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Motivated by diverse secure requirements of multi-user in UAV systems, we propose a collaborative secret and covert transmission method for multi-antenna ground users to unmanned aerial vehicle (UAV) communications. Specifically, based on the power domain non-orthogonal multiple access (NOMA), two ground users with distinct security requirements, named Bob and Carlo, superimpose their signals and transmit the combined signal to the UAV named Alice. An adversary Willie attempts to simultaneously eavesdrop Bob's confidential message and detect whether Carlo is transmitting or not. We derive close-form expressions of the secrecy connection probability (SCP) and the covert connection probability (CCP) to evaluate the link reliability for wiretap and covert transmissions, respectively. Furthermore, we bound the secrecy outage probability (SOP) from Bob to Alice and the detection error probability (DEP) of Willie to evaluate the link security for wiretap and covert transmissions, respectively. To characterize the theoretical benchmark of the above model, we formulate a weighted multi-objective optimization problem to maximize the average of secret and covert transmission rates subject to constraints SOP, DEP, the beamformers of Bob and Carlo, and UAV trajectory parameters. To solve the optimization problem, we propose an iterative optimization algorithm using successive convex approximation and block coordinate descent (SCA-BCD) methods. Our results reveal the influence of design parameters of the system on the wiretap and covert rates, analytically and numerically. In summary, our study fills the gaps in joint secret and covert transmission for multi-user multi-antenna uplink UAV communications and provides insights to construct such systems.

We revisit the problem of statistical sequence matching between two databases of sequences initiated by Unnikrishnan (TIT 2015) and derive theoretical performance guarantees for the generalized likelihood ratio test (GLRT). We first consider the case where the number of matched pairs of sequences between the databases is known. In this case, the task is to accurately find the matched pairs of sequences among all possible matches between the sequences in the two databases. We analyze the performance of the GLRT by Unnikrishnan and explicitly characterize the tradeoff between the mismatch and false reject probabilities under each hypothesis in both large and small deviations regimes. Furthermore, we demonstrate the optimality of Unnikrishnan's GLRT test under the generalized Neyman-Person criterion for both regimes and illustrate our theoretical results via numerical examples. Subsequently, we generalize our achievability analyses to the case where the number of matched pairs is unknown, and an additional error probability needs to be considered. When one of the two databases contains a single sequence, the problem of statistical sequence matching specializes to the problem of multiple classification introduced by Gutman (TIT 1989). For this special case, our result for the small deviations regime strengthens previous result of Zhou, Tan and Motani (Information and Inference 2020) by removing unnecessary conditions on the generating distributions., Comment: Extended version of ISIT paper

With the development of social media, rumors have been spread broadly on social media platforms, causing great harm to society. Beside textual information, many rumors also use manipulated images or conceal textual information within images to deceive people and avoid being detected, making multimodal rumor detection be a critical problem. The majority of multimodal rumor detection methods mainly concentrate on extracting features of source claims and their corresponding images, while ignoring the comments of rumors and their propagation structures. These comments and structures imply the wisdom of crowds and are proved to be crucial to debunk rumors. Moreover, these methods usually only extract visual features in a basic manner, seldom consider tampering or textual information in images. Therefore, in this study, we propose a novel Vision and Graph Fused Attention Network (VGA) for rumor detection to utilize propagation structures among posts so as to obtain the crowd opinions and further explore visual tampering features, as well as the textual information hidden in images. We conduct extensive experiments on three datasets, demonstrating that VGA can effectively detect multimodal rumors and outperform state-of-the-art methods significantly.

LiDAR and cameras are complementary sensors for 3D object detection in autonomous driving. However, it is challenging to explore the unnatural interaction between point clouds and images, and the critical factor is how to conduct feature alignment of heterogeneous modalities. Currently, many methods achieve feature alignment by projection calibration only, without considering the problem of coordinate conversion accuracy errors between sensors, leading to sub-optimal performance. In this paper, we present GraphAlign, a more accurate feature alignment strategy for 3D object detection by graph matching. Specifically, we fuse image features from a semantic segmentation encoder in the image branch and point cloud features from a 3D Sparse CNN in the LiDAR branch. To save computation, we construct the nearest neighbor relationship by calculating Euclidean distance within the subspaces that are divided into the point cloud features. Through the projection calibration between the image and point cloud, we project the nearest neighbors of point cloud features onto the image features. Then by matching the nearest neighbors with a single point cloud to multiple images, we search for a more appropriate feature alignment. In addition, we provide a self-attention module to enhance the weights of significant relations to fine-tune the feature alignment between heterogeneous modalities. Extensive experiments on nuScenes benchmark demonstrate the effectiveness and efficiency of our GraphAlign.

