PASCAL: A Learning-aided Cooperative Bandwidth Control Policy for Hierarchical Storage Systems

Nowadays, the Hierarchical Storage System (HSS) is considered as an ideal model to meet the cost-performance demand. The data migration between storing tiers of HSS is the way to achieve the cost-performance goal. The bandwidth control is to limit the maximum amount of data migration. Most of previous research about HSS focus on studying the data migration policy instead of bandwidth control. However, the recent research about cache and networking optimization suggest that the bandwidth control has significant impact on the system performance. Few previous work achieves a satisfactory bandwidth control in HSS since it is hard to control bandwidth for so many data migration tasks simultaneously. In this paper, we first give a stochastic programming model to formalize the bandwidth control problem in HSS. Then we propose a learning-aided bandwidth control policy for HSS, named \Pascal{}, which learns to control the bandwidth of different data migration task in an cooperative way. We implement \Pascal{} on a commercial HSS and compare it with three strong baselines over a group of workloads. Our evaluation on the physical system shows that \Pascal{} can effectively decrease 1.95X the tail latency and greatly improve throughput stability (2X $\downarrow$ throughput jitter), and meanwhile keep the throughput at a relatively high level

Colossal switchable photocurrents in topological Janus transition metal dichalcogenides

Nonlinear optical properties, such as bulk photovoltaic effects, possess great potential in energy harvesting, photodetection, rectification, etc. To enable efficient light-current conversion, materials with strong photo-responsivity are highly desirable. In this work, we predict that monolayer Janus transition metal dichalcogenides (JTMDs) in the 1T' phase possess colossal nonlinear photoconductivity owing to their topological band mixing, strong inversion symmetry breaking, and small electronic bandgap. 1T' JTMDs have inverted bandgaps on the order of 10 meV and are exceptionally responsive to light in the terahertz (THz) range. By first-principles calculations, we reveal that 1T' JTMDs possess shift current (SC) conductivity as large as $2300 ~\rm nm \cdot \mu A / V^2$, equivalent to a photo-responsivity of $2800 ~\rm mA/W$. The circular current (CC) conductivity of 1T' JTMDs is as large as $10^4~ \rm nm \cdot \mu A / V^2$. These remarkable photo-responsivities indicate that the 1T' JTMDs can serve as efficient photodetectors in the THz range. We also find that external stimuli such as the in-plane strain and out-of-plane electric field can induce topological phase transitions in 1T' JTMDs and that the SC can abruptly flip their directions. The abrupt change of the nonlinear photocurrent can be used to characterize the topological transition and has potential applications in 2D optomechanics and nonlinear optoelectronics

Contrastive Clustering

In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feature space. Therein, the instance- and cluster-level contrastive learning are respectively conducted in the row and column space by maximizing the similarities of positive pairs while minimizing those of negative ones. Our key observation is that the rows of the feature matrix could be regarded as soft labels of instances, and accordingly the columns could be further regarded as cluster representations. By simultaneously optimizing the instance- and cluster-level contrastive loss, the model jointly learns representations and cluster assignments in an end-to-end manner. Extensive experimental results show that CC remarkably outperforms 17 competitive clustering methods on six challenging image benchmarks. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19\% (39\%) performance improvement compared with the best baseline

CSTNet: A Dual-Branch Convolutional Network for Imaging of Reactive Flows using Chemical Species Tomography

Chemical Species Tomography (CST) has been widely used for in situ imaging of critical parameters, e.g. species concentration and temperature, in reactive flows. However, even with state-of-the-art computational algorithms the method is limited due to the inherently ill-posed and rank-deficient tomographic data inversion, and by high computational cost. These issues hinder its application for real-time flow diagnosis. To address them, we present here a novel CST-based convolutional neural Network (CSTNet) for high-fidelity, rapid, and simultaneous imaging of species concentration and temperature. CSTNet introduces a shared feature extractor that incorporates the CST measurement and sensor layout into the learning network. In addition, a dual-branch architecture is proposed for image reconstruction with crosstalk decoders that automatically learn the naturally correlated distributions of species concentration and temperature. The proposed CSTNet is validated both with simulated datasets, and with measured data from real flames in experiments using an industry-oriented sensor. Superior performance is found relative to previous approaches, in terms of robustness to measurement noise and millisecond-level computing time. This is the first time, to the best of our knowledge, that a deep learning-based algorithm for CST has been experimentally validated for simultaneous imaging of multiple critical parameters in reactive flows using a low-complexity optical sensor with severely limited number of laser beams.Comment: Submitted to IEEE Transactions on Neural Networks and Learning System

