Human Motion Capture Algorithm Based on Inertial Sensors

On the basis of inertial navigation, we conducted a comprehensive analysis of the human body kinematics principle. From the direction of two characteristic parameters, namely, displacement and movement angle, we calculated the attitude of a node during the human motion capture process by combining complementary and Kalman filters. Then, we evaluated the performance of the proposed attitude strategy by selecting different platforms as the validation object. Results show that the proposed strategy for the real-time tracking of the human motion process has higher accuracy than the traditional strategy

QChecker: Detecting Bugs in Quantum Programs via Static Analysis

Static analysis is the process of analyzing software code without executing the software. It can help find bugs and potential problems in software that may only appear at runtime. Although many static analysis tools have been developed for classical software, due to the nature of quantum programs, these existing tools are unsuitable for analyzing quantum programs. This paper presents QChecker, a static analysis tool that supports finding bugs in quantum programs in Qiskit. QChecker consists of two main modules: a module for extracting program information based on abstract syntax tree (AST), and a module for detecting bugs based on patterns. We evaluate the performance of QChecker using the Bugs4Q benchmark. The evaluation results show that QChecker can effectively detect various bugs in quantum programs.Comment: This paper will be appeared in the proceedings of the 4th International Workshop on Quantum Software Engineering (Q-SE 2023) on May 14, 202

ImageCaptioner2^2: Image Captioner for Image Captioning Bias Amplification Assessment

Most pre-trained learning systems are known to suffer from bias, which typically emerges from the data, the model, or both. Measuring and quantifying bias and its sources is a challenging task and has been extensively studied in image captioning. Despite the significant effort in this direction, we observed that existing metrics lack consistency in the inclusion of the visual signal. In this paper, we introduce a new bias assessment metric, dubbed $ImageCaptioner^2$, for image captioning. Instead of measuring the absolute bias in the model or the data, $ImageCaptioner^2$ pay more attention to the bias introduced by the model w.r.t the data bias, termed bias amplification. Unlike the existing methods, which only evaluate the image captioning algorithms based on the generated captions only, $ImageCaptioner^2$ incorporates the image while measuring the bias. In addition, we design a formulation for measuring the bias of generated captions as prompt-based image captioning instead of using language classifiers. Finally, we apply our $ImageCaptioner^2$ metric across 11 different image captioning architectures on three different datasets, i.e., MS-COCO caption dataset, Artemis V1, and Artemis V2, and on three different protected attributes, i.e., gender, race, and emotions. Consequently, we verify the effectiveness of our $ImageCaptioner^2$ metric by proposing AnonymousBench, which is a novel human evaluation paradigm for bias metrics. Our metric shows significant superiority over the recent bias metric; LIC, in terms of human alignment, where the correlation scores are 80% and 54% for our metric and LIC, respectively. The code is available at https://eslambakr.github.io/imagecaptioner2.github.io/

An Empirical Study of Bugs in Quantum Machine Learning Frameworks

Quantum computing has emerged as a promising domain for the machine learning (ML) area, offering significant computational advantages over classical counterparts. With the growing interest in quantum machine learning (QML), ensuring the correctness and robustness of software platforms to develop such QML programs is critical. A necessary step for ensuring the reliability of such platforms is to understand the bugs they typically suffer from. To address this need, this paper presents the first comprehensive study of bugs in QML frameworks. We inspect 391 real-world bugs collected from 22 open-source repositories of nine popular QML frameworks. We find that 1) 28% of the bugs are quantum-specific, such as erroneous unitary matrix implementation, calling for dedicated approaches to find and prevent them; 2) We manually distilled a taxonomy of five symptoms and nine root cause of bugs in QML platforms; 3) We summarized four critical challenges for QML framework developers. The study results provide researchers with insights into how to ensure QML framework quality and present several actionable suggestions for QML framework developers to improve their code quality.Comment: This paper will be appeared in the proceedings of the 2023 IEEE International Conference on Quantum Software (QSW 2023), July 2-8, 202

HRS-Bench: Holistic, Reliable and Scalable Benchmark for Text-to-Image Models

In recent years, Text-to-Image (T2I) models have been extensively studied, especially with the emergence of diffusion models that achieve state-of-the-art results on T2I synthesis tasks. However, existing benchmarks heavily rely on subjective human evaluation, limiting their ability to holistically assess the model's capabilities. Furthermore, there is a significant gap between efforts in developing new T2I architectures and those in evaluation. To address this, we introduce HRS-Bench, a concrete evaluation benchmark for T2I models that is Holistic, Reliable, and Scalable. Unlike existing bench-marks that focus on limited aspects, HRS-Bench measures 13 skills that can be categorized into five major categories: accuracy, robustness, generalization, fairness, and bias. In addition, HRS-Bench covers 50 scenarios, including fashion, animals, transportation, food, and clothes. We evaluate nine recent large-scale T2I models using metrics that cover a wide range of skills. A human evaluation aligned with 95% of our evaluations on average was conducted to probe the effectiveness of HRS-Bench. Our experiments demonstrate that existing models often struggle to generate images with the desired count of objects, visual text, or grounded emotions. We hope that our benchmark help ease future text-to-image generation research. The code and data are available at https://eslambakr.github.io/hrsbench.github.i

Faster Ray Tracing through Hierarchy Cut Code

We propose a novel ray reordering technique to accelerate the ray tracing process by encoding and sorting rays prior to traversal. Instead of spatial coordinates, our method encodes rays according to the cuts of the hierarchical acceleration structure, which is called the hierarchy cut code. This approach can better adapt to the acceleration structure and obtain a more reliable encoding result. We also propose a compression scheme to decrease the sorting overhead by a shorter sorting key. In addition, based on the phenomenon of boundary drift, we theoretically explain the reason why existing reordering methods cannot achieve better performance by using longer sorting keys. The experiment demonstrates that our method can accelerate secondary ray tracing by up to 1.81 times, outperforming the existing methods. Such result proves the effectiveness of hierarchy cut code, and indicate that the reordering technique can achieve greater performance improvement, which worth further research

