Abstract

Re-identification (ReID) is a critical challenge in computer vision, predominantly studied in the context of pedestrians and vehicles. However, robust object-instance ReID, which has significant im- plications for tasks such as autonomous exploration, long-term perception, and scene understanding, remains underexplored. In this work, we address this gap by proposing a novel dual-path object-instance re-identification transformer architecture that inte- grates multimodal RGB and depth information. By leveraging depth data, we demonstrate improvements in ReID across scenes that are cluttered or have varying illumination conditions. Additionally, we develop a ReID-based localization framework that enables accurate camera localization and pose identification across different view- points. We validate our methods using two custom-built RGB-D datasets, as well as multiple sequences from the open-source TUM RGB-D datasets. Our approach demonstrates significant improve- ments in both object instance ReID (mAP of 75.18) and localization accuracy (success rate of 83% on TUM-RGBD), highlighting the essential role of object ReID in advancing robotic perception. Our models, frameworks, and datasets have been made publicly avail- able.

Abstract

Abstract— Transgenic crops have become widespread but have associated health concerns. Testing for transgenic corn is slow and lengthy. In this paper, we used UV-Vis spectroscopy in combination with chemometrics and the PCA-SVM classifier to identify different types of corn samples from each other, including transgenic varieties. The presented methodology and the classifier can differentiate very well between the three different varieties of corn considered in this paper. Although there is a slight improvement in prediction accuracies compared to the state-of-the-art methods, our method is considerably more cost-effective and less time-consuming.

Abstract

Graph Neural Network (GNN) research is rapidly advancing due to GNNs’ capacity to learn distributed representations from graph-structured data. However, centralizing large volumes of real-world graph data for GNN training is often impractical due to privacy concerns, regulatory restrictions, and commercial competition. Federated learning (FL), a distributed learning paradigm, offers a solution by preserving data privacy with collaborative model training. Despite progress in training huge vision and language models, federated learning for GNNs remains underexplored. To address this challenge, we present a novel method for federated learning on GNNs based on spectral GNNs equipped with neural ordinary differential equations (ODE) for better information capture, showing promising results across both homophilic and heterophilic graphs. Our approach effectively handles non-Independent and Identically Distributed (non-IID) data, while also achieving performance comparable to existing methods that only operate on IID data. It is designed to be privacy-preserving and bandwidth-optimized, making it suitable for real-world applications such as social network analysis, recommendation systems, and fraud detection, which often involve complex, non-IID, and heterophilic graph structures. Our results in the area of federated learning on non-IID heterophilic graphs demonstrate significant improvements while also achieving better performance on homophilic graphs. This work highlights the potential of federated learning in diverse and challenging graph settings.

Abstract

The possibility of a quantum system to exhibit properties that are akin to both the classically held notions of being a particle and a wave, is one of the most intriguing aspects of the quantum description of nature. These aspects have been instrumental in understanding paradigmatic natural phenomena as well as to provide non-classical applications. A conceptual foundation for the wave nature of a quantum state has recently been presented, through the notion of quantum coherence. We introduce here a parallel notion for the particle nature of a quantum state of an arbitrary physical system. We provide elements of a resource theory of particleness, and give a quantification of the same. Finally, we provide evidence for a complementarity between the particleness thus introduced, and the coherence of an arbitrary quantum state.

Abstract

The fully entangled fraction (FEF) measures the proximity of a quantum state to maximally entangled states. FEF , in systems, is a significant benchmark for various quantum information processing protocols including teleportation. Quantum conditional entropy (QCE) on the other hand is a measure of correlation in quantum systems. Conditional entropies for quantum systems can be negative, marking a departure from conventional classical systems. The negativity of quantum conditional entropies plays a decisive role in tasks like state merging and dense coding. In the present work, we investigate the relation of these two important yardsticks. Our probe is mainly done in the ambit of states with maximally mixed marginals, with a few illustrations from other classes of quantum states. We start our study in two-qubit systems, where for the Werner states, we obtain lower bounds to its FEF when the conditional Rényi entropy is negative. We then obtain relations between FEF and QCE for two-qubit Weyl states. Moving on to two qudit states, we find a necessary and sufficient condition based on FEF, for the isotropic state to have negative conditional entropy. In two qudit systems, the relation between FEF and QCE is probed for the rank-deficient and generalized Bell diagonal states. FEF is intricately linked with k-copy nonlocality and k- copy steerability. The relations between FEF and QCE facilitates to find conditions for k- copy nonlocality and k- copy steerability based on QCE. We obtain such conditions for certain classes of states in two qubits and two qudits. Applications of the relations obtained are provided in the context of work extraction, faithful entanglement and entropic uncertainty relations.

