Search

There is an urgent need for the development of novel and truly rapid (equal or less than 1 hour) antibiotic susceptibility testing (AST) platforms in order to provide best antimicrobial prescribing practices and to help reduce the increasing global threat of antibiotic resistance. A 785 nm surface enhanced Raman spectroscopy (SERS) based phenotypic methodology is described that results in accurate minimum inhibitory concentration (MIC) determinations for all tested strain-antibiotic pairs. The SERS-AST procedure results in accurate MICs, the key quantitative measure of in vitro drug susceptibility, in 1 hour, including a 30-minute incubation period. The method is effective for both Gram positive and negative species, and for antibiotics with different initial primary targets of antibiotic activity, and for both bactericidal and bacteriostatic antibiotics. The molecular level mechanism of this methodology is described. Bacterial SERS spectra are due to secreted purine nucleotide degradation products (principally adenine, guanine, xanthine and hypoxanthine) resulting from water washing induced bacterial stringent response and the resulting (p)ppGpp alarmone mediates nucleobase formation from unneeded tRNA and rRNA. The rewiring of metabolic responses resulting from the secondary metabolic effects of antibiotic exposure during the 30-minute incubation period accounts for the dose dependence of the SERS spectral intensities which are used to accurately yield the MIC. This is the fastest demonstrated AST method yielding MICs.

Automatic prediction of amyotrophic lateral sclerosis (ALS) disease progression provides a more efficient and objective alternative than manual approaches. We propose ALS longitudinal speech transformer (ALST), a neural network-based automatic predictor of ALS disease progression from longitudinal speech recordings of ALS patients. By taking advantage of high-quality pretrained speech features and longitudinal information in the recordings, our best model achieves 91.0\% AUC, improving upon the previous best model by 5.6\% relative on the ALS TDI dataset. Careful analysis reveals that ALST is capable of fine-grained and interpretable predictions of ALS progression, especially for distinguishing between rarer and more severe cases. Code is publicly available.

The spread of various forms of offensive speech online is an important concern in social media. While platforms have been investing heavily in ways of coping with this problem, the question of privacy remains largely unaddressed. Models trained to detect offensive language on social media are trained and/or fine-tuned using large amounts of data often stored in centralized servers. Since most social media data originates from end users, we propose a privacy preserving decentralized architecture for identifying offensive language online by introducing Federated Learning (FL) in the context of offensive language identification. FL is a decentralized architecture that allows multiple models to be trained locally without the need for data sharing hence preserving users' privacy. We propose a model fusion approach to perform FL. We trained multiple deep learning models on four publicly available English benchmark datasets (AHSD, HASOC, HateXplain, OLID) and evaluated their performance in detail. We also present initial cross-lingual experiments in English and Spanish. We show that the proposed model fusion approach outperforms baselines in all the datasets while preserving privacy., Comment: Accepted to TRAC 2024 (Fourth Workshop on Threat, Aggression and Cyberbullying) at LREC-COLING 2024 (The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation)

The introduction of large language models (LLMs) has advanced natural language processing (NLP), but their effectiveness is largely dependent on pre-training resources. This is especially evident in low-resource languages, such as Sinhala, which face two primary challenges: the lack of substantial training data and limited benchmarking datasets. In response, this study introduces NSINA, a comprehensive news corpus of over 500,000 articles from popular Sinhala news websites, along with three NLP tasks: news media identification, news category prediction, and news headline generation. The release of NSINA aims to provide a solution to challenges in adapting LLMs to Sinhala, offering valuable resources and benchmarks for improving NLP in the Sinhala language. NSINA is the largest news corpus for Sinhala, available up to date., Comment: Accepted to LREC-COLING 2024 (The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation)

Text classification is an area of research which has been studied over the years in Natural Language Processing (NLP). Adapting NLP to multiple domains has introduced many new challenges for text classification and one of them is long document classification. While state-of-the-art transformer models provide excellent results in text classification, most of them have limitations in the maximum sequence length of the input sequence. The majority of the transformer models are limited to 512 tokens, and therefore, they struggle with long document classification problems. In this research, we explore on employing Model Fusing for long document classification while comparing the results with well-known BERT and Longformer architectures., Comment: Accepted in RANLP 2023

