When deploying a trained machine learning model in the real world, it is inevitable to receive inputs from out-of-distribution (OOD) sources. For instance, in continual learning settings, it is common to encounter OOD samples due to the non-stationarity of a domain. More generally, when we have access to a set of test inputs, the existing rich line of OOD detection solutions, especially the recent promise of distance-based methods, falls short in effectively utilizing the distribution information from training samples and test inputs. In this paper, we argue that empirical probability distributions that incorporate geometric information from both training samples and test inputs can be highly beneficial for OOD detection in the presence of test inputs available. To address this, we propose to model OOD detection as a discrete optimal transport problem. Within the framework of optimal transport, we propose a novel score function known as the \emph{conditional distribution entropy} to quantify the uncertainty of a test input being an OOD sample. Our proposal inherits the merits of certain distance-based methods while eliminating the reliance on distribution assumptions, a-prior knowledge, and specific training mechanisms. Extensive experiments conducted on benchmark datasets demonstrate that our method outperforms its competitors in OOD detection.

This study introduces the Supervised Magnitude-Altitude Scoring (SMAS) methodology, a machine learning-based approach, for analyzing gene expression data obtained from nonhuman primates (NHPs) infected with Ebola virus (EBOV). We utilize a comprehensive dataset of NanoString gene expression profiles from Ebola-infected NHPs, deploying the SMAS system for nuanced host-pathogen interaction analysis. SMAS effectively combines gene selection based on statistical significance and expression changes, employing linear classifiers such as logistic regression to accurately differentiate between RT-qPCR positive and negative NHP samples. A key finding of our research is the identification of IFI6 and IFI27 as critical biomarkers, demonstrating exceptional predictive performance with 100% accuracy and Area Under the Curve (AUC) metrics in classifying various stages of Ebola infection. Alongside IFI6 and IFI27, genes, including MX1, OAS1, and ISG15, were significantly upregulated, highlighting their essential roles in the immune response to EBOV. Our results underscore the efficacy of the SMAS method in revealing complex genetic interactions and response mechanisms during EBOV infection. This research provides valuable insights into EBOV pathogenesis and aids in developing more precise diagnostic tools and therapeutic strategies to address EBOV infection in particular and viral infection in general.

This paper investigates the weaknesses of image watermarking techniques. We present WAVES (Watermark Analysis Via Enhanced Stress-testing), a novel benchmark for assessing watermark robustness, overcoming the limitations of current evaluation methods.WAVES integrates detection and identification tasks, and establishes a standardized evaluation protocol comprised of a diverse range of stress tests. The attacks in WAVES range from traditional image distortions to advanced and novel variations of diffusive, and adversarial attacks. Our evaluation examines two pivotal dimensions: the degree of image quality degradation and the efficacy of watermark detection after attacks. We develop a series of Performance vs. Quality 2D plots, varying over several prominent image similarity metrics, which are then aggregated in a heuristically novel manner to paint an overall picture of watermark robustness and attack potency. Our comprehensive evaluation reveals previously undetected vulnerabilities of several modern watermarking algorithms. We envision WAVES as a toolkit for the future development of robust watermarking systems. The project is available at https://wavesbench.github.io/

This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase Folding and Convolutional Neural Network (CNN) system to detect exoplanets using the transit method. We devise a fast folding algorithm parallelized on a GPU to amplify low signal-to-noise ratio transit signals, allowing a search at high precision and speed. A CNN trained on two million synthetic light curves reports a score indicating the likelihood of a planetary signal at each period. While the GPFC method has broad applicability across period ranges, this research specifically focuses on detecting ultra-short-period planets with orbital periods less than one day. GPFC improves on speed by three orders of magnitude over the predominant Box-fitting Least Squares (BLS) method. Our simulation results show GPFC achieves $97%$ training accuracy, higher true positive rate at the same false positive rate of detection, and higher precision at the same recall rate when compared to BLS. GPFC recovers $100\%$ of known ultra-short-period planets in $\textit{Kepler}$ light curves from a blind search. These results highlight the promise of GPFC as an alternative approach to the traditional BLS algorithm for finding new transiting exoplanets in data taken with $\textit{Kepler}$ and other space transit missions such as K2, TESS and future PLATO and Earth 2.0.

Job Search, Glassdoor, and so on, (3) gender, to predict the The rapid development of technology not only provides a risk of personal insolvency. It is not hard to know by common lot of convenience to people's production and life, but also sense that unemployed employment status can be the real brings a lot of potential risks (Li et al. 2022; Chakraborty cause of an increase in personal insolvency risk among these et al. 2018; Guan et al. 2023a,b; Chu et al. 2023b), such as three predictors. Gender is also not directly related to the business risks, financial risks, medical risks, industry risks, personal insolvency risk. In addition, we also know that the credit risks, and so on. To prevent risks, a better way is to unemployed job status is more likely to increase the activity build an accurate risk prediction model before risks occur in job-hunting apps. Therefore, we can observe a correlation instead of finding a solution after the risk outbreak. Although rather than a causal relationship between the risk of personal artificial intelligence has seen tremendous recent successes in insolvency and the activity in job-hunting apps. Based on many areas (Luan and Tsai 2021; Zhu et al. 2023; Wang et al. this dataset, if we run a general prediction model, it is not 2023; Shi et al. 2023; Liu et al. 2023; Chen, Rezayi, and Li difficult to observe this result that the employment status and 2023), it is often unable to produce trustworthy results on risk the activity in job-hunting apps are relatively important features prediction tasks, mainly due to a lack of interpretability, no for the risk of personal insolvency due to the spurious insight into cause relationships, and low precision and recall.

