Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Robotic Navigation: An Evaluation of Single and Multi-Objective Reinforcement Learning Strategies

This study presents a comparative analysis between single-objective and multi-objective reinforcement learning methods for training a robot to navigate effectively to an end goal while efficiently avoiding obstacles. Traditional reinforcement learning techniques, namely Deep Q-Network (DQN), Deep Deterministic Policy Gradient (DDPG), and Twin Delayed DDPG (TD3), have been evaluated using the Gazebo simulation framework in a variety of environments with parameters such as random goal and robot starting locations. These methods provide a numerical reward to the robot, offering an indication of action quality in relation to the goal. However, their limitations become apparent in complex settings where multiple, potentially conflicting, objectives are present. To address these limitations, we propose an approach employing Multi-Objective Reinforcement Learning (MORL). By modifying the reward function to return a vector of rewards, each pertaining to a distinct objective, the robot learns a policy that effectively balances the different goals, aiming to achieve a Pareto optimal solution. This comparative study highlights the potential for MORL in complex, dynamic robotic navigation tasks, setting the stage for future investigations into more adaptable and robust robotic behaviors.
ECHO: An Automated Contextual Inquiry Framework for Anonymous Qualitative Studies using Conversational Assistants

Qualitative research studies often employ a contextual inquiry, or a field study that involves in-depth observation and interviews of a small sample of study participants, in-situ, to gain a robust understanding of the reasons and circumstances that led to the participant's thoughts, actions, and experiences regarding the domain of interest. Contextual inquiry, especially in sensitive data studies, can be a challenging task due to reasons such as participant privacy, as well as physical constraints such as in-person presence and manual analysis of the qualitative data gathered. In this work, we discuss Enqu\^ete Contextuelle Habile Ordinateur (ECHO); a virtual-assistant framework to automate the erstwhile manual process of conducting contextual inquiries and analysing the respondents' subjective qualitative data. ECHO automates the contextual inquiry pipeline, while not compromising on privacy preservation or response integrity. Its adaptive conversational interface enables respondents to provide unstructured or semi-structured responses in free-form natural language, allowing researchers to explore larger narratives in participant response data. It supports response-driven exploratory questions and automates coding methodologies for qualitative data, thus enabling the inquirer to dive deeper into correlated questions and to do better cause-effect analysis. It focuses on addressing the limitations of manual annotation, bringing standardisation to free-form text, and eliminating perspective bias amongst different reviewers of subjective responses. A participatory mental health study was conducted on 167 young adults bifurcated into two focus groups; one of which was administered a conventional contextual inquiry, and the other via ECHO, virtually. ECHO outperformed on participant transparency, response detail and median time required for end-to-end inquiry completion, per participant.
Evaluating ChatGPT as a Question Answering System: A Comprehensive Analysis and Comparison with Existing Models

In the current era, a multitude of language models has emerged to cater to user inquiries. Notably, the GPT-3.5 Turbo language model has gained substantial attention as the underlying technology for ChatGPT. Leveraging extensive parameters, this model adeptly responds to a wide range of questions. However, due to its reliance on internal knowledge, the accuracy of responses may not be absolute. This article scrutinizes ChatGPT as a Question Answering System (QAS), comparing its performance to other existing QASs. The primary focus is on evaluating ChatGPT's proficiency in extracting responses from provided paragraphs, a core QAS capability. Additionally, performance comparisons are made in scenarios without a surrounding passage. Multiple experiments, exploring response hallucination and considering question complexity, were conducted on ChatGPT. Evaluation employed well-known Question Answering (QA) datasets, including SQuAD, NewsQA, and PersianQuAD, across English and Persian languages. Metrics such as F-score, exact match, and accuracy were employed in the assessment. The study reveals that, while ChatGPT demonstrates competence as a generative model, it is less effective in question answering compared to task-specific models. Providing context improves its performance, and prompt engineering enhances precision, particularly for questions lacking explicit answers in provided paragraphs. ChatGPT excels at simpler factual questions compared to"how"and"why"question types. The evaluation highlights occurrences of hallucinations, where ChatGPT provides responses to questions without available answers in the provided context.
Harnessing Retrieval-Augmented Generation (RAG) for Uncovering Knowledge Gaps

