Embodied intelligence requires agents to interact with 3D environments in real time based on language instructions. A foundational task in this domain is ego-centric 3D visual grounding. However, the point clouds rendered from RGB-D images retain a large amount of redundant background data and inherent noise, both of which can interfere with the manifold structure of the target regions. Existing point cloud enhancement methods often require a tedious process to improve the manifold, which is not suitable for real-time tasks. We propose Proxy Transformation suitable for multimodal task to efficiently improve the point cloud manifold. Our method first leverages Deformable Point Clustering to identify the point cloud sub-manifolds in target regions. Then, we propose a Proxy Attention module that utilizes multimodal proxies to guide point cloud transformation. Built upon Proxy Attention, we design a submanifold transformation generation module where textual information globally guides translation vectors for different submanifolds, optimizing relative spatial relationships of target regions. Simultaneously, image information guides linear transformations within each submanifold, refining the local point cloud manifold of target regions. Extensive experiments demonstrate that Proxy Transformation significantly outperforms all existing methods, achieving an impressive improvement of 7.49% on easy targets and 4.60% on hard targets, while reducing the computational overhead of attention blocks by 40.6%. These results establish a new SOTA in ego-centric 3D visual grounding, showcasing the effectiveness and robustness of our approach.

The Domain Name System (DNS) is designed to be distributed, which aims to provide services with low latency and great reliability. However, after decades of development and changes in Internet business models, various aspects of the DNS ecosystem have begun to show signs of centralization. To investigate the centralization from the viewpoint of hosting service providers, we develop an automated method based on similarity among NS domains and co-hosting relationship to identify the hosting providers for authoritative name servers, so that we can identify hosting providers in DNS zone file to count the number of domains which a hosting provider host. This tool demonstrates greater accuracy than previous methods and our testing demonstrates the ability to identify hosting service providers for most domains in real-world measurement tasks. Using this tool, we conducted measurements on the dataset combined with .com, .net and .org TLD zones. We find that the top 10 providers collectively host over 54.19% of domains while top 100 providers host over 82.99% domains, which shows a significant level of centralization in hosting service providers within the DNS. Through an analysis of NSone’s NS domains and a statistical examination of top providers’ NS domains, we find that directly identifying the base domain as the provider is inappropriate. Furthermore, we discover relationships among hosting providers and between hosting providers and infrastructure that are more complex than previously anticipated. Finally, based on our research findings, we offer corresponding suggestions to mitigate the continued development of centralization.

Enabling intelligent agents to comprehend and interact with 3D environments through natural language is crucial for advancing robotics and human-computer interaction. A fundamental task in this field is ego-centric 3D visual grounding, where agents locate target objects in real-world 3D spaces based on verbal descriptions. However, this task faces two significant challenges: (1) loss of fine-grained visual semantics due to sparse fusion of point clouds with ego-centric multi-view images, (2) limited textual semantic context due to arbitrary language descriptions. We propose DenseGrounding, a novel approach designed to address these issues by enhancing both visual and textual semantics. For visual features, we introduce the Hierarchical Scene Semantic Enhancer, which retains dense semantics by capturing fine-grained global scene features and facilitating cross-modal alignment. For text descriptions, we propose a Language Semantic Enhancer that leverage large language models to provide rich context and diverse language descriptions with additional context during model training. Extensive experiments show that DenseGrounding significantly outperforms existing methods in overall accuracy, achieving improvements of **5.81%** and **7.56%** when trained on the comprehensive full training dataset and smaller mini subset, respectively, further advancing the SOTA in ego-centric 3D visual grounding. Our method also achieves **1st place** and receives **Innovation Award** in the 2024 Autonomous Grand Challenge Multi-view 3D Visual Grounding Track, validating its effectiveness and robustness.

Bursty mixed noise composed of impulsive noise with memory and white gaussian noise exists in many practical applications. However, existing signal demodulation algorithms are designed without considering the characteristics of the bursty mixed noise, resulting in performance degradation. Therefore, an accurate model for the bursty mixed noise needs to be built to design the corresponding signal demodulation algorithms. In this paper, we propose a novel statistical model based on the mul-tivariate student distribution for the bursty mixed noise, based on which we design the maximum likelihood (ML) demodulation method and analyze the theoretical bit error rate performance. Simulation results show that the proposed model can describe the bursty noise well and there is remarkable demodulation gain of the ML method as compared with conventional ones.

