Miracle-dz/UniFAR-FTUs huggingface dataset:
PIV measurement of high-Reynolds-number homogeneous and isotropic turbulence in an enclosed flow apparatus with fan agitation

Enclosed flow apparatuses with negligible mean flow are emerging as alternatives to wind tunnels for laboratory studies of homogeneous and isotropic turbulence (HIT) with or without aerosol particles, especially in experimental validation of Direct Numerical Simulation (DNS). It is desired that these flow apparatuses generate HIT at high Taylor-microscale Reynolds numbers (Rλ ? >) and enable accurate measurement of turbulence parameters including kinetic energy dissipation rate and thereby Rλ ? >. We have designed an enclosed, fan-driven, highly symmetric truncated-icosahedron ‘soccer ball’ airflow apparatus that enables particle imaging velocimetry (PIV) and other whole-field flow measurement techniques. To minimize gravity effect on inertial particles and improve isotropy, we chose fans instead of synthetic jets as flow actuators. We developed explicit relations between Rλ ? > and physical as well as operational parameters of enclosed HIT chambers. To experimentally characterize turbulence in this near-zero-mean flow chamber, we devised a new two-scale PIV approach utilizing two independent PIV systems to obtain both high resolution and large field of view. Velocity measurement results show that turbulence in the apparatus achieved high homogeneity and isotropy in a large central region (48 mm diameter) of the chamber. From PIV-measured velocity fields, we obtained turbulence dissipation rates and thereby Rλ ? > by using the second-order velocity structure function. A maximum Rλ ? > of 384 was achieved. Furthermore, experiments confirmed that the root mean square (RMS) velocity increases linearly with fan speed, and Rλ ? > increases with the square root of fan speed. Characterizing turbulence in such apparatus paves the way for further investigation of particle dynamics in particle-laden homogeneous and isotropic turbulence.
Meta-Maintanance for Dockerfiles: Are We There Yet?

Docker allows for the packaging of applications and dependencies, and its instructions are described in Dockerfiles. Nowadays, version pinning is recommended to avoid unexpected changes in the latest version of a package. However, version pinning in Dockerfiles is not yet fully realized (only 17k of the 141k Dockerfiles we analyzed), because of the difficulties caused by version pinning. To maintain Dockerfiles with version-pinned packages, it is important to update package versions, not only for improved functionality, but also for software supply chain security, as packages are changed to address vulnerabilities and bug fixes. However, when updating multiple version-pinned packages, it is necessary to understand the dependencies between packages and ensure version compatibility, which is not easy. To address this issue, we explore the applicability of the meta-maintenance approach, which aims to distribute the successful updates in a part of a group that independently maintains a common artifact. We conduct an exploratory analysis of 7,914 repositories on GitHub that hold Dockerfiles, which retrieve packages on GitHub by URLs. There were 385 repository groups with the same multiple package combinations, and 208 groups had Dockerfiles with newer version combinations compared to others, which are considered meta-maintenance applicable. Our findings support the potential of meta-maintenance for updating multiple version-pinned packages and also reveal future challenges.
Comparison of Face Recognition on RGB and Grayscale Color with Deep Learning in Forensic Science

In forensic science face recognition, we cannot request high-quality face images from sources, but we have face images from CCTV grayscale on the crime scene at night, face images in RGB mode from Web Cameras, etc. This research needs to find a satisfying method of face recognition in forensic science to identify the “Who's face? ” at the request of a police investigator. The experiment uses Siamese neural network face recognition of both RGB and GRAY color modes to compare and show the performance of both color modes. The evaluation shows a confusion matrix, F1-score ROC/AUC, and a strong recommend with Likelihood ratio (LR) that supports court in evidence identification recommended by NIST and ENFSI.
Benchmarking Deep Learning Architectures for Urban Vegetation Points Segmentation

