Cong Huy Nguyen, Son Dinh Nguyen, Guanlin Li, Tuan Dung Nguyen, Aditya Narayan Sankaran, Mai Huy Thong, Thanh Trung Nguyen, Mai Hong Son, Reza Farahbakhsh, Phi Le Nguyen, Noel Crespi. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Attenuation artifacts remain a critical challenge in cardiac Myocardial Perfusion Imaging (MPI) using Single-Photon Emission Computed Tomography (SPECT), often degrading diagnostic accuracy and clinical interpretability. While hybrid SPECT and Computed Tomography (CT) systems mitigate these artifacts using CT-derived attenuation maps, their high cost, radiation exposure, and limited accessibility restrict widespread clinical use. To address these challenges, we propose DiffCAS, an inference-time CT-free diffusion model for physics-aware multi-slice attenuation correction in cardiac SPECT. DiffCAS integrates a Brownian Bridge diffusion process with physics-guided supervision, enabling the generation of attenuation-corrected (AC) images directly from non-attenuation-corrected (NAC) inputs. Specifically, a physics-aware reconstruction module predicts voxel-wise attenuation coefficients and path lengths, then combines them via the Beer-Lambert law into an attenuation correction factor applied at each diffusion step, keeping the AC images physically consistent. The model introduces two key innovations that jointly enhance structural understanding and physics consistency. The first is multi-slice contextual learning, which captures cross-slice anatomical dependencies and improves spatial coherence in reconstructed images. The second is the 3D Computed Tomography Vision Transformer that models long-range volumetric structures and provides physics-consistent attenuation priors to guide the diffusion process. To enable CT-free attenuation correction, DiffCAS introduces the teacher-student distillation framework that transfers physics-informed knowledge from CT-conditioned training to a student network that requires no CT input at inference time, ensuring stability and interpretability. Evaluations on the CardiAC dataset, which comprises 424 patient studies with paired NAC and AC, and CT-based attenuation maps, demonstrate the strong performance of DiffCAS, evaluated using global pixel-level metrics and myocardium-specific clinical metrics. The proposed method surpasses state-of-the-art image generative methods, achieving superior reconstruction accuracy, structural consistency, and diagnostic reliability. These results highlight the proposed DiffCAS as a clinically promising, inference-time CT-free solution for attenuation correction in cardiac SPECT imaging.

Positron Emission Tomography (PET) and Computed Tomography (CT) are essential for diagnosing, staging, and monitoring various diseases, particularly cancer. Despite their importance, the use of PET/CT systems is limited by the necessity for radioactive materials, the scarcity of PET scanners, and the high cost associated with PET imaging. In contrast, CT scanners are more widely available and significantly less expensive. In response to these challenges, our study addresses the issue of generating PET images from CT images, aiming to reduce both the medical examination cost and the associated health risks for patients. Our contributions are twofold: First, we introduce a conditional diffusion model named CPDM, which, to our knowledge, is one of the initial attempts to employ a diffusion model for translating from CT to PET images. Second, we provide the largest CT-PET dataset to date, comprising 2,028,628 paired CT-PET images, which facilitates the training and evaluation of CT-to-PET translation models. For the CPDM model, we incorporate domain knowledge to develop two conditional maps: the Attention map and the Attenuation map. The former helps the diffusion process focus on areas of interest, while the latter improves PET data correction and ensures accurate diagnostic information. Experimental evaluations across various benchmarks demonstrate that CPDM surpasses existing methods in generating high-quality PET images in terms of multiple metrics. The source code and data samples are available at https://github.com/thanhhff/CPDM.

Friendly jamming and relay are effective schemes in physical layer security (PLS) for enhancing security in wireless communication . By deploying unmanned aerial vehicle (UAV)-assisted Non-Orthogonal Multiple Access (NOMA) transmission can extend coverage and enhancing spectrum efficiency. This paper studies the physical layer security of an UAV-based relay NOMA system, consisting of a source, multiple users, and an eavesdropper. To enhance secrecy performance, an additional UAV is employed to transmit jamming signals to the eavesdropper. Moreover, for a more practical approach, we also consider the imperfect collaboration between the jammer device and the legitimate user. The minimum average secrecy rate (MASR) of the users is maximized, assuming that the eavesdropper is capable of intercepting signals both from the source and from the relay UAV. An efficient iterative algorithm is proposed to solve the MASR maximum problem by optimizing UAV trajectories, transmit power, and power allocation coefficients. Simulation results demonstrate that the proposed system achieves 238% better MASR than the system without friendly jamming signals and 633% better than the non-optimal system. In addition, the ability to decode the received signal using successive interference cancellation also significantly affects the MASR of users in the system.

