Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Surface-Enhanced Raman Scattering of Rhodamine 6G from repeated dewetted gold thin film

This study reports the fabrication of gold nanoislands by thermal dewetting of gold thin film deposited on a silicon wafer to study the SERS of Rhodamine 6G molecules at an ultra-low concentration. It provides a comparative study of single dewetting and repeated dewetting, showing that repeated dewetting provides ten times higher SERS enhancement than the other. Repeated dewetting reduces the gap between the particles and increases the number of plasmonic hotspots. We analyzed the SERS by using DI water and ethanol as the solvent for R6G, using a laser excitation of 532 nm. We also reported the single-molecule detection of the R6G molecule using our SERS substrate. This simple fabrication method of SERS substrate can be used in the future to detect analytes at a single-molecule regime.
Taming irregular EDA applications on GPUs

Recently general purpose computing on graphic processing units (GPUs) is rising as an exciting new trend in high-performance computing. Thus it is appealing to study the potential of GPU for Electronic Design Automation (EDA) applications. However, EDA generally involves irregular data structures such as sparse matrix and graph operations, which pose significant challenges for efficient GPU implementations. In this paper, we propose high-performance GPU implementations for two important irregular EDA computing patterns, Sparse-Matrix Vector Product (SMVP) and graph traversal. On a wide range of EDA problem instances, our SMVP implementations outperform all published work and achieve a speedup of one order of magnitude over the CPU baseline. Upon such a basis, both timing analysis and linear system solution can be considerably accelerated. We also introduce a SMVP based formulation for Breadth-First Search and observe considerable speedup on GPU implementations. Our results suggest that the power of GPU computing can be successfully unleashed through designing GPU-friendly algorithms and/or re-organizing computing structures of current algorithms.
Norovirus epidemiology in South African children <5 years hospitalised for diarrhoeal illness between 2009 and 2013

SUMMARY Public health interest in norovirus (NoV) has increased in recent years following improved diagnostics, global burden estimates and the development of NoV vaccine candidates. This study aimed to describe the detection rate, clinical characteristics and environmental features associated with NoV detection in hospitalized children <5 years with diarrhoea in South Africa (SA). Between 2009 and 2013, prospective diarrhoeal surveillance was conducted at four sites in SA. Stool specimens were collected and screened for NoVs and other enteric pathogens using molecular and serological assays. Epidemiological and clinical data were compared in patients with or without detection of NoV. The study detected NoV in 15% (452/3103) of hospitalized children <5 years with diarrhoea with the majority of disease in children <2 years (92%; 417/452). NoV-positive children were more likely to present with diarrhoea and vomiting (odds ratio (OR) 1·3; 95% confidence interval (CI) 1·1–1·7; P = 0·011) with none-to-mild dehydration (adjusted OR 0·5; 95% CI 0·3–0·7) compared with NoV-negative children. Amongst children testing NoV positive, HIV-infected children were more likely to have prolonged hospitalization and increased mortality compared with HIV-uninfected children. Continued surveillance will be important to consider the epidemic trends and estimate the burden and risk of NoV infection in SA.
An adhesive surface sampling technique for airborne viruses

SUMMARY A mixture of sucrose, glycerol and bovine serum albumin produces a stable coating in a Petri dish which remains adhesive for up to an hour when exposed in a slit sampler. Virus aerosols can be collected on this surface followed the direct addition of cell cultures to demonstrate the presence of viable virus. The technique is applicable to the Andersen sampler. A modified version of this sampler has been produced with the same particle collection efficiency as the standard Andersen sampler. The plaque counts obtained the adhesive surface sampling technique are believed to give an indication of the number of particles collected bearing viable virus.
Change of Plans: Optimizing for Power, Reliability and Timeliness for Cost-Conscious Real-Time Systems

