Miracle-dz/UniFAR-FTUs huggingface dataset:
Video segmentation of industrial smoke based on dynamic fully convolutional network-Gaussian mixture model and multi-scale fusion attention module

Abstract. Accurately segmenting industrial smoke in videos plays a crucial role in assessing pollution levels based on smoke image evaluation. However, existing fully convolutional networks (FCNs) face challenges in precisely segmenting the edges of industrial smoke and exhibit low extraction and segmentation accuracy for small target smoke. To address this issue, we propose a video segmentation method specifically designed for industrial smoke. This method utilizes the dynamic FCN-Gaussian mixture model (GMM) along with a multi-scale fusion module and an attention module. The FCN-GMM effectively extracts dynamic feature information from spatiotemporal data, capturing motion in video or image sequences while preserving spatial details. The key innovation of FCN-GMM lies in integrating dynamic and static networks through a neural network, enabling the capture of features in both the temporal and spatial domains. Our approach begins by constructing a dynamic feature extraction network that captures spatial and temporal feature information separately during the training process, thereby enhancing the extraction of smoke edges. Additionally, we introduce a mechanism for multi-scale feature fusion and an attention module to effectively extract information related to small target smoke. Our experimental results demonstrate that our network accurately segments significant target smoke compared with FCNs. Furthermore, the network prioritizes the consideration of smoke edge information and improves the extraction of small target smoke, thereby enhancing the overall accuracy of smoke image segmentation with an increase of up to 10% in the intersection over union index.
Self-supervised remote sensing image change detection based on high frequency feature and gate attention-guided optimization unit

Abstract. Remote sensing image change detection (CD) is a technical method to analyze and compare remote sensing images covering the same area with different phases to determine the process of surface change, which has important application value in natural resource management, land monitoring, and natural disaster monitoring. At present, the commonly used supervised remote sensing image CD technique requires a large number of labeled samples to train the network, which brings a large human and material cost. In contrast, the unsupervised remote sensing image CD technique does not require the construction of labeled samples; however, there is no direct correspondence between the unsupervised learning task and the downstream CD. To address the aforementioned issues, a self-supervised remote sensing image CD method based on a high frequency feature enhancement module (HFFEM) and gate attention-guided optimization unit (GAGOU) Siamese-like network is proposed. First, the network is trained using a self-supervised learning diagram to extract features that are beneficial to remote sensing image CD and better serve the downstream CD task. After that, a clustering loss function and a contrastive loss function are used to optimize the network. Second, to enhance the feature extraction capability for change edges and to address the degradation of network performance due to the entrainment of redundant features during feature fusion, HFFEM and GAGOU are proposed, respectively. Finally, comprehensive simulation experiments were conducted on the IKONOS multi-spectral datasets Mina and Riyadh, the open CD dataset Onera satellite change detection (OSCD), and the heterogeneous dataset Shuguang, showing the effectiveness of the proposed algorithms based on evaluation metrics, such as overall accuracy, kappa coefficient, and F1 score.
Hollow core photonic crystal fiber based carbon monoxide sensor design applicable for hyperbilirubinemia diagnosis

Abstract. One of the modern ways to diagnose diseases is through exhale gas of patients, which is non-invasive and therefore desirable to doctors. The composition of exhaled breath contains valuable information for identifying hyperbilirubinemia disease. The accumulation of bilirubin and its lack of excretion causes jaundice, and the production of bilirubin has a direct relationship with exhaled carbon monoxide (CO). A CO diagnostic sensor based on a photonic crystal fiber (PCF) that can detect hyperbilirubinemia has been designed. This method can be used repeatedly to detect and treated jaundice in time. The PCF is designed as a hexagon. The holes in the cladding are circular and the holes in the core are circular and oval. Finite element method is used to do all the numerical simulations. COMSOL Multiphysics software was used to obtain the results. In this setup, CO absorption wavelength is 1.567 μm, and the relative sensitivity of 64.28% is reported with confinement loss of 3.81 × 10 − 3 dB / m.
Generating synthetic data and training muzzle flash detection systems using GANs

Researching gun muzzle flash detection can be costly and time-consuming, as data collection requires specialized equipment to be set up at various ranges and angles. This process is further complicated by the need to hire licensed weapon handlers for each weapon class, and by the scarcity of shooting ranges. To address this, we propose a novel approach that uses Generative Adversarial Networks (GANs) to speed up the research process. Specifically, we train a deep convolutional GAN (DCGAN) to generate synthetic muzzle flash waveforms, which can then be used to augment limited training data for deep-learning classifier models. We evaluate the performance of the DCGAN using a lightweight deep-learning model based on ResNet and explore the possibility of re-purposing the trained discriminator as a classifier.
Enhanced target recognition using quadratic correlation filter networks