The paper presents a methodology for uncovering knowledge gaps on the internet using the Retrieval Augmented Generation (RAG) model. By simulating user search behaviour, the RAG system identifies and addresses gaps in information retrieval systems. The study demonstrates the effectiveness of the RAG system in generating relevant suggestions with a consistent accuracy of 93%. The methodology can be applied in various fields such as scientific discovery, educational enhancement, research development, market analysis, search engine optimisation, and content development. The results highlight the value of identifying and understanding knowledge gaps to guide future endeavours.
A Machine Learning Approach to Robustly Determine Orientation Fields and Analyze Defects in Active Nematics

Active nematics are dense systems of rodlike particles that consume energy to drive motion at the level of the individual particles. They are found in a variety of natural systems, such as biological tissues, and artificial materials such as suspensions of self-propelled colloidal particles or synthetic microswimmers. Active nematics have attracted significant attention in recent years because of their spectacular nonequilibrium collective spatiotemporal dynamics, which may enable applications in fields such as robotics, drug delivery, and materials science. The orientation field, which measures the direction and degree of alignment of the local nematic director, is a crucial characteristic of an active nematic and is essential for detecting and studying topological defects. However, determining the orientation field is a significant challenge in many experimental systems. Although orientation fields can be derived from images of active nematics using traditional imaging processing methods, the results and accuracy of such methods are highly sensitive to the settings of the algorithm. These settings must be tuned from image-to-image due to experimental noise, intrinsic noise of the imaging technology, and perturbations caused by changes in experimental conditions. This sensitivity currently limits the automatic analysis of active ne-matics. To overcome this limitation, we have developed a machine learning model for extracting reliable orientation fields from raw experimental images, which enables accurate automatic analysis of topological defects. Application of the algorithm to experimental data demonstrates that the approach is robust and highly generalizable to experimental settings that were unseen in the training data. These results suggest that it will be a useful tool for investigating active nematics, and the approach may be generalized to other active matter systems.
Speaker Diarization For Multiple-Distant-Microphone Meetings Using Several Sources of Information

Human-machine interaction in meetings requires the localization and identification of the speakers interacting with the system as well as the recognition of the words spoken. A seminal step toward this goal is the field of rich transcription research, which includes speaker diarization together with the annotation of sentence boundaries and the elimination of speaker disfluencies. The sub-area of speaker diarization attempts to identify the number of participants in a meeting and create a list of speech time intervals for each such participant. In this paper, we analyze the correlation between signals coming from multiple microphones and propose an improved method for carrying out speaker diarization for meetings with multiple distant microphones. The proposed algorithm makes use of acoustic information and information from the delays between signals coming from the different sources. Using this procedure, we were able to achieve state-of-the-art performance in the NIST spring 2006 rich transcription evaluation, improving the Diarization Error Rate (DER) by 15% to 20% relative to previous systems.
Analyse des effets directionnels dans l'infrarouge thermique dans le cas des couverts végétaux continus : modélisation et application à la correction des données spatiales

