On-Line Load Balancing with Task Buffer

On-line load balancing is one of the most important problems for applications with resource allocation. It aims to assign tasks to suitable machines and balance the load among all of the machines, where the tasks need to be assigned to a machine upon arrival. In practice, tasks are not always required to be assigned to machines immediately. In this paper, we propose a novel on-line load balancing model with task buffer, where the buffer can temporarily store tasks as many as possible. Three algorithms, namely LPTCP1_α, LPTCP2_α, and LPTCP3_β, are proposed based on the Longest Processing Time (LPT) algorithm and a variety of planarization algorithms. The planarization algorithms are proposed for reducing the difference among each element in a set. Experimental results show that our proposed algorithms can effectively solve the on-line load balancing problem and have good performance in large scale experiments

Metric-aligned Sample Selection and Critical Feature Sampling for Oriented Object Detection

Arbitrary-oriented object detection is a relatively emerging but challenging task. Although remarkable progress has been made, there still remain many unsolved issues due to the large diversity of patterns in orientation, scale, aspect ratio, and visual appearance of objects in aerial images. Most of the existing methods adopt a coarse-grained fixed label assignment strategy and suffer from the inconsistency between the classification score and localization accuracy. First, to align the metric inconsistency between sample selection and regression loss calculation caused by fixed IoU strategy, we introduce affine transformation to evaluate the quality of samples and propose a distance-based label assignment strategy. The proposed metric-aligned selection (MAS) strategy can dynamically select samples according to the shape and rotation characteristic of objects. Second, to further address the inconsistency between classification and localization, we propose a critical feature sampling (CFS) module, which performs localization refinement on the sampling location for classification task to extract critical features accurately. Third, we present a scale-controlled smooth $L_1$ loss (SC-Loss) to adaptively select high quality samples by changing the form of regression loss function based on the statistics of proposals during training. Extensive experiments are conducted on four challenging rotated object detection datasets DOTA, FAIR1M-1.0, HRSC2016, and UCAS-AOD. The results show the state-of-the-art accuracy of the proposed detector

Improving gold recovery from a refractory ore via Na₂SO₄ assisted roasting and alkaline Na₂S leaching

Gold recovery from refractory gold ores with controlled roasting remained well below 80%. Na2SO4 was added in an O-2-enriched single stage roasting of a refractory gold ore to improve its gold recovery. Changes in physicochemical properties of the calcines suggested that this reduced the sintering as well as facilitated the formation of pores and a water soluble phase within the calcine. Thermodynamic analysis and leaching results demonstrated that Na2S solutions could effectively remove Sb species from the calcine. An extraction process that includes Na2SO4 assisted roasting and alkaline Na2S leaching is shown to be able to achieve a gold recovery of over 95% from the refractory ore

Seasonality and trend prediction of scarlet fever incidence in mainland China from 2004 to 2018 using a hybrid SARIMA-NARX model

Background Scarlet fever is recognized as being a major public health issue owing to its increase in notifications in mainland China, and an advanced response based on forecasting techniques is being adopted to tackle this. Here, we construct a new hybrid method incorporating seasonal autoregressive integrated moving average (SARIMA) with a nonlinear autoregressive with external input(NARX) to analyze its seasonality and trend in order to efficiently prevent and control this re-emerging disease. Methods Four statistical models, including a basic SARIMA, basic nonlinear autoregressive (NAR) method, traditional SARIMA-NAR and new SARIMA-NARX hybrid approaches, were developed based on scarlet fever incidence data between January 2004 and July 2018 to evaluate its temporal patterns, and their mimic and predictive capacities were compared to discover the optimal using the mean absolute percentage error, root mean square error, mean error rate, and root mean square percentage error. Results The four preferred models identified were comprised of the SARIMA(0,1,0)(0,1,1)12, NAR with 14 hidden neurons and five delays, SARIMA-NAR with 33 hidden neurons and five delays, and SARIMA-NARX with 16 hidden neurons and 4 delays. Among which presenting the lowest values of the aforementioned indices in both simulation and prediction horizons is the SARIMA-NARX method. Analyses from the data suggested that scarlet fever was a seasonal disease with predominant peaks of summer and winter and a substantial rising trend in the scarlet fever notifications was observed with an acceleration of 9.641% annually, particularly since 2011 with 12.869%, and moreover such a trend will be projected to continue in the coming year. Conclusions The SARIMA-NARX technique has the promising ability to better consider both linearity and non-linearity behind scarlet fever data than the others, which significantly facilitates its prevention and intervention of scarlet fever. Besides, under current trend of ongoing resurgence, specific strategies and countermeasures should be formulated to target scarlet fever

Effect of low frequency magnetic fields on melanoma: tumor inhibition and immune modulation