We investigate the transport properties within a holographic model characterized by a novel gauge-axion coupling. A key innovation is the introduction of the direct coupling between axion fields, the antisymmetric tensor, and the gauge field in our bulk theory. This novel coupling term leads to the emergence of nondiagonal components in the conductivity tensor. An important characteristic is that the off-diagonal elements manifest antisymmetry. Remarkably, the conductivity behavior in this model akin to that of Hall conductivity. Additionally, this model can also achieve metal-insulator transition., Comment: 29 pages, 11 figures, Published version

We study the successive refinement setting of Shannon cipher system (SCS) under the maximal leakage secrecy metric for discrete memoryless sources under bounded distortion measures. Specifically, we generalize the threat model for the point-to-point rate-distortion setting of Issa, Wagner and Kamath (T-IT 2020) to the multiterminal successive refinement setting. Under mild conditions that correspond to partial secrecy, we characterize the asymptotically optimal normalized maximal leakage region for both the joint excess-distortion probability (JEP) and the expected distortion reliability constraints. Under JEP, in the achievability part, we propose a type-based coding scheme, analyze the reliability guarantee for JEP and bound the leakage of the information source through compressed messages. In the converse part, by analyzing a guessing scheme of the eavesdropper, we prove the optimality of our achievability result. Under expected distortion, the achievability part is established similarly to the JEP counterpart. The converse proof proceeds by generalizing the corresponding results for the rate-distortion setting of SCS by Schieler and Cuff (T-IT 2014) to the successive refinement setting. Somewhat surprisingly, the normalized maximal leakage regions under both JEP and expected distortion constraints are identical under certain conditions, although JEP appears to be a stronger reliability constraint.

The sixth generation (6G) wireless systems are envisioned to enable the paradigm shift from "connected things" to "connected intelligence", featured by ultra high density, large-scale, dynamic heterogeneity, diversified functional requirements and machine learning capabilities, which leads to a growing need for highly efficient intelligent algorithms. The classic optimization-based algorithms usually require highly precise mathematical model of data links and suffer from poor performance with high computational cost in realistic 6G applications. Based on domain knowledge (e.g., optimization models and theoretical tools), machine learning (ML) stands out as a promising and viable methodology for many complex large-scale optimization problems in 6G, due to its superior performance, generalizability, computational efficiency and robustness. In this paper, we systematically review the most representative "learning to optimize" techniques in diverse domains of 6G wireless networks by identifying the inherent feature of the underlying optimization problem and investigating the specifically designed ML frameworks from the perspective of optimization. In particular, we will cover algorithm unrolling, learning to branch-and-bound, graph neural network for structured optimization, deep reinforcement learning for stochastic optimization, end-to-end learning for semantic optimization, as well as federated learning for distributed optimization, for solving challenging large-scale optimization problems arising from various important wireless applications. Through the in-depth discussion, we shed light on the excellent performance of ML-based optimization algorithms with respect to the classical methods, and provide insightful guidance to develop advanced ML techniques in 6G networks.

Due to increasing demands of seamless connection and massive information exchange across the world, the integrated satellite-terrestrial communication systems develop rapidly. To shed lights on the design of this system, we consider an uplink communication model consisting of a single satellite, a single terrestrial station and multiple ground users. The terrestrial station uses decode-and-forward (DF) to facilitate the communication between ground users and the satellite. The channel between the satellite and the terrestrial station is assumed to be a quasi-static shadowed Rician fading channel, while the channels between the terrestrial station and ground users are assumed to experience independent quasi-static Rayleigh fading. We consider two cases of channel state information (CSI) availability. When instantaneous CSI is available, we derive the instantaneous achievable sum rate of all ground users and formulate an optimization problem to maximize the sum rate. When only channel distribution information (CDI) is available, we derive a closed-form expression for the outage probability and formulate another optimization problem to minimize the outage probability. Both optimization problems correspond to scheduling algorithms for ground users. For both cases, we propose low-complexity user scheduling algorithms and demonstrate the efficiency of our scheduling algorithms via numerical simulations., Comment: To appear in IEEE Transactions on Wireless Communications

We revisit $M$-ary classification of Gutman (TIT 1989), where one is tasked to determine whether a testing sequence is generated with the same distribution as one of the $M$ training sequences or not. Our main result is a two-phase test, its theoretical analysis and its optimality guarantee. Specifically, our two-phase test is a special case of a sequential test with only two decision time points: the first phase of our test is a fixed-length test with a reject option, the second-phase of our test proceeds only if a reject option is decided in the first phase and the second phase of our test does \emph{not} allow a reject option. To provide theoretical guarantee for our test, we derive achievable error exponents using the method of types and derive a converse result for the optimal sequential test using the techniques recently proposed by Hsu, Li and Wang (ITW, 2022) for binary classification. Analytically and numerically, we show that our two phase test achieves the performance of an optimal sequential test with proper choice of test parameters. In particular, similarly as the optimal sequential test, our test does not need a final reject option to achieve the optimal error exponent region while an optimal fixed-length test needs a reject option to achieve the same region. Finally, we specialize our results to binary classification when $M=2$ and to $M$-ary hypothesis testing when the ratio of the lengths of training sequences and testing sequences tends to infinity so that generating distributions can be estimated perfectly., Comment: submitted to IEEE Trans. Inf. Theory