Les donnees de teledetection dans l'infrarouge thermique (IRT) sont une source indispensable d'information pour estimer les flux de surface et suivre le fonctionnement des agro-ecosystemes. Cependant, les mesures de temperature de surface sont sujettes a des effets directionnels tres importants (presence de 'hot spot') pouvant entrainer une erreur allant jusqu'a une dizaine de degres Celsius. Ils doivent etre pris en compte en vue des applications operationnelles. Le travail propose ici vise a modeliser l'anisotropie directionnelle des couverts vegetaux pour mettre au point des methodes operationnelles de correction des mesures satellitaires de temperature de surface. Il est largement motive par les projets du CNES visant a elaborer une mission spatiale nouvelle combinant une haute resolution spatiale et des capacites fortes de revisite dans l'IRT. Deux etapes de travail ont ete menees. La premiere repose sur l'utilisation du modele deterministe de transfert Sol-Vegetation-Atmosphere SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes), capable de simuler les radiances directionnelles dans l'optique et l'IRT. Dans ce manuscrit, il est valide par rapport a des mesures de terrain et sa capacite a simuler correctement les effets d'anisotropie demontree. Il est ensuite utilise pour etudier de facon systematique la sensibilite de l'anisotropie directionnelle a la structure de la canopee, a son etat hydrique, au forcage meteorologique et aux configurations angulaires solaire et de visee. Les consequences en terme d'impact combine des caracteristiques orbitales des satellites, de la position geographique des sites observes et de la date d'acquisition sur l'anisotropie sont discutees. La seconde etape vise a proposer un modele parametrique simplifie (dit RL). SCOPE est ici utilise en tant que generateur de donnees. Le modele RL se revele robuste et capable de restituer avec succes les signatures directionnelles sur le plan geometrique (position du hot spot) comme pour l'amplitude des effets directionnels. Une comparaison avec le seul autre modele parametrique utilise jusqu'alors en teledetection IRT (le modele de Vinnikov) confirme les qualites du modele RL, ce qui en fait un candidat potentiel pour les chaines de traitement des futures donnees satellitaires.
Lymphoid tissues contribute to plasma viral clonotypes early after antiretroviral therapy interruption in SIV-infected rhesus macaques

The rebound-competent viral reservoir, composed of a virus that is able to persist during antiretroviral therapy (ART) and mediate reactivation of systemic viral replication and rebound viremia after ART interruption (ATI), remains the biggest obstacle to treating HIV infection. A better understanding of the cellular and tissue origins and the dynamics of viral populations that initiate rebound upon ATI could help develop therapeutic strategies for reducing the rebound-competent viral reservoir. In this study, barcoded simian immunodeficiency virus (SIV), SIVmac239M, was used to infect rhesus macaques to enable monitoring of viral barcode clonotypes contributing to virus detectable in plasma after ATI. Blood and tissues from secondary lymphoid organs (spleen, mesenteric lymph nodes, and inguinal lymph nodes) and from the colon, ileum, lung, liver, and brain were analyzed using viral barcode sequencing, intact proviral DNA assay, single-cell RNA sequencing, and combined CODEX and RNAscope in situ hybridization. Four of seven animals had viral barcodes detectable by deep sequencing of plasma at necropsy, although plasma viral RNA remained below 22 copies per milliliter. Among the tissues studied, mesenteric lymph nodes, inguinal lymph nodes, and spleen contained viral barcodes detected in plasma. CD4+ T cells were the main cell type harboring viral RNA after ATI. Furthermore, T cell zones in lymphoid tissues showed higher viral RNA abundance than B cell zones for most animals. These findings are consistent with lymphoid tissues contributing to the virus present in plasma early after ATI. SIV clonotypes that reemerge early after antiretroviral therapy interruption are also observed in secondary lymphoid tissues. Editor’s summary A major hurdle to treating HIV is the continued presence of the rebound-competent viral reservoir, which persists during antiretroviral therapy (ART) and serves as a source of viremia during ART interruption (ATI). Here, Solis-Leal et al. infected rhesus macaques with a barcoded strain of simian immunodeficiency virus, SIVmac239M, that enabled tracking of viral clonotypes over time and between tissues. The macaques underwent two separate ATIs, and the authors compared barcodes present in the blood and tissues 1 week after ATI. The authors found that the re-emerged viral clonotypes in plasma at ATI-2 mainly overlapped with those found in the secondary lymphoid tissues such as the spleen and lymph nodes; they also found that CD4+ T cells harbored the most viral RNA after ATI. These data suggest that secondary lymphoid organs are likely the primary viral reservoir driving rebound early after ATI. —Courtney Malo
Campylobacter prophage diversity reveals pervasive recombination between prophages from different Campylobacter species