Deep eutectic solvents enable the enhanced production of n-3 PUFA-enriched triacylglycerols

Efficient synthesis of n‐3 PUFA‐enriched triacylglycerol (TAG) by the esterification of glycerol with n‐3 PUFA in deep eutectic solvents (DES) is reported. There was a 1.2‐fold increase of TAG yield in DES compared with that in the solvent‐free system. Adsorption of the produced water by DES during esterification contributed to enhance the conversion efficiency by changing the reaction equilibrium. DES also served as an effective solvent for enriching the n‐3 PUFA of TAG in the upper layer of reaction media. A TAG yield of 55% was achieved under the optimal condition. Practical Applications: Enzymatic synthesis of n‐3 PUFA‐enriched triacylglycerol (TAG) is challenged by low yields. Here, deep eutectic solvents show great potential for enhancing the production of n‐3 PUFA‐enriched TAG

Downregulation of N-Acetylglucosaminyltransferase GCNT3 by miR-302b-3p Decreases Non-Small Cell Lung Cancer (NSCLC) Cell Proliferation, Migration and Invasion

Background/Aims: GCNT3 is a member of N-acetylglucosaminyltransferase family involved with mucin biosynthesis. GCNT3 aberrant expression is known to promote the progression of several human cancers. However, its role in tumorigenesis and the progression of non-small cell lung cancer (NSCLC) has not been well-characterized. Our study investigated the functional mechanisms of GCNT3 regulated by microRNAs (miRNAs) in NSCLC. Methods: The differential expression of mRNAs in NSCLC tissues and matched adjacent non-cancerous lung tissues from patients in Xuanwei, Yunnan province, China, was screened via mRNA microarray. The expression of GCNT3 and its correlation with NSCLC progression was measured in 92 paired tumor tissues and adjacent normal tissues. The functions of GCNT3 in NSCLC cells and its underlying mechanisms were measured using siRNA and GCNT3-expression vectors. The miRNA immunoprecipitation (miRIP) method was used to identify the miRNAs targeting GCNT3. The protein were measured using western blot assay, and the mRNAs were measured by quantitative real-time PCR (qRT-PCR) assay. Cell proliferation was measured using Cell Counting Kit-8 (CCK-8) and a colony forming assays; cell migration and invasion assays were performed using 24-well Transwell chambers with 8-μm pores filter, and analyses of the cell cycle and apoptosis were performed via flow cytometric analysis. The dual luciferase reporter assay was performed to confirm whether GCNT3 gene was a direct target of miR-302b-3p. Results: GCNT3 was found to be highly expressed in both NSCLC tissues and cell lines, and higher expression correlated significantly with advanced tumor-node-metastasis (TNM) stage, positive lymph node metastasis, and poor overall survival. Knockdown of GCNT3 inhibited the proliferation, migration and invasion ability of NSCLC cells, while overexpression facilitated these activities. Further mechanistic experiments using miRIP and dual luciferase reporter assays revealed that GCNT3 was a direct target of miR-302b-3p. Low expression of miR-302b-3p was found in NSCLC cells and negatively correlated with GCNT3 levels, while miR-302b-3p overexpression inhibited the proliferation, migration and invasion of NSCLC cells. Co-transfection with miR-302b-3p and the expression vector of GCNT3 abrogated the effects of mir-302b-3p, confirming that miR-302b-3p inhibited NSCLC progression by targeting GCNT3. Western blotting revealed that E-cadherin, N-cadherin, vimentin, p-Erk and cyclin D1 were downstream molecules of miR-302b-3p/GCNT3 pathway. Conclusion: miR-302b-3p/GCNT3 axis regulated cell proliferation, migration, and invasion by activating the Erk signaling pathway and epithelial-mesenchymal transition (EMT), which was identified as a potential therapeutic target for NSCLC

Revealing the missing expressed genes beyond the human reference genome by RNA-Seq

<p>Abstract</p> <p>Background</p> <p>The complete and accurate human reference genome is important for functional genomics researches. Therefore, the incomplete reference genome and individual specific sequences have significant effects on various studies.</p> <p>Results</p> <p>we used two RNA-Seq datasets from human brain tissues and 10 mixed cell lines to investigate the completeness of human reference genome. First, we demonstrated that in previously identified ~5 Mb Asian and ~5 Mb African novel sequences that are absent from the human reference genome of NCBI build 36, ~211 kb and ~201 kb of them could be transcribed, respectively. Our results suggest that many of those transcribed regions are not specific to Asian and African, but also present in Caucasian. Then, we found that the expressions of 104 RefSeq genes that are unalignable to NCBI build 37 in brain and cell lines are higher than 0.1 RPKM. 55 of them are conserved across human, chimpanzee and macaque, suggesting that there are still a significant number of functional human genes absent from the human reference genome. Moreover, we identified hundreds of novel transcript contigs that cannot be aligned to NCBI build 37, RefSeq genes and EST sequences. Some of those novel transcript contigs are also conserved among human, chimpanzee and macaque. By positioning those contigs onto the human genome, we identified several large deletions in the reference genome. Several conserved novel transcript contigs were further validated by RT-PCR.</p> <p>Conclusion</p> <p>Our findings demonstrate that a significant number of genes are still absent from the incomplete human reference genome, highlighting the importance of further refining the human reference genome and curating those missing genes. Our study also shows the importance of <it>de novo </it>transcriptome assembly. The comparative approach between reference genome and other related human genomes based on the transcriptome provides an alternative way to refine the human reference genome.</p