Orthogonal frequency division multiplexing (OFDM) system is susceptible to carrier frequency offset (CFO). Therefore, CFO estimation and compensation is a necessary component for OFDM systems. In high-mobility environments, extremely large Doppler frequency shift will introduce severe CFO that is far beyond the subcarrier spacing. Conventional CFO estimation algorithms typically estimate the integer and fractional parts of CFO separately. This strategy is computationally expensive and time-consuming. To avert this deficiency, inspired by the Chinese remainder theorem, we propose a maximum likelihood estimation approach (CRT-MLE) to simultaneously estimate the integer and fractional parts of OFDM CFO. For the proposed CRT-MLE method, the CFO can be directly estimated via multiple sequences of different lengths. This approach can achieve a very large CFO estimation range up to the total number of OFDM subcarriers without significant additional computational complexity. Furthermore, we show that the proposed algorithm can approach the Cramér-Rao Bound. Finally, extensive simulation results show that our proposed CRT-MLE method is advantageous regarding estimation range and performance compared to baselines.

Zero-knowledge scalable transparent arguments of knowledge (zk-STARKs) are a promising approach to solving the blockchain scalability problem while maintaining security, decentralization and privacy. However, compared with zero-knowledge proofs with trusted setups deployed in existing scalability solutions, zk-STARKs are usually less efficient. In this paper, we introduce Ligerolight, an optimized zk-STARK for the arithmetic circuit satisfiability problem following the framework of Ligero (ACM CCS 2017) and Aurora (Eurocrypt 2019) based on interactive oracle proof, which could be used for blockchain scalability. Evaluations show that Ligerolight has performance advantages compared with existing zk-STARKs. The prover time is 30% faster than Aurora to generate proof for computing an authentication path of a Merkle tree with 32 leaves. The proof size is about 131KB, one-tenth of Ligero and 50% smaller than Aurora. The verifier time is 2 times as fast as Aurora. Underlying Ligerolight is a new batch zero-knowledge inner product argument, allowing to prove multiple inner product relations once. Using this argument, we build a batch multivariate polynomial commitment with poly-logarithmic communication complexity and verification. This polynomial commitment is particularly efficient when opening multiple points in multiple polynomials at one time, and may be of independent interest in constructing scalability solutions.

This article studies the multirobot efficient search (MuRES) for a nonadversarial moving target problem, whose objective is usually defined as either minimizing the target's expected capture time or maximizing the target's capture probability within a given time budget. Different from canonical MuRES algorithms, which target only one specific objective, our proposed algorithm, named distributional reinforcement learning-based searcher (DRL-Searcher), serves as a unified solution to both MuRES objectives. DRL-Searcher employs distributional reinforcement learning (DRL) to evaluate the full distribution of a given search policy's return, that is, the target's capture time, and thereafter makes improvements with respect to the particularly specified objective. We further adapt DRL-Searcher to the use case without the target's real-time location information, where only the probabilistic target belief (PTB) information is provided. Lastly, the recency reward is designed for implicit coordination among multiple robots. Comparative simulation results in a range of MuRES test environments show the superior performance of DRL-Searcher to state of the arts. Additionally, we deploy DRL-Searcher to a real multirobot system for moving target search in a self-constructed indoor environment with satisfying results.

Minimum shift keying (MSK) is a continuous phase modulation with both high spectrum efficiency and power efficiency, and has been widely used in wireless communication systems. Existing demodulation algorithms of MSK signals mainly consider either white Gaussian noise (WGN) or non-Gaussian impulsive noise (IN). However, the MSK signals suffer from the mixture of WGN and IN in many practical scenarios. For these environments, directly applying these demodulation algorithms could not achieve desirable bit error rate (BER) performance. In this paper, we address the problem of demodulation of MSK signals under the mixture of WGN and IN. Based on the approximate probability density function, the Viterbi algorithm based demodulation scheme is proposed by calculating the maximum-likelihood (ML) branch metric and the corresponding theoretical BER performance is analyzed. Furthermore, several popular branch metrics are compared in terms of both BER and complexity. Simulations results show that the proposed ML branch metric can obtain the best BER performance under all the scenarios, and the theoretical BER matches the simulation results well.

Communication systems suffer from the mixed noise which contains both non-Gaussian impulsive noise (IN) and white Gaussian noise (WGN) in many scenarios. However, there is little literature on the channel capacity under the mixed noise. In this paper, we analyze the existence of the capacity under p-th moment constraint and prove that there are only finite mass points in capacity-achieving distribution. Furthermore, we discuss the capacity relating to specific modulation format under the mixed noise and derive a lower bound with closed form. Numerical results reveal that the capacity of the mixed noise channel will decrease as the impulsiveness of the mixed noise becomes significant and the obtained analytical capacity lower bound is tight.