This paper revisits a class of convex Finite-Sum Coupled Compositional Stochastic Optimization (cFCCO) problems with many applications, including group distributionally robust optimization (GDRO), learning with imbalanced data, reinforcement learning, and learning to rank. To better solve these problems, we introduce an efficient single-loop primal-dual block-coordinate proximal algorithm, dubbed ALEXR. This algorithm leverages block-coordinate stochastic mirror ascent updates for the dual variable and stochastic proximal gradient descent updates for the primal variable. We establish the convergence rates of ALEXR in both convex and strongly convex cases under smoothness and non-smoothness conditions of involved functions, which not only improve the best rates in previous works on smooth cFCCO problems but also expand the realm of cFCCO for solving more challenging non-smooth problems such as the dual form of GDRO. Finally, we present lower complexity bounds to demonstrate that the convergence rates of ALEXR are optimal among first-order block-coordinate stochastic algorithms for the considered class of cFCCO problems.

Space-Air-Ground Integrated Networks (SAGINs), which incorporate space and aerial networks with terrestrial wireless systems, are vital enablers of the emerging sixth-generation (6G) wireless networks. Besides bringing significant benefits to various applications and services, SAGINs are envisioned to extend high-speed broadband coverage to remote areas, such as small towns or mining sites, or areas where terrestrial infrastructure cannot reach, such as airplanes or maritime use cases. However, due to the limited power and storage resources, as well as other constraints introduced by the design of terrestrial networks, SAGINs must be intelligently configured and controlled to satisfy the envisioned requirements. Meanwhile, Artificial Intelligence (AI) is another critical enabler of 6G. Due to massive amounts of available data, AI has been leveraged to address pressing challenges of current and future wireless networks. By adding AI and facilitating the decision-making and prediction procedures, SAGINs can effectively adapt to their surrounding environment, thus enhancing the performance of various metrics. In this work, we aim to investigate the interplay of AI and SAGINs by providing a holistic overview of state-of-the-art research in AI-enabled SAGINs. Specifically, we present a comprehensive overview of some potential applications of AI in SAGINs. We also cover open issues in employing AI and detail the contributions of SAGINs in the development of AI. Finally, we highlight some limitations of the existing research works and outline potential future research directions.

Time-series anomaly detection deals with the problem of detecting anomalous timesteps by learning normality from the sequence of observations. However, the concept of normality evolves over time, leading to a "new normal problem", where the distribution of normality can be changed due to the distribution shifts between training and test data. This paper highlights the prevalence of the new normal problem in unsupervised time-series anomaly detection studies. To tackle this issue, we propose a simple yet effective test-time adaptation strategy based on trend estimation and a self-supervised approach to learning new normalities during inference. Extensive experiments on real-world benchmarks demonstrate that incorporating the proposed strategy into the anomaly detector consistently improves the model's performance compared to the baselines, leading to robustness to the distribution shifts.

In harsh environments such as those found in nuclear facilities, the use of robotic systems is crucial for performing tasks that would otherwise require human intervention. This is done to minimize the risk of human exposure to dangerous levels of radiation, which can have severe consequences for health and even be fatal. However, the telemanipulation systems employed in these environments are becoming increasingly intricate, relying heavily on sophisticated control methods and local master devices. Consequently, the cognitive burden on operators during labor-intensive tasks is growing. To tackle this challenge, operator intention detection based on task learning can greatly enhance the performance of robotic tasks while reducing the reliance on human effort in teleoperation, particularly in a glovebox environment. By accurately predicting the operator's intentions, the robot can carry out tasks more efficiently and effectively, with minimal input from the operator. In this regard, we propose the utilization of Convolutional Neural Networks, a machine learning approach, to learn and forecast the operator's intentions using raw force feedback spatiotemporal data. Through our experimental study on glovebox tasks for nuclear applications, such as radiation survey and object grasping, we have achieved promising outcomes. Our approach holds the potential to enhance the safety and efficiency of robotic systems in harsh environments, thus diminishing the risk of human exposure to radiation while simultaneously improving the precision and speed of robotic operations.

Recent years have seen an exponential rise in complex software-driven functionality in vehicles, leading to a rising number of electronic control units (ECUs), network capabilities, and interfaces. These expanded capabilities also bring-in new planes of vulnerabilities making intrusion detection and management a critical capability; however, this can often result in more ECUs and network elements due to the high computational overheads. In this paper, we present a consolidated ECU architecture incorporating an Intrusion Detection System (IDS) for Automotive Controller Area Network (CAN) along with traditional ECU functionality on an off-the-shelf hybrid FPGA device, with near-zero overhead for the ECU functionality. We propose two quantised multi-layer perceptrons (QMLP's) as isolated IDSs for detecting a range of attack vectors including Denial-of-Service, Fuzzing and Spoofing, which are accelerated using off-the-shelf deep-learning processing unit (DPU) IP block from Xilinx, operating fully transparently to the software on the ECU. The proposed models achieve the state-of-the-art classification accuracy for all the attacks, while we observed a 15x reduction in power consumption when compared against the GPU-based implementation of the same models quantised using Nvidia libraries. We also achieved a 2.3x speed up in per-message processing latency (at 0.24 ms from the arrival of a CAN message) to meet the strict end-to-end latency on critical CAN nodes and a 2.6x reduction in power consumption for inference when compared to the state-of-the-art IDS models on embedded IDS and loosely coupled IDS accelerators (GPUs) discussed in the literature.