Vegetation is crucial for sustainable and resilient cities providing various ecosystem services and well-being of humans. However, vegetation is under critical stress with rapid urbanization and expanding infrastructure footprints. Consequently, mapping of this vegetation is essential in the urban environment. Recently, deep learning for point cloud semantic segmentation has shown significant progress. Advanced models attempt to obtain state-of-the-art performance on benchmark datasets, comprising multiple classes and representing real world scenarios. However, class specific segmentation with respect to vegetation points has not been explored. Therefore, selection of a deep learning model for vegetation points segmentation is ambiguous. To address this problem, we provide a comprehensive assessment of point-based deep learning models for semantic segmentation of vegetation class. We have selected four representative point-based models, namely PointCNN, KPConv (omni-supervised), RandLANet and SCFNet. These models are investigated on three different datasets, specifically Chandigarh, Toronto3D and Kerala, which are characterized by diverse nature of vegetation, varying scene complexity and changing per-point features. PointCNN achieves the highest mIoU on the Chandigarh (93.32%) and Kerala datasets (85.68%) while KPConv (omni-supervised) provides the highest mIoU on the Toronto3D dataset (91.26%). The paper develops a deeper insight, hitherto not reported, into the working of these models for vegetation segmentation and outlines the ingredients that should be included in a model specifically for vegetation segmentation. This paper is a step towards the development of a novel architecture for vegetation points segmentation.
Enhancing ECG Analysis of Implantable Cardiac Monitor Data: An Efficient Pipeline for Multi-Label Classification

Implantable Cardiac Monitor (ICM) devices are demonstrating as of today, the fastest-growing market for implantable cardiac devices. As such, they are becoming increasingly common in patients for measuring heart electrical activity. ICMs constantly monitor and record a patient's heart rhythm and when triggered - send it to a secure server where health care professionals (denote HCPs from here on) can review it. These devices employ a relatively simplistic rule-based algorithm (due to energy consumption constraints) to alert for abnormal heart rhythms. This algorithm is usually parameterized to an over-sensitive mode in order to not miss a case (resulting in relatively high false-positive rate) and this, combined with the device's nature of constantly monitoring the heart rhythm and its growing popularity, results in HCPs having to analyze and diagnose an increasingly growing amount of data. In order to reduce the load on the latter, automated methods for ECG analysis are nowadays becoming a great tool to assist HCPs in their analysis. While state-of-the-art algorithms are data-driven rather than rule-based, training data for ICMs often consist of specific characteristics which make its analysis unique and particularly challenging. This study presents the challenges and solutions in automatically analyzing ICM data and introduces a method for its classification that outperforms existing methods on such data. As such, it could be used in numerous ways such as aiding HCPs in the analysis of ECGs originating from ICMs by e.g. suggesting a rhythm type.
Reduction in muscle fibre number during the adaptive radiation of notothenioid fishes: a phylogenetic perspective

SUMMARY The fish fauna of the continental shelf of the Southern Ocean is dominated by a single sub-order of Perciformes, the Notothenioidei, which have unusually large diameter skeletal muscle fibres. We tested the hypothesis that in fast myotomal muscle a high maximum fibre diameter (FDmax) was related to a reduction in the number of muscle fibres present at the end of the recruitment phase of growth. We also hypothesized that the maximum fibre number (FNmax) would be negatively related to body size, and that both body size and size-corrected FNmax would show phylogenetic signal (tendency for related species to resemble each other). Finally, we estimated ancestral values for body size and FNmax. A molecular phylogeny was constructed using 12S mitochondrial rRNA sequences. A total of 16 species were studied from the Beagle Channel, Tierra del Fuego (5-11°C), Shag Rocks, South Georgia (0.5-4°C), and Adelaide Island, Antarctic Peninsula (-1.5 to 0.5°C). The absence of muscle fibres of less than 10 μm diameter was used as the criterion for the cessation of fibre recruitment. FDmax increased linearly with standard length (SL), reaching 500-650 μm in most species. Maximum body size was a highly significant predictor of species variation in FNmax, and both body size and size-corrected FNmax showed highly significant phylogenetic signal (P<0.001). Estimates of trait values at nodes of the maximum likelihood phylogenetic tree were consistent with a progressive reduction in fibre number during part of the notothenioid radiation, perhaps serving to reduce basal energy requirements to compensate for the additional energetic costs of antifreeze production. For example, FNmax in Chaenocephalus aceratus (12 700±300, mean ± s.e.m., N=18) was only 7.7% of the value found in Eleginops maclovinus (164 000±4100, N=17), which reaches a similar maximum length (85 cm). Postembryonic muscle fibre recruitment in teleost fish normally involves stratified followed by mosaic hyperplasia. No evidence for this final phase of growth was found in two of the most derived families (Channichthyidae and Harpagiferidae). The divergence of the notothenioids in Antarctica after the formation of the Antarctic Polar Front and more recent dispersal north would explain the high maximum diameter and low fibre number in the derived sub-Antarctic notothenioids. These characteristics of notothenioids may well restrict their upper thermal tolerance, particularly for Champsocephalus esox and similar Channichthyids that lack respiratory pigments.
Bioprocess development to produce a hyperthermostable S‐methyl‐5′‐thioadenosine phosphorylase in Escherichia coli