Macrophage/microglia polarization for the treatment of diabetic retinopathy

Macrophages/microglia are immune system defense and homeostatic cells that develop from bone marrow progenitor cells. According to the different phenotypes and immune responses of macrophages (Th1 and Th2), the two primary categories of polarized macrophages/microglia are those conventionally activated (M1) and alternatively activated (M2). Macrophage/microglial polarization is a key regulating factor in the development of inflammatory disorders, cancers, metabolic disturbances, and neural degeneration. Macrophage/microglial polarization is involved in inflammation, oxidative stress, pathological angiogenesis, and tissue healing processes in ocular diseases, particularly in diabetic retinopathy (DR). The functional phenotypes of macrophages/microglia affect disease progression and prognosis, and thus regulate the polarization or functional phenotype of microglia at different DR stages, which may offer new concepts for individualized therapy of DR. This review summarizes the involvement of macrophage/microglia polarization in physiological situations and in the pathological process of DR, and discusses the promising role of polarization in personalized treatment of DR

Anti-PD-1 immunotherapy combined with stereotactic body radiation therapy and GM-CSF for the treatment of advanced malignant PEComa: A case report

BackgroundPerivascular epithelioid cell neoplasm (PEComa) is a rare mesenchymal tumour. Due to its low incidence, a standard treatment regimen for PEComa has not yet been established. Radiotherapy has a synergistic effect with PD-1 inhibitors and GM-CSF. We treated advanced malignant PEComa with a triple regimen of PD-1 inhibitor, SBRT and GM-CSF to provide better therapeutic effect.Case presentationA 63-year-old woman was diagnosed with malignant PEComa after presenting with postmenopausal vaginal bleeding. Despite two surgeries, the neoplasm eventually metastasized throughout the body. We formulated triple therapy with SBRT, a PD-1 inhibitor, and GM-CSF for the patient. The patient’s local symptoms were controlled at the radiotherapy site, and the lesions at the unirradiated sites were also relieved.ConclusionsFor the first time, a triple regimen of PD-1 inhibitor, SBRT and GM-CSF was used in the treatment of malignant PEComa and achieved good efficacy. Considering the lack of prospective clinical studies in PEComa, we believe that this triple therapy is a good-quality regimen for advanced malignant PEComa

Correlations among the plasma concentrations of first-line anti-tuberculosis drugs and the physiological parameters influencing concentrations

Background: The plasma concentrations of the four most commonly used first-line anti-tuberculosis (TB) drugs, isoniazid (INH), rifampicin (RMP), ethambutol (EMB), and pyrazinamide (PZA), are often not within the therapeutic range. Insufficient drug exposure could lead to drug resistance and treatment failure, while excessive drug levels may lead to adverse reactions. The purpose of this study was to identify the physiological parameters influencing anti-TB drug concentrations.Methods: A retrospective cohort study was conducted. The 2-h plasma concentrations of the four drugs were measured by using the high-performance liquid chromatography-tandem mass spectrometry method.Results: A total of 317 patients were included in the study. The proportions of patients with INH, RMP, EMB, and PZA concentrations within the therapeutic range were 24.3%, 31.5%, 27.8%, and 18.6%, respectively. There were positive associations between the concentrations of INH and PZA and RMP and EMB, but negative associations were observed between the concentrations of INH and RMP, INH and EMB, RMP and PZA, and EMB and PZA. In the multivariate analysis, the influencing factors of the INH concentration were the PZA concentration, total bile acid (TBA), serum potassium, dose, direct bilirubin, prealbumin (PA), and albumin; those of the RMP concentration were PZA and EMB concentrations, weight, α-l-fucosidase (AFU), drinking, and dose; those of the EMB concentration were the RMP and PZA concentrations, creatinine, TBA and indirect bilirubin; and those of the PZA concentration were INH, RMP and EMB concentrations, sex, weight, uric acid and drinking.Conclusion: The complex correlations between the concentrations of the four first-line anti-TB drugs lead to a major challenge in dose adjustment to maintain all drugs within the therapeutic window. Levels of TBA, PA, AFU, and serum potassium should also be considered when adjusting the dose of the four drugs