ethicalabs/Research-Intent-Collab huggingface dataset:
Knowledge, Attitude and Practice of Health Care Professionals towards Antimicrobial Resistance and its stewardship at tertiary care teaching Hospital

Background: Irrational antibiotic use leads to resistance, a serious problem which is increasing tremendously and requires urgent response. The goal of this study is to evaluate knowledge, attitude, and practice of health care professionals (HCPs) regarding antimicrobial resistance (AMR) and its stewardship. Methods: This questionnaire based prospective cross-sectional study was conducted in 216 HCPs of Universal College of Medical Sciences (UCMS). Chi-square analysis was used to see the association of knowledge, attitude, and practice (KAP) with other demographic variables. Pearson correlation was done to analyze the correlation between knowledge and attitude, knowledge and practice, and attitude and practice. Results: Among all participants, 33% had good knowledge, majority of them (78.2%) had positive attitude and 43.5% had good practice related to antimicrobial resistance and its stewardship. There was no significant correlation between knowledge and practice, as well as between practice and attitude. However, a weak positive correlation was found between knowledge and attitude (r=0.186; p=0.006). Conclusion: The majority of health care professionals showed a positive attitude towards antibiotic resistance and its stewardship. However, in spite of positive attitude of HCPs, the level of knowledge and practice were found to be poor.
Developing resilience and cyber physical protection capabilities for critical aviation infrastructures

Ο τομέας των Μεταφορών αποτελεί Κρίσιμη Υποδομή (ΚΥ) και βασικό πυλώνα της Οικονομίας, συνδέοντας ανθρώπους, κράτη, πολιτισμούς σε τοπικό και διεθνές επίπεδο. H δυσλειτουργία των υποδομών και των μέσων μεταφοράς επιφέρει σημαντικές αρνητικές επιπτώσεις στο κοινωνικοοικονομικό γίγνεσθαι, μέχρι ανθρώπινες απώλειες, επομένως οι ΚΥ των Μεταφορών πρέπει να προστατεύονται επαρκώς, τόσο από φυσικές απειλές, όσο και από απειλές του κυβερνοχώρου.Η διατριβή επικεντρώθηκε στον τομέα των Αερομεταφορών, ο οποίος αποτελεί το πιο διασυνδεδεμένο σύστημα ανταλλαγής πληροφοριών. Οι κυβερνοεπιθέσεις απειλούν τις ψηφιακές υποδομές της Αεροπλοΐας και μπορεί να επιφέρουν σοβαρό αντίκτυπο στην ασφάλεια των πτήσεων. Στον τομέα της διαχείρισης της εναέριας κυκλοφορίας, προτάθηκε ένα εκτεταμένο μοντέλο αντιμετώπισης των νέων απειλών, προτάθηκαν μέτρα ενίσχυσης της ανθεκτικότητας της Αεροπλοΐας από απειλές του κυβερνοχώρου και συγκροτήθηκε μια ολιστική στρατηγική άμυνας, πρόληψης και μετριασμού των απειλών αυτών. Περαιτέρω, διερευνήθηκε η ανθεκτικότητα των σύγχρονων αερολιμένων στον τομέα της κυβερνοασφάλειας, καθώς τα σύγχρονα αεροδρόμια αναβαθμίζουν τις υποδομές τους με την προσθήκη αυτοματοποιημένων διαδικασιών για τη καλύτερη εξυπηρέτηση των επιβατών. Παρουσιάστηκε μια ολοκληρωμένη ανάλυση σεναρίων κακόβουλων επιθέσεων σε σύγχρονα αεροδρόμια, για τον εντοπισμό απειλών, κινδύνων και την εφαρμογή βέλτιστων πρακτικών κυβερνοασφάλειας και ενίσχυσης της ανθεκτικότητας των υποδομών τους.Η έρευνα εστιάστηκε, επίσης, στον ταχύτερα αναπτυσσόμενο τομέα της Αεροπλοΐας, τα Συστήματα Μη Επανδρωμένων Αεροσκαφών (ΣΜΕΑ/Unmanned Aircraft Systems), τα οποία αυξάνονται σε τεχνική πολυπλοκότητα και πτητική αξιοπιστία, παρέχουν νέες επιχειρησιακές δυνατότητες και ταυτόχρονα θέτουν σημαντικές προκλήσεις κυβερνοασφάλειας. Αναλύθηκαν σενάρια επίθεσης σε κρίσιμες αεροπορικές υποδομές και προτάθηκαν τεχνικές λύσεις για την ενίσχυση της ανθεκτικότητας και την υπεράσπιση των αερολιμενικών υποδομών από επιθέσεις με UAS. Τέλος, αναπτύχθηκε μεθοδολογία και εργαλείο λογισμικού για την αξιολόγηση των αλληλεξαρτήσεων συμβάντων, που επιφέρουν καθυστερήσεις σε διασυνδεδεμένα αεροδρόμια. Το λογισμικό εντοπίζει τα κρισιμότερα αεροδρόμια. Διακρίνει, επίσης, τις δυσμενέστερες αλυσίδες αλληλεξαρτώμενων δρομολογίων, που θα πρέπει να αποφεύγονται από τους επιμελητές πτήσεων, για τον περιορισμό των επιπτώσεων των καθυστερήσεων στο αεροπορικό δίκτυο.
Natural Language Processing and User Modeling: Synergies and Limitations

