Miracle-dz/UniFAR-FTUs huggingface dataset:
MATE: Benchmarking Multi-Agent Reinforcement Learning in Distributed Target Coverage Control

We introduce the Multi-Agent Tracking Environment ( MATE ), a novel multi-agent environment simulates the target coverage control problems in the real world. MATE hosts an asymmetric cooperative-competitive game consisting of two groups of learning agents—“cameras” and “targets”—with opposing interests. Speciﬁcally, “cameras”, a group of directional sensors, are mandated to actively control the directional perception area to maximize the coverage rate of targets. On the other side, “targets” are mobile agents that aim to transport cargo between multiple randomly assigned warehouses while minimizing the exposure to the camera sensor networks. To showcase the practicality of MATE, we benchmark the multi-agent reinforcement learning (MARL) algorithms from different aspects, including cooperation, communication, scalability, robustness, and asymmetric self-play. We start by reporting results for cooperative tasks using MARL algorithms (MAPPO, IPPO, QMIX, MADDPG) and the results after augmenting with multi-agent communication protocols (TarMAC, I2C). We then evaluate the effectiveness of the popular self-play techniques (PSRO, ﬁctitious self-play) in an asymmetric zero-sum competitive game. This process of co-evolution between cameras and targets helps to realize a less exploitable
Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network

Measuring the attribution of input features toward the model output is one of the popular post-hoc explanations on the Deep Neural Networks (DNNs). Among various approaches to compute the attribution, the gradient-based methods are widely used to generate attributions, because of its ease of implementation and the model-agnostic characteristic. However, existing gradient integration methods such as Integrated Gradients (IG) suffer from (1) the noisy attributions which cause the unreliability of the explanation, and (2) the selection for the integration path which determines the quality of explanations. FullGrad (FG) is an another approach to construct the reliable attributions by focusing the locality of piece-wise linear network with the bias gradient. Although FG has shown reasonable performance for the given input, as the shortage of the global property, FG is vulnerable to the small perturbation, while IG which includes the exploration over the input space is robust. In this work, we design a new input attribution method which adopt the strengths of both local and global attributions. In particular, we propose a novel approach to distill input features using weak and extremely positive contributor masks. We aggregate the intermediate local attributions obtained from the distillation sequence to provide reliable attribution. We perform the quantitative evaluation compared to various attribution methods and show that our method outperforms others. We also provide the qualitative result that our method obtains object-aligned and sharp attribution heatmap.
Safe Opponent-Exploitation Subgame Refinement

In zero-sum games, an NE strategy tends to be overly conservative confronted with opponents of limited rationality, because it does not actively exploit their weaknesses. From another perspective, best responding to an estimated opponent model is vulnerable to estimation errors and lacks safety guarantees. Inspired by the recent success of real-time search algorithms in developing superhuman AI, we investigate the dilemma of safety and opponent exploitation and present a novel real-time search framework, called Safe Exploitation Search (SES), which continuously interpolates between the two extremes of online strategy reﬁnement. We provide SES with a theoretically upper-bounded exploitability and a lower-bounded evaluation performance. Additionally, SES enables computationally efﬁcient online adaptation to a possibly updating opponent model, while previous safe exploitation methods have to recompute for the whole game. Empirical results show that SES signiﬁcantly outperforms NE baselines and previous algorithms while keeping exploitability low at the same time.
Natural image synthesis for the retina with variational information bottleneck representation

In the early visual system, high dimensional natural stimuli are encoded into the trains of neuronal spikes that transmit the information to the brain to produce perception. However, is all the visual scene information required to explain the neuronal responses? In this work, we search for answers to this question by developing a joint model of the natural visual input and neuronal responses using the Information Bottleneck (IB) framework that can represent features of the input data into a few latent variables that play a role in the prediction of the outputs. The correlations between data samples acquired from published experiments on ex-vivo retinas are accounted for in the model by a Gaussian Process (GP) prior. The proposed IB-GP model performs competitively to the state-of-the-art feedforward convolutional networks in predicting spike responses to natural stimuli. Finally, the IB-GP model is used in a closed-loop iterative process to obtain reduced-complexity inputs that elicit responses as elicited by the original stimuli. We found three properties of the retina’s IB-GP model. First, the reconstructed stimuli from the latent variables show robustness in spike prediction across models. Second, surprisingly the dynamics of the high-dimensional stimuli and RGCs’ responses are very well represented in the embeddings of the IB-GP model. Third, the minimum stimuli consist of different patterns: Gabor-type locally high-frequency filters, on-and off-center Gaussians, or a mixture of both. Overall, this work demonstrates that the IB-GP model provides a principled approach for joint learning of the stimuli and retina codes, capturing dynamics of the stimuli-RGCs in the latent space which could help better understand the computation of the early visual system.
Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs

