Miracle-dz/UniFAR-FTUs huggingface dataset:
Long-time behavior of solutions of superlinear systems of differential equations

This paper establishes the precise asymptotic behavior, as time t tends to inﬁnity, for nontrivial, decaying solutions of genuinely nonlinear systems of ordinary diﬀerential equations. The lowest order term in these systems, when the spatial variables are small, is not linear, but rather positively homogeneous of a degree greater than one. We prove that the solution behaves like ξt − p , as t → ∞ , for a nonzero vector ξ and an explicit number p > 0.
Singular perturbation analysis for a coupled KdV-ODE system

Asymptotic stability is with no doubts an essential property to be studied for any system. This analysis often becomes very difficult for coupled systems and even harder when different timescales appear. The singular perturbation method allows to decouple a full system into what are called the reduced order system and the boundary layer system, to get simpler stability conditions for the original system. In the infinite-dimensional setting, we do not have a general result making sure this strategy works. This papers is devoted to this analysis for some systems coupling the Korteweg-the Vries equation and an ordinary differential equation with different timescales. More precisely, We obtain stability results and Tikhonov-type theorems.
Offline and online energy-efficient monitoring of scattered uncertain logs using a bounding model

Monitoring the correctness of distributed cyber-physical systems is essential. Detecting possible safety violations can be hard when some samples are uncertain or missing. We monitor here black-box cyber-physical system, with logs being uncertain both in the state and timestamp dimensions: that is, not only the logged value is known with some uncertainty, but the time at which the log was made is uncertain too. In addition, we make use of an over-approximated yet expressive model, given by a non-linear extension of dynamical systems. Given an offline log, our approach is able to monitor the log against safety specifications with a limited number of false alarms. As a second contribution, we show that our approach can be used online to minimize the number of sample triggers, with the aim at energetic efficiency. We apply our approach to three benchmarks, an anesthesia model, an adaptive cruise controller and an aircraft orbiting system.
Stealthy Peers: Understanding Security Risks of WebRTC-Based Peer-Assisted Video Streaming

—As an emerging service for in-browser content delivery, peer-assisted delivery network (PDN) is reported to offload up to 95% of bandwidth consumption for video streaming, significantly reducing the cost incurred by traditional CDN services. With such benefits, PDN services significantly impact today’s video streaming and content delivery model. However, their security implications have never been investigated. In this paper, we report the first effort to address this issue, which is made possible by a suite of methodologies, e.g., an automatic pipeline to discover PDN services and their customers, and a PDN analysis framework to test the potential security and privacy risks of these services. Our study has led to the discovery of 3 representative PDN providers, along with 134 websites and 38 mobile apps as their customers. Most of these PDN customers are prominent video streaming services with millions of monthly visits or app downloads (from Google Play). Also found in our study are another 9 top video/live streaming websites with each equipped with a proprietary PDN solution. Most importantly, our analysis on these PDN services has brought to light a series of security risks, which have never been reported before, including free riding of the public PDN services, video segment pollution, exposure of video viewers’ IPs to other peers, and resource squatting. All such risks have been studied through controlled experiments and measurements, under the guidance of our institution’s IRB. We have responsibly disclosed these security risks to relevant PDN providers, who have acknowledged our findings, and also discussed the avenues to mitigate these risks.
Spuriosity Rankings: Sorting Data for Spurious Correlation Robustness

We present a framework for ranking images within their class based on the strength of spurious cues present. By measuring the gap in accuracy on the highest and lowest ranked images (we call this spurious gap), we assess spurious feature reliance for 89 diverse ImageNet models, ﬁnding that even the best models underperform in images with weak spurious presence. However, the effect of spurious cues varies far more dramatically across classes, emphasizing the crucial, often overlooked, class-dependence of the spurious correlation problem. While most spurious features we observe are clarifying (i.e. improving test-time accuracy when present, as is typically expected), we surprisingly ﬁnd many cases of confusing spurious features, where models perform better when they are absent. We then close the spurious gap by training new classiﬁcation heads on lowly ranked (i.e. without common spurious cues) images, resulting in improved effective robustness to distribution shifts (ObjectNet, ImageNet-R, ImageNet-Sketch). We also pro-pose a second metric to assess feature reliability, ﬁnding that spurious features are generally less reliable than non-spurious (core) ones, though again, spurious features can be more reliable for certain classes. To enable our analysis, we annotated 5 , 000 feature-class dependencies over all of ImageNet as core or spurious using minimal human super-vision. Finally, we show the feature discovery and spurios-ity ranking framework can be extended to other datasets like CelebA and WaterBirds in a lightweight fashion with only linear layer
Regret Minimization with Dynamic Benchmarks in Repeated Games