Quadratic Correlation Filters (QCFs) have been shown to be useful at separating target areas of interest from background clutter for two-class discrimination. However, extension to multi-class discrimination is not straightforward, as QCFs primarily maximize the distance of the response of a filtered target area from clutter. Several attempts have been made to extend QCFs to multi-class discrimination using support vector machines and convolutional neural networks. In addition, detection and recognition of targets that are considered “unresolved” are still elusive for neural network architectures like YOLO which have minimum target size requirements. This work will show that the localization aspect of a QCF neural network layer paired with feature extraction layers of a purely convolutional neural network provides a robust solution to this problem. We will compare the recognition accuracy to YOLO outputs, since this is the current state-of-the-art for target localization and recognition for autonomous vehicles.
The Future of Fixed-term Employment in India

The industrial relations (IR) environment in India and its labour market, have been undergoing significant transformations in multiple domains for around two decades after the economic liberalization. Gillan and Lambert (2013) observe several restructurings at the workplace level in the country. These include, among others, farming out job functions or services to any third party through outsourcing, engaging contract labour in the organized sector, the tendency among managers to avoid acknowledging the right of trade unions to collectively bargain and the prevalence of voluntary retirement schemes for having numerical flexibility of the workforce.
Using Domain-Specific, Immediate Feedback to Support Students Learning Computer Programming to Make Music

Broadening participation in computer science has been widely studied, creating many different techniques to attract, motivate, and engage students. A common meta-strategy is to use an outside domain as a hook, using the concepts in that domain to teach computer science. These domains are selected to interest the student, but students often lack a strong background in these domains. Therefore, a strategy designed to increase students' interest, motivation, and engagement could actually create more barriers for students, who now are faced with learning two new topics. To reduce this potential barrier in the domain of music, this paper presents the use of automated, immediate feedback during programming activities at a summer camp that uses music to teach foundational programming concepts. The feedback guides students musically, correcting notes that are out-of-key or rhythmic phrases that are too long or short, allowing students to focus their learning on the computer science concepts. This paper compares the correctness of students that received automated feedback with students that did not, which shows the effectiveness of the feedback. Follow up focus groups with students confirmed this quantitative data, with students claiming that the feedback was not only useful but that the activities would be much more challenging without the feedback.
NUMERICAL STUDY ON FLOW AND HEAT TRANSFER IN A ELLIPTICAL AND CIRCULAR FINNED TUBE

A three-dimensional numerical simulation was carried out to study tie flow and heat transfer characters outside the elliptical finned elliptical tube.The influence of fin parameters and inlet flow velocity on heat transfer coefficient and flowed friction was researched.With comparison of the circular finned circular tube which has the identical structure parameters(equivalent diameter, thickness of fin and the surface area),in the identical condition,the heat transfer coefficients of elliptical tube and circular tube nearly are less different,but the flowed friction of elliptical tube reduced obviously.With the theory of field synergy,it was found that the local heat transfer on the fin located on the windward side is better than that on the leeward side.With the fin area increasing on the windward side and reducing that on the leeward side properly,the heat transfer could be enhanced,and at the same time,the flowed friction could be reduced.
Choosing a Didactic Basis for an Instructional Video: What Are the Implications For Novice Programmers?

Much work on instructional videos in computing education focuses on the overall impact and technical aspects of videos, such as motivation and length. However, it might be significant how the underlying pedagogical theory, the didactic basis, determines the delivery of the content. We conducted a randomized experiment to investigate the research question: How does the didactic basis of an instructional video affect code writing performance and self-efficacy given the basic skill of novice programmers? Our data included two cohorts of 133 and 428 CS1 students from the Fall semesters of 2021/22 and 2022/23, respectively. In cohort 1, videos based on language-sensitive teaching led to significantly better results in writing code in object orientation for novices with medium basic skills than videos based on worked examples. This result could not be replicated in cohort 2. We found no effect on novice self-efficacy in either cohort.
More (Sema|Meta)phors: Additional Perspectives on Analogy Use from Concurrent Programming Students

A concurrent computing course is filled with challenges for upper-level programming students. Understanding concurrency provides deeper insight into many modern computing and programming language behaviors, but the subject matter can be difficult even for relatively proficient students. It can be a challenge to help students navigate and understand these unfamiliar topics. While there is a difference in general programming familiarity, teaching this novel material is not unlike some challenges faced when engaging introductory students with first programming concepts. In this work, we explore the use of analogy by students while learning a novel programming methodology. We investigate perceptions of the utility of analogy and creation of analogies in the concurrent course. We also examine perceptions of analogy value across students' computing education and factors which impacted their use or disuse of provided or student-generated analogies. This exploration suggests that pedagogical analogy design can be memorable and significant for student understanding. It further suggests that analogies inherent in concept naming and foundational examples may have even greater salience. While not all students create analogies, those that do share both unique examples and additions to existing examples that helped them understand core concepts. Students had mixed responses on whether analogy as a tool was used in their lower-level courses. Despite this, most found analogies to be useful, with a majority finding them even more useful in upper-level programming courses.
The effect of modified friction by the outsole tread patterns on joint biomechanics during tennis specific movements