Summary: The fields of user modeling and natural language processing have been closely linked since the early days of user modeling. Natural language systems consult user models in order to improve their understanding of users' requirements and to generate appropriate and relevant responses. At the same time, the information natural language systems obtain from their users is expected to increase the accuracy of their user models. In this paper, we review natural language systems for generation, understanding and dialogue, focusing on the requirements and limitations these systems and user models place on each other. We then propose avenues for future research.
A Unified Platform for Radiology Report Generation and Clinician-Centered AI Evaluation

ABSTRACT                                    Generative AI models have demonstrated strong potential in radiology report generation, but their clinical adoption depends on physician trust. In this study, we conducted a radiology-focused Turing test to evaluate how well attendings and residents distinguish AI-generated reports from those written by radiologists, and how their confidence and decision time reflect trust. we developed an integrated web-based platform comprising two core modules: Report Generation and Report Evaluation. Using the web-based platform, eight participants evaluated 48 anonymized X-ray cases, each paired with two reports from three comparison groups: radiologist vs. AI model 1, radiologist vs. AI model 2, and AI model 1 vs. AI model 2. Participants selected the AI-generated report, rated their confidence, and indicated report preference. Attendings outperformed residents in identifying AI-generated reports (49.9% vs. 41.1%) and exhibited longer decision times, suggesting more deliberate judgment. Both groups took more time when both reports were AI-generated. Our findings highlight the role of clinical experience in AI acceptance and the need for design strategies that foster trust in clinical applications. The project page of the evaluation platform is available at:                   https://zachatalay89.github.io/Labsite                   .
Integrated Seismic Program (ISP): A New Python GUI‐Based Software for Earthquake Seismology and Seismic Signal Processing

Integrated Seismic Program (ISP) is a graphical user interface designed to facilitate and provide a user&amp;#8208;friendly framework for performing diverse common and advanced tasks in seismological research. ISP is composed of six main modules for earthquake location, time&amp;#8211;frequency analysis and advanced signal processing, implementation of array techniques, seismic moment tensor inversion, receiver function computation and and a new module for ambient noise tomography.Recently the Ambient Noise Tomography module has been upgraded with a tool specifically designed to synchronize Ocean Bottom Seismometers (OBSs) and to denoise the OBSs seismogram of the vertical component from tilt and compliance noise. The new tool has extensively been tested with data from UPFLOW project (https://upflow-eu.github.io).In addition, several support tools are available, allowing the user to create an event database, download data from International Federation of Digital Seismograph Networks services, inspect the background noise, and compute synthetic seismograms.ISP is written in Python3, supported by several open&amp;#8208;source and/or publicly available tools. Its modular design allows for new features to be added in a collaborative development environment.
Knowledge and Attitude towards Sexual and Reproductive Health and Rights: A Study of First- and Final-Year Healthcare Students of the University of Buenos Aires, Argentina

