Large-scale Continuous Gesture Recognition Using Convolutional Neural Networks

This paper addresses the problem of continuous gesture recognition from sequences of depth maps using convolutional neutral networks (ConvNets). The proposed method first segments individual gestures from a depth sequence based on quantity of movement (QOM). For each segmented gesture, an Improved Depth Motion Map (IDMM), which converts the depth sequence into one image, is constructed and fed to a ConvNet for recognition. The IDMM effectively encodes both spatial and temporal information and allows the fine-tuning with existing ConvNet models for classification without introducing millions of parameters to learn. The proposed method is evaluated on the Large-scale Continuous Gesture Recognition of the ChaLearn Looking at People (LAP) challenge 2016. It achieved the performance of 0.2655 (Mean Jaccard Index) and ranked $3^{rd}$ place in this challenge

Doubly Robust Estimation under Covariate-Induced Dependent Left Truncation

In prevalent cohort studies with follow-up, the time-to-event outcome is subject to left truncation leading to selection bias. For estimation of the distribution of time-to-event, conventional methods adjusting for left truncation tend to rely on the (quasi-)independence assumption that the truncation time and the event time are "independent" on the observed region. This assumption is violated when there is dependence between the truncation time and the event time possibly induced by measured covariates. Inverse probability of truncation weighting leveraging covariate information can be used in this case, but it is sensitive to misspecification of the truncation model. In this work, we apply the semiparametric theory to find the efficient influence curve of an expected (arbitrarily transformed) survival time in the presence of covariate-induced dependent left truncation. We then use it to construct estimators that are shown to enjoy double-robustness properties. Our work represents the first attempt to construct doubly robust estimators in the presence of left truncation, which does not fall under the established framework of coarsened data where doubly robust approaches are developed. We provide technical conditions for the asymptotic properties that appear to not have been carefully examined in the literature for time-to-event data, and study the estimators via extensive simulation. We apply the estimators to two data sets from practice, with different right-censoring patterns

Position estimation of an outer rotor permanent magnet synchronous machine using linear hall-effect sensors and neural networks

This thesis presents and evaluates a new method for estimating the angular position for an outer rotor permanent magnet synchronous machine (PMSM). PMSMs are increasingly used as prime movers in electric vehicles such as cars and bicycles, and the precise control of these machines requires reliable feedback of the rotational position of the rotor. Conventional methods of achieving this feedback signal rely on either physically connected sensors or the implementation of sensorless methods, each of which has certain drawbacks. The proposed method uses an array of linear Hall-effect sensors located in the leakage magnetic field of the rotor. These sensors detect the rotation-dependent changing field, which is fed into a machine-learning based neural network algorithm to interpret the signals. Due to the use of machine-learning, the algorithm will first need to be trained to properly correlate the sensor signals to the rotor angle. Data sets of training signals are acquired with commercial sensors and an outer rotor PMSM, and offline training steps and results are discussed. The main objective is to design a cost-effective position estimation system that is comparable to encoders and resolvers in functionality and performance, without the limitations of sensorless position estimation methods

To identify how service quality and brand image impact on customer satisfaction and customer loyalty in China's mobile phone markets on youth consumers.

The relationship between service quality and customer satisfaction Many theorists have suggested that high customer expectations of service quality can lead to better service performance which, in turn, positively influences customer satisfaction with service. This can then perpetuate a cycle of positive reinforcing behavior. Cronin and Taylor (1992) mentioned that there is a preponderance of evidence that service quality is an antecedent to service satisfaction. Rust and Oliver (1994) describe customer satisfaction as being a reaction to a service incident (or long-term service relationship.) The relationship between brand image and customer satisfaction Different brand have a differing quality reputation, the author expects the service quality to be directly related to the brand type. The effect of brand name on consumer perceptions is viewed in the marketing literature as a signaling effect. Rao and Monroe (1999) empirically examine this relationship and find a positive relationship between quality reputation associated with the brand and consumer perceptions of quality. Among the established signals of quality (brand, price, physical appearance, and store name), brand was found to be the most important (Dawar and Parker, 1994). The relationship between customer satisfaction and customer loyalty Anderson and Sullivan (1993), in analyzing data from a study of customer satisfaction among Swedish customers, find that stated customer loyalty is strongly related to stated satisfaction across product categories. A study conducted by Woodside, Frey, and Daly (2000) uncovers a significant association between overall patient satisfaction and intent to choose the hospital again. Research frame and the linkage to the project In order to implement an efficient and sustainable aim, this research will be built upon a conceptual framework based on the research motive, objectives and literature review described above. Since the aim of this research is to identify how service quality and brand image impact on customer satisfaction and customer loyalty in China's mobile phone markets on youth generation, a comprehensive literature review has been examined the above four aspects in detail and the inter-links have also been unrevealed. The literature review has set up a holistic theory frame to lay a foundation for the author to do further research and evaluate the theories discussed in this chapter. For example, through examining the literature review, the author found many theorists have done some research on the correlation among the four dimensions and some of the research results have been put forward at the end of this chapter, which reflect the objective 1 and also is the source of the hypotheses that will be raised in the next chapter. The literature review chapter also has provided the theoretical support and tools for the author to evaluate the customer service. As the industry focus on customers and providing service, keeping brand image, building brand loyalty and improving customer service is the major tasks of any mobile phone maker. Therefore, the author will focus on the emphasis of the literature review when conducting the survey, analyzing the current situation of the industry, discovering problems and developing relevant strategies. Considering these different theories and studies, mobile phone industry in China will be able to come across with a more spherical view and choose the best way for the future development

Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation

Program synthesis has been long studied with recent approaches focused on directly using the power of Large Language Models (LLMs) to generate code according to user intent written in natural language. Code evaluation datasets, containing curated synthesis problems with input/output test-cases, are used to measure the performance of various LLMs on code synthesis. However, test-cases in these datasets can be limited in both quantity and quality for fully assessing the functional correctness of the generated code. Such limitation in the existing benchmarks begs the following question: In the era of LLMs, is the code generated really correct? To answer this, we propose EvalPlus -- a code synthesis benchmarking framework to rigorously evaluate the functional correctness of LLM-synthesized code. In short, EvalPlus takes in the base evaluation dataset and uses an automatic input generation step to produce and diversify large amounts of new test inputs using both LLM-based and mutation-based input generators to further validate the synthesized code. We extend the popular HUMANEVAL benchmark and build HUMANEVAL+ with 81x additionally generated tests. Our extensive evaluation across 14 popular LLMs demonstrates that HUMANEVAL+ is able to catch significant amounts of previously undetected wrong code synthesized by LLMs, reducing the pass@k by 15.1% on average! Moreover, we even found several incorrect ground-truth implementations in HUMANEVAL. Our work not only indicates that prior popular code synthesis evaluation results do not accurately reflect the true performance of LLMs for code synthesis but also opens up a new direction to improve programming benchmarks through automated test input generation

NeuRI: Diversifying DNN Generation via Inductive Rule Inference

Deep Learning (DL) is prevalently used in various industries to improve decision-making and automate processes, driven by the ever-evolving DL libraries and compilers. The correctness of DL systems is crucial for trust in DL applications. As such, the recent wave of research has been studying the automated synthesis of test-cases (i.e., DNN models and their inputs) for fuzzing DL systems. However, existing model generators only subsume a limited number of operators, lacking the ability to pervasively model operator constraints. To address this challenge, we propose NeuRI, a fully automated approach for generating valid and diverse DL models composed of hundreds of types of operators. NeuRI adopts a three-step process: (i) collecting valid and invalid API traces from various sources; (ii) applying inductive program synthesis over the traces to infer the constraints for constructing valid models; and (iii) using hybrid model generation which incorporates both symbolic and concrete operators. Our evaluation shows that NeuRI improves branch coverage of TensorFlow and PyTorch by 24% and 15% over the state-of-the-art model-level fuzzers. NeuRI finds 100 new bugs for PyTorch and TensorFlow in four months, with 81 already fixed or confirmed. Of these, 9 bugs are labelled as high priority or security vulnerability, constituting 10% of all high-priority bugs of the period. Open-source developers regard error-inducing tests reported by us as "high-quality" and "common in practice"

The emerging roles of ferroptosis in cells of the central nervous system

Ferroptosis is morphologically characterized by shrunken mitochondria and biochemically characterized by iron overload, lipid peroxidation and lipid reactive oxygen species (ROS) accumulation; these phenomena are suppressed by iron chelation, genetic inhibition of cellular iron uptake, and intervention on other pathways such as lipid metabolism. The induction of ferroptosis may be related to pathological cellular conditions in the central nervous system (CNS); thus, ferroptosis may cause disability via CNS damage. Here, we review the role of ferroptosis in the main cells of the CNS, including glial cells, neurons, and pericytes; in various diseases of the CNS; and in the interaction of glia and neurons in CNS diseases. Some small molecules and traditional Chinese drugs which inhibit ferroptosis in cells of the CNS are shown as potential therapeutic strategies for neurological diseases

Dietary Glycemic Index, Glycemic Load, and Risk of Coronary Heart Disease, Stroke, and Stroke Mortality: A Systematic Review with Meta-Analysis

Background: The relationship between dietary glycemic index, glycemic load and risk of coronary heart disease (CHD), stroke, and stroke-related mortality is inconsistent. Methods: We systematically searched the MEDLINE, EMBASE, and Science Citation Index Expanded databases using glycemic index, glycemic load, and cardiovascular disease and reference lists of retrieved articles up to April 30, 2012. We included prospective studies with glycemic index and glycemic load as the exposure and incidence of fatal and nonfatal CHD, stroke, and stroke-related mortality as the outcome variable. Pooled relative risks (RR) and 95% confidence intervals (CI) were calculated using random-effects models. Results: Fifteen prospective studies with a total of 438,073 participants and 9,424 CHD cases, 2,123 stroke cases, and 342 deaths from stroke were included in the meta-analysis. Gender significantly modified the effects of glycemic index and glycemic load on CHD risk, and high glycemic load level was associated with higher risk of CHD in women (RR = 1.49, 95%CI 1.27−1.73), but not in men (RR = 1.08, 95%CI 0.91−1.27). Stratified meta-analysis by body mass index indicated that among overweight and obese subjects, dietary glycemic load level were associated with increased risk of CHD (RR = 1.49, 95%CI 1.27−1.76; P for interaction = 0.003). Higher dietary glycemic load, but not glycemic index, was positively associated with stroke (RR = 1.19, 95% CI 1.00−1.43). There is a linear dose-response relationship between dietary glycemic load and increased risk of CHD, with pooled RR of 1.05 (95%CI 1.02−1.08) per 50-unit increment in glycemic load level. Conclusion: High dietary glycemic load is associated with a higher risk of CHD and stroke, and there is a linear dose-response relationship between glycemic load and CHD risk. Dietary glycemic index is slightly associated with risk of CHD, but not with stroke and stroke-related death. Further studies are needed to verify the effects of gender and body weight on cardiovascular diseases