Multipath QUIC (MPQUIC) is an emerging multi-path transport protocol that lets a mobile client simultaneously use several wireless networks (e.g., Wi-Fi and cellular) in 5G and beyond. MPQUIC's performance heavily relies on its scheduler, which determines a path or several ones for sending packets in the upcoming time slot. Despite numerous efforts, the traditional design of MPQUIC schedulers can not handle wireless networks' dynamicity. Recently, a learning-based approach has shown the potential to bypass such limitations of the MPQUIC scheduler with various learning-based schedulers proposed in the literature. However, the existing works only consider the scheduling task in a single client context. When applying such a scheduler to multiple client scenarios (likely to occur in practice), they suffer from a so-called rush scheduling phenomenon. More specifically, the packet forwarding decisions made by a scheduler are only accountable to one client, resulting in conflicts of interest with other clients' schedulers. Consequently, it may harm the network performance. This paper addresses the issue and designs a learning-based MPQUIC scheduler considering the existence of multiple clients. To the best of our knowledge, this is the first work to do so. We propose MuLeS, a learning-based scheduler for MPQUIC in the multi-client scenario. MuLeS uses a central controller, which allows it to observe the state of all flows in the network. Our evaluation results show that MuLeS outperforms contemporary schedulers in terms of various metrics, including download time and loss rate. Notably, MuLeS reduces the average download time by 7%-16% compared to the other schedulers.

With the increasing demand for video streaming services, efficient video streaming with MPQUIC in mobile wireless networks is gaining interest. Although MPQUIC can leverage multiple network connections (e.g., Wi-Fi, 5G) for simultaneous transfer, improving bandwidth and resilience, its performance is heavily dependent on the MPQUIC scheduler. In mobile scenarios, network fluctuations challenge the scheduler to adapt to dynamic conditions while maintaining good video streaming quality. Addressing this issue, this paper proposes a novel MPQUIC scheduler named LoGra (i.e., LSTM-DDPG integrated MPQUIC Scheduler) with two advanced features. First, LoGra utilizes Long Short-Term Memory (LSTM) to model the temporal correlation of network conditions over time and handle the challenges posed by mobility patterns. Second, it leverages the Deep Deterministic Policy Gradient (DDPG), with its self-learning capabilities and the strength of deep neural networks, to analyze the informative temporal feature vector to influence scheduling decisions. We have implemented the proposed scheduler and compared it to existing ones. The results show that the LoGra scheduler effectively manages multipath communication in heterogeneous wireless networks under mobility scenarios. Moreover, compared to existing schedulers, LoGra achieves significant improvements in data transmission, both in general metrics (loss, goodput) and streaming-related metrics (bitrate, freezing time).

In the era of 5G and beyond, mobile devices usually can access several heterogeneous wireless networks (e.g., Wi-Fi and 5G). To simultaneously and efficiently utilize the accessible network resources, muli path transport protocols, such as MPTCP and MPQUIC, have shown much potential. In these protocols, scheduling is one of the critical processes to ensure the performance of the multipath transmission. Although there have been many proposed multipath schedulers in the literature, they have not performed well in heterogeneous networks, especially when the network conditions vary (i.e., dynamicity). In this paper, we propose a novel Q-learning-based Multipath scheduler for data transmission optimization (Q-SAT), aiming to bypass the existing limitation. By leveraging the self-learning ability of reinforcement learning, Q-SAT can instantly observe environmental changes and deploy appropriate path selection to optimize data transmission time. As a result, Q-SAT efficiently schedules multipath communication in heterogeneous wireless networks with different dynamicity levels. We have implemented Q-SAT with MPQUIC and extensively evaluated Q-SAT in an emulated environment and a real network. The evaluation results show that Q-SAT improves the data transmission time by at least 10% in the emulation and 26% in the actual deployment compared to the state-of-the-art schedulers.

Target coverage and connectivity are two of the most crucial issues in handling wireless sensor networks. However, maintaining these two factors is challenging due to the energy constraint of sensors. To this end, wireless charging has emerged as a promising solution to prolong the sensor's lifetime. In a wireless charging sensor network, a mobile charger moves around the network, stops at several charging locations and charges the sensor via electromagnetic waves. In this study, we investigate the problem of optimizing the charging location and charging time of the mobile charger to ensure the target coverage and connectivity of the network. Our main idea is to leverage the Deep Reinforcement Learning approach. Specifically, the mobile charger will act as an agent, which receives a state including the energy information of the sensors. The mobile charger then decides the following charging location and charging time using the state information and the knowledge learned in the past. Experimental results have shown that our algorithm can extend the network lifetime (i.e., the time until the network coverage and connectivity are not guaranteed) up to 245.9 times compared to the existing algorithms.