In repeated games, strategies are often evaluated by their ability to guarantee the performance of the single best action that is selected in hindsight (a property referred to as Hannan consistency, or no-regret). However, the effectiveness of the single best action as a yardstick to evaluate strategies is limited, as any static action may perform poorly in common dynamic settings. We propose the notion of dynamic benchmark consistency, which requires a strategy to asymptotically guarantee the performance of the best dynamic sequence of actions selected in hindsight subject to a constraint on the number of action changes the corresponding dynamic benchmark admits. We show that dynamic benchmark consistent strategies exist if and only if the number of changes in the benchmark scales sublinearly with the horizon length. Further, our main result establishes that the set of empirical joint distributions of play that may emerge, when all players deploy such strategies, asymptotically coincides with the set of Hannan equilibria (also referred to as coarse correlated equilibria) of the stage game. This general characterization allows one to leverage analyses developed for frameworks that consider static benchmarks, which we demonstrate by bounding the social efficiency of the possible outcomes in our setting. Together, our results imply that dynamic benchmark consistent strategies introduce the following Pareto-type improvement over no-regret strategies: They enable stronger individual guarantees against arbitrary strategies of the other players, while maintaining the same worst-case guarantees on the social welfare, when all players adopt these strategies.
Online Resource Allocation for Reusable Resources

We study a general model on reusable resource allocation under model uncertainty. A heterogeneous population of customers arrive at the decision maker’s (DM’s) platform sequentially. Upon observing a customer’s type, the DM selects an allocation decision, which leads to rewards earned and resources occupied. Each resource unit is occupied for a random duration, and the unit is available for another allocation after the usage duration. Our model captures numerous applications involving admission control and assortment planning. The DM aims to simultaneously maximize multiple types of rewards, while satisfying the resource constraints and being uncertain about the customers’ arrival process. We develop a near-optimal algorithm that achieves (1 − (cid:15) ) fraction of the optimal expected rewards, where the error parameter (cid:15) decays to zero as the resource capacity units and the length of the horizon grow. The algorithm iteratively applies the Multiplicative Weight Update algorithm in a novel manner, which balances the trade-oﬀ among the amounts of rewards earned, resources occupied and usage durations.
A New Locally Divergence-Free Path-Conservative Central-Upwind Scheme for Ideal and Shallow Water Magnetohydrodynamics

We develop a new second-order unstaggered path-conservative central-upwind (PCCU) scheme for ideal and shallow water magnetohydrodynamics (MHD) equations. The new scheme possesses several important properties: it locally preserves the divergence-free constraint, it does not rely on any (approximate) Riemann problem solver, and it robustly produces high-resolution and non-oscillatory results. The derivation of the scheme is based on the Godunov-Powell nonconservative modifications of the studied MHD systems. The local divergence-free property is enforced by augmenting the modified systems with the evolution equations for the corresponding derivatives of the magnetic field components. These derivatives are then used to design a special piecewise linear reconstruction of the magnetic field, which guarantees a non-oscillatory nature of the resulting scheme. In addition, the proposed PCCU discretization accounts for the jump of the nonconservative product terms across cell interfaces, thereby ensuring stability. We test the proposed PCCU scheme on several benchmarks for both ideal and shallow water MHD systems. The obtained numerical results illustrate the performance of the new scheme, its robustness, and its ability not only to achieve high resolution, but also preserve the positivity of computed quantities such as density, pressure, and water depth.
Controlled Text Generation using T5 based Encoder-Decoder Soft Prompt Tuning and Analysis of the Utility of Generated Text in AI

Controlled text generation is a very important task in the arena of natural language processing due to its promising applications. In order to achieve this task we mainly introduce the novel soft prompt tuning method of using soft prompts at both encoder and decoder levels together in a T5 model and investigate the performance as the behaviour of an additional soft prompt related to the decoder of a T5 model in controlled text generation remained unexplored. Then we also investigate the feasibility of steering the output of this extended soft prompted T5 model at decoder level and finally analyse the utility of generated text to be used in AI related tasks such as training AI models with an interpretability analysis of the classifier trained with synthetic text, as there is a lack of proper analysis of methodologies in generating properly labelled data to be utilized in AI tasks. Through the performed in-depth intrinsic and extrinsic evaluations of this generation model along with the artificially generated data, we found that this model produced better results compared to the T5 model with a single soft prompt at encoder level and the sentiment classifier trained using this artificially generated data can produce comparable classification results to the results of a classifier trained with real labelled data and also the classifier decision is interpretable with respect to the input text content.
Perspective Fields for Single Image Camera Calibration