In many practical applications, the communication system suffers from mixed Gaussian-impulsive noise consisting of both Gaussian white noise and non-Gaussian impulsive noise. Obtaining the optimal signal detection algorithm under these scenarios requires the estimate of the mixed Gaussian-impulsive noise parameters. Unfortunately, the estimation accuracy will deteriorate in the presence of the transmitted signal. To solve the problem, we propose a blind source separation method with a neural network, namely U-net++, to separate the transmitted signal from the mixed noise. Then, the parameter estimation can be implemented with the recovered noise samples. An adaptive clipping preprocessing module and a novel loss function of the network are designed based on the statistical property of the mixed noise. Results show that our algorithm outperforms existing baselines on both source separation and parameter estimation.

Existing E2E systems would suffer performance degradation when applied in the environments where the channel conditions deviate from the trained ones. To address this problem, we propose a comprehensive method to develop an E2E communication system that possesses strong adaptability under dynamically varying unknown environments. From the system structure prospective, our system consists of the impulse generative adversarial network (IGAN) and transceiver. Within the E2E system, a novel transceiver is proposed, which maps information bit sequences to symbol vectors to introduce the inter-symbols correlation such that the signals can be detected without pilots. Hence, the transceiver is more flexible in a changing fading environment while achieving higher spectrum efficiency. On top of that, the model-agnostic meta-learning (MAML) is utilized to meta-train the IGAN to enable fast adaptation to practical noise samples. The transceiver is then general-trained based on the designed fading channel set that includes various time and frequency selectivity and the simulated noise set. Simulation results show that our proposed E2E system can outperform conventional approaches both in terms of BER, overheads and adaptability.

Tensor-based spectrum cartography (SC) has received increasing interests for recovering multi-dimensional radio map (RM) from sparse measurements. However, existing tensor-based SC methods largely depends on an ideal assumption, that the sparse measurements are regularly located on grids. However, this assumption is largely unrealistic since the RM is continuous in essence, and can be measured at arbitrary positions deviating from the pre-divided grids. This work addresses the problem of parametric SC from irregular off-grid samplings. The main idea is combining interpolation with the multi-linear rank-$(L,L,1)$block-term tensor decomposition (LL1). The interpolation is first adopted to guarantee the uniqueness of LL1, under the guidance of the proposed sampling pattern theorem. Then, the power spectrum density (PSD) and spatial loss field (SLF) of each emitter can be smoothly estimated, and SC is completed via the aggregation model. For the whole procedure, the uncertainty derived from interpolation is grid-wisely specified, and imposed as a restriction. Simulations verified that the proposed method outperforms the baselines based on on-grid samplings in harsher environments.

Traditionally, a communication waveform is designed by experts based on communication theory and their experiences on a case-by-case basis. In this paper, we propose a new waveform design paradigm with the knowledge graph (KG)-based intelligent recommendation system. The proposed paradigm aims to improve the design efficiency by structural characterization of existing waveforms and intelligently utilizing the knowledge learned from them. To achieve this goal, we first build a communication waveform knowledge graph (CWKG) with a first-order neighbor node, for which both structured semantic knowledge and numerical parameters of a waveform are integrated by representation learning. Based on the developed CWKG, we further propose an intelligent communication waveform recommendation system (CWRS) to generate waveform candidates. In the CWRS, an improved involution1D operator is introduced according to the characteristics of KG-based waveform representation, and the multi-head self-attention is adopted to weigh the influence of various components. Meanwhile, multilayer perceptron-based collaborative filtering is used to evaluate the matching degree between the requirement and the waveform candidate. Simulation results show that the proposed CWKG-based CWRS can recommend waveform candidates with high reliability, and utilize existing waveform knowledge much more effectively than existing methods.

Joint spectrum cartography and disaggregation from sparse spatial observations has been proven to be theoretically feasible based on block-term tensor decomposition (BTD) model. However, the existing BTD framework suffers from inherent drawbacks in terms of numerical stability, complexity and noise robustness. To combat with these drawbacks, we propose a new Constrained-BTD (CBTD) framework in this letter by fully utilizing practical traits of geographical power density spectrum (PSD) and spatial loss field (SLF). The cornerstone of CBTD framework is formulating the joint PSD and SLF estimation as a constrained matrix factorization problem, instead of addressing the factors of BTD. Further, a projection gradient-based (PG) algorithm, which has sublinear convergence, is proposed to handle the restrictions of PSD and SLF by projection on manifolds. Compared with the baseline methods, simulations verify that the proposed approach obtains better performances in terms of stability, complexity and noise robustness.

