Collaborative Edge Caching: a Meta Reinforcement Learning Approach with Edge Sampling

Current learning-based edge caching schemes usually suffer from dynamic content popularity, e.g., in the emerging short video platforms, users' request patterns shift significantly over time and across different edges. An intuitive solution for a specific local edge cache is to collect more request histories from other edge caches. However, uniformly merging these request histories may not perform satisfactorily due to heterogeneous content distributions on different edges. To solve this problem, we propose a collaborative edge caching framework. First, we design a meta-learning-based collaborative strategy to guarantee that the local model can timely meet the continually changing content popularity. Then, we design an edge sampling method to select more "valuable" neighbor edges to participate in the local training. To evaluate the proposed framework, we conduct trace-driven experiments to demonstrate the effectiveness of our design: it improves the average cache hit rate by up to $10.12\%$ (normalized) compared with other baselines.Comment: Published on IEEE International Conference on Multimedia and Expo 2023 (ICME2023

Integrating spatial continuous wavelet transform and normalized difference vegetation index to map the agro-pastoral transitional zone in Northern China

The agro-pastoral transitional zone (APTZ) in Northern China is one of the most important ecological barriers of the world. The commonly-used method to identify the spatial distribution of ATPZ is to apply a threshold rule on climatic or land use indicators. This approach is highly subjective, and the quantity standards vary among the studies. In this study, we adopted the spatial continuous wavelet transform (SCWT) technique to detect the spatial fluctuation in normalized difference vegetation index (NDVI) sequences, and as such identify the APTZ. To carry out this analysis, the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI 1-month data (MODND1M) covering the period 2006–2015 were used. Based on the spatial variation in NDVI, we identified two sub-regions within the APTZ. The temporal change of APTZ showed that although vegetation spatial pattern changed annually, certain areas appeared to be stable, while others showed higher sensitivity to environmental variance. Through correlation analysis between the dynamics of APTZ and precipitation, we found that the mean center of the APTZ moved toward the southeast during dry years and toward the northwest during humid years. By comparing the APTZ spatial pattern obtained in the present study with the outcome following the traditional approach based on mean annual precipitation data, it can be concluded that our study provides a reliable basis to advance the methodological framework to identify accurately transitional zones. The identification framework is of high importance to support decision-making in land use management in Northern China as well as other similar regions around the world

Beyond physical entrainment: Competitive and cooperative mental stances during identical joint-action tasks differently affect inter-subjective neural synchrony and judgments of agency

10.1080/17470919.2020.1727949Social Neuroscience1503368-37

DAGC: Data-Volume-Aware Adaptive Sparsification Gradient Compression for Distributed Machine Learning in Mobile Computing

Distributed machine learning (DML) in mobile environments faces significant communication bottlenecks. Gradient compression has emerged as an effective solution to this issue, offering substantial benefits in environments with limited bandwidth and metered data. Yet, they encounter severe performance drop in non-IID environments due to a one-size-fits-all compression approach, which does not account for the varying data volumes across workers. Assigning varying compression ratios to workers with distinct data distributions and volumes is thus a promising solution. This study introduces an analysis of distributed SGD with non-uniform compression, which reveals that the convergence rate (indicative of the iterations needed to achieve a certain accuracy) is influenced by compression ratios applied to workers with differing volumes. Accordingly, we frame relative compression ratio assignment as an $n$-variables chi-square nonlinear optimization problem, constrained by a fixed and limited communication budget. We propose DAGC-R, which assigns the worker handling larger data volumes the conservative compression. Recognizing the computational limitations of mobile devices, we DAGC-A, which are computationally less demanding and enhances the robustness of the absolute gradient compressor in non-IID scenarios. Our experiments confirm that both the DAGC-A and DAGC-R can achieve better performance when dealing with highly imbalanced data volume distribution and restricted communication

FILLING THE GAP WITH MISSING DATA

Ph.DDOCTOR OF PHILOSOPHY (FOS

Highly Portable, Sensor-Based System for Human Fall Monitoring

Falls are a very dangerous situation especially among elderly people, because they may lead to fractures, concussion, and other injuries. Without timely rescue, falls may even endanger their lives. The existing optical sensor-based fall monitoring systems have some disadvantages, such as limited monitoring range and inconvenience to carry for users. Furthermore, the fall detection system based only on an accelerometer often mistakenly determines some activities of daily living (ADL) as falls, leading to low accuracy in fall detection. We propose a human fall monitoring system consisting of a highly portable sensor unit including a triaxis accelerometer, a triaxis gyroscope, and a triaxis magnetometer, and a mobile phone. With the data from these sensors, we obtain the acceleration and Euler angle (yaw, pitch, and roll), which represents the orientation of the user’s body. Then, a proposed fall detection algorithm was used to detect falls based on the acceleration and Euler angle. With this monitoring system, we design a series of simulated falls and ADL and conduct the experiment by placing the sensors on the shoulder, waist, and foot of the subjects. Through the experiment, we re-identify the threshold of acceleration for accurate fall detection and verify the best body location to place the sensors by comparing the detection performance on different body segments. We also compared this monitoring system with other similar works and found that better fall detection accuracy and portability can be achieved by our system

Targeted inhibition of human hematological cancers in vivo by doxorubicin encapsulated in smart lipoic acid-crosslinked hyaluronic acid nanoparticles

The chemotherapy of hematological cancers is challenged by its poor selectivity that leads to low therapeutic efficacy and pronounced adverse effects. Here, we report that doxorubicin encapsulated in lipoic acid-crosslinked hyaluronic acid nanoparticles (LACHA-DOX) mediate highly efficacious and targeted inhibition of human hematological cancers including LP-1 human multiple myeloma (MM) and AML-2 human acute myeloid leukemia xenografted in nude mice. LACHA-DOX had a size of ca. 183 nm and a DOX loading content of ca. 12.0 wt.%. MTT and flow cytometry assays showed that LACHA-DOX possessed a high targetability and antitumor activity toward CD44 receptor overexpressing LP-1 human MM cells and AML-2 human acute myeloid leukemia cells. The in vivo and ex vivo images revealed that LACHA-DOX achieved a significantly enhanced accumulation in LP-1 and AML-2 tumor xenografts. Notably, LACHA-DOX effectively suppressed LP-1 as well as AML-2 tumor growth and drastically increased mice survival rate as compared to control groups receiving free DOX or PBS. Histological analyses exhibited that LACHA-DOX caused little damage to the major organs like liver and heart. This study provides a proof-of-concept that lipoic acid-crosslinked hyaluronic acid nanoparticulate drugs may offer a more safe and effective treatment modality for CD44 positive hematological malignancies