Geometric camera calibration is often required for applications that understand the perspective of the image. We propose Perspective Fields as a representation that models the local perspective properties of an image. Perspective Fields contain per-pixel information about the camera view, parameterized as an Up-vector and a Latitude value. This representation has a number of advantages; it makes minimal assumptions about the camera model and is invariant or equivariant to common image editing operations like cropping, warping, and rotation. It is also more interpretable and aligned with human perception. We train a neural network to predict Perspective Fields and the predicted Perspective Fields can be converted to calibration parameters easily. We demonstrate the robustness of our approach under various scenarios compared with camera calibration-based methods and show example applications in image compositing. Project page: https://jinlinyi.github. io/PerspectiveFields/.
Objects as Spatio-Temporal 2.5D points

Determining accurate bird’s eye view (BEV) positions of objects and tracks in a scene is vital for various perception tasks including object interactions mapping, scenario extraction etc., however, the level of supervision required to accomplish that is extremely challenging to procure. We propose a light-weight, weakly supervised method to estimate 3D position of objects by jointly learning to regress the 2D object detections and scene’s depth prediction in a single feed-forward pass of a network. Our proposed method extends a center-point based single-shot object detector [48], and introduces a novel object representation where each object is modeled as a BEV point spatio-temporally, without the need of any 3D or BEV annotations for training and LiDAR data at query time. The approach leverages readily available 2D object supervision along with LiDAR point clouds (used only during training) to jointly train a single network, that learns to predict 2D object detection alongside the whole scene’s depth, to spatio-temporally model object tracks as points in BEV. The proposed method is computationally over ∼ 10x efﬁcient compared to recent SOTA approaches [1, 38] while achieving comparable ac-curacies on KITTI tracking benchmark.
Signatures of storm sudden commencements in the electric field measured at geostationary orbit (GEOS-2)

GEOS-2 carried a long wire boom antenna to measure the ambient electric field. In view of the long successful operation of the spacecraft a vast amount of electric field data was accumulated between 1978 and 1983. A survey of the data set revealed about 90 sudden occurrences of large amplitude Pc5 type waves. It turned out that about half of these events were associated with SSC's recorded on ground. We describe how the electric field and the computed E × B/B2 plasma drift changed from the undistrubed pre-SSC periods to the initial tens of seconds after the sudden compression occurred. The typical picture obtained from these events is that SSC's detected by GEOS-2 near local noon show a clear compressional plasma drift pattern, i.e., E × B/B2 points tailward. Close to the flanks or in the nightside these signatures contain not only a tailward flow proper but also a radially inward component. The magnetic field signatures show that the compressional component is strongest around local noon with a remarkable decrease via the flanks toward local midnight. Our data indicates that SSC's are hardly detectable in the geostationary orbit near local midnight. Based on simple geometrical considerations and within the accuracy of the ground recording times of the SSC's (±30s) we derive propagation velocities for the radially inward travelling compressive wave of 950 km/s and for azimuthal directions in the outer magnetosphere 1100 km/s.
Discovering the mesoscale for chains of conflict

Abstract Conflicts, like many social processes, are related events that span multiple scales in time, from the instantaneous to multi-year development, and in space, from one neighborhood to continents. Yet, there is little systematic work on connecting the multiple scales, formal treatment of causality between events, and measures of uncertainty for how events are related to one another. We develop a method for extracting causally related chains of events that addresses these limitations with armed conflict. Our method explicitly accounts for an adjustable spatial and temporal scale of interaction for clustering individual events from a detailed data set, the Armed Conflict Event & Location Data Project. With it, we discover a mesoscale ranging from a week to a few months and tens to hundreds of kilometers, where long-range correlations and nontrivial dynamics relating conflict events emerge. Importantly, clusters in the mesoscale, while extracted from conflict statistics, are identifiable with mechanism cited in field studies. We leverage our technique to identify zones of causal interaction around conflict hotspots that naturally incorporate uncertainties. Thus, we show how a systematic, data-driven, and scalable procedure extracts social objects for study, providing a scope for scrutinizing and predicting conflict and other processes.
Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement Learning