BACKGROUND: We previously found that the low frequency magnetic fields (LF-MF) inhibited gastric and lung cancer cell growth. We suppose that exposure to LF-MF may modulate immune function so as to inhibit tumor. We here investigated whether LF-MF can inhibit the proliferation and metastasis of melanoma and influence immune function. METHODS: The effect of MF on the proliferation, cell cycle and ultrastracture of B16-F10 in vitro was detected by cell counting Kit-8 assay, flow cytometry, and transmission electron microscopy. Lung metastasis mice were prepared by injection of 2 × 10(5) B16-F10 melanoma cells into the tail vein in C57BL/6 mice. The mice were then exposed to an LF-MF (0.4 T, 7.5 Hz) for 43 days. Survival rate, tumor markers and the innate and adaptive immune parameters were measured. RESULTS: The growth of B16-F10 cells was inhibited after exposure to the LF-MF. The inhibition was related to induction of cell cycle arrest and decomposition of chromatins. Moreover, the LF-MF prolonged the mouse survival rate and inhibited the proliferation of B16-F10 in melanoma metastasis mice model. Furthermore, the LF-MF modulated the immune response via regulation of immune cells and cytokine production. In addition, the number of Treg cells was decreased in mice with the LF-MF exposure, while the numbers of T cells as well as dendritic cells were significantly increased. CONCLUSION: LF-MF inhibited the growth and metastasis of melanoma cancer cells and improved immune function of tumor-bearing mice. This suggests that the inhibition may be attributed to modulation of LF-MF on immune function and LF-MF may be a potential therapy for treatment of melanoma

MicroRNAs in apoptosis, autophagy and necroptosis

MicroRNAs (miRNAs) are endogenous 22 nt non-coding RNAs that target mRNAs for cleavage or translational repression. Numerous miRNAs regulate programmed cell death including apoptosis, autophagy and necroptosis. We summarize how miRNAs regulate apoptotic, autophagic and necroptotic pathways and cancer progression. We also discuss how miRNAs link different types of cell death

An Efficient Approach to Solve the Large-Scale Semidefinite Programming Problems

Solving the large-scale problems with semidefinite programming (SDP) constraints is of great importance in modeling and model reduction of complex system, dynamical system, optimal control, computer vision, and machine learning. However, existing SDP solvers are of large complexities and thus unavailable to deal with large-scale problems. In this paper, we solve SDP using matrix generation, which is an extension of the classical column generation. The exponentiated gradient algorithm is also used to solve the special structure subproblem of matrix generation. The numerical experiments show that our approach is efficient and scales very well with the problem dimension. Furthermore, the proposed algorithm is applied for a clustering problem. The experimental results on real datasets imply that the proposed approach outperforms the traditional interior-point SDP solvers in terms of efficiency and scalability

Weak-Key Leakage Resilient Cryptography

In traditional cryptography, the standard way of examining the security of a scheme is to analyze it in a black-box manner, capturing no side channel attacks which exploit various forms of unintended information leakages and do threaten the practical security of the scheme. One way to protect against such attacks aforementioned is to extend the traditional models so as to capture them. Early models rely on the assumption that only computation leaks information, and are incapable of capturing memory attacks such as cold boot attacks. Thus, Akavia et al.(TCC \u2709) formalize the general model of key-leakage attacks to cover them. However, most key-leakage attacks in reality tend to be weak key leakage attacks which can be viewed as a nonadaptive version of the key-leakage attacks. Powerful as those may be, the existing constructions of cryptographic schemes in adaptive key-leakage attacks model still have some drawbacks such as they are quite inefficient or they can only tolerate a small amount of leakage. Therefore, we mainly consider models that cover weak key-leakage attacks and the corresponding constructions in them. We extend the transformation paradigm presented by Naor and Segev that can transform from any chosen-plaintext secure public-key encryption (PKE) scheme to a chosen-plaintext weak key-leakage secure PKE scheme. Our extensions are two-fold. Firstly, we extend the paradigm into chosen-ciphertext attack scenarios and prove that the properties of it still hold in these scenarios. We also give an instantiation based on DDH assumption in this setting. Additionally, we extend the paradigm to cover more side channel attacks under the consideration of different types of leakage functions. We further consider attacks which require the secret key still has enough min-entropy after leaking and prove the original paradigm is still applicable in this case with chosen-ciphertext attacks. Attacks that require the secret key is computationally infeasible to recover given the leakage information are taken into consideration as well. And we formalize the informal discusses by Naor and Segev in (Crypto\u27 09) on how to adapt the original paradigm in this new models

Exogenous treatment with melatonin enhances waterlogging tolerance of kiwifruit plants

Waterlogging stress has an enormous negative impact on the kiwifruit yield and quality. The protective role of exogenous melatonin on water stress has been widely studied, especially in drought stress. However, the research on melatonin-induced waterlogging tolerance is scarce. Here, we found that treatment with exogenous melatonin could effectively alleviate the damage on kiwifruit plants in response to waterlogging treatment. This was accompanied by higher antioxidant activity and lower ROS accumulation in kiwifruit roots during stress period. The detection of changes in amino acid levels of kiwifruit roots during waterlogging stress showed a possible interaction between melatonin and amino acid metabolism, which promoted the tolerance of kiwifruit plants to waterlogging. The higher levels of GABA and Pro in the roots of melatonin-treated kiwifruit plants partly contributed to their improved waterlogging tolerance. In addition, some plant hormones were also involved in the melatonin-mediated waterlogging tolerance, such as the enhancement of ACC accumulation. This study discussed the melatonin-mediated water stress tolerance of plants from the perspective of amino acid metabolism for the first time