Occlusion Boundary Estimation (OBE) identifies boundaries arising from both inter-object occlusions and self-occlusion within individual objects. This task is closely related to Monocular Depth Estimation (MDE), which infers depth from a single image, as Occlusion Boundaries (OBs) provide critical geometric cues for resolving depth ambiguities, while depth can conversely refine occlusion reasoning. In this paper, we aim to systematically model and exploit this mutually beneficial relationship. To this end, we propose MoDOT, a novel framework for joint estimation of depth and OBs, which incorporates a new Cross-Attention Strip Module (CASM) to leverage mid-level OB features for depth prediction, and a novel OB-Depth Constraint Loss (OBDCL) to enforce geometric consistency. To facilitate this study, we contribute OB-Hypersim, a large-scale photorealistic dataset with precise depth and self-occlusion-handled OB annotations. Extensive experiments on two synthetic datasets and NYUD-v2 demonstrate that MoDOT achieves significantly better performance than single-task baselines and multi-task competitors. Furthermore, models trained solely on our synthetic data demonstrate strong generalization to real-world scenes without fine-tuning, producing depth maps with sharper boundaries and improved geometric fidelity. Collectively, these results underscore the significant benefits of jointly modeling OBs and depth. Code and resources are available at HERE.

Mental states, a complex union of cognitive, emotional, and perceptual conditions, fundamentally shape how individuals perceive and interact with their surroundings. Detecting these states is vital, as it reveals the underlying processes that govern behaviour and enables targeted interventions across diverse fields such as mental health, education, and human-computer interaction. Generalisability across subjects and trials is essential to ensure that these interventions are effective and reliable in varied real-world settings, thereby enhancing their practical applicability. In this paper, we introduce an end-to-end optimised pipeline for classifying mental states from electroencephalography (EEG) signals. Through quantitative studies of data preprocessing and feature enhancement of continuous data collected under less stringent conditions, our pipeline utilises specially designed, cutting-edge, lightweight classifiers and achieves new state-of-the-art performance. Specifically addressing the challenge of generalisability in EEG signal research, our pipeline demonstrates robust performance, achieving a peak accuracy of 79.1% and an average of 71.9% in cross-subject scenarios, and a high of 89.3% with an average of 85.4% in cross-trial evaluations.

Modern energy systems increasingly rely on complex, multi-energy microgrids incorporating diverse energy carriers, including hydrogen-based technologies, yet their efficient dispatch remains challenging due to the need for precise coordination across heterogeneous resources. Existing multi-agent reinforcement learning approaches often fail to capture the nuanced interactions between different energy vectors and agents. We propose an Attention-boosted Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) algorithm that enhances inter-agent coordination through self-attention mechanisms in the critic network, enabling more effective feature extraction and global value assessment. Comprehensive simulations demonstrate our approach significantly outperforms baseline methods, reducing operational costs by up to 37.67% and carbon emissions by 34.1%, while maintaining superior power balance across microgrids. These results establish attention-enhanced MATD3 as a promising solution for optimizing complex energy systems with high renewable penetration and diverse storage technologies.

The trading of data is becoming increasingly important as it holds substantial value. A blockchain-based data marketplace can provide a secure and transparent platform for data exchange. To facilitate this, developing a fair data exchange protocol for digital goods has garnered considerable attention in recent decades. The Zero Knowledge Contingent Payment (ZKCP) protocol enables trustless fair exchanges with the aid of blockchain and zero-knowledge proofs. However, applying this protocol in a practical data marketplace is not trivial.In this paper, several potential attacks are identified when applying the ZKCP protocol in a practical public data marketplace. To address these issues, we propose SmartZKCP, an enhanced solution that offers improved security measures and increased performance. The protocol is formalized to ensure fairness and secure against potential attacks. Moreover, SmartZKCP offers efficiency optimizations and minimized communication costs. Evaluation results show that SmartZKCP is both practical and efficient, making it applicable in a data exchange marketplace.

Multiparty private set intersection (mPSI) protocol is capable of finding the intersection of multiple sets securely without revealing any other information. However, its limitation lies in processing only those elements present in every participant's set, which proves inadequate in scenarios where certain elements are common to several, but not all, sets.

