Multi-access edge computing provides localized resources within mobile networks to address the requirements of emerging latency-sensitive and computing-intensive applications. At the edge, dynamic requests necessitate sophisticated resource management for adaptive network slicing. This involves optimizing resource allocations, scaling functions, and load balancing to utilize only essential resources under constrained network scenarios. However, existing solutions largely assume static slice counts, ignoring the re-optimization overhead associated with management algorithms when slices fluctuate. Moreover, many approaches rely on simplified energy models that overlook intertemporal resource scheduling and are predominantly evaluated through simulations, neglecting critical practical considerations. This paper presents an incremental cooperative Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for resource management in dynamic edge slicing. The proposed approach optimizes long-term slicing benefits by reducing delay and energy consumption while minimizing retraining overhead in response to slice variations. Furthermore, we implement an urban-wide edge computing testbed based on OpenStack and Kubernetes to validate the algorithm’s performance. Experimental results demonstrate that our incremental MADDPG method outperforms benchmark strategies in aggregated slicing utility and reduces training energy consumption by up to 50% compared to the re-optimization approach.

MOTIVATION: In the era of precision medicine, performing comparative analysis over diverse patient populations is a fundamental step toward tailoring healthcare interventions. However, the aspect of fairly selecting molecular features across multiple patients is often overlooked. RESULTS: To address this challenge, we introduce FALAFL (FAir muLti-sAmple Feature seLection), an algorithmic approach based on combinatorial optimization. FALAFL is designed to perform feature selection in sequencing data which ensures a balanced selection of features from all patient samples in a cohort. We have applied FALAFL to the problem of selecting lineage-informative CpG sites within a cohort of colorectal cancer patients subjected to low-coverage single-cell methylation sequencing. Our results demonstrate that FALAFL can rapidly and robustly determine the optimal set of CpG sites, which are each well covered by cells across the vast majority of the patients, while ensuring that in each patient, a large proportion of these sites have high read coverage. An analysis of the FALAFL-selected sites reveals that their tumor lineage-informativeness exhibits a strong correlation across a spectrum of diverse patient profiles. Furthermore, these universally lineage-informative sites are highly enriched in the inter-CpG island regions. We hope that FALAFL will aid in designing panels for diagnostic and prognostic purposes and help propel fair data science practices in the exploration of complex diseases. AVAILABILITY AND IMPLEMENTATION: The source code is available at: https://github.com/algo-cancer/FALAFL.

Remote conference applications are increasingly widely used, but currently, their improper data encryption methods, proprietary implementations, and dial-in access cause concerns about privacy breaches. As such, there is a need for trustworthy and secure solutions for these production tools. In this paper, we present mTunnel, a transparent software layer in the host system for securing conference applications without sacrificing the key functionalities and convenience. The basic idea of mTunnel is to encrypt sensitive data, such as audio, video, text, etc., before it is obtained by untrusted application clients. mTunnel leverages the audio and video streaming capabilities of the conference applications to tunnel the encrypted content to the remote end. mTunnel involves a software framework to accommodate the media interception and representation through I/O virtualization based on virtual drivers. Moreover, mTunnel supports complete E2EE group conversations even in a mixed IP and public switched telephone network (PSTN). We implement mTunnel and evaluate it with several commercial products. Results show its feasibility and overhead.

A method to improve protein function prediction for sparsely annotated PPI networks is introduced. The method extends the DSD majority vote algorithm introduced by Cao et al. to give confidence scores on predicted labels and to use predictions of high confidence to predict the labels of other nodes in subsequent rounds. We call this a majority vote cascade. Several cascade variants are tested in a stringent cross-validation experiment on PPI networks from S. cerevisiae and D. melanogaster, and we show that for many different settings with several alternative confidence functions, cascading improves the accuracy of the predictions. A list of the most confident new label predictions in the two networks is also reported. Code and networks for the cross-validation experiments appear at http://bcb.cs.tufts.edu/cascade.

The resource requirements of application-specific accelerators challenge embedded system designers who have a tight area budget but must cover a range of possible software kernels. We propose an early detection methodology (ReconfAST) to identify computationally similar synthesizable kernels to build Shared Accelerators (SAs). SAs are specialized hardware accelerators that execute very different software kernels but share the common hardware functions between them. SAs increase the fraction of workloads covered by specialized hardware by detecting similarities in dataflow and control flow between seemingly very different workloads. Existing methods use either dynamic traces or analyze register transfer level (RTL) implementations to find these similarities which require deep knowledge of RTL and time-consuming design process.