This paper investigates the quickest change detection of quantum states in a universal setting: specifically, where the post-change quantum state is not known a priori. We establish the asymptotic optimality of a two-stage approach in terms of worst average delay to detection. The first stage employs block POVMs with classical outputs that preserve quantum relative entropy to arbitrary precision. The second stage leverages a recently proposed windowed-CUSUM algorithm that is known to be asymptotically optimal for quickest change detection with an unknown post-change distribution in the classical setting.

Deep Reinforcement Learning (DRL) has shown promise for social navigation, yet its real-world deployment remains hindered by a persistent sim-to-real gap arising from simplified first-order dynamics and context-specific human state estimation pipelines. This work presents a unified framework that addresses these limitations to produce dynamically feasible navigation policies suitable for real-world deployment. First, theoretical analysis reveals that tracking error between simulated and actual robot position decays exponentially with increased control order, motivating the use of higher-order control inputs as DRL action space. A second-order control formulation tailored to differential drive robots is developed, complemented by a stochastic iterative Linear Quadratic Regulator (iLQR) that pretrains the policy via a divergence minimization objective. Second, to avoid the added system complexity of camera-LiDAR fusion, a cluster-based human tracking pipeline using only 2D LiDAR is introduced. Human detections are associated according to both spatial proximity and velocity similarity, enabling reliable differentiation of nearby pedestrians and yielding stable velocity estimates through temporal aggregation. Third, we introduce an unbiased residual gating block to balance reaction- and memory-based behaviors while handling time-varying crowd sizes, both critical for social navigation. The resulting policy, KinematicRL, consistently improves kinematic performance and adapts to varying number of detected humans. Experiments in real-world environments demonstrate that, when combined with the proposed tracking pipeline, KinematicRL can be deployed on a real differential drive robot with minimal modifications.

Point lattices play an important role in various fields of computer science, including communication, cryptography, optimization, and machine learning. In this work, we introduce the concept of Locally Correctable Lattices (LCLs)—a class of lattices with an efficient reconstruction algorithm that can recover any index of a lattice point by querying only a small portion of a corrupted word. LCLs facilitate efficient partial decoding in the presence of noise, benefiting applications such as communication systems. We present two families of LCL constructions, each offering a range of trade-offs between lattice density, error tolerance, and the query complexity required for reconstruction.

The study of group testing aims to develop strategies to identify a small set of defective items among a large population using a few pooled tests. The established techniques have been highly beneficial in a broad spectrum of applications ranging from channel communication to identifying COVID-19-infected individuals efficiently. Despite significant research on group testing and its variants since the 1940s, testing strategies robust to deletion noise have not been explored. Deletion errors, common in practical systems like wireless communication and data storage, cause asynchrony in tests, rendering current group testing methods ineffective. In this work, we introduce non-adaptive group testing strategies resilient to deletion noise. We establish the necessary and sufficient conditions for successfully identifying defective items despite adversarial deletions of test outcomes. The study also presents constructions of testing matrices with a nearoptimal number of tests and develops efficient and super-efficient recovery algorithms.

THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD.” 1-bit compressed sensing (1bCS) is a quantized signal acquisition technique to compress highdimensional sparse signals. The goal in 1bCS is to design sensing matrices$A \in \mathbb{R}^{m \times n}$with the fewest possible rows that enable efficient and accurate recovery of sparse signals$x \in \mathbb{R}^{n}$from 1-bit measurements of the form$sign (A x)$. In this work, we focus on designing sensing matrices$A$that enable super-efficient recovery of the support set of sparse signals. Our results prove that with a slight increase in the number of measurements, we can in fact obtain sublinear-time (in$n$) algorithms for support recovery. Furthermore, we also show that the proposed techniques can be modified to achieve resilience against bounded number of adversarial errors.

Hoeffding’s formulation and solution to the universal hypothesis testing (UHT) problem had a profound impact on many subsequent works dealing with asymmetric hypotheses. In this work, we introduce a quantum universal hypothesis testing framework that serves as a quantum analog to Hoeffding’s UHT. Motivated by Hoeffding’s approach, which estimates the empirical distribution and uses it to construct the test statistic, we employ quantum state tomography to reconstruct the unknown state prior to forming the test statistic. Leveraging the concentration properties of quantum state tomography, we establish the exponential consistency of the proposed test: the type II error probability decays exponentially quickly, with the exponent determined by the trace distance between the true state and the nominal state.

Most existing sparse representation based approaches for attributed scattering center (ASC) extraction adopt traditional iterative optimization algorithms, which suffer from lengthy computation time and limited precision. This paper presents a solution by introducing an interpretable network that can effectively and rapidly extract ASC via deep unfolding. Initially, we create a dictionary containing reliable prior knowledge and apply it to iterative shrinkage-thresholding algorithm (ISTA). Then, we unfold ISTA to a neural network, employing it to autonomously and precisely optimize the hyperparameters. The interpretability in physics is retained by applying a dictionary with physical meaning. The experiments are conducted on multiple test sets with diverse data distribution and demonstrate the superior performance and generalizability of our method.