The sliding motion in tennis caused by proper friction of tennis shoes enables quick change of direction (Pavailler & Horvais, 2014) and reduces the load on the joints (Damm et al., 2014). Accordingly, tennis shoe manufacturers are changing the design of the outsole pattern so that the intentional sliding motion appears. However, there is a lack of accurate criteria or data in designing the level and direction of friction for the pattern of the outsole of tennis shoes.
The Bernstein decomposition and the Bernstein centre

What is the Bernstein decomposition? In rough terms, it expresses the category R(G) of smooth complex representations of a reductive p-adic group G as the product of certain indecomposable full subcategories, often called the components of R(G). The primary reference is [Ber84] where the decomposition forms a crucial step in a description of the centre of the abelian category R(G). This is the theory of the Bernstein centre which we also discuss below. The notes [BR92] present a somewhat different approach to both the Bernstein decomposition and the theory of the Bernstein centre. These have very strongly influenced the account presented here. In particular, we directly follow [BR92] in expressing each component of R(G) as a module category via the construction of suitable progenerators. (The necessary elementary categorical algebra is reviewed in the body of the chapter.) This breaks into two cases, the cuspidal and the non-cuspidal. The cuspidal case is elementary. The non-cuspidal case, however, is far from elementary. It relies principally on a deep result of Bernstein, often referred to as the Second Adjoint Theorem. I have omitted the long and non-trivial proof of this theorem. A full account, following the approach of [BR92], is to appear in a forthcoming book by David Renard [Ren]. (Note we cannot appeal to Bushnell’s alternative proof of the Second Adjoint Theorem [Bus01] because of its dependence on [Ber84] — see Remark 1.8.1.4 below.) Working within the associated module categories in the two cases, cuspidal and non-cuspidal, we recover Bernstein’s description of the centre of R(G). Here the treatment of the cuspidal case roughly parallels [BR92] but the precise route we have taken was prompted by [BH03, Section 8]. In the non-cuspidal case, our treatment diverges from [BR92] and is closer in spirit to [Ber84]. The introductions to the individual sections give an overview of the stages (and detours) on the way to the final decomposition and the description of the Bernstein centre. In Section 1.2 and in parts of Sections 1.3 and 1.4, I have borrowed heavily from my notes from a beautiful set of lectures given by Robert Kottwitz at the University of Chicago in the early nineties. Finally, I am grateful to David Renard for alerting me to a gap in a preliminary version of this article.
Do sex differences exist in foot shape?: a hierarchical cluster analysis

Footwear brands have been trying to provide a well-fitting shoe for a wide variety of foot shapes in order to meet the customer expectations. A typical example is that different shoe lasts have been used in shoe design to accommodate the different dimensions of the foot shapes between male and female. In fact, men’s feet are longer and wider than women’s feet (e.g., Wunderlich and Cavanagh, 2001), which should be related to the anthropometric differences between the sexes. The majority of previous research on the foot shape differences between the sexes has been conducted using 1D or 2D measures, such as arch height, foot circumference and so on. For these variables, relatively small differences between the sexes have been observed when normalized to the foot length (Krauss et al., 2011). However, little is known regarding the 3D shape difference by taking into account the size differences.
The SVD Applied to the Identification of the Basic Parameters of Synchronous Machine

The singular value decomposition(SVD)has been applied to the identification of the parameters of the synchronous machine.The means would obtain the shortest vector solution of the LSE whether Jaccobi matrix is singular or not,to improve the convergence,and to reduce the iteration number of estimation algorthm.The method presented in the paper is successful by indentified the parameters of the 200 MW turbogenerator.
Medicinal plants: A promising source of anti-diabetic agents in sub-Sahara Africa

