Communication-Oriented Model Fine-Tuning for Packet-Loss Resilient Distributed Inference Under Highly Lossy IoT Networks

The distributed inference (DI) framework has gained traction as a technique for real-time applications empowered by cutting-edge deep machine learning (ML) on resource-constrained Internet of things (IoT) devices. In DI, computational tasks are offloaded from the IoT device to the edge server via lossy IoT networks. However, generally, there is a communication system-level trade-off between communication latency and reliability; thus, to provide accurate DI results, a reliable and high-latency communication system is required to be adapted, which results in non-negligible end-to-end latency of the DI. This motivated us to improve the trade-off between the communication latency and accuracy by efforts on ML techniques. Specifically, we have proposed a communication-oriented model tuning (COMtune), which aims to achieve highly accurate DI with low-latency but unreliable communication links. In COMtune, the key idea is to fine-tune the ML model by emulating the effect of unreliable communication links through the application of the dropout technique. This enables the DI system to obtain robustness against unreliable communication links. Our ML experiments revealed that COMtune enables accurate predictions with low latency and under lossy networks

Quantitative Evaluation of the Use of Actigraphy for Neurological and Psychiatric Disorders

Quantitative and objective evaluation of disease severity and/or drug effect is necessary in clinical practice. Wearable accelerometers such as an actigraph enable long-term recording of a patient’s movement during activities and they can be used for quantitative assessment of symptoms due to various diseases. We reviewed some applications of actigraphy with analytical methods that are sufficiently sensitive and reliable to determine the severity of diseases and disorders such as motor and nonmotor disorders like Parkinson’s disease, sleep disorders, depression, behavioral and psychological symptoms of dementia (BPSD) for vascular dementia (VD), seasonal affective disorder (SAD), and stroke, as well as the effects of drugs used to treat them. We believe it is possible to develop analytical methods to assess more neurological or psychopathic disorders using actigraphy records

Distillation-Based Semi-Supervised Federated Learning for Communication-Efficient Collaborative Training with Non-IID Private Data

This study develops a federated learning (FL) framework overcoming largely incremental communication costs due to model sizes in typical frameworks without compromising model performance. To this end, based on the idea of leveraging an unlabeled open dataset, we propose a distillation-based semi-supervised FL (DS-FL) algorithm that exchanges the outputs of local models among mobile devices, instead of model parameter exchange employed by the typical frameworks. In DS-FL, the communication cost depends only on the output dimensions of the models and does not scale up according to the model size. The exchanged model outputs are used to label each sample of the open dataset, which creates an additionally labeled dataset. Based on the new dataset, local models are further trained, and model performance is enhanced owing to the data augmentation effect. We further highlight that in DS-FL, the heterogeneity of the devices’ dataset leads to ambiguous of each data sample and lowing of the training convergence. To prevent this, we propose entropy reduction averaging, where the aggregated model outputs are intentionally sharpened. Moreover, extensive experiments show that DS-FL reduces communication costs up to 99 percent relative to those of the FL benchmark while achieving similar or higher classification accuracy

Role of cysteine residues in the V(V)-reductase activity of Vanabin2

Ascidians (tunicates or sea squirts) accumulate extremely high levels of vanadium as the reduced form V(III) in extremely acidic vacuoles in their blood cells. Several key proteins related to vanadium accumulation have been isolated from vanadium-rich ascidians and their physiological functions characterized. Of these, vanabins are small, cysteine-rich proteins that have been identified only in vanadium-rich ascidians. Our previous study revealed that Vanabin2 can act as a V(V)-reductase. The current study examines the role of cysteine and several other amino acid residues of Vanabin2 in V(V)-reduction. When all eighteen cysteine residues of Vanabin2 were substituted with serine residues, the V(V)-reductase activity was lost. Substitutions of three, structurally clustered cysteines in three different regions resulted in a moderate decrease in reductase activity, suggesting that more than a single cysteine pair is responsible for the V(V)-reductase activity of Vanabin2. Mutations in the V(IV)-binding domains caused either an increase or decrease in activity but no mutation caused the complete loss of activity. These results suggest that some pairs, but more than a single pair, of cysteine residues are necessary for the V(V)-reductase activity of Vanabin2

Metal Ion Selectivity of the Vanadium(V)-Reductase Vanabin2

In a previous study, Vanabin2, a member of a family of V(IV)-binding proteins, or Vanabins, was shown to act as a V(V)-reductase. The current study assesses the ability of Vanabin2 to reduce various transition metal ions in vitro. An NADPH-coupled oxidation assay yielded no evidence of reduction activity with the hexavalent transition metal anions, MoVIO42- and WVIO42-, or with three divalent cations, Mn(II), Ni(II), and Co(II). Although Cu(II) is readily reduced by glutathione and is gradually oxidized in air, this process was not affected by the presence of Vanabin2. In the experiments conducted thus far, Vanabin2 acts only as a V(V)-reductase. This high selectivity may account for the metal ion selectivity of vanadium accumulation in ascidians