The large-scale adoption of embedded devices naturally sees them deployed under widely varying constraints matching an equally wide number of environments and applications. Such devices may be under hard timing constraints as a result of their domain (real-time), but other constraints, such as reliability and power, are becoming increasingly relevant. In particular, battery-powered devices are limited in runtime due to their long-running average power (amortized power) consumption. We propose a real-time scheduling algorithm optimizing for reliability under an amortized energy budget. Weakly-hard real-time tasks can tolerate patterns of failures and successes, and we leverage this property as the driver of reliability in a constrained environment. Reliability against transient faults is increased by dynamically switching the protected subset of the task set based on the observed history of task success/fails. By performing such a change of plans only for tasks in need, we can cost-effectively boost reliability while lowering energy consumption. Our method optimizes reliability for a given deadline and energy budget. We explore the behavior of our method under a wide range of energy and time constrained environments, illustrating the trade-off between amortized power, timeliness and reliability, enabling application designers to make cost-conscious decisions.
Classification of Online News Articles Using Optimized Deep Learning Models

Text mining has gained considerable importance in recent years, especially in the last few. Users can now get information from many different places, such as digital media, print media, electronic media, and others. Text categorization is the method by which researchers organize the huge quantities of unstructured data generated as a result of the broad availability of text in numerous formats. In the accessible corpus of study, these specialists have identified a multitude of techniques for transforming the dispersed text into a defined and organized volume. In this study, we explore the text classification process, classifiers, and multiple feature extraction approaches utilizing the word2vec model, the Google pre-trained model (Google News 300), the RNN, LSTM, GRU, bidirectional, BERT, and the Convolutional 1D model with the Maxout layer. The results of a comparison of the classifiers that are currently available and how they work are shown in an effective way.
Design and Mathematical Modelling of Statistically Distributed Rotated Dual Split Elliptical CSRR Loaded SIW Antenna for Narrow Beam Highly Reduced SLL Based Sub-6 GHz Applications

In this article, statistically distributed Rotated Dual Split Elliptical Complementary Split Ring Resonator (RDS-ECSRR) loaded substrate integrated waveguide (SIW) antenna with highly reduced side lobe level (SLL) and narrow half power beam-width (HPBW) is proposed. Both offset and rotation of RDS-ECSRR are distributed in statistical distributions to control the SLL and HPBW. The offset has been distributed using Uniform and Normal distribution whereas rotation of RDS-ECSRR is distributed using regular arithmetic progression (AP). A maximum of $\approx$ 13.2 dB realized gain with nearly 22.3 dB reduction in SLL and $\leq 11.1^{\circ}$ HPBW has been achieved using proposed Normal distributed RDS-ECSRR loaded SIW antenna. A closed form empirical relationship with at least 95% co-efficient bound has been modeled to predict the non-linear nature of SLL and HPBW reduction based on offset and rotation optimization. 2D and 3D radiation pattern for each statistical distribution has been illustrated to showcase the reduction in HPBW and SLL in each sub-section of this article.
BER-SISF: Inter-segment Fusion Network Using Emotional Similarity Information for Bimodal Emotion Recognition

For bimodal emotion recognition (BER), a key challenge is to effectively aggregate information from different modalities to improve emotion recognition performance. In this study, we present a dual-modality emotion recognition model that utilizes a segment-level, inter-modal similarity-based fusion method alongside a joint emotion feature fusion technique. This approach leverages inter-modal similarities to create central representation vectors for input sequences, thereby boosting realtime emotion recognition robustness. The model also integrates modality-specific emotional information with cross-modal affective similarities for improved information aggregation. The proposed model is validated on the public benchmark dataset IEMOCAP and achieves weighted accuracies of 84.23% in fourclass emotion recognition tasks, respectively, surpassing other state-of-the-art methods.
Evaluation of ghrelin level and appetite regulation in patients with acute exacerbations of chronic obstructive pulmonary disease

