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To meet the increasing computing needs of various application fields, Field programmable gate array (FPGA) has been widely deployed. In FPGA-based processing, hardware tasks can be better accelerated by allocating appropriate computing resources. Therefore, FPGA-based hardware task scheduling has become one of the mainstream research directions in academia and industry. However, the optimization objectives of existing FPGA-based hardware task scheduling methods are relatively scattered. In this regard, this paper summarizes the research status of hardware task dynamic scheduling from the three essential elements of FPGA processing:time, resources, and power consumption. This paper analyzes, sorts out, categorizes the ideas and implementations of various scheduling methods and analyzes and evaluates optimization effects of various scheduling methods from multiple dimensions. Then, the shortcomings of the existing methods are summarized and some practical applications are introduced. Finally, the research direction of task scheduling based on FPGA is prospected and summarized.

PurposeThere is a strong link between occupation and self-rated health. Existing research has revealed the effects of occupation on self-rated health outcomes and the corresponding mechanisms. However, there is a lack of research on the effects of state services on self-rated health in China. Therefore, this study focuses on exploring the effects of serving as a state functionary in China on self-rated health to enrich research in related fields.MethodBased on the data of 14,138 individuals collected from the 2016 China Labour-Force Dynamics Survey, the logit model was used to investigate the effects of serving as a state functionary on self-rated health and the difference in the effects across different populations.ResultsThe results show that (1) serving as a state functionary has a significant positive effect on self-rated health; (2) self-rated health of elderly state functionaries is higher than that of younger state functionaries; (3) self-rated health of state functionaries in non-eastern regions is higher than that of state functionaries in eastern regions; and (4) state functionaries with lower education have higher self-rated health than highly-educated state functionaries; (5) Higher self-rated health of state functionaries is achieved primarily through better work time, better work environment and lower relative deprivation.ConclusionServing as a state functionary in China has a significant positive correlation with self-rated health, with differences across populations of state functionaries. This study expands the current literature on the effects of occupation on self-rated health in the context of China.

Perovskite solar cells have received substantial attention due to their potential for low-cost photovoltaic devices on flexible or rigid substrates. Thiocyanate (SCN)-containing additives, such as MASCN (MA = methylammonium), have been shown to control perovskite film crystallization and the film microstructure to achieve effective room-temperature perovskite absorber processing. Nevertheless, the crystallization pathways and mechanisms of perovskite formation involved in MASCN additive processing are far from clear. Using in situ X-ray diffraction and photoluminescence, we investigate the crystallization pathways of MAPbI3 and reveal the mechanisms of additive-assisted perovskite formation during spin coating and subsequent N2 drying. We confirm that MASCN induces large precursor aggregates in solution and, during spin coating, promotes the formation of the perovskite phase with lower nucleation density and overall larger initial nuclei size, which forms upon reaching supersaturation in solution, in addition to intermediate solvent-complex phases. Finally, during the subsequent N2 drying, MASCN facilitates the dissociation of these precursor aggregates and the solvate phases, leading to further growth of the perovskite crystals. Our results show that the nature of the intermediate phases and their formation/dissociation kinetics determine the nucleation and growth of the perovskite phase, which subsequently impact the film microstructure. These findings provide mechanistic insights underlying room-temperature, additive-assisted perovskite processing and help guide further development of such facile room-temperature synthesis routes.

In order to overcome the difficulties encountered in the current debugging of different equipment, to reduce the workload of different equipment debugging, and to improve the standardization of the interface and power quality, this paper proposes a construction method of multiple communication protocol conversion system between auxiliary devices to improve power quality. The construction method is based on the principle of embedded intelligent protocol converter, which can be compatible with a variety of common communication protocols on the market. Based on this, the construction method takes the power line carrier communication as the carrier, and takes the COAP-lot protocol as the conversion target protocol, which can provide two major functions for the power quality auxiliary equipment, namely, data exchange and plug and play. The research results show that the construction method can greatly reduce the debugging workload of different equipment, and break the limitations of traditional power quality improvement equipment.

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This paper addresses the new problem of automated screening of coronavirus disease 2019 (COVID-19) based on chest X-rays, which is urgently demanded toward fast stopping the pandemic. However, robust and accurate screening of COVID-19 from chest X-rays is still a globally recognized challenge because of two bottlenecks: 1) imaging features of COVID-19 share some similarities with other pneumonia on chest X-rays, and 2) the misdiagnosis rate of COVID-19 is very high, and the misdiagnosis cost is expensive. While a few pioneering works have made much progress, they underestimate both crucial bottlenecks. In this paper, we report our solution, discriminative cost-sensitive learning (DCSL), which should be the choice if the clinical needs the assisted screening of COVID-19 from chest X-rays. DCSL combines both advantages from fine-grained classification and cost-sensitive learning. Firstly, DCSL develops a conditional center loss that learns deep discriminative representation. Secondly, DCSL establishes score-level cost-sensitive learning that can adaptively enlarge the cost of misclassifying COVID-19 examples into other classes. DCSL is so flexible that it can apply in any deep neural network. We collected a large-scale multi-class dataset comprised of 2,239 chest X-ray examples: 239 examples from confirmed COVID-19 cases, 1,000 examples with confirmed bacterial or viral pneumonia cases, and 1,000 examples of healthy people. Extensive experiments on the three-class classification show that our algorithm remarkably outperforms state-of-the-art algorithms. It achieves an accuracy of 97.01%, a precision of 97%, a sensitivity of 97.09%, and an F1-score of 96.98%. These results endow our algorithm as an efficient tool for the fast large-scale screening of COVID-19., Comment: Under review

This paper presents a new solution to reduce the thermal interference of vertical U-tube ground heat exchanger (UGHE): pipe insulation installed on the upward branch pipe of the U-tube. 3-dimensional numerical models of the UGHE with and without pipe insulation were developed to simulate the heat transfer process around the UGHE. The models were verified with previously published data. The effects of pipe insulation on outlet temperature, soil temperature, heat exchange rate per unit length of borehole depth and soil temperature recovery ratio were investigated. The effects of inlet velocity of heat transfer fluid (HTF) and the thermal conductivity of backfill material were also studied to fully assess the effects of the insulation. The results indicate that the average outlet temperature of HTF, soil temperature near the upward branch pipe and soil temperature recovery ratio all increased and the thermal interference can be effectively weakened when pipe insulation was installed.