In distributed detection systems, energy-efficient ordered transmission (EEOT) schemes are able to reduce the number of transmissions required to make a final decision. In this work, we investigate the effect of data falsification attacks on the performance of EEOT-based systems. We derive the probability of error for an EEOT-based system under attack and find an upper bound (UB) on the expected number of transmissions required to make the final decision. Moreover, we tighten this UB by solving an optimization problem via integer programming (IP). We also obtain the FC's optimal threshold which guarantees the optimal detection performance of the EEOT-based system. Numerical and simulation results indicate that it is possible to reduce transmissions while still ensuring the quality of the decision with an appropriately designed threshold.

Mobile crowdsensing has become a promising sensing paradigm with the popularization of mobile devices. In this paper, we focus on an opportunistic mobile crowdsensing scenario where there are multiple task requesters and users, who move in an opportunistic way in the target environment. When a task requester encounters a user, he can assign some of his held tasks to the user and receive corresponding task results when they re-encounter sometime later. In this paper, we study how to minimize the largest makespan of all requesters for the task result collections. To address this issue, we propose a cluster based largest makespan sensitive online task assignment (C-LOTA) algorithm. C-LOTA first performs two-phase clustering which clusters the users into different clusters, one for each task requester, based on their relativeness to the task requesters and also the task workloads at different requesters. C-LOTA then iteratively performs greedy intra-cluster task assignment such that largest task is firstly assigned and the first idle user always takes the task, until all tasks are assigned. We present the detailed algorithm design of C-LOTA. We deduce its computation complexity. Simulation results show that C-LOTA can achieve much better performance compared with existing work.

Answer selection is an important method to achieve better user experience in question answering (QA) systems and it is essential in ensuring better QA matching performance. Improving mutual information between QA pairs is a useful way to obtain the matching degree improvement and question rewriting has been proven a helpful way in utilizing mutual interaction between questions and answers. In this research, we focus on syntax tree aware question rewriting inspired by the thought of integrating syntactic information into question answering. Besides, to improve the quality of rewriting, we employ the generative adversarial network for rewriting optimization, which consists of a syntax tree aware rewriting model and a discriminator. The quality information given by the discriminator guides the optimizing of the rewriting model in the training phase. The experimental study has shown the effectiveness of syntax tree aware question rewriting and utilizing the generative adversarial network for rewriting.

Image semantic segmentation is a challenging problem for low‐level computer vision. Recently, deep convolutional neural networks (DCNNs) have been proved to achieve outstanding performance in image semantic segmentation. Most current methods still have some problems in segmenting the object edges and the integrity of objects. In this study, the authors first construct the difference‐pooling module in the DCNNs to extract the object edge gradients and get finer boundary in segmentation results. Then the combination of the pyramid pooling module and the atrous spatial pyramid pooling extracts the image global features and the context structure information by building long‐distance dependency between pixels, which is just like a simple fully connected conditional random field (CRF). Different from other methods, the proposed method does not need extra pre‐processing and post‐processing steps, such as extracting gradient features by the traditional algorithm and building context relationships by CRF. Finally, the experimental results on the PASCAL VOC2012 benchmark indicate that the proposed model can obtain the finer boundaries and more complete parts.

Since adverse drug reactions (ADRs) represent a significant health problem all over the world, ADR detection has become an important research topic in drug safety surveillance. As many potential ADRs cannot be detected though premarketing review, drug safety currently depends heavily on postmarketing surveillance. Particularly, current postmarketing surveillance in the United States primarily relies on the FDA Adverse Event Reporting System (FAERS). However, the effectiveness of such spontaneous reporting systems for ADR detection is not as good as expected because of the extremely high underreporting ratio of ADRs. Moreover, it often takes the FDA years to complete the whole process of collecting reports, investigating cases, and releasing alerts. Given the prosperity of social media, many online health communities are publicly available for health consumers to share and discuss any healthcare experience such as ADRs they are suffering. Such health-consumer-contributed content is timely and informative, but this data source still remains untapped for postmarketing drug safety surveillance. In this study, we propose to use (1) association mining to identify the relations between a drug and an ADR and (2) temporal analysis to detect drug safety signals at the early stage. We collect data from MedHelp and use the FDA's alerts and information of drug labeling revision as the gold standard to evaluate the effectiveness of our approach. The experiment results show that health-related social media is a promising source for ADR detection, and our proposed techniques are effective to identify early ADR signals.

The Internet is an ideal platform for business-to-consumer (B2C) and business-to-business (B2B) electronic commerce where businesses and consumers conduct commerce activities such as searching for consumer products, promoting business, managing supply chain and making electronic transactions. With the advance of Web 2.0 technologies and the popularity of social media sites, social commerce offers new opportunities of social interaction between electronic commerce consumers as well as social interaction between consumers and e-retailers. The user contributed content provides a tremendous amount of information that may assist in electronic commerce services. Social network analysis and mining has been a powerful tool for electronic commerce vendors and marketing companies to understand the user behavior which is useful for identifying potential customers of their products. However, the capability of social network analysis and mining diminishes when the social network data is incomplete, especially when there are only limited ties available. The social networks extracted from explicit relationships in social media are usually sparse. Many social media users who have similar interest may not have direct interactions with one another or purchase the same products. Therefore, the explicit relationships between electronic commerce users are not sufficient to construct social networks for effective social network analysis and mining. In this work, we propose the temporal analysis techniques to identify implicit relationships for enriching the social network structure. We have conducted an experiment on Digg.com, which is a social media site for users to discover and share content from anywhere of the Web. The experiment shows that the temporal analysis techniques outperform the baseline techniques that only rely on explicit relationships.