Sensor scheduling for maximizing the network lifetime and achieving the full area coverage is a paramount problem in wireless sensor networks. Although considerable effort has been devoted, this problem is still a challenge to the research community. The approximation algorithms proposed so far couldn't guarantee the performance ratio. In this paper, we first formulate the problem under linear programming model which can help to determine the exact optimal solution. Then, in order to reduce the time complexity, we propose a (1 +∊)-approximation algorithm based on divide-and-conquer technique. The main idea is to divide the network into sub-regions, then determine the suboptimal solution for every sub-region and combine them to obtain the total solution of the whole network. Moreover, with the aim of speeding up the suboptimal solution finding process, we propose an approximation algorithm using the column generation approach. The experiment results show the superiority of our proposed algorithms over the existing ones.

Wireless sensor networks (WSNs) are used in many mission-critical applications, such as target tracking on a battlefield, emergency alarms, and disaster detection. In such applications, QoS provisioning in the timeliness domain is indispensable. Moreover, because of the diversity of sensory data, QoS provisioning should support not only one but multiple levels of end-to-end delay constraints. As a result of several characteristics such as the limitations on the energy supply, available storage and computational capacity of the sensor nodes, guaranteeing timely delivery in WSNs is a challenging problem. To overcome these limitations, several lightweight and stateless QoS-based geographic routing protocols have been proposed. The existing protocols work well in networks without routing holes (i.e., regions with no working sensors). However, with the occurrence of routing holes, they suffer from the so-called local minimum phenomenon and traffic congestion around the hole boundary. In this paper, we consider the presence of routing holes and propose a delay-guaranteed geographic routing protocol called DEHA that can support multiple end-to-end delay levels. The main idea is to achieve early awareness of the presence of a routing hole and then to utilize this awareness in determining a routing path that can avoid the hole. Simulation results show that our protocol outperforms the existing protocols in terms of several performance metrics, including packet delivery ratio, energy efficiency, and load balancing.

The occurrence of multiple holes in wireless sensor networks poses many challenges in designing routing protocols. The traditional scheme is forwarding packets along the hole perimeters. However, this scheme leads to two serious problems: data concentration around the hole boundaries and routing path enlargement Recently, several approaches have been proposed to address these two problems, wherein a common idea is to form forbidden areas around the holes from which packets are kept to stay away. However, due to the static nature of the forbidden areas and routing paths, the existing protocols cannot solve these two problems thoroughly. In this paper, we propose a novel protocol for bypassing multiple holes in wireless sensor networks which can balance the traffic over the network while ensuring the constant stretch property of the routing path. Our main idea is to use elastic forbidden areas and dynamic routing paths. The theoretical analysis proves that the routing path stretch of the proposed protocol can be controlled to be as small as 1 + ϵ (for any predefined ϵ > 0), and the simulation experiments show that our protocol strongly outperforms state-of-the-art protocols in terms of load balancing.

Nowadays, cloud-based storage services are rapidly growing and becoming an emerging trend in data storage field. There are many problems when designing an efficient storage engine for cloud-based systems with some requirements such as big-file processing, lightweight meta-data, low latency, parallel I/O, deduplication, distributed, high scalability. Key-value stores played an important role and showed many advantages when solving those problems. This paper presents about Big File Cloud (BFC) with its algorithms and architecture to handle most of problems in a big-file cloud storage system based on key-value store. It is done by proposing low-complicated, fixed-size meta-data design, which supports fast and highly-concurrent, distributed file I/O, several algorithms for resumable upload, download and simple data deduplication method for static data. This research applied the advantages of ZDB - an in-house key-value store which was optimized with auto-increment integer keys for solving big-file storage problems efficiently. The results can be used for building scalable distributed data cloud storage that support big-file with size up to several terabytes.

This paper presents an energy efficient inverter based readout circuit for implantable pressure bridge piezo-resistive sensor which can achieve 9 bit resolution with 28.8-mV input voltage range. Only one bridge branch is utilized with interchanging supply voltage to achieve net differential input voltage range, hence reducing the power consumption by a half. A gain compensated technique is applied for inverter based switched capacitor amplifier to achieve both power efficiency and high resolution. A two-step auto calibration is applied to eliminate the offset from non-ideal effects of the switched-capacitor amplifier (SC-amp) and comparator delay. The readout system is implemented and simulated in TSMC 90 nm CMOS technology. With supply voltage of 1.2 V, simulation results show that the circuit can achieve 9.4 bit resolution while consuming only 35 µW during 320 µs conversion time. The digital output code has little sensitivity to temperature variation.