Historically, conditional random fields (CRFs) were popular tools in a variety of application areas from computer vision to natural language processing, but due to their higher computational cost and weaker practical performance, they have, in many situations, fallen out of favor and been replaced by end-to-end deep neural network (DNN) solutions. More recently, combined DNN-CRF approaches have been considered, but their speed and practical performance still falls short of the best performing pure DNN solutions. In this work, we present a generic combined approach in which a log-supermodular CRF acts as a regularizer to encourage similarity between outputs in a structured prediction task. We show that this combined approach is widely applicable, practical (it incurs only a moderate overhead on top of the base DNN solution) and, in some cases, it can rival carefully engineered pure DNN solutions for the same structured prediction task.
Revisit last-iterate convergence of mSGD under milder requirement on step size

Understanding convergence of stochastic gradient descent (SGD) based optimization algorithms can help deal with enormous machine learning problems. To ensure last-iterate convergence of SGD and momentum-based SGD (mSGD), the existing studies usually constrain the step size ϵ n to decay as (cid:80) + ∞ n =1 ϵ 2 n < + ∞ , which however is rather conservative and may lead to slow convergence in the early stage of the iteration. In this paper, we relax this requirement by studying an alternate step size for the mSGD. First, we relax the requirement of the decay on step size to (cid:80) + ∞ n =1 ϵ 2+ η 0 n < + ∞ (0 ≤ η 0 < 1 / 2) . This implies that a larger step size, such as ϵ n = 1 √ n can be utilized for accelerating the mSGD in the early stage. Under this new step size and some common conditions, we prove that the gradient norm of mSGD for a class of non-convex loss functions asymptotically decays to zero. In addition, we show that this step size can indeed help make the iterates of mSGD converge into a neighborhood of the stationary points quicker in the early stage. Finally, we establish the convergence of mSGD under a constant step size ϵ n ≡ ϵ > 0 by removing a common requirement in the literature on strong convexity of the loss functions. Some experiments are given to illustrate the developed results.
Entropy-Driven Mixed-Precision Quantization for Deep Network Design: Appendix

According to the Maximum Entropy Principle [6] and its successful deep learning applications [17, 1, 25, 19], a deep neural network can be regarded as an information system, and the differential entropy of the last output feature map represents the expressiveness of the system. Given by the following theorem: Theorem 1. For any continuous distribution P(x) of mean μ and variance σ, its differential entropy is maximized when P(x) is a Gaussian distribution N (μ, σ).
SoftPatch: Unsupervised Anomaly Detection with Noisy Data

Although mainstream unsupervised anomaly detection (AD) algorithms perform well in academic datasets, their performance is limited in practical application due to the ideal experimental setting of clean training data. Training with noisy data is an inevitable problem in real-world anomaly detection but is seldom discussed. This paper considers label-level noise in image sensory anomaly detection for the first time. To solve this problem, we proposed a memory-based unsupervised AD method, SoftPatch, which efficiently denoises the data at the patch level. Noise discriminators are utilized to generate outlier scores for patch-level noise elimination before coreset construction. The scores are then stored in the memory bank to soften the anomaly detection boundary. Compared with existing methods, SoftPatch maintains a strong modeling ability of normal data and alleviates the overconfidence problem in coreset. Comprehensive experiments in various noise scenes demonstrate that SoftPatch outperforms the state-of-the-art AD methods on the MVTecAD and BTAD benchmarks and is comparable to those methods under the setting without noise.
Learning Fractional White Noises in Neural Stochastic Differential Equations

Differential equations play important roles in modeling complex physical systems. Recent advances present interesting research directions by combining differential equations with neural networks. By including noise, stochastic differential equations (SDEs) allows us to model data with uncertainty and measure imprecision. There are many variants of noises known to exist in many real-world data. For example, previously white noises are idealized and induced by Brownian motions. Nevertheless, there is a lack of machine learning models that can handle such noises. In this paper, we introduce a generalized fractional white noise to existing models and propose an efficient approximation of noise sample paths based on classical integration methods and sparse Gaussian processes. Our experimental results demonstrate that the proposed model can capture noise characteristics such as continuity from various time series data, therefore improving model fittings over existing models. We examine how we can apply our approach to score-based generative models, showing that there exists a case of our generalized noise resulting in a better image generation measure.
ZERODUR strength modeling with Weibull statistical distributions