Statistical inference, such as hypothesis testing and calculating a confidence interval, is an important tool for accessing uncertainty in machine learning and statistical problems. Stochastic gradient methods, such as stochastic gradient descent (SGD), have recently been successfully applied to point estimation in large scale machine learning problems. In this work, we present novel stochastic gradient methods for statistical inference in large scale machine learning problems. Unregularized M -estimation using SGD. Using SGD with a fixed step size, we demonstrate that the average of such SGD sequences can be used for statistical inference, after proper scaling. An intuitive analysis using the Ornstein-Uhlenbeck process suggests that such averages are asymptotically normal. From a practical perspective, our SGD-based inference procedure is a first order method, and is well-suited for large scale problems. To show its merits, we apply it to both synthetic and real datasets, and demonstrate that its accuracy is comparable to classical statistical methods, while requiring potentially far less computation. Approximate Newton-based statistical inference using only stochastic gradients for unregularized M -estimation. We present a novel inference framework for convex empirical risk minimization, using approximate stochastic Newton steps. The proposed algorithm is based on the notion of finite differences and allows the approximation of a Hessian-vector product from first-order information. In theory, our method efficiently computes the statistical error covariance in M -estimation for unregularized convex learning problems, without using exact second order information, or resampling the entire data set. In practice, we demonstrate the effectiveness of our framework on large-scale machine learning problems, that go even beyond convexity: as a highlight, our work can be used to detect certain adversarial attacks on neural networks. High dimensional linear regression statistical inference using only stochastic gra- dients. As an extension of the approximate Newton-based statistical inference algorithm for unregularized problems, we present a similar algorithm, using only stochastic gradients, for statistical inference in high dimensional linear regression, where the number of features is much larger than the number of samples. Stochastic gradient methods for time series analysis. We present a novel stochastic gradient descent algorithm for time series analysis, which correctly captures correlation structures in a time series dataset during optimization. Instead of uniformly sampling indices in vanilla SGD, we uniformly sample contiguous blocks of indices, where the block length depends on the dataset.

We consider the problem of sparsity-constrained $M$-estimation when both explanatory and response variables have heavy tails (bounded 4-th moments), or a fraction of arbitrary corruptions. We focus on the $k$-sparse, high-dimensional regime where the number of variables $d$ and the sample size $n$ are related through $n \sim k \log d$. We define a natural condition we call the Robust Descent Condition (RDC), and show that if a gradient estimator satisfies the RDC, then Robust Hard Thresholding (IHT using this gradient estimator), is guaranteed to obtain good statistical rates. The contribution of this paper is in showing that this RDC is a flexible enough concept to recover known results, and obtain new robustness results. Specifically, new results include: (a) For $k$-sparse high-dimensional linear- and logistic-regression with heavy tail (bounded 4-th moment) explanatory and response variables, a linear-time-computable median-of-means gradient estimator satisfies the RDC, and hence Robust Hard Thresholding is minimax optimal; (b) When instead of heavy tails we have $O(1/\sqrt{k}\log(nd))$-fraction of arbitrary corruptions in explanatory and response variables, a near linear-time computable trimmed gradient estimator satisfies the RDC, and hence Robust Hard Thresholding is minimax optimal. We demonstrate the effectiveness of our approach in sparse linear, logistic regression, and sparse precision matrix estimation on synthetic and real-world US equities data.

We provide a novel -- and to the best of our knowledge, the first -- algorithm for high dimensional sparse regression with constant fraction of corruptions in explanatory and/or response variables. Our algorithm recovers the true sparse parameters with sub-linear sample complexity, in the presence of a constant fraction of arbitrary corruptions. Our main contribution is a robust variant of Iterative Hard Thresholding. Using this, we provide accurate estimators: when the covariance matrix in sparse regression is identity, our error guarantee is near information-theoretically optimal. We then deal with robust sparse regression with unknown structured covariance matrix. We propose a filtering algorithm which consists of a novel randomized outlier removal technique for robust sparse mean estimation that may be of interest in its own right: the filtering algorithm is flexible enough to deal with unknown covariance. Also, it is orderwise more efficient computationally than the ellipsoid algorithm. Using sub-linear sample complexity, our algorithm achieves the best known (and first) error guarantee. We demonstrate the effectiveness on large-scale sparse regression problems with arbitrary corruptions.

performance. Abstract. This paper presents the preparation for phase change materials(PCM) with different phase change temperature for use in refrigerated transport equipment. The optimal preparation condition of HSD-1 as the PCM was confirmed. The thermo-physical properties of the modified HSD-1 were investigated by cooling curve method and differential scanning calorimeter (DSC) method. The effect of nucleating agent and thickener were deeply researched. The results indicate that the phase change temperature of HSD-1 is 4.2˚C and the latent heat is 318.1 kJ/kg. The modified HSD-1 has good heat stability. With the injection of 0.0567g of PS as the nucleating agent into the HSD-1, the degree of super-cooling decreases by 5˚C. By adding SJ as the thickener, the temperature of the system is stable and no phase separation is observed. These results show that the modified HSD-1 can be used in refrigerated transport equipment.