Background Appetite reduction is a major cause of cachexia in acute exacerbations of chronic obstructive pulmonary disease (AECOPD). This study tested the correlation of appetite and circulating levels of acylated ghrelin in patients with AECOPD. Methods Thirty-six patients with AECOPD and 23 healthy adults were enrolled in this study. Circulating total ghrelin, acylated ghrelin, and obestatin levels, Simplified Nutritional Appetite Questionnaire (SNAQ) score, and caloric intake were compared in patients and healthy controls. Additionally, the above parameters were compared between admission and discharge in the patients with AECOPD. Results Compared with healthy controls, SNAQ scores and caloric intake were significantly lower in patients with AECOPD, but there were no significant differences in total ghrelin, acyl ghrelin, or obestatin levels. In patients with AECOPD, the total ghrelin level was significantly higher at admission than on discharge, the SNAQ score and caloric intake were significantly increased at discharge when compared with admission, and there was no significant difference in acylated ghrelin level between admission and discharge. Conclusion We demonstrated lower appetite scores and caloric intake in patients with AECOPD, but could not confirm that these effects were caused by insufficient levels of the orexigenic peptide, acyl ghrelin. Further studies are needed to confirm our findings and to determine the mechanism regulating appetite in patients with AECOPD.
Visual Representations and Guitar Skill 37

Music Perception volume 30, issue 1, pp. 37–47. issn 0730-7829, electronic issn 1533-8312. © 2012 by the regents of the university of california all rights reserved. please direct all requests for permission to photocopy or reproduce article content through the university of california press’s rights and permissions website, http://www.ucpressjournals. com/reprintinfo.asp. doi: 10.1525/mp.2012. 30.1. 37 matthew j. c. crump Brooklyn College of CUNY
Multi-Scale Edge-Guided Context Aggregation Network for Single Image Deraining

DCNNs have shown remarkable results in the task of image deraining. However, many existing methods for single image deraining do not take into account the restoration of edge textures in the image. Some approaches use a backbone network to handle both image deraining and detail restoration. To overcome these limitations, We created a dual-branch structure named Multi-Scale Edge-Guided Context Aggregation Network (MSEGCA-Net) that utilizes a coarse-to-fine approach. This allows us to attain excellent deraining performance while maintaining image resolution. Firstly, we present a main image deraining branch to obtain coarse deraining information from the rainy image, as well as an auxiliary edge texture detection branch to obtain edge texture information. Then, different from previous methods relying on direct guidance from the edge textures, We introduce a new connection block, Edge-Guided Context Aggregation Block (EGCAB), that combines coarse deraining information with edge texture details. This aggregated information guides the primary image deraining branch to produce the ultimate derained image. Our method was evaluated on three datasets: Rain200L, Rain1200, and Rain1400. Both quantitative and qualitative comparisons indicate that our approach excels over current leading deraining methods, showcasing superior robustness against rain and accuracy in preserving image details.
The Effect of Age, Gender, and Insertion Site on Marginal Bone Loss around Endosseous Implants: Results from a 3-Year Trial with Premium Implant System

Objectives. The goal of this study was to evaluate bone changes around endosseous implants in partially edentulous patients. Materials and Methods. A total of 632 two-stage implants were placed in 252 patients. The implants had straight emergence profile, ZirTi surface, 3.3 to 5 mm diameter, and 8.5 to 13 mm length. Bone levels were assessed on orthopantomography immediately after surgery and after 36 months and marginal bone loss (MBL) was calculated from their difference. Results. Cumulative survival rate was 98.73%. Overall MBL was 0.8 mm ± 0.03 (mean ± SEM). Higher MBL was observed around implants in the maxilla than in the mandible (P < 0.007). A relation between implant diameter and MBL (P < 0.0001) was observed in male and, more limitedly, female patients. Older patients had higher MBL in the maxilla, but not in the mandible (P < 0.0001). MBL progressively increased with age in male patients, but reached a peak already in the 50–60 years age group in the female subset (P < 0.001). Conclusions. The overall MBL is consistent with the available literature. Site difference and patient age and gender appear to significantly affect MBL, representing important factors to be considered during implant placement.
An In-Depth Study of Handwritten Digit Recognition Methods: An Exploration of Deep Learning and Machine Learning Approaches