Nucleoside phosphorylases are important biocatalysts for the chemo‐enzymatic synthesis of nucleosides and their analogs which are, among others, used for the treatment of viral infections or cancer. S‐methyl‐5′‐thioadenosine phosphorylases (MTAP) are a group of nucleoside phosphorylases and the thermostable MTAP of Aeropyrum pernix (ApMTAP) was described to accept a wide range of modified nucleosides as substrates. Therefore, it is an interesting biocatalyst for the synthesis of nucleoside analogs for industrial and therapeutic applications. To date, thermostable nucleoside phosphorylases were produced in shake flask cultivations using complex media. The drawback of this approach is low volumetric protein yields which hamper the wide‐spread application of the thermostable nucleoside phosphorylases in large scale. High cell density (HCD) cultivations allow the production of recombinant proteins with high volumetric yields, as final optical densities >100 can be achieved. Therefore, in this study, we developed a suitable protocol for HCD cultivations of ApMTAP. Initially, optimum expression conditions were determined in 24‐well plates using a fed‐batch medium. Subsequently, HCD cultivations were performed using E. coli BL21‐Gold cells, by employing a glucose‐limited fed‐batch strategy. Comparing different growth rates in stirred‐tank bioreactors, cultivations revealed that growth at maximum growth rates until induction resulted in the highest yields of ApMTAP. On a 500‐mL scale, final cell dry weights of 87.1–90.1 g L−1 were observed together with an overproduction of ApMTAP in a 1.9%–3.8% ratio of total protein. Compared to initially applied shake flask cultivations with terrific broth (TB) medium the volumetric yield increased by a factor of 136. After the purification of ApMTAP via heat treatment and affinity chromatography, a purity of more than 90% was determined. Activity testing revealed specific activities in the range of 0.21 ± 0.11 (low growth rate) to 3.99 ± 1.02 U mg−1 (growth at maximum growth rate). Hence, growth at maximum growth rate led to both an increased expression of the target protein and an increased specific enzyme activity. This study paves the way towards the application of thermostable nucleoside phosphorylases in industrial applications due to an improved heterologous expression in Escherichia coli.
Local image fitting-based active contour for vector-valued images

Variational active contour seeks to segment or extract desired object boundaries for further analysis. The model can be divided into global segmentation and selective segmentation. Selective segmentation, which focuses on segmenting a particular object, is preferable to the global model. Recently, a number of selective segmentation models have been developed to precisely extract an object on grayscale images. Nevertheless, if the input image is vector-valued (colour), these models merely convert it to a grayscale image, resulting in data loss owing to the reduction in image dimension. Furthermore, they may have poor segmentation performance due to the intensity inhomogeneous images. Therefore, a new model on variational selective active contour for segmenting vector-valued images has been proposed that incorporates the concepts of local image fitting and distance-based fitting terms into a variational minimization energy functional. Moreover, a Gaussian function was used as a regularizer to replace the computationally expensive Total Variation term. Then, the proposed model’s Euler Lagrange equation has been provided to solve the model. When segmenting an object in inhomogeneous intensity images, the result of the proposed model was about 30% more accurate based on the Jaccard value and about 3 times faster than other existing methods.
A study of travel behaviour sustainability in Saudi Arabia: Any evidence of sustainable behaviour?

Purpose – The purpose of this study was to investigate and model travel behaviour and attitudes for households in Saudi Arabia.Design/methodology/approach – A household survey was used to collect data on household travel patterns and socio‐economic variables in the city of Tabuk in Saudi Arabia. The population of Tabuk city is just under 500,000 people, of which approximately 83 per cent are Saudi nationals and 17 per cent non‐Saudi nationals. The average household size is about six persons.Findings – Although there have been a large number of research investigations into travel behaviour in Western countries, there is a huge lack of studies in this area in some countries, such as Saudi Arabia. The specific characteristics of socio‐economic patterns as well as travel behaviour attitudes in this country make it a very interesting and unique area, which has its own characteristics and patterns, hence the need for the research.Originality/value – Although there have been a large number of research investigat.
DDA-Net: A Discrepancy-Based Domain Adaptation Network for CSI Feedback Transferability