The domain of Botany is rich with metaphorical terms. Those terms play an important role in the description and identification of flowers and plants. However, the identification of such terms in discourse is an arduous task. This leads in some cases to committing errors during translation processes and lexicographic tasks. The process is even more challenging when it comes to machine translation, both in the cases of single-word terms and multi-word terms. One of the recent concerns of Natural Language Processing (NLP) applications and Machine Translation (MT) technologies is the automatic identification of metaphor-based words in discourse through Deep Learning (DL). In this study, we seek to fill this gap through the use of thirteen popular transformer based models, as well as ChatGPT, and we show that discriminative models perform better than GPT-3.5 model with our best performer reporting 92.2349% F1 score in metaphoric flower and plant names identification task., Comment: Accepted for SEPLN 2023

The widespread of offensive content online, such as hate speech and cyber-bullying, is a global phenomenon. This has sparked interest in the artificial intelligence (AI) and natural language processing (NLP) communities, motivating the development of various systems trained to detect potentially harmful content automatically. These systems require annotated datasets to train the machine learning (ML) models. However, with a few notable exceptions, most datasets on this topic have dealt with English and a few other high-resource languages. As a result, the research in offensive language identification has been limited to these languages. This paper addresses this gap by tackling offensive language identification in Sinhala, a low-resource Indo-Aryan language spoken by over 17 million people in Sri Lanka. We introduce the Sinhala Offensive Language Dataset (SOLD) and present multiple experiments on this dataset. SOLD is a manually annotated dataset containing 10,000 posts from Twitter annotated as offensive and not offensive at both sentence-level and token-level, improving the explainability of the ML models. SOLD is the first large publicly available offensive language dataset compiled for Sinhala. We also introduce SemiSOLD, a larger dataset containing more than 145,000 Sinhala tweets, annotated following a semi-supervised approach., Comment: Accepted to Language Resources and Evaluation, Springer

The widespread of offensive content online has become a reason for great concern in recent years, motivating researchers to develop robust systems capable of identifying such content automatically. With the goal of carrying out a fair evaluation of these systems, several international competitions have been organized, providing the community with important benchmark data and evaluation methods for various languages. Organized since 2019, the HASOC (Hate Speech and Offensive Content Identification) shared task is one of these initiatives. In its fourth iteration, HASOC 2022 included three subtracks for English, Hindi, and Marathi. In this paper, we report the results of the HASOC 2022 Marathi subtrack which provided participants with a dataset containing data from Twitter manually annotated using the popular OLID taxonomy. The Marathi track featured three additional subtracks, each corresponding to one level of the taxonomy: Task A - offensive content identification (offensive vs. non-offensive); Task B - categorization of offensive types (targeted vs. untargeted), and Task C - offensive target identification (individual vs. group vs. others). Overall, 59 runs were submitted by 10 teams. The best systems obtained an F1 of 0.9745 for Subtrack 3A, an F1 of 0.9207 for Subtrack 3B, and F1 of 0.9607 for Subtrack 3C. The best performing algorithms were a mixture of traditional and deep learning approaches.

Multiword expression (MWE) is a sequence of words which collectively present a meaning which is not derived from its individual words. The task of processing MWEs is crucial in many natural language processing (NLP) applications, including machine translation and terminology extraction. Therefore, detecting MWEs in different domains is an important research topic. In this paper, we explore state-of-the-art neural transformers in the task of detecting MWEs in flower and plant names. We evaluate different transformer models on a dataset created from Encyclopedia of Plants and Flower. We empirically show that transformer models outperform the previous neural models based on long short-term memory (LSTM)., Comment: Submitted to The 5th Workshop on Multi-word Units in Machine Translation and Translation Technology at Europhras2022

Multiword expressions (MWEs) present groups of words in which the meaning of the whole is not derived from the meaning of its parts. The task of processing MWEs is crucial in many natural language processing (NLP) applications, including machine translation and terminology extraction. Therefore, detecting MWEs is a popular research theme. In this paper, we explore state-of-the-art neural transformers in the task of detecting MWEs.We empirically evaluate several transformer models in the dataset for SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM). We show that transformer models outperform the previous neural models based on long short-term memory (LSTM). The code and pre-trained model will be made freely available to the community., Comment: Accepted to Europhras 2022

The task of machine reading comprehension (MRC) is a useful benchmark to evaluate the natural language understanding of machines. It has gained popularity in the natural language processing (NLP) field mainly due to the large number of datasets released for many languages. However, the research in MRC has been understudied in several domains, including religious texts. The goal of the Qur'an QA 2022 shared task is to fill this gap by producing state-of-the-art question answering and reading comprehension research on Qur'an. This paper describes the DTW entry to the Quran QA 2022 shared task. Our methodology uses transfer learning to take advantage of available Arabic MRC data. We further improve the results using various ensemble learning strategies. Our approach provided a partial Reciprocal Rank (pRR) score of 0.49 on the test set, proving its strong performance on the task., Comment: Accepted to OSACT5 Co-located with LREC 2022