This article explains how deep learning and machine learning techniques are being utilized to replace humans in a variety of activities, like item classification and sound addition. It emphasizes handwritten text recognition (HTR), which involves computers interpreting handwritten content from sources like paper documents and touchscreens. For recognizing handwritten digits, the study analyzes three algorithms: support vector machines (SVM), multilayer perception (MLP), and convolutional neural networks (CNN). This comparison aims to evaluate the correctness and execution times of different approaches. The evaluation is based on parameters like the dataset used, the number of epochs, algorithm complexity, accuracy, and hardware specifications. The study compares the effectiveness of CNN, Multilayer Perception (MLP), and Support Vector Machines using handwritten digits from the MNIST dataset. It evaluates these algorithms based on criteria such as runtime, complexity, rate of precision, the count of epochs, and the quantity of hidden layers specifically within deep learning techniques. SVM had the highest training data accuracy, but CNN outperformed others on the test dataset. The article’s primary goal is to identify the most precise model for recognizing handwritten digits. It achieves this by assessing the performance of different algorithms and comparing them based on various characteristics.
Visualizing Wellness: The Impact of Deep Learning on Healthcare Image Analysis

Deep convolution networks have quickly established itself as the main method for deciphering medical pictures due to their widespread use. This extensive study examined almost 300 recent papers, mostly from the preceding year, that summarized the basic artificial intelligence ideas related to medical picture analysis. Applications of deep learning were explored in detail in a number of disciplines, including object recognition, picture grouping, classification, and registering. This book presents a succinct summary of studies conducted in the fields of neurological disorders, retinal cells, asthma, malignant scans, breasts, cardiovascular and cardiac pelvic, and nerve imaging. Study conducted concludes our assessment with a review of the state-of-the-art at this time, a thorough investigation of outstanding problems, and suggestions for further research. All areas, including computerized pathology, musculoskeletal, pulmonary, the heart, breasts, muscles and tendons, retinal, and neurology imaging, are summarized in a summary of current studies. This thorough review ends with an assessment of the field's current state, a critical analysis of ongoing issues, and specific suggestions for further research and development.
Profiling Analysis for Enhancing the Performance of Graph Neural Networks

Since the turn of the century, Graph Neural Networks (GNNs) have come into the forefront for collaborative filtering (CF). While not immediately recognized by the average user, GNNs are providing users CF recommendations across all aspects of life. The Graph Convolutional Network (GCN) has provided a strong foundation for CF and node classification in recent years. A great number of models are based on the design of the GCN. GNN models are highly complex and require large labeled datasets making training costly. Analyzing the performance characteristics of GNNs provides insight into opportunities for model enhancement and hardware acceleration. In this paper, we describe and analyze two GNN models: LightGCN and ExpressGNN. In LightGCN we find an abundance of elementwise operations yielding to high levels of pipeline stalls. With ExpressGNN, we find most kernels are based in GEMM (General Matrix Multiplication) and GEMV (General Matrix-Vector Multiplication) operations. ExpressGNN has the possibility to greatly benefit from accelerators, including the use of tensor cores in Nvidia's GPUs.
Exploring Part-of-Speech Tagging Models in Malaysia's Multilingual

This study delves into the realm of part-of-speech (POS) tagging within Malaysia's multilingual context. We investigate the efficacy of CRF-QTAG, semi-supervised CRF models and rule-based systems within Bahasa Rojak Analytics—a Malay-based language processing system. By analyzing these models' performances, we observed the profound impact of retraining on their accuracy. While CRF-QTAG and semi-supervised CRF models showcased substantial improvements post-retraining, the rigidity of the rule-based system led to underperformance. The study sheds light on challenges posed by linguistic nuances in code-mixed languages and the dependence on labeled data. Our findings highlight the potential of semi-supervised models in addressing data scarcity issues and adapting to linguistic evolution. Additionally, we advocate for further research aimed at refining rule-based approaches by emphasizing linguistic comprehension and rule definition for enhanced adaptability and accuracy. Addressing these challenges can potentially pave the way for more inclusive and precise language technologies tailored to Malaysia's diverse linguistic fabric.
UWB Antipodal Antenna With Half-Elliptical Parasitic Patch Loaded with Cylindrical Dielectric For Microwave Breast Imaging Application

