SCOPE: Scalable Composite Optimization for Learning on Spark

Many machine learning models, such as logistic regression~(LR) and support vector machine~(SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization~(DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better performance than traditional batch methods. However, most of these DSO methods are not scalable enough. In this paper, we propose a novel DSO method, called \underline{s}calable \underline{c}omposite \underline{op}timization for l\underline{e}arning~({SCOPE}), and implement it on the fault-tolerant distributed platform \mbox{Spark}. SCOPE is both computation-efficient and communication-efficient. Theoretical analysis shows that SCOPE is convergent with linear convergence rate when the objective function is convex. Furthermore, empirical results on real datasets show that SCOPE can outperform other state-of-the-art distributed learning methods on Spark, including both batch learning methods and DSO methods

An Analysis of Background Interference on Fire Debris

AbstractIn this study, the controlled burn experiments of carpets with and without gasoline in this study and commonly encountered substrates produced complex chromatograms producing peaks that were identified by mass spectrometry and comparison with reference standards and each other. The result shown that many of the compounds frequently encountered as a result of either combustion products or pyrolysis products of carpets detected in fresh gasoline as well. These compounds as background interferences that detect weather the gasoline exist in carpet combustion products or not

The Analysis of UV on No Traces Combustion-supporting in Fire Residue

AbstractIn this paper, the ethyl nitrite which ethanol and sodium nitrite's reaction product has the UV absorption peaks in 300nm∼ 400nm to qualitative identification for ethanol. The methanol has the similar absorption peaks in the same range, but these peaks have blue shift or red shift as the steric effects. Quantitative analysis of residual ethanol by the establishment of standard curves and recovery tests and other data analysis. Thus complete the qualitative and quantitative identification of ethanol

Robust Correlation Tracking for UAV Videos via Feature Fusion and Saliency Proposals

Following the growing availability of low-cost, commercially available unmanned aerial vehicles (UAVs), more and more research efforts have been focusing on object tracking using videos recorded from UAVs. However, tracking from UAV videos poses many challenges due to platform motion, including background clutter, occlusion, and illumination variation. This paper tackles these challenges by proposing a correlation filter-based tracker with feature fusion and saliency proposals. First, we integrate multiple feature types such as dimensionality-reduced color name (CN) and histograms of oriented gradient (HOG) features to improve the performance of correlation filters for UAV videos. Yet, a fused feature acting as a multivector descriptor cannot be directly used in prior correlation filters. Therefore, a fused feature correlation filter is proposed that can directly convolve with a multivector descriptor, in order to obtain a single-channel response that indicates the location of an object. Furthermore, we introduce saliency proposals as re-detector to reduce background interference caused by occlusion or any distracter. Finally, an adaptive template-update strategy according to saliency information is utilized to alleviate possible model drifts. Systematic comparative evaluations performed on two popular UAV datasets show the effectiveness of the proposed approach

Household Catastrophic Medical Expenses in Eastern China: Determinants and Policy Implications

Background: Much of research on household catastrophic medical expenses in China has focused on less developed areas and little is known about this problem in more developed areas. This study aimed to analyse the incidence and determinants of catastrophic medical expenses in eastern China. Methods: Data were obtained from a health care utilization and expense survey of 11,577 households conducted in eastern China in 2008. The incidence of household catastrophic medical expenses was calculated using the method introduced by the World Health Organization. A multi-level logistic regression model was used to identify the determinants. Results: The incidence of household catastrophic medical expenses in eastern China ranged from 9.24% to 24.79%. Incidence of household catastrophic medical expenses was lower if the head of household had a higher level of education, labor insurance coverage, while the incidence was higher if they lived in rural areas, had a family member with chronic diseases, had a child younger than 5 years old, had a person at home who was at least 65 years old, and had a household member who was hospitalized. Moreover, the impact of the economic level on catastrophic medical expenses was non-linear. The poorest group had a lower incidence than that of the second lowest income group and the group with the highest income had a higher incidence than that of the second highest income group. In addition, region was a significant determinant. Conclusions: Reducing the incidence of household catastrophic medical expenses should be one of the priorities of health policy. It can be achieved by improving residents’ health status to reduce avoidable health services such as hospitalization. It is also important to design more targeted health insurance in order to increase financial support for such vulnerable groups as the poor, chronically ill, children, and senior populations

A 0.76-pJ/Pulse 0.1-1 Gpps Microwatt IR-UWB CMOS Pulse Generator with Adaptive PSD Control Using A Limited Monocycle Precharge Technique

Document Version Author final version (often known as postprint) Link to publication from Aalborg University Citation for published version (APA)

Adenosine Kinase on Deoxyribonucleic Acid Methylation: Adenosine Receptor-Independent Pathway in Cancer Therapy

Methylation is an important mechanism contributing to cancer pathology. Methylation of tumor suppressor genes and oncogenes has been closely associated with tumor occurrence and development. New insights regarding the potential role of the adenosine receptor-independent pathway in the epigenetic modulation of DNA methylation offer the possibility of new interventional strategies for cancer therapy. Targeting DNA methylation of cancer-related genes is a promising therapeutic strategy; drugs like 5-Aza-2′-deoxycytidine (5-AZA-CdR, decitabine) effectively reverse DNA methylation and cancer cell growth. However, current anti-methylation (or methylation modifiers) are associated with severe side effects; thus, there is an urgent need for safer and more specific inhibitors of DNA methylation (or DNA methylation modifiers). The adenosine signaling pathway is reported to be involved in cancer pathology and participates in the development of tumors by altering DNA methylation. Most recently, an adenosine metabolic clearance enzyme, adenosine kinase (ADK), has been shown to influence methylation on tumor suppressor genes and tumor development and progression. This review article focuses on recent updates on ADK and its two isoforms, and its actions in adenosine receptor-independent pathways, including methylation modification and epigenetic changes in cancer pathology

CoderEval: A Benchmark of Pragmatic Code Generation with Generative Pre-trained Models

Code generation models based on the pre-training and fine-tuning paradigm have been increasingly attempted by both academia and industry, resulting in well-known industrial models such as Codex, CodeGen, and PanGu-Coder. To evaluate the effectiveness of these models, multiple existing benchmarks are proposed, including only cases of generating a standalone function, i.e., a function that may invoke or access only built-in functions and standard libraries. However, non-standalone functions, which typically are not included in the existing benchmarks, constitute more than 70% of the functions in popular open-source projects, and evaluating models' effectiveness on standalone functions cannot reflect these models' effectiveness on pragmatic code generation scenarios. To help bridge the preceding gap, in this paper, we propose a benchmark named CoderEval, consisting of 230 Python and 230 Java code generation tasks carefully curated from popular real-world open-source projects and a self-contained execution platform to automatically assess the functional correctness of generated code. CoderEval supports code generation tasks from six levels of context dependency, where context refers to code elements such as types, APIs, variables, and consts defined outside the function under generation but within the dependent third-party libraries, current class, file, or project. CoderEval can be used to evaluate the effectiveness of models in generating code beyond only standalone functions. By evaluating three code generation models on CoderEval, we find that the effectiveness of these models in generating standalone functions is substantially higher than that in generating non-standalone functions. Our analysis highlights the current progress and pinpoints future directions to further improve a model's effectiveness by leveraging contextual information for pragmatic code generation