Electron-electron interactions (EEIs) in 2D van der Waals structures is one of the topics with high current interest in physics. We report the observation of a negative parabolic magnetoresistance (MR) in multilayer 2D semiconductor InSe beyond the low-field weak localization/antilocalization regime, and provide evidence for the EEI origin of this MR behavior. Further, we analyze this negative parabolic MR and other observed quantum transport signatures of EEIs (temperature dependent conductance and Hall coefficient) within the framework of Fermi liquid theory and extract the gate voltage tunable Fermi liquid parameter $F_0^\sigma$ which quantifies the electron spin-exchange interaction strength.

Stimulated Raman scattering (SRS) microscopy allows for high-speed label-free chemical imaging of biomedical systems. The imaging sensitivity of SRS microscopy is limited to ~10 mM for endogenous biomolecules. Electronic pre-resonant SRS allows detection of sub-micromolar chromophores. However, label-free SRS detection of single biomolecules having extremely small Raman cross-sections (~10-30 cm2 sr-1) remains unreachable. Here, we demonstrate plasmon-enhanced stimulated Raman scattering (PESRS) microscopy with single-molecule detection sensitivity. Incorporating pico-Joule laser excitation, background subtraction, and a denoising algorithm, we obtained robust single-pixel SRS spectra exhibiting the statistics of single-molecule events. Single-molecule detection was verified by using two isotopologues of adenine. We further demonstrated the capability of applying PESRS for biological applications and utilized PESRS to map adenine released from bacteria due to starvation stress. PESRS microscopy holds the promise for ultrasensitive detection of molecular events in chemical and biomedical systems.

Spin coating technique was employed to fabricate CuO films at different spin speeds for different time duration, and they were annealed at different temperatures for different time durations in air. These thin films were characterized using UV Visible spectrometer and solar cell simulator. As the spinning rate increases, the corresponding band gap energy of the thin film increases. However, the annealing temperature does not affect the optical band gap. Moreover, the optical band gap does not depend on the annealing time and spin time. It was observed that the optical band gap decreases with the number of CuO layers. The optical band gap values of CuO thin films were between 1.843 and 1.869 eV. Inclusion of the two additives enhances the photocurrent, photovoltage and efficiency. Photocurrent density can be enhanced from 0.1464 to 1.172 mA per cm2 using the additive ethylene glycol. Properties of CuO films synthesized using sol gel dip coating technique were compared with those of CuO films grown using spin coating method., Comment: 11 pages, 4 tables, 4 figures

Manipulating the electron spin with the aid of spin-orbit coupling (SOC) is an indispensable element of spintronics. Electrostatically gating a material with strong SOC results in an effective magnetic field which can in turn be used to govern the electron spin. In this work, we report the existence and electrostatic tunability of Rashba SOC in multilayer InSe. We observed a gate-voltage-tuned crossover from weak localization (WL) to weak antilocalization (WAL) effect in quantum transport studies of InSe, which suggests an increasing SOC strength. Quantitative analyses of magneto-transport studies and energy band diagram calculations provide strong evidence for the predominance of Rashba SOC in electrostatically gated InSe. Furthermore, we attribute the tendency of the SOC strength to saturate at high gate voltages to the increased electronic density of states-induced saturation of the electric field experienced by the electrons in the InSe layer. This explanation of nonlinear gate voltage control of Rashba SOC can be generalized to other electrostatically gated semiconductor nanomaterials in which a similar tendency of spin-orbit length saturation was observed (e.g. nanowire field effect transistors), and is thus of broad implications in spintronics. Identifying and controlling the Rashba SOC in InSe may serve pivotally in devising III-VI semiconductor-based spintronic devices in the future., Comment: 7 pages, 5 figures

Sol-gel spin coating technique was employed to synthesize CuO thin films at 1500 rpm, 2200 rpm and 2400 rpm for 30 seconds, and subsequently annealed at 150-550{\deg}C for 1 hour. Multi-layered CuO thin films with 3 and 5 layers were prepared. These thin films were characterized using XRD, FTIR and polarizing light microscopy. The XRD and FTIR analysis confirmed the crystallization of CuO phase in thin films annealed above 350 {\deg}C, and corresponding crystallite sizes were observed to be increasing with the annealing temperature. The Scherrer formula was applied for (111) and peaks of XRD patterns of CuO thin films. According to the data of peak, average crystallite size increased from 0.801 to 55.2 0A as annealing temperature was increased from 350 to 500 0C., Comment: 10 pages, 4 figures