This paper introduces an Ultra-Wideband (UWB) Antipodal Antenna (AA) featuring leaf-shaped stubs, a half-elliptical parasitic patch, and a cylindrical lens strategically integrated into its design, aimed at enhancing its suitability for microwave breast imaging applications. The initial focus of this development is on crafting and optimizing the performance of the leaf-shaped stubs. Subsequently, in pursuit of heightened high-frequency gain performance, a half-elliptical parasitic structure is introduced both above and below the substrate. To further elevate the antenna’s gain capabilities, a circular cylindrical dielectric lens is seamlessly integrated. The proposed configuration demonstrates a simulated return loss exceeding 10 dB across the frequency range of 1.4 to 16 GHz, with a projected peak gain of 12.4 dBi at 13 GHz. Notably, this innovative device has proven effective in the successful detection of tumors within a planar breast phantom.
HEM and TM Excitation Modes For a Three-Port Wideband MIMO Cylindrical Dielectric Resonator Antenna at a mm-Wave Frequency

This article is brief about multiple-input multiple output (MIMO) cylindrical dielectric resonator antenna (CDRA). Two resonant modes, hybrid electromagnetic and transverse electromagnetic HEM 11 and TM 01 mode excitement, have been applied in MIMO CDRA for 5G mm wave communication. HEM and TM mode generates strong electric and magnetic dipoles achieved by using a slot aperture coupling technique through T-shaped microstrip feed lines. A prototype was successfully verified and experimentally demonstrated by the commercially available low-loss dielectric substrates Rogers RT 6010 and RO 4350. The proposed CDRA antenna was designed using CST simulation software and tested by a vector network analyzer. Further, less than −15dB isolation has also been attained at all three ports, resulting in no mutual coupling between the antenna elements.
Sentiment Analysis for Product Reviews using Long Short-Term Memory with Layer Normalization

Sentiment analysis is an important tool in the hands of service providers and sellers to gain insights into customer behaviour. The work reported in this paper aims to classify customer sentiment in a sample of 3,000 customer reviews into positive and negative categories, using two methods: long short-term memory (LSTM), and LSTM with layer normalization. Before applying these two methods, the data is pre-processed using data exploration, emoji conversion, lemmatization, and tokenization. The findings indicate that LSTM with layer normalization gives slightly better results as compared to LSTM.
Low-distortion subspace embeddings in input-sparsity time and applications to robust linear regression

Low-distortion embeddings are critical building blocks for developing random sampling and random projection algorithms for common linear algebra problems. We show that, given a matrix A ∈ R<sup>n x d</sup> with n >> d and a p ∈ [1, 2), with a constant probability, we can construct a low-distortion embedding matrix Π ∈ R<sup>O(poly(d)) x n</sup> that embeds A<sub>p</sub>, the l<sub>p</sub> subspace spanned by A's columns, into (R<sup>O(poly(d))</sup>, |~cdot~|<sub>p</sub>); the distortion of our embeddings is only O(poly(d)), and we can compute Π A in O(nnz(A)) time, i.e., input-sparsity time. Our result generalizes the input-sparsity time l<sub>2</sub> subspace embedding by Clarkson and Woodruff [STOC'13]; and for completeness, we present a simpler and improved analysis of their construction for l<sub>2</sub>. These input-sparsity time l<sub>p</sub> embeddings are optimal, up to constants, in terms of their running time; and the improved running time propagates to applications such as (1 pm ε)-distortion l<sub>p</sub> subspace embedding and relative-error l<sub>p</sub> regression. For l<sub>2</sub>, we show that a (1+ε)-approximate solution to the l<sub>2</sub> regression problem specified by the matrix A and a vector b ∈ R<sup>n</sup> can be computed in O(nnz(A) + d<sup>3</sup> log(d/ε) /ε^2) time; and for l<sub>p</sub>, via a subspace-preserving sampling procedure, we show that a (1 pm ε)-distortion embedding of A<sub>p</sub> into R<sup>O(poly(d))</sup> can be computed in O(nnz(A) ⋅ log n) time, and we also show that a (1+ε)-approximate solution to the l<sub>p</sub> regression problem min<sub>x ∈ R<sup>d</sup></sub> |A x - b|<sub>p</sub> can be computed in O(nnz(A) ⋅ log n + poly(d) log(1/ε)/ε<sup>2</sup>) time. Moreover, we can also improve the embedding dimension or equivalently the sample size to O(d<sup>3+p/2</sup> log(1/ε) / ε<sup>2</sup>) without increasing the complexity.