The decisive influence on breakage strength of brittle materials such as the low expansion glass ceramic ZERODUR is the surface condition. For polished or etched surfaces it is essential if micro cracks are present and how deep they are. Ground surfaces have many micro cracks caused by the generation process. Here only the depths of the micro cracks are relevant. In any case presence and depths of micro cracks are statistical by nature. The Weibull distribution is the model used traditionally for the representation of such data sets. It is based on the weakest link ansatz. The use of the two or three parameter Weibull distribution for data representation and reliability prediction depends on the underlying crack generation mechanisms. Before choosing the model for a specific evaluation, some checks should be done. Is there only one mechanism present or is it to be expected that an additional mechanism might contribute deviating results? For ground surfaces the main mechanism is the diamond grains’ action on the surface. However, grains breaking from their bonding might be moved by the tool across the surface introducing a slightly deeper crack. It is not to be expected that these scratches follow the same statistical distribution as the grinding process. Hence, their description with the same distribution parameters is not adequate. Before including them a dedicated discussion should be performed. If there is additional information available influencing the selection of the model, for example the existence of a maximum crack depth, this should be taken into account also. Micro cracks introduced by small diamond grains on tools working with limited forces cannot be arbitrarily deep. For data obtained with such surfaces the existence of a threshold breakage stress should be part of the hypothesis. This leads to the use of the three parameter Weibull distribution. A differentiation based on the data set alone without preexisting information is possible but requires a large data set. With only 20 specimens per sample such differentiation is not possible. This requires 100 specimens per set, the more the better. The validity of the statistical evaluation methods is discussed with several examples. These considerations are of special importance because of their consequences on the prognosis methods and results. Especially the use of the two parameter Weibull distribution for high strength surfaces has led to non-realistic results. Extrapolation down to low acceptable probability of failure covers a wide range without data points existing and is mainly influenced by the slope determined by the high strength specimens. In the past this misconception has prevented the use of brittle materials for stress loads, which they could have endured easily.
Monitoring grazing intensity: an experiment with canopy spectra applied to satellite remote sensing

Abstract. The quantification of grassland grazing intensity (GI) and its detailed spatial distribution are important for grassland management and ecological protection. Remote sensing has great potential in these areas, but its use is still limited. This study analyzed the impacts of grazing on biophysical properties of vegetation and suggested using biomass to quantify GI because of its stability and interpretability. In comparison to a single spectral index, such as the red edge index (REI), combining REI and a cellulose absorption ratio index calculated from hyperspectral data performs better for biomass estimation. Further, an auxiliary spectral index, called the grazing monitoring index (GMI), was developed based on differences in spectral reflectance in the infrared range. Experiments in a grazing area of the Inner Mongolia grassland indicated that GMI can identify GI, with three range intervals (GMI <0, 0–1, and ≥1) used to describe the biomass distribution. The results showed that combining GMI and biomass was more successful than existing approaches for identifying the grassland variability resulting from the spatial heterogeneity of grazing behavior. The thresholds of biomass for four GI levels (ungrazed, lightly grazed, moderately grazed, and heavily grazed) could be determined by the intersections of biomass distributions. In addition, the approach developed at the on-ground canopy scale was extended to remotely sensed Hyperion data. The results showed that the approach could successfully identify the grazing treatments of blocks in the experimental grazing area. Overall, our study provides inspiration and ideas for using satellite remote sensing for evaluating plant production, standing biomass, and livestock impacts.
Origin of the Gas in the Extended Narrow-Line Region of Nearby Seyfert Galaxies: The Case of Mrk 6

Abstract Ionization cones are one of the most important pieces of evidence supporting the AGN unified model (Antonucci & Miller 1985; Urry & Padovani 1995). Until now, the physical processes at work in the cones are not completely understood. A still open question concerns the origin of the gas. To study the origin of the ionized gas, we first selected a sample of nearby (z<0.03) Seyfert galaxies showing extended [O iii] λ5007 emission. We then observed these galaxies with the MultiPupil Fiber Spectrograph (MPFS) at the 6-m telescope of the Special Astrophysical Observatory (Russia). Here we present very preliminary results on the properties of the narrow-line region (NLR) of the intermediate Seyfert galaxy Mrk 6.
COVID-19 Detection by Using Handcrafted Features Extracted From Chest CT-Scan Images

Coronavirus illness, which was initially diagnosed in 2019 but has propagated rapidly across the globe, has led to increased fatalities. According to professional physicians who examined chest CT scans, COVID-19 behaves differently than various viral cases of pneumonia. Even though the illness only recently emerged, a number of research investigations have been performed wherein the progression of the disease impacts mostly on the lungs are identified using thoracic CT scans. In this work, automated identification of COVID-19 is used by using machine learning classifier trained on more than 1000+ lung CT Scan images. As a result, immediate diagnosis of COVID-19, which is very much necessary in the opinion of healthcare specialists, is feasible. To improve detection accuracy, the feature extraction method are applied on regions of interests. Feature extraction approaches, including Discrete Wavelet Transform (DWT), Grey Level Cooccurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), and Grey-Level Size Zone Matrix (GLSZM) algorithms are used. Then the classification by using Support Vector Machines (SVM) is used. The classification accuracy is assessed by using precision, specificity, accuracy, sensitivity and F-score measures. Among all feature extraction methods, the GLCM approach has given the optimum classification accuracy of 95.6%.
Control Plane TCP Attacks Detection and Prevention in SDN Networks using Deep Learning Model