Abstract The rising burden of Diabetes mellitus (DM) globally and particularly in sub-Sahara Africa calls for alternative treatment solutions. This is because the currently available drugs for its management are limited due to undesirable adverse effects and high cost. Thus, this review explores diabetes and summarizes its treatment options, focusing mainly on medicinal plants therapy. Information on twenty-five selected medicinal plants from sub-Sahara Africa having hypoglycemic and anti-diabetic potentials was obtained via electronic search of major databases, such as Pubmed/Medline, Scopus, Google Scholar and web of science. Predominant bioactive compounds found in these plants include tannins, carpaine, terpenoids, hexadecenoic acid, luteolin, saponins, glycosides, rutin, quercetin, vindoline and kaempferol. Robust evidence indicates that these medicinal plants and their bioactive components exert their antidiabetic potentials via different mechanisms, including: regeneration of pancreatic β-cell and insulin secretion; inhibition of α-amylase, inhibition of intestinal glucose absorption and liver glucose production; antioxidative stress; limitation of glycogen degradation and gluconeogenesis; anti-inflammatory, immunoregulatory. DM imposes a tremendous burden in the region, and its prevalence is not abating; thus the rich flora of the region with known hypoglycemic and antidiabetic efficacy could be explored as a complementary therapy in its management.
Metabolic-Pathway-Presence-Heatmap (MPPH): Constructing phylogenetic trees based on metabolic pathways

Genome sequencing has revolutionized the study of biological systems, enabling exploration of species origins, evolution, and identification. However, traditional methods for constructing phylogenetic trees based on raw sequence data require substantial computational resources and may be challenging for biologists with limited computer knowledge. To address this, a lightweight tree-building tool was developed, Metabolic-Pathway-Presence-Heatmap (MPPH), leveraging Python programming and the KEGG metabolomics database to construct phylogenetic trees based on metabolic pathway information. This approach reduces computational and time requirements while focusing the analysis on metabolic pathways. The tool provides a rapid and reliable option for biologists to investigate the evolutionary and taxonomic aspects of species. Additionally, the tool incorporates a heatmap feature, allowing users to visualize the presence or absence of metabolic pathways across multiple species. The code is available at http://github.com/DeweyYihengDu/Metabolic-Pathway-Presence-Heatmap.
The genome sequence of the Montseny horsehair worm, Gordionus montsenyensis sp. nov., a key resource to investigate Ecdysozoa evolution

Nematomorpha, also known as Gordiacea or Gordian worms, are a phylum of parasitic organisms that belong to the Ecdysozoa, a clade of invertebrate animals characterized by molting. They are one of the less scientifically studied animal phyla, and many aspects of their biology and evolution are still unknown, partially due to the lack of genomic resources for this phylum. As part of the European Reference Genome Atlas pilot effort to generate reference genomes for European biodiversity, we present the taxonomic description and chromosome-level genome assembly of a newly described species of Nematomorpha (Gordionus montsenyensis Schmidt-Rhaesa & Fernández sp. nov.). The final assembly has a total length of 288 Mb in 396 scaffolds with an N50 of 64.4 Mb, 97% of which is scaffolded into 5 pseudochromosomes. The circular mitochondrial genome was also assembled into a 15-kilobases sequence. Gene annotation predicted 10,320 protein-coding genes in the nuclear genome. In this study, we contribute a key genomic resource to not only explore the evolution of Ecdysozoa, but also to further our understanding on the genomic basis of parasitic lifestyles. In addition, we describe a species new to science from this enigmatic animal phyla.
Two independent DNA repair pathways cause mutagenesis in template switching deficient Saccharomyces cerevisiae

Upon DNA replication stress, cells utilize the post-replication repair pathway to repair single-stranded DNA and maintain genome integrity. Post-replication repair is divided into two branches: error-prone translesion synthesis, signaled by PCNA mono-ubiquitination, and error-free template switching, signaled by PCNA poly-ubiquitination. In Saccharomyces cerevisiae, Rad5 is involved in both branches of repair during DNA replication stress. When the PCNA poly-ubiquitination function of Rad5 is disrupted, Rad5 recruits translesion synthesis polymerases to stalled replication forks, resulting in mutagenic repair. Details of how mutagenic repair is carried out, as well as the relationship between Rad5-mediated mutagenic repair and the canonical PCNA-mediated mutagenic repair, remain to be understood. We find that Rad5-mediated mutagenic repair requires the translesion synthesis polymerase ζ but does not require other yeast translesion polymerase activities. Furthermore, we show that Rad5-mediated mutagenic repair is independent of PCNA binding by Rev1 and so is separable from canonical mutagenic repair. In the absence of error-free template switching, both modes of mutagenic repair contribute additively to replication stress response in a replication timing-independent manner. Cellular contexts where error-free template switching is compromised are not simply laboratory phenomena, as we find that a natural variant in RAD5 is defective in PCNA poly-ubiquitination and therefore defective in error-free repair, resulting in Rad5- and PCNA-mediated mutagenic repair. Our results highlight the importance of Rad5 in regulating spontaneous mutagenesis and genetic diversity in S. cerevisiae through different modes of post-replication repair.