Providing sexual and reproductive healthcare services of high quality is key to improving people’s health, especially women and adolescents and, thus, to reducing maternal mortality and morbidity. Successful outcomes of sexual and reproductive healthcare services depend, among other factors, on competent health providers, particularly medical doctors, midwives and nurses. The thesis “Knowledge and Attitude towards Sexual and Reproductive Health (SRH) and Rights- A Study of First and Final Year Healthcare Students of the University of Buenos Aires, Argentina” assessed what students learn about SRH and rights at the School of Medicine. Self-administered, anonymous questionnaires were administered to 1,489 first and final-year students from different fields between 2011 and 2013. The results revealed important gaps and deficiencies in knowledge in core topics of SRH care among soon-to-be healthcare providers, which could raise serious barriers to health and rights for the population. There is an urgent need to improve SRH care education.
Improving Patient Safety through Enhanced Reporting Systems: A Quality Assurance Approach

atient safety is a pivotal aspect of healthcare quality assurance. This quality improvement study investigates the implementation and impact of enhanced reporting systems on patient safety in hospital settings. By addressing current challenges and reviewing best practices, the paper presents a structured approach to improve reporting systems, ultimately aiming to reduce adverse events and enhance patient outcomes.
Comparative Analysis of Large Language Model Inference Serving Systems: A Performance Study of vLLM and HuggingFace TGI

The deployment of Large Language Models (LLMs) in production environments requires efficient inference serving systems that balance throughput, latency, and resource utilization. This paper presents a comprehensive empirical evaluation of two prominent open-source LLM serving frameworks: vLLM and HuggingFace Text Generation Inference (TGI). We benchmark these systems across multiple dimensions including throughput performance, end-to-end latency, GPU memory utilization, and scalability characteristics using LLaMA-2 models ranging from 7B to 70B parameters. Our experiments reveal that vLLM achieves up to 24x higher throughput than TGI under high-concurrency workloads through its novel PagedAttention mechanism, while TGI demonstrates lower tail latencies for interactive single-user scenarios. We provide detailed performance profiles for different deployment scenarios and offer practical recommendations for system selection based on workload characteristics. Our findings indicate that the choice between these frameworks should be guided by specific use-case requirements: vLLM excels in high-throughput batch processing scenarios, while TGI is better suited for latency-sensitive interactive applications with moderate concurrency.
Return Augmented Decision Transformer for Off-Dynamics Reinforcement Learning

We study offline off-dynamics reinforcement learning (RL) to utilize data from an easily accessible source domain to enhance policy learning in a target domain with limited data. Our approach centers on return-conditioned supervised learning (RCSL), particularly focusing on Decision Transformer (DT) type frameworks, which can predict actions conditioned on desired return guidance and complete trajectory history. Previous works address the dynamics shift problem by augmenting the reward in the trajectory from the source domain to match the optimal trajectory in the target domain. However, this strategy can not be directly applicable in RCSL owing to (1) the unique form of the RCSL policy class, which explicitly depends on the return, and (2) the absence of a straightforward representation of the optimal trajectory distribution. We propose the Return Augmented (REAG) method for DT type frameworks, where we augment the return in the source domain by aligning its distribution with that in the target domain. We provide the theoretical analysis demonstrating that the RCSL policy learned from REAG achieves the same level of suboptimality as would be obtained without a dynamics shift. We introduce two practical implementations REAG$_\text{Dara}^{*}$ and REAG$_\text{MV}^{*}$ respectively. Thorough experiments on D4RL datasets and various DT-type baselines demonstrate that our methods consistently enhance the performance of DT type frameworks in off-dynamics RL.
Towards Scholarly Communication 2.0: Peer-to-Peer Review &amp; Ranking in Open Access Preprint Repositories

In this paper we present our unified peer-to-peer review model for Open Access preprint repositories. Its objective is to improve the efficiency and effectivity of digital scholarly communication. The key elements of this model are standardized quality assessment instruments, public and private communication channels, special rankings and novel incentives. The model allows scholars to proficiently evaluate both the manuscripts and their peer reviews. These scrutinized manuscripts and peer reviews will then be made available to the relevant parties. These standardized quality assessments allow for new quality metrics for papers and peer reviews. The Reviewer Impact, which represents the peer review proficiency and peer review output of scholars, is one such metric. The model includes diverse rankings for scholars to appear in to receive better odds of having their own manuscripts noticed, read, peer reviewed and cited. Their specific ranking is proportional to their Reviewer Impact and the overall quality of their manuscripts. The Open Access preprint repository model is a suitable foundation for our model because of its high degree of accessibility, but little to no certification of its deposited manuscripts. With this combination we envision a novel, Open Access, peer-to-peer scholarly communication model that functions independently of, but not incompatibly with, the traditional journal publishing model: Scholarly Communication 2.0.
Corporate Social Responsibility and Business Ethics in the Central and Eastern Europe