The scientific outcomes of the 2022 Landslide4Sense (L4S) competition organized by the Institute of Advanced Research in Artificial Intelligence (IARAI) are presented here. The objective of the competition is to automatically detect landslides based on large-scale multiple sources of satellite imagery collected globally. The 2022 L4S aims to foster interdisciplinary research on recent developments in deep learning (DL) models for the semantic segmentation task using satellite imagery. In the past few years, DL-based models have achieved performance that meets expectations on image interpretation, due to the development of convolutional neural networks (CNNs). The main objective of this article is to present the details and the best-performing algorithms featured in this competition. The winning solutions are elaborated with state-of-the-art models like the Swin Transformer, SegFormer, and U-Net. Advanced machine learning techniques and strategies such as hard example mining, self-training, and mix-up data augmentation are also considered. Moreover, we describe the L4S benchmark data set in order to facilitate further comparisons, and report the results of the accuracy assessment online. The data is accessible on \textit{Future Development Leaderboard} for future evaluation at \url{https://www.iarai.ac.at/landslide4sense/challenge/}, and researchers are invited to submit more prediction results, evaluate the accuracy of their methods, compare them with those of other users, and, ideally, improve the landslide detection results reported in this article.

We study the mismatched successive refinement problem where one uses Gaussian codebooks to compress an arbitrary memoryless source with successive minimum Euclidean distance encoding under the quadratic distortion measure. Specifically, we derive achievable refined asymptotics under both the joint excess-distortion probability (JEP) and the separate excess-distortion probabilities (SEP) criteria. For both second-order and moderate deviations asymptotics, we consider two types of codebooks: the spherical codebook where each codeword is drawn independently and uniformly from the surface of a sphere and the i.i.d. Gaussian codebook where each component of each codeword is drawn independently from a Gaussian distribution. We establish the achievable second-order rate-region under JEP and we show that under SEP any memoryless source satisfying mild moment conditions is strongly successively refinable. When specialized to a Gaussian memoryless source (GMS), our results provide an alternative achievability proof with specific code design. We show that under JEP and SEP, the same moderate deviations constant is achievable. For large deviations asymptotics, we only consider the i.i.d. Gaussian codebook since the i.i.d. Gaussian codebook has better performance than the spherical codebook in this regime for the one layer mismatched rate-distortion problem (Zhou, Tan, Motani, TIT, 2019). We derive achievable exponents of both JEP and SEP and specialize our results to a GMS, which appears to be a novel result of independent interest.

Point cloud semantic segmentation has attracted attentions due to its robustness to light condition. This makes it an ideal semantic solution for autonomous driving. However, considering the large computation burden and bandwidth demanding of neural networks, putting all the computing into vehicle Electronic Control Unit (ECU) is not efficient or practical. In this paper, we proposed a light weighted point cloud semantic segmentation network based on range view. Due to its simple pre-processing and standard convolution, it is efficient when running on deep learning accelerator like DPU. Furthermore, a near sensor computing system is built for autonomous vehicles. In this system, a FPGA-based deep learning accelerator core (DPU) is placed next to the LiDAR sensor, to perform point cloud pre-processing and segmentation neural network. By leaving only the post-processing step to ECU, this solution heavily alleviate the computation burden of ECU and consequently shortens the decision making and vehicles reaction latency. Our semantic segmentation network achieved 10 frame per second (fps) on Xilinx DPU with computation efficiency 42.5 GOP/W., Comment: accepted by ISCAS 2022

Sparse code multiple access (SCMA) is an emerging paradigm for efficient enabling of massive connectivity in future machine-type communications (MTC). In this letter, we conceive the uplink transmissions of the low-density parity check (LDPC) coded SCMA system. Traditional receiver design of LDPC-SCMA system, which is based on message passing algorithm (MPA) for multiuser detection followed by individual LDPC decoding, may suffer from the drawback of the high complexity and large decoding latency, especially when the system has large codebook size and/or high overloading factor. To address this problem, we introduce a novel receiver design by applying the expectation propagation algorithm (EPA) to the joint detection and decoding (JDD) involving an aggregated factor graph of LDPC code and sparse codebooks. Our numerical results demonstrate the superiority of the proposed EPA based JDD receiver over the conventional Turbo receiver in terms of both significantly lower complexity and faster convergence rate without noticeable error rate performance degradation.