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State estimation is highly critical for accurately observing the dynamic behavior of the power grids and minimizing risks from cyber threats. However, existing state estimation methods encounter challenges in accurately capturing power system dynamics, primarily because of limitations in encoding the grid topology and sparse measurements. This paper proposes a physics-informed graphical learning state estimation method to address these limitations by leveraging both domain physical knowledge and a graph neural network (GNN). We employ a GNN architecture that can handle the graph-structured data of power systems more effectively than traditional data-driven methods. The physics-based knowledge is constructed from the branch current formulation, making the approach adaptable to both transmission and distribution systems. The validation results of three IEEE test systems show that the proposed method can achieve lower mean square error more than 20% than the conventional methods., Comment: 11 pages, 17 figures, journal accepted

We generalize the type-based multiple access framework proposed by Mergen and Tong (2006) to the case of unsourced multiple access. In the proposed framework, each device tracks the state of a physical/digital process, quantizes this state, and communicates it to a common receiver through a shared channel in an uncoordinated manner. The receiver aims to estimate the type of the states, i.e., the set of states and their multiplicity in the sequence of states reported by all devices. We measure the type estimation error using the Wasserstein distance. Considering an example of multi-target position tracking, we show that type estimation can be performed effectively via approximate message passing. Furthermore, we determine the quantization resolution that minimizes the type estimation error by balancing quantization distortion and communication error., Comment: accepted to the 25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC); simulation code available at: https://github.com/khachoang1412/TUMA

We investigate the age of information (AoI) in a scenario where energy-harvesting devices send status updates to a gateway following the slotted ALOHA protocol and receive no feedback. We let the devices adjust the transmission probabilities based on their current battery level. Using a Markovian analysis, we derive analytically the average AoI. We further provide an approximate analysis for accurate and easy-to-compute approximations of both the average AoI and the age-violation probability (AVP), i.e., the probability that the AoI exceeds a given threshold. We also analyze the average throughput. Via numerical results, we investigate two baseline strategies: transmit a new update whenever possible to exploit every opportunity to reduce the AoI, and transmit only when sufficient energy is available to increase the chance of successful decoding. The two strategies are beneficial for low and high update-generation rates, respectively. We show that an optimized policy that balances the two strategies outperforms them significantly in terms of both AoI metrics and throughput. Finally, we show the benefit of decoding multiple packets in a slot using successive interference cancellation and adapting the transmission probability based on both the current battery level and the time elapsed since the last transmission., Comment: Submitted to IEEE Transaction of Communications. A short version [arXiv:[2310.00348] was presented at GLOBECOM 2023. Simulation code: https://github.com/khachoang1412/AoI_slottedALOHA_energyHarvesting. arXiv admin note: text overlap with arXiv:2310.00348

We propose a distributed implementation for integrated sensing and communication (ISAC) backed by a massive multiple input multiple output (CF-mMIMO) architecture without cells. Distributed multi-antenna access points (APs) simultaneously serve communication users (UEs) and emit probing signals towards multiple specified zones for sensing. The APs can switch between communication and sensing modes, and adjust their transmit power based on the network settings and sensing and communication operations' requirements. By considering local partial zero-forcing and maximum-ratio-transmit precoding at the APs for communication and sensing, respectively, we first derive closed-form expressions for the spectral efficiency (SE) of the UEs and the mainlobe-to-average-sidelobe ratio (MASR) of the sensing zones. Then, a joint operation mode selection and power control design problem is formulated to maximize the SE fairness among the UEs, while ensuring specific levels of MASR for sensing zones. The complicated mixed-integer problem is relaxed and solved via successive convex approximation approach. We further propose a low-complexity design, where AP mode selection is designed through a greedy algorithm and then power control is designed based on this chosen mode. Our findings reveal that the proposed scheme can consistently ensure a sensing success rate of $100\%$ for different network setups with a satisfactory fairness among all UEs., Comment: The manuscript has been submitted to IEEE TWC

In 1998, O'Loan et al. introduced a simplified bus route model to illustrate bus dynamics. However, fluctuations in passenger numbers make it challenging to achieve an exact solution for the model's stationary state, as these fluctuations can impact bus behavior. In this study, we present an exactly solvable model for a dual bus route system that builds upon O'Loan et al.'s model. This dual model allows us to comprehensively analyze bus route dynamics. Our model introduces additional parameters not previously considered by O'Loan et al. to account for neighboring effects and align with an observation in the original model, which can influence the average stationary current and velocity of buses. When the neighboring effect is weak, our model behaves similarly to O'Loan et al.'s bus route model. However, with a strong neighboring effect, our model exhibits intriguing characteristics. Additionally, in some limiting cases, our model recovers well-known models such as the simple exclusion process, its generalization, the cooperative exclusion process, and the RNA polymerase model during the elongation stage.