This Issue begins with a paper by Kirchmayer, Remišová and Lašáková on ethical leadership in public and private organisations in Slovakia. Authentic leadership and interpersonal conflicts in Poland are further taken up by Sypniewska and Gigol. Perceptions of the ethical climate in Serbian tourism industry are explored by Dragin, Jovanović, Mijatov, Majstorović and Dragin. Prus takes us to the promotion of sustainable agriculture through the focused higher education on agriculture in Poland. Saveanu, Abrudan, Saveanu and Matei call for finding out predictors of CSR in small and medium enterprises operating in Romania. Potocan, Mulej and Nedelko at-tempt empirical investigation of employees’ attitudes towards natural, social and economic aspects of CSR in Slovenian organisations during two periods – economic crisis and recovery for a post-transition context. Rybnikova and Toleikienė turn to formal and informal elements of ethics management infrastructure in Lithuanian local government. This Issue concludes with a research note on the development towards corporate sustainability (morality and responsibility) in Estonian business by Kooskora and Cundiff.
Rule of law, judicial independence and government of the judiciary in Spain

One of the pillars on which the rule of law is based, regardless of whether one starts from a formal or substantive conception of the rule of law, has been and continues to be judicial independence. The courts play an essential role in ensuring that the executive and legislative powers do not exceed the limits of the norms that serve as the legal basis for their actions, which, if exceeded, imply the nullity or invalidity of such actions. Consequently, the courts that are called upon to control legislative and executive activity must be independent and free from any interference that might subject them to the will of the political power. Otherwise, there is a risk that they will not observe these limits or that they will interpret them extensively, "legitimizing legislative or executive actions that should be annulled".
Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction

To improve the prediction of crop yields at an aggregate scale, we developed a data assimilation-crop modeling framework that incorporates remotely sensed soil moisture and leaf area index (LAI) into a crop model using sequential data assimilation. The core of the framework is an Ensemble Kalman Filter (EnKF) used to control crop model runs, assimilate remote sensing (RS) data and update model state variables. We modified the Decision Support System for Agro-technology Transfer – Cropping System Model (DSSAT-CSM)-Maize model (Jones et al., 2003) to be able to stop and start simulations at any given time in the growing season, such that the EnKF can update model state variables as RS data become available. The data assimilation-crop modeling framework was evaluated against 2003–2009 maize yields in Story County, Iowa, USA, assimilating AMSR-E soil moisture and MODIS-LAI data independently and simultaneously. Assimilating LAI or soil moisture independently slightly improved the correlation of observed and simulated yields (R = 0.51 and 0.50) compared to no data assimilation (open-loop; R = 0.47) but prediction errors improved with reductions in MBE and RMSE by 0.5 and 0.5 Mg ha− 1 respectively for LAI assimilation while these were reduced by 1.8 and 1.1 Mg ha− 1 for soil moisture assimilation. Yield correlation improved more when both soil moisture and LAI were assimilated (R = 0.65) suggesting a cause–effect interaction between soil moisture and LAI, prediction errors (MBE and RMSE) were also reduced by 1.7 and 1.8 Mg ha− 1 with respect to open-loop simulations. Results suggest that assimilation of LAI independently might be preferable when conditions are extremely wet while assimilation of soil moisture + LAI might be more suitable when conditions are more nominal. AMSR-E soil moisture tends to be more biased under the presence of high vegetation (i.e., when crops are fully developed) and that updating rootzone soil moisture by near-surface soil moisture assimilation under very wet conditions could increase the modeled percolation causing excessive nitrogen (N) leaching hence reducing crop yields even with water stress reduced at a minimum due to soil moisture assimilation. However, applying the data assimilation-crop modeling framework strategically by considering a-priori information on climate condition expected during the growing season may improve yield prediction performance substantially, in our case with higher correlation (R = 0.80) and more reductions in MBE and RMSE (2.5 and 3.3 Mg ha− 1) compared to when there is no data assimilation. Scaling AMSR-E soil moisture to the climatology of the model did not improve our data assimilation results because the model is also biased. Better soil moisture products e.g., from Soil Moisture Active Passive (SMAP) mission, may solve the soil moisture data issue in the near future.
Transitions and Thermodynamics on Species Graphs of Chemical Reaction Networks

