deep learning has shown great power in processing remote sensing data, especially for fine-grained remote sensing ship image classification. However, the lack of a large amount of effective training data greatly limits the performance of neural networks. Based on current data augmentation methods, images of ships on the sea generated for remote sensing have the problem of distortion, blurring, and poor diversity. To tackle this problem, we propose a novel progressive remote sensing ship image data augmentation method that combines ship simulation samples and a neural style transfer (NST) based network to generate a large amount of transferred remote sensing ship images. Our method consists of two stages. The first stage uses a visible light imaging simulation system to generate ship simulation samples through three-dimensional models of real images. This stage can significantly increase the diversity of the training dataset. For the second stage, to eliminate the domain gap between real ship images and ship simulation samples, a few real images and a newly designed NST-based network called Sim2RealNet are employed to realize style transfer from simulation samples to real images. The proposed method was applied to a variety of ship targets to verify its effectiveness compared to other data augmentation methods on remote sensing image classification tasks. The experimental results demonstrate the effectiveness of the proposed method.
Overcoming coarse coding in visual cortex via multiplexing: neural correlations differ dramatically when stimulus bundles are presented

Sensory receptive fields are large enough that they can contain more than one perceptible stimulus. How, then, can the brain encode information about each of the stimuli that may be present at once? We recently showed that when more than one stimulus is present, single neurons can fluctuate between coding one vs. the other(s) across some time period, suggesting a form of neural multiplexing of different stimuli (Caruso et al., 2018). Here we investigate how such fluctuations are coordinated across the neural population by analyzing the spike count (“noise”) correlations observed between pairs of visual cortex (V1, V4) neurons when stimuli are presented jointly. We found a novel pattern of V1 noise correlations: distinct distributions of positive and negative values, depending on whether the two neurons in the pair have similar or different preferences for the component stimuli. Distinct distributions of spike count correlations based on tuning preferences were also seen in V4 for adjacent stimuli, but were not observed in either structure for single stimuli or when the two gratings were superimposed and formed a single plaid. Importantly, the bimodal correlation patterns were most pronounced among pairs of neurons showing the strongest evidence for multiplexing. These findings suggest multiple stimuli evoke different response dynamics than those evoked by single stimuli, lending support to the multiplexing hypothesis and suggesting a means by which information about multiple stimuli can be preserved despite the apparent coarseness of sensory coding.How the brain separates information about multiple objects despite overlap in the neurons responsive to each item is not well understood. Here we show that pairs of neurons in primate V1 and V4 show unique patterns of correlated firing when there are multiple objects in the visual scene. These patterns were most pronounced in neurons that showed fluctuating activity consistent with switching between encoding each object across time (i.e. time division multiplexing). Both strongly positive and strongly negative correlations were observed, depending on whether the neurons in the pair exhibited similar or different stimulus preferences. These patterns were absent when only one object was presented, suggesting that they may play a key role in preserving information about multiple items.
Similar cognitive processing synchronizes brains, hearts, and eyes

Neural, physiological and behavioral signals synchronize between human subjects in a variety of settings. Multiple hypotheses have been proposed to explain this interpersonal synchrony, but there is no clarity under which conditions it arises, for which signals, or whether there is a common underlying mechanism. We hypothesized that similar cognitive processing of a shared stimulus is the source of interpersonal synchrony, measured here as inter-subject correlation. To test this we presented informative videos to participants in an attentive and distracted condition and subsequently measured information recall. Inter-subject correlation was observed for electro-encephalography, gaze position, pupil size and heart rate, but not respiration and head movements. The strength of correlation was co-modulated in the different signals, changed with attentional state, and predicted subsequent recall of information presented in the videos. There was robust within-subject coupling between brain, heart and eyes, but not respiration or head movements. The results suggest that inter-subject correlation is the result of similar cognitive processing and thus emerges only for those signals that exhibit a robust brain-body connection. While physiological and behavioral fluctuations may be driven by multiple features of the stimulus, correlation with other individuals is co-modulated by the level of attentional engagement with the stimulus. Significance The synchronization of human neural, physiological and behavioral signals happens in a variety of settings. In this work we show that physiological synchrony requires only two things, namely, effective cognitive processing of a common stimulus, and a robust coupling between brain activity and the physiological signal in question. We confirm this theory for heart rate, pupil size, gaze position and saccade rate, as positive examples, and respiration and head movements as negative examples. We show that the strength of this correlation is co-modulated, i.e. correlation is modulated in unison for all signals in which correlation is detected. We propose that this common modulation is the result of attentional engagement with the stimulus.
Perception affects the brain’s metabolic response to sensory stimulation