The deep learning (DL)-based channel state information (CSI) feedback methods have been intensively explored in recent years. Most existing works are trained offline based on the prestored datasets. However, in real-world deployment, the pretrained model may not fit the field environment due to the wireless environment changes. In a previous study, a supervised learning approach has been introduced to deal with this CSI feedback transferability problem by finetuning the model. Nevertheless, the transmission for the original uncompressed CSI data will cause intense traffic in air interface. In this paper, a novel unsupervised transfer learning framework named Discrepancy-based Domain Adaptation Network (DDA-Net) is proposed to solve this problem. By minimizing the discrepancy between the CSI codeword datasets from the pretrained environment and field environment, the encoder and decoder are finetuned to extract the common features in both environments, so that the DL-based CSI feedback model can also work properly in a drifted environment without transmitting any original uncompressed CSI data. Simulation results show that the DDA approach can augment CSI feedback reconstruction accuracy and combat overfitting problems in the deployment environment. Compared to the existing supervised learning approach, the DDA approach can achieve similar CSI recovery performance without transmitting the original uncompressed CSI data, reducing considerable transmission traffic.
Deformer: Denoising Transformer for Improved Audio Music Genre Classification

Audio music genre classification is performed to categorize audio music into various genres. Traditional approaches based on convolutional recurrent neural networks do not consider long temporal information, and their sequential structures result in longer training times and convergence difficulties. To overcome these problems, a traditional transformer-based approach was introduced. However, this approach employs pre-training based on momentum contrast (MoCo), a technique that increases computational costs owing to its reliance on extracting many negative samples and its use of highly sensitive hyperparameters. Consequently, this complicates the training process and increases the risk of learning imbalances between positive and negative sample sets. In this paper, a method for audio music genre classification called Deformer is proposed. The Deformer learns deep representations of audio music data through a denoising process, eliminating the need for MoCo and additional hyperparameters, thus reducing computational costs. In the denoising process, it employs a prior decoder to reconstruct the audio patches, thereby enhancing the interpretability of the representations. By calculating the mean squared error loss between the reconstructed and real patches, Deformer can learn a more refined representation of the audio data. The performance of the proposed method was experimentally compared with that of two distinct baseline models: one based on S3T and one employing a residual neural network-bidirectional gated recurrent unit (ResNet-BiGRU). The Deformer achieved an 84.5% accuracy, surpassing both the ResNet-BiGRU-based (81%) and S3T-based (81.1%) models, highlighting its superior performance in audio classification.
Unveiling Vulnerability of Self-Attention

Pre-trained language models (PLMs) are shown to be vulnerable to minor word changes, which poses a big threat to real-world systems. While previous studies directly focus on manipulating word inputs, they are limited by their means of generating adversarial samples, lacking generalization to versatile real-world attack. This paper studies the basic structure of transformer-based PLMs, the self-attention (SA) mechanism. (1) We propose a powerful perturbation technique \textit{HackAttend}, which perturbs the attention scores within the SA matrices via meticulously crafted attention masks. We show that state-of-the-art PLMs fall into heavy vulnerability that minor attention perturbations $(1\%)$ can produce a very high attack success rate $(98\%)$. Our paper expands the conventional text attack of word perturbations to more general structural perturbations. (2) We introduce \textit{S-Attend}, a novel smoothing technique that effectively makes SA robust via structural perturbations. We empirically demonstrate that this simple yet effective technique achieves robust performance on par with adversarial training when facing various text attackers. Code is publicly available at \url{github.com/liongkj/HackAttend}.
Patterned Orientations of Liquid Crystals on Affinity Microcontact Printed Proteins

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Power efficient data gathering and aggregation in wireless sensor networks

Recent developments in processor, memory and radio technology have enabled wireless sensor networks which are deployed to collect useful information from an area of interest. The sensed data must be gathered and transmitted to a base station where it is further processed for end-user queries. Since the network consists of low-cost nodes with limited battery power, power efficient methods must be employed for data gathering and aggregation in order to achieve long network lifetimes.In an environment where in a round of communication each of the sensor nodes has data to send to a base station, it is important to minimize the total energy consumed by the system in a round so that the system lifetime is maximized. With the use of data fusion and aggregation techniques, while minimizing the total energy per round, if power consumption per node can be balanced as well, a near optimal data gathering and routing scheme can be achieved in terms of network lifetime.So far, besides the conventional protocol of direct transmission, two elegant protocols called LEACH and PEGASIS have been proposed to maximize the lifetime of a sensor network. In this paper, we propose two new algorithms under name PEDAP (Power Efficient Data gathering and Aggregation Protocol), which are near optimal minimum spanning tree based routing schemes, where one of them is the power-aware version of the other. Our simulation results show that our algorithms perform well both in systems where base station is far away from and where it is in the center of the field. PEDAP achieves between 4x to 20x improvement in network lifetime compared with LEACH, and about three times improvement compared with PEGASIS.