Chemical reaction networks (CRNs) exhibit complex dynamics governed by their underlying network structure. In this paper, we propose a novel approach to study the dynamics of CRNs by representing them on species graphs (S-graphs). By scaling concentrations by conservation laws, we obtain a graph representation of transitions compatible with the S-graph, which allows us to treat the dynamics in CRNs as transitions between chemicals. We also define thermodynamic-like quantities on the S-graph from the introduced transitions and investigate their properties, including the relationship between specieswise forces, activities, and conventional thermodynamic quantities. Remarkably, we demonstrate that this formulation can be developed for a class of irreversible CRNs, while for reversible CRNs, it is related to conventional thermodynamic quantities associated with reactions. The behavior of these specieswise quantities is numerically validated using an oscillating system (Brusselator). Our work provides a novel methodology for studying dynamics on S-graphs, paving the way for a deeper understanding of the intricate interplay between the structure and dynamics of chemical reaction networks.
Whitening for Self-Supervised Representation Learning

ICML 2021
Toward Lightweight and Fast Decoders for Diffusion Models in Image and Video Generation

We investigate methods to reduce inference time and memory footprint in stable diffusion models by introducing lightweight decoders for both image and video synthesis. Traditional latent diffusion pipelines rely on large Variational Autoencoder decoders that can slow down generation and consume considerable GPU memory. We propose custom-trained decoders using lightweight Vision Transformer and Taming Transformer architectures. Experiments show up to 15% overall speed-ups for image generation on COCO2017 and up to 20 times faster decoding in the sub-module, with additional gains on UCF-101 for video tasks. Memory requirements are moderately reduced, and while there is a small drop in perceptual quality compared to the default decoder, the improvements in speed and scalability are crucial for large-scale inference scenarios such as generating 100K images. Our work is further contextualized by advances in efficient video generation, including dual masking strategies, illustrating a broader effort to improve the scalability and efficiency of generative models.
LOSS OF THE SELF IN POSTMODERN DISCOURSE. DECONSTRUCTING AND RECONSTRUCTING IDENTITY

The topic of the present article concerns the problem of deconstructing and reconstructing identity. In modernism we come across two contrary desires: the desire for a fixed identity and the desire to go beyond it. Under the concepts ‘the self’, ‘space’, ‘time’, ‘rationality’, ‘causality’, ‘society’, ‘history’ we not only mean the world, but also ourselves. The aim of the research is to study the alienated, absurd, existentialist anti-heroes which are all in the same position, failing to find the clear meaning of a personal, identifiable form of existence. In the postmodern age, the notion of a unique identity that used to command an aura has been lost due to the human ability to create exact reproductions. No one exactly knows what is meant by the term ‘postmodernism’. All that one knows is that it is some kind of a reaction against or an extension of modernism which ultimately turns on itself.
Symmetry Fractionalization, Defects, and Gauging of Topological Phases

We examine the interplay of symmetry and topological order in $2+1$ dimensional topological phases of matter. We present a definition of the \it topological symmetry \rm group, which characterizes the symmetry of the emergent topological quantum numbers of a topological phase, and we describe its relation with the microscopic symmetry of the underlying physical system. We derive a general framework to characterize and classify symmetry fractionalization in topological phases, including phases that are non-Abelian and symmetries that permute the quasiparticle types and/or are anti-unitary. We develop a theory of extrinsic defects (fluxes) associated with elements of the symmetry group, which provides a general classification of symmetry-enriched topological phases derived from a topological phase of matter $\mathcal{C}$ with symmetry group $G$. The algebraic theory of the defects, known as a $G$-crossed braided tensor category $\mathcal{C}_{G}^{\times}$, allows one to compute many properties, such as the number of topologically distinct types of defects associated with each group element, their fusion rules, quantum dimensions, zero modes, braiding exchange transformations, a generalized Verlinde formula for the defects, and modular transformations of the $G$-crossed extensions of topological phases. We also examine the promotion of the global symmetry to a local gauge invariance, wherein the extrinsic $G$-defects are turned into deconfined quasiparticle excitations, which results in a different topological phase $(\mathcal{C}_{G}^{\times})^{G}$. A number of instructive and/or physically relevant examples are studied in detail.