Time-Correlated Sparsification for Efficient Over-the-Air Model Aggregation in Wireless Federated Learning

Federated edge learning (FEEL) is a promising distributed machine learning (ML) framework to drive edge intelligence applications. However, due to the dynamic wireless environments and the resource limitations of edge devices, communication becomes a major bottleneck. In this work, we propose time-correlated sparsification with hybrid aggregation (TCS-H) for communication-efficient FEEL, which exploits jointly the power of model compression and over-the-air computation. By exploiting the temporal correlations among model parameters, we construct a global sparsification mask, which is identical across devices, and thus enables efficient model aggregation over-the-air. Each device further constructs a local sparse vector to explore its own important parameters, which are aggregated via digital communication with orthogonal multiple access. We further design device scheduling and power allocation algorithms for TCS-H. Experiment results show that, under limited communication resources, TCS-H can achieve significantly higher accuracy compared to the conventional top-K sparsification with orthogonal model aggregation, with both i.i.d. and non-i.i.d. data distributions

Beyond Transmitting Bits: Context, Semantics, and Task-Oriented Communications

Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, thereby providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications

Zoledronic acid induces apoptosis via stimulating the expressions of ERN1, TLR2, and IRF5 genes in glioma cells

Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor that affects older people. Although the current therapeutic approaches for GBM include surgical resection, radiotherapy, and chemotherapeutic agent temozolomide, the median survival of patients is 14.6 months because of its aggressiveness. Zoledronic acid (ZA) is a nitrogen-containing bisphosphonate that exhibited anticancer activity in different cancers. The purpose of this study was to assess the potential effect of ZA in distinct signal transduction pathways in U87-MG cells. In this study, experiments performed on U87-MG cell line (Human glioblastoma-astrocytoma, epithelial-like cell line) which is an in vitro model of human glioblastoma cells to examine the cytotoxic and apoptotic effects of ZA. IC50 dose of ZA, 25 μM, applied on U87-MG cells during 72 h. ApoDIRECT In Situ DNA Fragmentation Assay was used to investigate apoptosis of U87MG cells. The quantitative reverse transcription polymerase chain reaction (qRT-PCR) (LightCycler480 System) was carried out for 48 gene expression like NF-κB, Toll-like receptors, cytokines, and inteferons. Our results indicated that ZA (IC50 dose) increased apoptosis 1.27-fold in U87MG cells according to control cells. According to qRT-PCR data, expression levels of the endoplasmic reticulum-nuclei-1 (ERN1), Toll-like receptor 2 (TLR2), and human IFN regulatory factor 5 (IRF5) tumor suppressor genes elevated 2.05-, 2.08-, and 2.3-fold by ZA, respectively, in U87MG cells. Our recent results indicated that ZA have a key role in GBM progression and might be considered as a potential agent in glioma treatment. © 2015, International Society of Oncology and BioMarkers (ISOBM)

Clinical and parasitological detection of babesia canis canis in dogs: first report from turkey

This study was aimed to describe clinical, biochemical and haemathological findings in three dogs with Babesia canis canis diagnosed by Giemsa staining and Polymerase Chain Reaction (PCR) methods. In Case I and II severe icterus, anemia, haemoglobinemia, haemoglobinuria, thrombocytopenia, tachypnea, tachycardia, liver damage and myositis were determined. In addition in Case I cholecystitis and in Case II renal damage were developed. In Case III mild anemia and icterus along with liver damage and myositis were developed and clinical signs were mild. Following treatment Case II and III completely recovered while Case I died. In conclusion, B. canis canis has been determined clinically and parasitologically for first time in Turkey

Correntropy: Implications of nonGaussianity for the moment expansion and deconvolution

Accepted versio

Metalearning-based alternating minimization algorithm for nonconvex optimization

In this article, we propose a novel solution for nonconvex problems of multiple variables, especially for those typically solved by an alternating minimization (AM) strategy that splits the original optimization problem into a set of subproblems corresponding to each variable and then iteratively optimizes each subproblem using a fixed updating rule. However, due to the intrinsic nonconvexity of the original optimization problem, the optimization can be trapped into a spurious local minimum even when each subproblem can be optimally solved at each iteration. Meanwhile, learning-based approaches, such as deep unfolding algorithms, have gained popularity for nonconvex optimization; however, they are highly limited by the availability of labeled data and insufficient explainability. To tackle these issues, we propose a meta-learning based alternating minimization (MLAM) method that aims to minimize a part of the global losses over iterations instead of carrying minimization on each subproblem, and it tends to learn an adaptive strategy to replace the handcrafted counterpart resulting in advance on superior performance. The proposed MLAM maintains the original algorithmic principle, providing certain interpretability. We evaluate the proposed method on two representative problems, namely, bilinear inverse problem: matrix completion and nonlinear problem: Gaussian mixture models. The experimental results validate the proposed approach outperforms AM-based methods

Accelerated diabetic wound healing by topical application of combination oral antidiabetic agents-loaded nanofibrous scaffolds: An in vitro and in vivo evaluation study

The combination of oral antidiabetic drugs, pioglitazone, metformin, and glibenclamide, which also exhibit the strongest anti-inflammatory action among oral antidiabetic drugs, were loaded into chitosan/gelatin/polycaprolactone (PCL) by electrospinning and polyvinyl pyrrolidone (PVP)/PCL composite nanofibrous scaffolds by pressurized gyration to compare the diabetic wound healing effect. The combination therapies significantly accelerated diabetic wound healing in type-1 diabetic rats and organized densely packed collagen fibers in the dermis, it also showed better regeneration of the dermis and epidermis than single drug-loaded scaffolds with less inflammatory cell infiltration and edema. The formation of the hair follicles started in 14 days only in the combination therapy and lower proinflammatory cytokine levels were observed compared to single drug-loaded treatment groups. The combination therapy increased the wettability and hydrophilicity of scaffolds, demonstrated sustained drug release over 14 days, has high tensile strength and suitable cytocompatibility on L929 (mouse fibroblast) cell and created a suitable area for the proliferation of fibroblast cells. Consequently, the application of metformin and pioglitazone-loaded chitosan/gelatin/PCL nanofibrous scaffolds to a diabetic wound area offer high bioavailability, fewer systemic side effects, and reduced frequency of dosage and amount of drug

The International Deep Brain Stimulation Registry and Database for Gilles de la Tourette Syndrome: How Does It Work?

Tourette Syndrome (TS) is a neuropsychiatric disease characterized by a combination of motor and vocal tics. Deep brain stimulation (DBS), already widely utilized for Parkinson's disease and other movement disorders, is an emerging therapy for select and severe cases of TS that are resistant to medication and behavioral therapy. Over the last two decades, DBS has been used experimentally to manage severe TS cases. The results of case reports and small case series have been variable but in general positive. The reported interventions have, however, been variable, and there remain non-standardized selection criteria, various brain targets, differences in hardware, as well as variability in the programming parameters utilized. DBS centers perform only a handful of TS DBS cases each year, making large-scale outcomes difficult to study and to interpret. These limitations, coupled with the variable effect of surgery, and the overall small numbers of TS patients with DBS worldwide, have delayed regulatory agency approval (e.g., FDA and equivalent agencies around the world). The Tourette Association of America, in response to the worldwide need for a more organized and collaborative effort, launched an international TS DBS registry and database. The main goal of the project has been to share data, uncover best practices, improve outcomes, and to provide critical information to regulatory agencies. The international registry and database has improved the communication and collaboration among TS DBS centers worldwide. In this paper we will review some of the key operation details for the international TS DBS database and registry