Quantum discord and non-Markovianity of quantum dynamics

The problem of recognizing (non-)Markovianity of a quantum dynamics is revisited through analyzing quantum correlations. We argue that instantaneously-vanishing quantum discord provides a necessary and sufficient condition for Markovianity of a quantum map. This is used to introduce a measure of non-Markovianity. This measure, however, requires demanding knowledge about the system and the environment. By using a quantum correlation monogamy property and an ancillary system, we propose a simplified measure with less requirements. Non-Markovianity is thereby decided by quantum state tomography of the system and the ancilla.Comment: 5 pages, 3 figure

Potential Protective Link Between Type I Diabetes and Parkinson's Disease Risk and Progression

BACKGROUND: Epidemiological studies suggested an association between Parkinson's disease (PD) and type 2 diabetes, but less is known about type 1 diabetes (T1D) and PD. OBJECTIVE: This study sought to explore the association between T1D and PD. METHODS: We used Mendelian randomization, linkage disequilibrium score regression, and multi-tissue transcriptome-wide analysis to examine the association between PD and T1D. RESULTS: Mendelian randomization showed a potentially protective role of T1D for PD risk (odds ratio [OR], 0.97; 95% confidence interval [CI], 0.94-0.99; P = 0.039), as well as motor (OR, 0.94; 95% CI, 0.88-0.99; P = 0.044) and cognitive progression (OR, 1.50; 95% CI, 1.08-2.09; P = 0.015). We further found a negative genetic correlation between T1D and PD (rg = -0.17; P = 0.016), and we identified eight genes in cross-tissue transcriptome-wide analysis that were associated with both traits. CONCLUSIONS: Our results suggest a potential genetic link between T1D and PD risk and progression. Larger comprehensive epidemiological and genetic studies are required to validate our findings. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

The Impact of Explanations on AI Competency Prediction in VQA

Explainability is one of the key elements for building trust in AI systems. Among numerous attempts to make AI explainable, quantifying the effect of explanations remains a challenge in conducting human-AI collaborative tasks. Aside from the ability to predict the overall behavior of AI, in many applications, users need to understand an AI agent's competency in different aspects of the task domain. In this paper, we evaluate the impact of explanations on the user's mental model of AI agent competency within the task of visual question answering (VQA). We quantify users' understanding of competency, based on the correlation between the actual system performance and user rankings. We introduce an explainable VQA system that uses spatial and object features and is powered by the BERT language model. Each group of users sees only one kind of explanation to rank the competencies of the VQA model. The proposed model is evaluated through between-subject experiments to probe explanations' impact on the user's perception of competency. The comparison between two VQA models shows BERT based explanations and the use of object features improve the user's prediction of the model's competencies.Comment: Submitted to HCCAI 202

Burn Patients Infected With Metallo-Beta-Lactamase-Producing Pseudomonas aeruginosa: Multidrug-Resistant Strains

Background: Metallo-beta-lactamase (MBL) producing Pseudomonas aeruginosa in the burn patients is a leading cause of morbidity and mortality and remains a serious health concern among the clinicians. Objectives: The aim of this study was to detect MBL-producing P. aeruginosa in burn patients and determine multidrug-resistant (MDR) strains, and respective resistance patterns. Patients and Methods: In this cross-sectional study, 270 strains of P. aeruginosa were isolated from the burn patients referred to Ghotbeddin Burn Hospital, Shiraz, Iran. Among them, 55 MBL-producing P. aeruginosa strains were isolated from 55 patients hospitalized in burn unit. Minimum inhibitory concentrations (MICs) and MBLs were determined by the E-test method. Results: Of the 55 burn cases, 29 (53%) were females and 26 (47%) males. Injured burn patients’ ages ranged from 16 to 87 years, with maximum number of cases in the age group of 16 to 36 years (n, 40; 72.7%). Overall, 32 cases were accidental (60%), and 22 were suicidal burns (40%). Of the 55 burn patients, 17 cases were expired (30%). All deaths were due to chemical exposures. In antibiotic susceptibility testing by E-test method, ceftazidime was the most effective one and 35 isolates (63.5%) were resistant to all the 11 tested antibiotics. Conclusions: Routine microbiological surveillance and careful in vitro testing of antibiotics prior to prescription and strict adherence to hospital antibiotic policy may help to prevent, treat, and control MDR and pandrug-resistant (PDR) P. aeruginosa strains in burn units

Behavioral responses of 3S tourism visitors : evidence from a Mediterranean island destination

This empirical research deepens the current knowledge of tourism destination images by conceptualizing the effects of sun, sand, and sea (3S) tourism. The study investigates its impact on tourist attitude toward 3S tourism. This project also examines the impact of attitudes toward 3S tourism on visit intentions and word-of-mouth intentions as two behavioral outcomes. The study focuses on the mental representations of 3S tourism by tourists. Destination images (mental representations of destinations) can be defined, operationalized, and measured in a variety of ways; this study investigates the images and attitudes held by tourists toward 3S tourism in Cyprus, along with their desired behavioral responses. In the study, 410 survey questionnaires were administered to tourists during the summer of 2017. The results revealed that images of 3S tourism had a positive impact on tourist attitudes. Visit intentions and word-of-mouth intentions were enhanced by improving tourists' attitudes toward 3S tourism