Adaptation Video-text Retrieval (UDA VR), assuming that training (source) data and testing (target) data are from different domains.

Ourmethodologyincorporates a certain amount of backtracking when needed, allowing changes in causally preceding variables tominimize deviations from realistic scenarios. Specifically, we introduce a novel optimization framework that permits but also controls the extent of backtracking with a "naturalness" criterion. Empirical experiments demonstrate the effectiveness of our method.

Lemma 2 (Chernoff bound for irreducible Markov chains). The proof is based on the argument given in Appendix A.2 of [7], adapted though for the case of Markov chains. We start the analysis by establishing the relation between the expected regret, Equation 1, and its proxy,Equation17. For the first part, we show in Appendix C that the expected number of times that an arma {1,...,N}hasn'tbeenplayed,isoftheorderofO(loglogT). Assume that the one-parameter family of Markov chains on the finite state space S, together with the reward functionf: S R, satisfy conditions (18), (19), (20), (21), and (22).

Ifweassumethereward function is a linear combination of features, it is often the case that the number of featuresk is much lessthanthetotalnumber ofstate-action pairs. When learning a posterior from demonstrations we use Bayesian IRL [4]. Bayesian IRL uses Markov chain Monte Carlo (MCMC) sampling to sample from the posterior P(R|D). The step size was tuned to result in an accept ratio close to0.4. Ifso, then we stop gradient ascent.

Graph-based Anomaly Detection models have gained widespread adoption in recent years, identifying suspicious nodes by aggregating neighborhood information. However, most existing studies overlook the pervasive issues of missing and adversarially obscured node attributes, which can undermine aggregation stability and prediction reliability. To mitigate this, we propose FGC-Comp, a lightweight, classifier-agnostic, and deployment-friendly attribute completion module-designed to enhance neighborhood aggregation under incomplete attributes. We partition each node's neighbors into three label-based groups, apply group-specific transforms to the labeled groups while a node-conditioned gate handles unknowns, fuse messages via residual connections, and train end-to-end with a binary classification objective to improve aggregation stability and prediction reliability under missing attributes. Experiments on two real-world fraud datasets validate the effectiveness of the approach with negligible computational overhead.

Electronic Health Records (EHR) systematically organize patient health data through standardized medical codes, serving as a comprehensive and invaluable source for predictive modeling. Graph neural networks (GNNs) have demonstrated effectiveness in modeling interactions between medical codes within EHR. However, existing GNN-based methods are inadequate due to: a) their reliance on pairwise relations fails to capture the inherent higher-order dependencies in clinical data, and b) the localized message-passing scheme limits representation power. To address these issues, this paper proposes a novel Structure-aware HyperGraph Transformer (SHGT) framework following three-fold ideas: a) employing a hypergraph structural encoder to capture higher-order interactions among medical codes, b) integrating the Transformer architecture to reason over the entire hypergraph, and c) designing a tailored loss function incorporating hypergraph reconstruction to preserve the hypergraph's original structure. Experiments on real-world EHR datasets demonstrate that the proposed SHGT outperforms existing state-of-the-art models on diagnosis prediction.

Underwater Acoustic Networks (UANs) have gained significant attention from both industry and academia due to their indisputable advantages in improving link reliability, increasing system capacity, expanding transmission range and so on. Acoustic communication is most widely used underwater communication as sound wave is not absorbed by water so easily like electromagnetic wave and optical wave [1]. UANs typically consist of acoustic-linked seabed sensors, autonomous underwater vehicles, and ground stations that provide links to onshore control centers. Due to the battery-powered network nodes, shallow water acoustic channel characteristics, such as low available bandwidth and highly varying multi-path, maximizing throughput while minimizing consumption has become a very challenging task [2]. Recent studies have discussed the challenges and opportunities of underwater cognitive communication [3], proposed cooperative automatic repeat request protocols for higher channel quality [4], and analyzed the impact of low transmission rates and long preambles on medium access control protocols [5]. Artificial intelligence (AI) has experienced significant growth in popularity in recent years, and many industries and research fields have explored its potential applications, including information theory, game theory, biological systems, and so on [6-9].

Predicting Quality of Service (QoS) data crucial for cloud service selection, where user privacy is a critical concern. Federated Graph Neural Networks (FGNNs) can perform QoS data prediction as well as maintaining user privacy. However, existing FGNN-based QoS predictors commonly implement on-device training on scattered explicit user-service graphs, thereby failing to utilize the implicit user-user interactions. To address this issue, this study proposes a high order collaboration-oriented federated graph neural network (HC-FGNN) to obtain accurate QoS prediction with privacy preservation. Concretely, it magnifies the explicit user-service graphs following the principle of attention mechanism to obtain the high order collaboration, which reflects the implicit user-user interactions. Moreover, it utilizes a lightweight-based message aggregation way to improve the computational efficiency. The extensive experiments on two QoS datasets from real application indicate that the proposed HC-FGNN possesses the advantages of high prediction accurate and privacy protection.

Commercial UAVs are an emerging security threat as they are capable of carrying hazardous payloads or disrupting air traffic. To counter UAVs, we introduce an autonomous 3D target encirclement and interception strategy. Unlike traditional ground-guided systems, this strategy employs autonomous drones to track and engage non-cooperative hostile UAVs, which is effective in non-line-of-sight conditions, GPS denial, and radar jamming, where conventional detection and neutralization from ground guidance fail. Using two noisy real-time distances measured by drones, guardian drones estimate the relative position from their own to the target using observation and velocity compensation methods, based on anti-synchronization (AS) and an X$-$Y circular motion combined with vertical jitter. An encirclement control mechanism is proposed to enable UAVs to adaptively transition from encircling and protecting a target to encircling and monitoring a hostile target. Upon breaching a warning threshold, the UAVs may even employ a suicide attack to neutralize the hostile target. We validate this strategy through real-world UAV experiments and simulated analysis in MATLAB, demonstrating its effectiveness in detecting, encircling, and intercepting hostile drones. More details: https://youtu.be/5eHW56lPVto.