This paper investigates the reliable shortest path (RSP) planning problem from the reinforcement learning perspective. Different from canonical path planning methods, which require at least the first- order statistic (mean) and second-order statistic (variance) information of travel time distribution, we target at the RSP planning problem without the assumption of knowing any travel time distribution characteristic beforehand, and propose a cascaded temporal difference learning (CTD) method, which simultaneously estimates the mean and variance of the executing path and thereby gradually makes improvements through the generalized policy iteration (GPI) scheme, as the ego vehicle interacts with the environment. Extensive simulation results demonstrate the applicability of the proposed method for RSP learning in various transportation networks.

Spectrum cartography aims to estimate multidimensional radio map from limited samples taken over a geographical region. The radio map, formulated as a third-order tensor, admits an approximated low rank CANDECOMP/PARAFAC (CP) decomposition (CPD). We formulate the spectrum cartography problem as a low-CP-rank tensor completion problem. To handle the sequential spectrum observations which have not been addressed in existing research, the time-varying spectrum cartography problem is handled via online tensor completion based on incremental CPD and then solved by block coordinate descent approach. In addition, we show that the incremental CPD generates a sequence of latent factors estimates converging to a stationary point. Numerical simulations show that our proposed algorithm has better performance than the baseline methods and is suitable for realtime radio map estimation.

A generalized framework of spectrum sensing disruption for a power-limited adversary is proposed in this paper. In the literature, a conventional sensing attack typically assumes that the adversary has perfect knowledge of the spectral usage status. The framework in this paper considers a more general case where there are uncertainties in the estimates at the adversary. These uncertainties are modeled utilizing the probability of detection and the probability of false alarm. Then, the sum of the conditional probabilities of false detection at the secondary within the spectral range of interest, conditioned on the adversary's estimated spectrum usage status, is maximized. It is shown that the optimal sensing attack, given perfect estimation is a special case of the proposed framework. When the adversary has perfect spectrum usage information, this framework reduces to a previously demonstrated optimal sensing disruption. When the adversary has imperfect information on the spectral status, the proposed framework is significantly more robust than conventional sensing attacks. Further, when the adversary's power budget increases, it asymptotically approaches the sensing disruption performance upper bound.

In this paper, the sensing disruption for a power limited adversary with estimation uncertainty is formulated and analyzed. The estimation uncertainty of the adversary is modeled in terms of its probability of false alarm in vacant bands and the probability of detection in busy bands. The strategy for the adversary is obtained by maximizing the sum of the conditional probabilities of false detection within the spectral range of interest, conditioned on the adversary's estimated spectrum usage status. The proposed algorithm is shown to be significantly more robust than conventional algorithms. It is shown in simulation results that, as the adversary's power budget increases, the proposed algorithm asymptotically approaches the performance upper bound when the adversary has perfect information on the spectrum usage status.

The tradeoff between spoofing and jamming a cognitive radio network by an intelligent adversary is analyzed in this paper. Due to the vulnerabilities of spectrum sensing noted in recent studies, a cognitive radio can be attacked during the sensing interval by an adversary who puts spoofing signals in unused bands. Further, once secondary users access unused bands, the adversary can use traditional jamming to interfere with them during transmission. For an energy-constrained intelligent adversary, a two step procedure is formulated to distribute the energy between spoofing and jamming, such that the average sum throughput of the secondary users is minimized. That is, we optimally spoof in the sensing duration and then optimally jam in the transmission slot. In a cluster-based cognitive radio network, when the number of spectral vacancies required by secondary users increases, the optimal attack for the intelligent adversary will shift from jamming only, to a combination of spoofing and jamming, to spoofing only.

In this paper, we consider an adaptive multi-user resource allocation for the downlink transmission of a multi-cluster tactical multicarrier DS CDMA network. The goal is to maximize the sum packet throughput, subject to transmit power constraints. Since the objective function turns out to be noncovex and nondifferentiable, we propose a simple iterative bisection algorithm. At each iteration, a closed-form expression is derived for the transmit power, subchannel, and modulation assignment, which significantly reduces the computational complexity. We also provide an optimization algorithm for the downlink transmission under the condition of imperfect channel knowledge, and investigate the effects of both channel estimation error and partial-band jamming.

Reliable detection of available spectrum is the foundation of cognitive radio technology. To improve the detection probability under sustainable false alarm rate, a distributed spectrum sensing scheme has been proposed. In this paper, we propose a new decision combination scheme, in which the credibility of local spectrum sensing is taken into account in the final decision at central access point and Dempster-Shafer's evidence theory is adopted to combine different sensing decisions from each cognitive user. Simulation results show that significant improvement in detection probability as well as reduction in false alarm rate is achieved by our proposal