The separation of the data and control planes and an integrated software-based administration strategy grounded on a central controller are two considerations put forth by the software-defined networking (SDN) paradigm to make it easier to deploy new applications and services. Those architectural concepts set the stage for a software-controlled network that is more flexible, efficient, and dynamic. With SDN networks, the network can be managed by software that uses minimal network hardware components and enables easy interconnection and installation. It provides the easiest mechanism for network administrators to deploy modern applications and security services. As a result, most network services will be more adaptable, programmable, and unrestricted by platforms. However, there are several difficulties and security-related problems with these centralized and programmable approaches. Security problems in the high- speed network are growing as network traffic grows as well. However, there are a variety of security weaknesses in the SDN architecture's components that might be used by intruders to execute malicious deeds and disrupt the infrastructure and its operations. This research proposes a deep learning-based attack classification method to classify the malicious and legitimate hosts and to block unwanted traffic in the SDN networks. This work mainly focuses on the detection and prevention of TCP-based assaults of the control plane in the SDN networks. The experimental results show better classification accuracy with minimum overhead in terms of efficiency.
Performance of Convolutional Recurrent Networks for Handwritten Text Recognition

The globe has been swept up in the popularity of the optical character recognition technology, which can analyze written characters in high-quality photographs. OCR, however, performs poorly in real-world settings for a number of reasons, including differences in the style of writing, varied spacing, poor character detection due to inconsistent handwriting, and complexity of detection due to low image quality. Hence, it is still difficult to recognize text from handwriting. Convolutional and Recurrent neural networks are the foundation of the most effective offline handwritten text recognition techniques currently available. But, they are susceptible to data loss while processing huge text pictures because they are able to learn only higher level features of source domain. To overcome this challenge, many designs based on gated CNN have been developed which also pick up the domain agnostic features. In this study, models based on various designs are constructed and then compared. To assess whether architecture has shown impressive outcomes in recognition of handwritten text, the precision, character error rate, and word error rate are measured.
Contrastive Positive Sample Propagation Along the Audio-Visual Event Line

Visual and audio signals often coexist in natural environments, forming audio-visual events (AVEs). Given a video, we aim to localize video segments containing an AVE and identify its category. It is pivotal to learn the discriminative features for each video segment. Unlike existing work focusing on audio-visual feature fusion, in this paper, we propose a new contrastive positive sample propagation (CPSP) method for better deep feature representation learning. The contribution of CPSP is to introduce the available full or weak label as a prior that constructs the exact positive-negative samples for contrastive learning. Specifically, the CPSP involves comprehensive contrastive constraints: pair-level positive sample propagation (PSP), segment-level and video-level positive sample activation (PSA<inline-formula><tex-math notation="LaTeX">$_{S}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mi>S</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="wang-ieq1-3223688.gif"/></alternatives></inline-formula> and PSA<inline-formula><tex-math notation="LaTeX">$_{V}$</tex-math><alternatives><mml:math><mml:msub><mml:mrow/><mml:mi>V</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="wang-ieq2-3223688.gif"/></alternatives></inline-formula>). Three new contrastive objectives are proposed (i.e., <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_{\text{avpsp}}$</tex-math><alternatives><mml:math><mml:msub><mml:mi mathvariant="script">L</mml:mi><mml:mtext>avpsp</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="wang-ieq3-3223688.gif"/></alternatives></inline-formula>, <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_ \text{spsa}$</tex-math><alternatives><mml:math><mml:msub><mml:mi mathvariant="script">L</mml:mi><mml:mtext>spsa</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="wang-ieq4-3223688.gif"/></alternatives></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$\mathcal {L}_\text{vpsa}$</tex-math><alternatives><mml:math><mml:msub><mml:mi mathvariant="script">L</mml:mi><mml:mtext>vpsa</mml:mtext></mml:msub></mml:math><inline-graphic xlink:href="wang-ieq5-3223688.gif"/></alternatives></inline-formula>) and introduced into both the fully and weakly supervised AVE localization. To draw a complete picture of the contrastive learning in AVE localization, we also study the self-supervised positive sample propagation (SSPSP). As a result, CPSP is more helpful to obtain the refined audio-visual features that are distinguishable from the negatives, thus benefiting the classifier prediction. Extensive experiments on the AVE and the newly collected VGGSound-AVEL100k datasets verify the effectiveness and generalization ability of our method.