Aligning a user query and video clips in cross-modal latent space and that with semantic concepts are two mainstream approaches for ad-hoc video search (AVS). However, the effectiveness of existing approaches is bottlenecked by the small sizes of available video-text datasets and the low quality of concept banks, which results in the failures of unseen queries and the out-of-vocabulary problem. This paper addresses these two problems by constructing a new dataset and developing a multi-word concept bank. Specifically, capitalizing on a generative model, we construct a new dataset consisting of 7 million generated text and video pairs for pre-training. To tackle the out-of-vocabulary problem, we develop a multi-word concept bank based on syntax analysis to enhance the capability of a state-of-the-art interpretable AVS method in modeling relationships between query words. We also study the impact of current advanced features on the method. Experimental results show that the integration of the above-proposed elements doubles the R@1 performance of the AVS method on the MSRVTT dataset and improves the xinfAP on the TRECVid AVS query sets for 2016-2023 (eight years) by a margin from 2% to 77%, with an average about 20%., Comment: Accepted in ICMR2024

We consider the numerical approximation for a class of radial Dunkl processes corresponding to arbitrary (reduced) root systems in $\mathbb{R}^{d}$. This class contains some well-known processes such as Bessel processes, Dyson's Brownian motions, and Wishart processes. We propose some semi--implicit and truncated Euler--Maruyama schemes for radial Dunkl processes, and study their rate of convergence with respect to the $L^{p}$-sup norm., Comment: 24 pages

Fairness of machine learning models in healthcare has drawn increasing attention from clinicians, researchers, and even at the highest level of government. On the other hand, the importance of developing and deploying interpretable or explainable models has been demonstrated, and is essential to increasing the trustworthiness and likelihood of adoption of these models. The objective of this study was to develop and implement a framework for addressing both these issues - fairness and explainability. We propose an explainable fairness framework, first developing a model with optimized performance, and then using an in-processing approach to mitigate model biases relative to the sensitive attributes of race and sex. We then explore and visualize explanations of the model changes that lead to the fairness enhancement process through exploring the changes in importance of features. Our resulting-fairness enhanced models retain high sensitivity with improved fairness and explanations of the fairness-enhancement that may provide helpful insights for healthcare providers to guide clinical decision-making and resource allocation., Comment: Accepted to the IEEE International Conference on Healthcare Informatics (IEEE ICHI 2024)

We study the kinematics of a pillar, namely G287.76-0.87, using three rotational lines of $^{12}$CO(5-4), $^{12}$CO(8-7), $^{12}$CO(11-10), and a fine structure line of [OI] $63\,\mu$m Southern Carina observed by SOFIA/GREAT. This pillar is irradiated by the associated massive star cluster Trumpler 16, which includes $\eta$~Carina. Our analysis shows that the relative velocity of the pillar with respect to this ionization source is small, $\sim 1\,\rm km\,s^{-1}$, and the gas motion in the tail is more turbulent than in the head. We also performed analytical calculations to estimate the gas column density in local thermal equilibrium (LTE) conditions, which yields $N_{\rm CO}$ as $(\sim 0.2 -5)\times 10^{17}\,\rm cm^{-2}$. We further constrain the gas's physical properties in non-LTE conditions using RADEX. The non-LTE estimations result in $n_{\rm H_{2}} \simeq 10^{5}\,\rm cm^{-3}$ and $N_{\rm CO} \simeq 10^{16}\,\rm cm^{-2}$. We found that the thermal pressure within the G287.76-0.87 pillar is sufficiently high to make it stable for the surrounding hot gas and radiation feedback if the winds are not active. While they are active, stellar winds from the clustered stars sculpt the surrounding molecular cloud into pillars within the giant bubble around $\eta$~Carina., Comment: 17 pages, 14 figures, accepted to ApJ

In the realm of food computing, segmenting ingredients from images poses substantial challenges due to the large intra-class variance among the same ingredients, the emergence of new ingredients, and the high annotation costs associated with large food segmentation datasets. Existing approaches primarily utilize a closed-vocabulary and static text embeddings setting. These methods often fall short in effectively handling the ingredients, particularly new and diverse ones. In response to these limitations, we introduce OVFoodSeg, a framework that adopts an open-vocabulary setting and enhances text embeddings with visual context. By integrating vision-language models (VLMs), our approach enriches text embedding with image-specific information through two innovative modules, eg, an image-to-text learner FoodLearner and an Image-Informed Text Encoder. The training process of OVFoodSeg is divided into two stages: the pre-training of FoodLearner and the subsequent learning phase for segmentation. The pre-training phase equips FoodLearner with the capability to align visual information with corresponding textual representations that are specifically related to food, while the second phase adapts both the FoodLearner and the Image-Informed Text Encoder for the segmentation task. By addressing the deficiencies of previous models, OVFoodSeg demonstrates a significant improvement, achieving an 4.9\% increase in mean Intersection over Union (mIoU) on the FoodSeg103 dataset, setting a new milestone for food image segmentation., Comment: CVPR 2024; 12 pages

Bike-sharing systems (BSSs) are deployed in over a thousand cities worldwide and play an important role in many urban transportation systems. BSSs alleviate congestion, reduce pollution and promote physical exercise. It is essential to explore the spatiotemporal patterns of bike-sharing demand, as well as the factors that influence these patterns, in order to optimise system operational efficiency. In this study, an optimised geo-temporal graph is constructed using trip data from Moby Bikes, a dockless BSS operator. The process of optimising the graph unveiled prime locations for erecting new stations during future expansions of the BSS. The Louvain algorithm, a community detection technique, is employed to uncover usage patterns at different levels of temporal granularity. The community detection results reveal largely self-contained sub-networks that exhibit similar usage patterns at their respective levels of temporal granularity. Overall, this study reinforces that BSSs are intrinsically spatiotemporal systems, with community presence driven by spatiotemporal dynamics. These findings may aid operators in improving redistribution efficiency., Comment: Accepted to publish in The 2024 IEEE 40th International Conference on Data Engineering Workshops (ICDEW&DASC-2024), pp. 1-8