Abstract

Effective decision-making in complex environments with multi-discrete action spaces poses significant challenges for agent architectures, particularly in image-based settings. While Decision Transformers have shown promise in various domains, their performance often suffers in environments where agents must handle multi-discrete actions. Existing enhancements to Decision Transformer architectures have yet to address this critical issue, limiting their ability to support agents in learning robust policies in these environments. To address this gap, we propose Multi-State Action Tokenisation (M-SAT), a novel approach designed to improve agent decision-making by tokenising actions at the individual action level and incorporating auxiliary state information. This disentanglement of actions improves both the performance of agents and the interpretability of individual actions within attention layers, fostering better visibility into agent decision processes. Importantly, M-SAT facilitates the development of more interpretable and transparent agents capable of making complex decisions in dynamic environments involving multi-discrete action spaces. We evaluate M-SAT on the challenging ViZDoom environments, focusing on scenarios with multi-discrete action spaces and image-based observations, such as Deadly Corridor, My Way Home and Death Match. Our approach demonstrates superior performance compared to baseline Decision Transformers, with no additional data or significant computational overheads. Furthermore, we observe that M-SAT does not require positional encoding to achieve high performance, with its removal occasionally leading to further improvements. These findings suggest that M-SAT enables more efficient and interpretable agent-based decision-making in multi-discrete action spaces.

Abstract

The study of information revivals, witnessing the violation of certain data-processing inequalities, has provided an important paradigm in the study of non-Markovian quantum stochastic processes. Although often used interchangeably, we argue here that the notions of “revivals” and “backflows,” i.e., flows of information from the environment back into the system, are distinct: an information revival can occur without any backflow ever taking place. In this paper, we examine in detail the phenomenon of noncausal revivals and relate them to the theory of short Markov chains and squashed non-Markovianity. We also provide an operational condition, in terms of system-only degrees of freedom, to witness the presence of genuine backflow that cannot be explained by noncausal revivals. As a byproduct, we demonstrate that focusing on processes with genuine backflows, while excluding those with only noncausal revivals, resolves the issue of nonconvexity of Markovianity, thus enabling the construction of a convex resource theory of genuine quantum non-Markovianity

Abstract

An influential mathematical model of memory, the temporal context model (TCM), posits that we encode items and their associations with temporal context (Howard & Kahana, 2002). Temporal context is conceived of as a recency-weighted average of past experiences. Critically, the model assumes that when an item is retrieved later, the associated temporal context is also obligatorily retrieved. Existing evidence for the idea of retrieved temporal context primarily comes from free-recall studies. However, free recall introduces some critical confounds that are difficult to resolve (Folkerts et al., 2018) and also encourages memory strategies that may mimic temporal context effects (Hintzman, 2011). Schwartz et al. (2005) showed that temporal context influences image recognition within short experimental lists. We extend this by using the Natural Scenes Dataset (NSD) to demonstrate that reinstating temporal context enhances recognition accuracy even across long timescales. We demonstrate that when the relevant temporal context is successfully reinstated for a reference image, the recognition accuracy of subsequent images increases. Critically, we show that this influence falls off with temporal distance at encoding from the reference image only when the temporal context is successfully retrieved, as predicted by TCM. Furthermore, the slope of this temporal gradient increases as a function of the strength of the influence of the retrieved temporal context. These findings extend our understanding of temporal context effects in episodic memory by showing that temporal context is retrieved even in tasks that do not encourage linking between items as a memory strategy.

Abstract

A demonstration of the implementation of vehicle occupancy detection on hardware-software is shown in this paper. For the purpose of validating applications for vehicle occupancy detection, a hardware Field Programmable Gate Array (FPGA) platform, also known as PYNQ-ZU, is a feasible embedded architecture. Automatic in-car occupancy monitoring is an important technology in modern transportation, with major implications for safety, energy efficiency, and smart vehicle management. One of the primary benefits of mmWave radar is its ability to accurately detect the number and location of vehicle occupants, mmWave radar ensures robust detection under all lighting and weather conditions. In our research, the proposed approach was applied to point cloud images. Following the generation of 3D point cloud images, two filters, Top-View (TV), and Front-View (FV), were used to improve vehicle occupancy detection. These filters transformed 3D images into 2D ones. TV filter was found to be more effective than the FV filter. After filtering the 2D images, Mexican Hat Wavelet Transform (MHWT) was used to extract features from them. Four machine learning methods were then used to determine vehicle seat occupancy, with Logistic Regression (LR) and Support Vector Machine (SVM) producing the highest results, with an accuracy of 98%. In comparison to existing methods, the proposed approach, which utilizes mmWave radar, TV Filter, MHWT, FPGA (PYNQ-ZU), and LR, was determined to significantly improve the accuracy of vehicle occupancy detection.

Abstract

A graph transactional database (GTD) is a collection of graphs. Frequent Top-k Subgraph Pattern Mining (FTSPM) involves finding the complete set of top-k frequently occurring subgraph patterns in a GTD. Most previous studies focused on finding these patterns in certain graphs by disregarding the crucial information regarding the existential probabilities that may exist between the edges of any two nodes. With this motivation, this paper proposes a novel model to discover top-k (frequently occurring) subgraphs in an uncertain graph transactional database. We introduce a novel algorithm, top-k uncertain subgraph miner (TUSM), to find all top-k subgraphs in the data. We also introduce an approximate top-k uncertain subgraph miner (ATUSM) algorithm to tackle the computational expansiveness of TUSM. Experimental results on synthetic and protein-protein interaction datasets demonstrate that the proposed model finds valuable information and the algorithms are efficient.