Processing of incoming sensory stimulation triggers an increase of cerebral perfusion and blood oxygenation (neurovascular response) as well as an alteration of the metabolic neurochemical profile (neurometabolic response). Here we show that perceived and unperceived isoluminant chromatic flickering stimuli designed to have similar neurovascular responses as measured by blood oxygenation level dependent functional MRI (BOLD-fMRI) in primary visual cortex (V1) have markedly different neurometabolic responses as measured by functional MRS. In particular, a significant regional buildup of lactate, an index of aerobic glycolysis, and glutamate, an index of malate-aspartate shuttle, occurred in V1 only when the flickering is perceived, without any relation with behavioral or physiological variables. Wheras the BOLD-fMRI signal in V1, a proxy for input to V1, was insensitive to flickering perception by design, the BOLD-fMRI signal in secondary visual areas was larger during perceived than unperceived flickering indicating increased output from V1. These results indicate that the upregulation of energy metabolism induced by visual stimulation depends on the type of information processing taking place in V1, and that 1H-fMRS provides unique information about local input/output balance that is not measured by BOLD-fMRI. Significance statement Visual perception has a measurable metabolic effect in the primary visual cortex (V1). Here we show that the upregulation of energy metabolism induced by isoluminant chromatic flickering depends on subjective visual perception. Within V1, perceived and unperceived stimuli that are contrast-matched to elicit similar blood-oxygenation level-dependent fMRI response are associated with clearly distinct neurochemical profiles. Specifically, regional accumulations of lactate (an index of aerobic glycolysis) and glutamate (an index of malate-aspartate shuttle) only develop during perceived stimuli, together with a larger activation of secondary visual areas. Our results imply a dissociation between metabolic and functional response, and indicate that that the upregulation of energy metabolism induced by visual stimulation depends on the type of information processing taking place in V1.
Neuromuscular connectomes across development reveal synaptic ordering rules

In mammals, the connections between motor neurons and muscle fibers profoundly reorganize in the early postnatal period. To better understand this synaptic rewiring we traced out all the connectivity in muscles at successive ages in the mouse using serial section scanning electron microscopy in a muscle at birth and Brainbow-based and XFP-based fluorescent reconstructions in neonatal and older muscles respectively. Our data indicate that axons prune about 85% of their branches in the first two weeks of postnatal life, and that while much of this pruning leaves neuromuscular junctions with only one remaining axon (a ∼8-fold reduction), it also causes a ∼6-fold reduction in the number of muscle fibers that possess more than one neuromuscular junction. Unexpectedly, the simplification of the wiring diagram was not haphazard but rather was constrained by the tendency for neurons to maintain co-innervation the longest with other neurons based on their proximity in an abstract rank order. This synaptic ordering preference was even significant at birth when connectivity was the most overlapping but became more striking as development proceeded and was even obvious in the few adult muscle fibers that retained more than one axon at different neuromuscular junctions. Analysis of properties of muscle fibers sharing axons at developing ages and changes in the physical distance between neuromuscular junctions that were maintained in young versus older muscles suggests that the rank order of motor neurons is based on their relative similarity in activity patterns. This same ranking governs both the close-proximity synaptic competitions within neuromuscular junctions and the long-distance competitions that remove or maintain synapses millimeters apart meaning that all neuromuscular rewiring is based on the same global activity ordering rule. We think it is likely that this ranking is related to the ultimate recruitment order of motor axon activity as first described by (Henneman, 1957). Thus the emerging structure of neuromuscular circuitry is a product of its function: initial nearly all-to-all connectivity gives rise to a well-organized system of axons, allowing for the orderly recruitment of neurons during a smoothly graded behavior.