ABSTRACT Prophages, bacteriophages integrated into bacterial genomes, play a critical role in the genetic diversity and evolution of bacterial species. In this study, we investigated the prophage population harbored in Campylobacter species. A total of 446 high-quality and complete genomes of Campylobacter isolated from different sources revealed 431 prophages. We analyzed the phylogenomic relationships between 490 phages and prophages, unveiling a wide diversity of undescribed prophages. Bacteriophage pangenomes revealed shared genetic repertoires among different phage genera. In addition, we found recombination events between prophages from different phage genera, mainly in prophages infecting Campylobacter jejuni and Campylobacter coli, showing distinct evolutionary histories in specific regions of the prophage genomes. Specifically, these regions showed variations in gene content, enrichment in structural genes and others in hypothetical proteins, genes involved in transcription, DNA-associated factors, and genes associated with lysis. Finally, Campylobacter prophages alter the host genetic repertoire by carrying virulence factors and antibiotic-resistance genes. IMPORTANCE Prophages play an important role in shaping the genetic diversity and evolution of their hosts. Acquisition or loss of prophages can lead to genomic variations, including changes in the bacterial phenotype promoted by recombination events, genetic repertoire exchanges and dissemination of virulence factors, and antibiotic resistance. By studying prophages in Campylobacter species, scientists can gain insights into the evolutionary patterns, pathogenicity mechanisms, epidemiology, and population dynamics of these species. This has implications for public health, antibiotic resistance surveillance, and the development of targeted therapeutic approaches. Prophages play an important role in shaping the genetic diversity and evolution of their hosts. Acquisition or loss of prophages can lead to genomic variations, including changes in the bacterial phenotype promoted by recombination events, genetic repertoire exchanges and dissemination of virulence factors, and antibiotic resistance. By studying prophages in Campylobacter species, scientists can gain insights into the evolutionary patterns, pathogenicity mechanisms, epidemiology, and population dynamics of these species. This has implications for public health, antibiotic resistance surveillance, and the development of targeted therapeutic approaches.
Coreness Variation Rule and Fast Updating Algorithm for Dynamic Networks

Coreness is one of the important indicators to measure the importance of a node. Traditionally, the coreness of a node is measured by k-core decomposition. However, to measure the coreness in a dynamic network, the k-core decomposition method becomes very time-consuming and inefficient, and cannot meet the need in very large real networks. Recently, the H operator method was proposed to calculate the coreness of a node, which provides a novel method to deal with the coreness of a node in a network. In this paper, we decode the coreness variation rule by a symmetric pair of experiments, i.e., deleting and adding edge, on real networks. Then, an algorithm to fast update the coreness of related nodes is proposed. Results on five real networks showed that the performance of the proposed algorithm was greatly enhanced and comprehensively superior to the k-core decomposition algorithm. Our study provides a promising way to optimize the algorithm of coreness calculation in the dynamic networks.
Quantification des échanges nappe-rivière au sein de l’hydrosystème Seine par modélisation multi-échelle

Compte tenu de l’evolution demographique et climatique planetaire, la gestion de la ressource en eau constitue un defi majeur auquel la communaute internationale devra faire face au cours du XXIeme siecle. A cet effet, l'identification de la continuite hydrique entre les eaux de surface et les eaux souterraines permet l'introduction de la notion de gestion integree de la ressource. L'application de ce principe de gestion au bassin de la Seine, a travers l'estimation des echanges nappe-riviere, est rendue possible par la mise en pratique du concept d'interface nappe-riviere emboitees au sein de travaux de modelisation. Pour cela une procedure de modelisation multi-echelles peut etre mise en place. Elle vise a integrer des informations locales au sein de modelisation a une echelle superieure. Dans ce memoire, une procedure de modelisation multi-echelles est mise en œuvre. Ce protocole de modelisation est initie par une estimation des flux d'eau regionaux au sein de l'hydrosysteme Seine. La coherence globale de ces flux est garantie par le developpement d'une methodologie de calibration de modeles couples en deux etapes. Ensuite les informations locales, que sont les heterogeneites de la plaine alluviale de la Bassee et de la representation des interfaces nappe-riviere du reseau secondaire, sont integrees au modele regional par une procedure de modelisation emboitee et de changement d'echelle des parametres hydrauliques. La mise en place de cette procedure a finalement permis l'estimation fine des echanges nappe-riviere sur la quasi-totalite (83%) du reseau hydrographique naturel du bassin de la Seine et ainsi de repondre aux recommandations de gestion integree de la ressource faites par la directive cadre sur l'eau.
Psychosocial safety climate (PSC) and working conditions, predictors of mental health and antidepressant and opioid use in Australia: a study protocol for longitudinal data linkage