Large language models (LLMs) have revolutionized code completion tasks. IDE plugins such as MarsCode can generate code recommendations, saving developers significant time and effort. However, current evaluation methods for code completion are limited by their reliance on static code benchmarks, which do not consider human interactions and evolving repositories. This paper proposes RepoSim, a novel benchmark designed to evaluate code completion tasks by simulating the evolving process of repositories and incorporating user behaviors. RepoSim leverages data from an IDE plugin, by recording and replaying user behaviors to provide a realistic programming context for evaluation. This allows for the assessment of more complex prompt strategies, such as utilizing recently visited files and incorporating user editing history. Additionally, RepoSim proposes a new metric based on users' acceptance or rejection of predictions, offering a user-centric evaluation criterion. Our preliminary evaluation demonstrates that incorporating users' recent edit history into prompts significantly improves the quality of LLM-generated code, highlighting the importance of temporal context in code completion. RepoSim represents a significant advancement in benchmarking tools, offering a realistic and user-focused framework for evaluating code completion performance.

How to obtain discriminative features has proved to be a core problem for image retrieval. Many recent works use convolutional neural networks to extract features. However, clutter and occlusion will interfere with the distinguishability of features when using convolutional neural network (CNN) for feature extraction. To address this problem, we intend to obtain high-response activations in the feature map based on the attention mechanism. We propose two attention modules, a spatial attention module and a channel attention module. For the spatial attention module, we first capture the global information and model the relation between channels as a region evaluator, which evaluates and assigns new weights to local features. For the channel attention module, we use a vector with trainable parameters to weight the importance of each feature map. The two attention modules are cascaded to adjust the weight distribution for the feature map, which makes the extracted features more discriminative. Furthermore, we present a scale and mask scheme to scale the major components and filter out the meaningless local features. This scheme can reduce the disadvantages of the various scales of the major components in images by applying multiple scale filters, and filter out the redundant features with the MAX-Mask. Exhaustive experiments demonstrate that the two attention modules are complementary to improve performance, and our network with the three modules outperforms the state-of-the-art methods on four well-known image retrieval datasets.

Visual place recognition (VPR) aims to estimate the geographical location of a query image by finding its nearest reference images from a large geo-tagged database. Most of the existing methods adopt convolutional neural networks to extract feature maps from images. Nevertheless, such feature maps are high-dimensional tensors, and it is a challenge to effectively aggregate them into a compact vector representation for efficient retrieval. To tackle this challenge, we develop an end-to-end convolutional neural network architecture named DMPCANet. The network adopts the regional pooling module to generate feature tensors of the same size from images of different sizes. The core component of our network, the Differentiable Multilinear Principal Component Analysis (DMPCA) module, directly acts on tensor data and utilizes convolution operations to generate projection matrices for dimensionality reduction, thereby reducing the dimensionality to one sixteenth. This module can preserve crucial information while reducing data dimensions. Experiments on two widely used place recognition datasets demonstrate that our proposed DMPCANet can generate low-dimensional discriminative global descriptors and achieve the state-of-the-art results.

System-on-chip (SoC) has migrated from single core to manycore architectures to cope with the increasing complexity of real-life applications. Application task mapping has a significant impact on the efficiency of manycore system (MCS) computation and communication. We present WAANSO, a scalable framework that incorporates a Wavelet Clustering based approach to cluster application tasks. We also introduce Ant Swarm Optimization (ASO) based on iterative execution of Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) for task clustering and mapping to the MCS processing elements. We have shown that WAANSO can significantly increase the MCS energy and performance efficiencies. Based on our experiments on a 64-core system, WAANSO improves energy efficiency by 19%, compared to baseline approaches, namely DPSO, ACO and branch and bound (B&B). Additionally, the performance improves by 65.86% compared to Density-Based Spatial Clustering of Applications with Noise (DBSCAN) baseline.

Discriminative local features obtained from activations of convolutional neural networks have proven to be essential for image retrieval. To improve retrieval performance, many recent works aim to obtain more powerful and discriminative features. In this work, we propose a new attention layer to assess the importance of local features and assign higher weights to those more discriminative. Furthermore, we present a scale and mask module to filter out the meaningless local features and scale the major components. This module not only reduces the impact of the various scales of the major components in images by scaling them on the feature maps, but also filters out the redundant and confusing features with the MAX-Mask. Finally, the features are aggregated into the image representation. Experimental evaluations demonstrate that the proposed method outperforms the state-of-the-art methods on standard image retrieval datasets.