Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing

Given significant air pollution problems, air quality index (AQI) monitoring has recently received increasing attention. In this paper, we design a mobile AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS, to efficiently build fine-grained AQI maps in realtime. Specifically, we first propose the Gaussian plume model on basis of the neural network (GPM-NN), to physically characterize the particle dispersion in the air. Based on GPM-NN, we propose a battery efficient and adaptive monitoring algorithm to monitor AQI at the selected locations and construct an accurate AQI map with the sensed data. The proposed adaptive monitoring algorithm is evaluated in two typical scenarios, a two-dimensional open space like a roadside park, and a three-dimensional space like a courtyard inside a building. Experimental results demonstrate that our system can provide higher prediction accuracy of AQI with GPM-NN than other existing models, while greatly reducing the power consumption with the adaptive monitoring algorithm

Game Theoretic Approaches to Massive Data Processing in Wireless Networks

Wireless communication networks are becoming highly virtualized with two-layer hierarchies, in which controllers at the upper layer with tasks to achieve can ask a large number of agents at the lower layer to help realize computation, storage, and transmission functions. Through offloading data processing to the agents, the controllers can accomplish otherwise prohibitive big data processing. Incentive mechanisms are needed for the agents to perform the controllers' tasks in order to satisfy the corresponding objectives of controllers and agents. In this article, a hierarchical game framework with fast convergence and scalability is proposed to meet the demand for real-time processing for such situations. Possible future research directions in this emerging area are also discussed

X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model

We introduce X-Adapter, a universal upgrader to enable the pretrained plug-and-play modules (e.g., ControlNet, LoRA) to work directly with the upgraded text-to-image diffusion model (e.g., SDXL) without further retraining. We achieve this goal by training an additional network to control the frozen upgraded model with the new text-image data pairs. In detail, X-Adapter keeps a frozen copy of the old model to preserve the connectors of different plugins. Additionally, X-Adapter adds trainable mapping layers that bridge the decoders from models of different versions for feature remapping. The remapped features will be used as guidance for the upgraded model. To enhance the guidance ability of X-Adapter, we employ a null-text training strategy for the upgraded model. After training, we also introduce a two-stage denoising strategy to align the initial latents of X-Adapter and the upgraded model. Thanks to our strategies, X-Adapter demonstrates universal compatibility with various plugins and also enables plugins of different versions to work together, thereby expanding the functionalities of diffusion community. To verify the effectiveness of the proposed method, we conduct extensive experiments and the results show that X-Adapter may facilitate wider application in the upgraded foundational diffusion model.Comment: Project page: https://showlab.github.io/X-Adapter

Conservative medical intervention as a complement to CDT for BCRL therapy: a systematic review and meta-analysis of randomized controlled trials

BackgroundThe effect of first-line complex decongestive therapy (CDT) for breast cancer-related lymphedema (BCRL) depending on various factors forces patients to seek additional treatment. Therefore, this meta-analysis was conducted to evaluate the effect of different conservative medical interventions as a complement to CDT. This is the first meta-analysis that includes various kinds of conservative treatments as adjunctive therapy to get broader knowledge and improve practical application value, which can provide recommendations to further improve BCRL patients’ health status.MethodsRCTs published before 18 December 2023 from PubMed, Embase, Cochrane Library, and Web of Science databases were searched. RCTs that compared the effects of conservative medical intervention were included. A random-effects or fixed-effects model was used based on the heterogeneity findings. Study quality was evaluated using the Cochrane risk of bias tool.ResultsSixteen RCTs with 690 participants were included, comparing laser therapy, intermittent pneumatic compression (IPC), extracorporeal shock wave therapy (ESWT), electrotherapy, ultrasound, diet or diet in combination with synbiotic supplement, traditional Chinese medicine (TCM), continuous passive motion (CPM), and negative pressure massage treatment (NMPT). The results revealed that conservative medical intervention as complement to CDT had benefits in improving lymphedema in volume/circumference of the upper extremity [SMD = −0.30, 95% CI = (−0.45, −0.15), P < 0.05, I2 = 51%], visual analog score (VAS) for pain [SMD = −3.35, 95% CI (−5.37, −1.33), P < 0.05, I2 = 96%], quality of life [SMD = 0.44, 95% CI (0.19, 0.69), P < 0.05, I2 = 0], and DASH/QuickDASH [SMD = −0.42, 95% CI (−0.70, −0.14), P < 0.05, I2 = 10%] compared with the control group. Subgroup analysis revealed that laser therapy and electrotherapy are especially effective (P < 0.05).ConclusionCombining conservative medical interventions with CDT appears to have a positive effect on certain BCRL symptoms, especially laser therapy and electrotherapy. It showed a better effect on patients under 60 years old, and laser therapy of low to moderate intensity (5–24 mW, 1.5–2 J/cm2) and of moderate- to long-term duration (≥36–72 sessions) showed better effects.Systematic review registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=354824, identifier CRD42022354824

A Novel Non-Volatile Inverter-based CiM: Continuous Sign Weight Transition and Low Power on-Chip Training

In this work, we report a novel design, one-transistor-one-inverter (1T1I), to satisfy high speed and low power on-chip training requirements. By leveraging doped HfO2 with ferroelectricity, a non-volatile inverter is successfully demonstrated, enabling desired continuous weight transition between negative and positive via the programmable threshold voltage (VTH) of ferroelectric field-effect transistors (FeFETs). Compared with commonly used designs with the similar function, 1T1I uniquely achieves pure on-chip-based weight transition at an optimized working current without relying on assistance from off-chip calculation units for signed-weight comparison, facilitating high-speed training at low power consumption. Further improvements in linearity and training speed can be obtained via a two-transistor-one-inverter (2T1I) design. Overall, focusing on energy and time efficiencies, this work provides a valuable design strategy for future FeFET-based computing-in-memory (CiM)