Discriminativeness is a desirable feature of image captions: captions should describe the characteristic details of input images. However, recent high-performing captioning models, which are trained with reinforcement learning (RL), tend to generate overly generic captions despite their high performance in various other criteria. First, we investigate the cause of the unexpectedly low discriminativeness and show that RL has a deeply rooted side effect of limiting the output words to high-frequency words. The limited vocabulary is a severe bottleneck for discriminativeness as it is difficult for a model to describe the details beyond its vocabulary. Then, based on this identification of the bottleneck, we drastically recast discriminative image captioning as a much simpler task of encouraging low-frequency word generation. Hinted by long-tail classification and debiasing methods, we propose methods that easily switch off-the-shelf RL models to discriminativeness-aware models with only a single-epoch fine-tuning on the part of the parameters. Extensive experiments demonstrate that our methods significantly enhance the discriminative-ness of off-the-shelf RL models and even outperform previous discriminativeness-aware methods with much smaller computational costs. Detailed analysis and human evaluation also verify that our methods boost the discriminativeness without sacrificing the overall quality of captions.1
Efficient Malware Analysis Using Metric Embeddings

Real-world malware analysis consists of a complex pipeline of classifiers and data analysis – from detection to classification of capabilities to retrieval of unique training samples from user systems. In this paper, we aim to reduce the complexity of these pipelines through the use of low-dimensional metric embeddings of Windows PE files, which can be used in a variety of downstream applications, including malware detection, family classification, and malware attribute tagging. Specifically, we enrich labeling of malicious and benign PE files with computationally-expensive, disassembly-based malicious capabilities information. Using this enhanced labeling, we derive several different types of efficient metric embeddings utilizing an embedding neural network trained via contrastive loss, Spearman rank correlation, and combinations thereof. Our evaluation examines performance on a variety of transfer tasks performed on the EMBER and SOREL datasets, demonstrating that low-dimensional, computationally-efficient metric embeddings maintain performance with little decay. This offers the potential to quickly retrain for a variety of transfer tasks at significantly reduced overhead and complexity. We conclude with an examination of practical considerations for the use of our proposed embedding approach, such as robustness to adversarial evasion and introduction of task-specific auxiliary objectives to improve performance on mission critical tasks.
InternVideo: General Video Foundation Models via Generative and Discriminative Learning

The foundation models have recently shown excellent performance on a variety of downstream tasks in computer vision. However, most existing vision foundation models simply focus on image-level pretraining and adpation, which are limited for dynamic and complex video-level understanding tasks. To fill the gap, we present general video foundation models, InternVideo, by taking advantage of both generative and discriminative self-supervised video learning. Specifically, InternVideo efficiently explores masked video modeling and video-language contrastive learning as the pretraining objectives, and selectively coordinates video representations of these two complementary frameworks in a learnable manner to boost various video applications. Without bells and whistles, InternVideo achieves state-of-the-art performance on 39 video datasets from extensive tasks including video action recognition/detection, video-language alignment, and open-world video applications. Especially, our methods can obtain 91.1% and 77.2% top-1 accuracy on the challenging Kinetics-400 and Something-Something V2 benchmarks, respectively. All of these results effectively show the generality of our InternVideo for video understanding. The code will be released at https://github.com/OpenGVLab/InternVideo .
A comparative study of emotion recognition methods using facial expressions

Understanding the facial expressions of our interlocutor is important to enrich the communication and to give it a depth that goes beyond the explicitly expressed. In fact, studying one's facial expression gives insight into their hidden emotion state. However, even as humans, and despite our empathy and familiarity with the human emotional experience, we are only able to guess what the other might be feeling. In the fields of artificial intelligence and computer vision, Facial Emotion Recognition (FER) is a topic that is still in full growth mostly with the advancement of deep learning approaches and the improvement of data collection. The main purpose of this paper is to compare the performance of three state-of-the-art networks, each having their own approach to improve on FER tasks, on three FER datasets. The first and second sections respectively describe the three datasets and the three studied network architectures designed for an FER task. The experimental protocol, the results and their interpretation are outlined in the remaining sections.
Antiwindup Design for Zero-Phase Repetitive Controllers

This paper addresses the antiwindup problem for linear systems equipped with the zero-phase repetitive controller (ZPRC). The antiwindup compensator is designed using a coprime factorization technique and conditions to characterize the sets of admissible references and disturbances are proposed. A numerical example illustrates the application and potentialities of the proposed methodology.