Introduction Work-related stress is a social determinant of global health that represents a huge cost to workers’ health and reduces work performance. In Australia, mental well-being is a pressing national issue—with one in five Australians experiencing mental disorders. Antidepressants are a first-line medication commonly used to treat mental disorders. Recently, Australia has seen a dramatic increase in the use of prescribed antidepressant medications to treat mental health related illnesses. Australia has also seen a dramatic increase in the use of prescribed opioid analgesics for non-cancer pain including opioid use for psychological distress and social stressors. It is plausible a rise in mental health problems and antidepressant and opioid medication use is partly attributable to the corporate climate for worker mental health (ie, the psychosocial safety climate, PSC). This research aims to identify how PSC and workplace conditions contribute to employee well-being and distress that culminate in antidepressant and opioid medication use. Methods/analysis Data will be collected through creative data linkage from the Australian Workplace Barometer (AWB), to medication data (via the Pharmaceutical Benefits Scheme, PBS). The participant sample will include 1372 working Australians from the AWB project from 2009 to 2021. Four waves of longitudinal data from 2009 to 2021 will be used to investigate the plausible link between Australia’s high levels of antidepressant and opioid use and distress at work. The project advances theory by probing the role corporate climate plays in work design, distress, mental health problems and antidepressant and opioid use. It will determine if antidepressant and opioid use has led to an underestimation of work stress effects. Proposed theoretical models will be analysed through linked data, using continuous time structural equation modelling, hierarchical linear modelling, logistic regression and cost estimation. Ethics and dissemination The study has been approved by the Human Research Ethics Committee of the University of South Australia (Ethics Protocol: 203003). Further, approval from the Australian Institute of Health and Welfare Ethics Committee was also granted for linkage of AWB data and PBS data (EthOS Application EO2022/1/1190). Results of the study will be disseminated through worldwide keynotes, key international settings, high-impact peer-reviewed journals, industry conference presentations and media outlets to reach managers, workers, and industry partners. Further, UniSA requires publications from public projects to be held in an institutional repository which fulfils the Australian Research Council’s Open Access Policy.
4. DATA REPORT: UTILIZING COLOR REFLECTANCE ANALYSIS AS A CARBONATE CONCENTRATION PROXY

Color reflectance data (400–700 nm) are routinely collected aboard the JOIDES Resolution and have been used to estimate the carbonate concentration in sediments (Balsam et al., 1997; Mix et al., 1995). A good summary of the recent usage of color reflectance data to estimate fractions of various minerals is published in Balsam et al. (1999). The purpose of this paper is to present models estimating the carbonate fraction for each site cored during Leg 181. These models (“Appendix A,” p. 6) were constructed using data collected aboard ship during Leg 181 (hereafter referred to as “shipboard data”) and supplementary data collected at the Ocean Drilling Program Gulf Coast Repository (ODP GCR) after the cruise (hereafter referred to as “laboratory data”). Each model is contained in a separate table and includes predicted carbonate fractions with corresponding depth values in meters composite depth (mcd) (Carter, McCave, Richter, Carter, et al., 1999). Graphic depictions for each model are included in “Appendix B,” p. 7.