UAS Path Planning for Dynamical Wildfire Monitoring with Uneven Importance

Unmanned Aircraft Systems (UASs) offer many benefits in wildfire monitoring when compared to traditional wildfire monitoring technologies. When planning the path of an UAS for wildfire monitoring, it is important to consider the uneven propagation nature of the wildfire because different parts of the fire boundary demand different levels of monitoring attention (importance) based on the propagation speed. In addition, many of the existing works adopt a centralized approach for the path planning of the UASs. However, the use of centralized approaches is often limited in terms of applicability and adaptability. This work focuses on developing decentralized UAS path planning algorithms to autonomously monitor a spreading wildfire considering uneven importance. The algorithms allow the UASs to focus on the most active regions of a wildfire while still covering the entire fire perimeter. When monitoring a relatively smaller and spatially static fire, a single UAS might be adequate for the task. However, when monitoring a larger wildfire that is evolving dynamically in space and time, efficient and optimized use of multiple UASs is required. Based on this need, we also focus on decentralized and importance-based multi-UAS path planning for wildfire monitoring. The design, implementation, analysis, and simulation results have been discussed in details for both single-UAS and multi-UAS path planning algorithms. Experiment results show the effectiveness and robustness of the proposed algorithms for dynamic wildfire monitoring

Exploring the Relationship between LLM Hallucinations and Prompt Linguistic Nuances: Readability, Formality, and Concreteness

As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination. While various mitigation techniques are emerging to address hallucination, it is equally crucial to delve into its underlying causes. Consequently, in this preliminary exploratory investigation, we examine how linguistic factors in prompts, specifically readability, formality, and concreteness, influence the occurrence of hallucinations. Our experimental results suggest that prompts characterized by greater formality and concreteness tend to result in reduced hallucination. However, the outcomes pertaining to readability are somewhat inconclusive, showing a mixed pattern

FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering

Automatic fact verification has received significant attention recently. Contemporary automatic fact-checking systems focus on estimating truthfulness using numerical scores which are not human-interpretable. A human fact-checker generally follows several logical steps to verify a verisimilitude claim and conclude whether its truthful or a mere masquerade. Popular fact-checking websites follow a common structure for fact categorization such as half true, half false, false, pants on fire, etc. Therefore, it is necessary to have an aspect-based (delineating which part(s) are true and which are false) explainable system that can assist human fact-checkers in asking relevant questions related to a fact, which can then be validated separately to reach a final verdict. In this paper, we propose a 5W framework (who, what, when, where, and why) for question-answer-based fact explainability. To that end, we present a semi-automatically generated dataset called FACTIFY-5WQA, which consists of 391, 041 facts along with relevant 5W QAs - underscoring our major contribution to this paper. A semantic role labeling system has been utilized to locate 5Ws, which generates QA pairs for claims using a masked language model. Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field. Lastly, we propose a robust fact verification system that takes paraphrased claims and automatically validates them. The dataset and the baseline model are available at https: //github.com/ankuranii/acl-5W-QAComment: Accepted at ACL main conference 202

The Troubling Emergence of Hallucination in Large Language Models -- An Extensive Definition, Quantification, and Prescriptive Remediations

The recent advancements in Large Language Models (LLMs) have garnered widespread acclaim for their remarkable emerging capabilities. However, the issue of hallucination has parallelly emerged as a by-product, posing significant concerns. While some recent endeavors have been made to identify and mitigate different types of hallucination, there has been a limited emphasis on the nuanced categorization of hallucination and associated mitigation methods. To address this gap, we offer a fine-grained discourse on profiling hallucination based on its degree, orientation, and category, along with offering strategies for alleviation. As such, we define two overarching orientations of hallucination: (i) factual mirage (FM) and (ii) silver lining (SL). To provide a more comprehensive understanding, both orientations are further sub-categorized into intrinsic and extrinsic, with three degrees of severity - (i) mild, (ii) moderate, and (iii) alarming. We also meticulously categorize hallucination into six types: (i) acronym ambiguity, (ii) numeric nuisance, (iii) generated golem, (iv) virtual voice, (v) geographic erratum, and (vi) time wrap. Furthermore, we curate HallucInation eLiciTation (HILT), a publicly available dataset comprising of 75,000 samples generated using 15 contemporary LLMs along with human annotations for the aforementioned categories. Finally, to establish a method for quantifying and to offer a comparative spectrum that allows us to evaluate and rank LLMs based on their vulnerability to producing hallucinations, we propose Hallucination Vulnerability Index (HVI). We firmly believe that HVI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making. In conclusion, we propose two solution strategies for mitigating hallucinations

Counter Turing Test CT^2: AI-Generated Text Detection is Not as Easy as You May Think -- Introducing AI Detectability Index

With the rise of prolific ChatGPT, the risk and consequences of AI-generated text has increased alarmingly. To address the inevitable question of ownership attribution for AI-generated artifacts, the US Copyright Office released a statement stating that 'If a work's traditional elements of authorship were produced by a machine, the work lacks human authorship and the Office will not register it'. Furthermore, both the US and the EU governments have recently drafted their initial proposals regarding the regulatory framework for AI. Given this cynosural spotlight on generative AI, AI-generated text detection (AGTD) has emerged as a topic that has already received immediate attention in research, with some initial methods having been proposed, soon followed by emergence of techniques to bypass detection. This paper introduces the Counter Turing Test (CT^2), a benchmark consisting of techniques aiming to offer a comprehensive evaluation of the robustness of existing AGTD techniques. Our empirical findings unequivocally highlight the fragility of the proposed AGTD methods under scrutiny. Amidst the extensive deliberations on policy-making for regulating AI development, it is of utmost importance to assess the detectability of content generated by LLMs. Thus, to establish a quantifiable spectrum facilitating the evaluation and ranking of LLMs according to their detectability levels, we propose the AI Detectability Index (ADI). We conduct a thorough examination of 15 contemporary LLMs, empirically demonstrating that larger LLMs tend to have a higher ADI, indicating they are less detectable compared to smaller LLMs. We firmly believe that ADI holds significant value as a tool for the wider NLP community, with the potential to serve as a rubric in AI-related policy-making.Comment: EMNLP 2023 Mai

Enhanced Heart Failure in Redox‐Dead Cys17Ser PKARIα Knock‐In Mice

Background PKARIα (protein kinase A type I‐α regulatory subunit) is redox‐active independent of its physiologic agonist cAMP. However, it is unknown whether this alternative mechanism of PKARIα activation may be of relevance to cardiac excitation–contraction coupling. Methods and Results We used a redox‐dead transgenic mouse model with homozygous knock‐in replacement of redox‐sensitive cysteine 17 with serine within the regulatory subunits of PKARIα (KI). Reactive oxygen species were acutely evoked by exposure of isolated cardiac myocytes to AngII (angiotensin II, 1 µmol/L). The long‐term relevance of oxidized PKARIα was investigated in KI mice and their wild‐type (WT) littermates following transverse aortic constriction (TAC). AngII increased reactive oxygen species in both groups but with RIα dimer formation in WT only. AngII induced translocation of PKARI to the cell membrane and resulted in protein kinase A–dependent stimulation of ICa (L‐type Ca current) in WT with no effect in KI myocytes. Consequently, Ca transients were reduced in KI myocytes as compared with WT cells following acute AngII exposure. Transverse aortic constriction–related reactive oxygen species formation resulted in RIα oxidation in WT but not in KI mice. Within 6 weeks after TAC, KI mice showed an enhanced deterioration of contractile function and impaired survival compared with WT. In accordance, compared with WT, ventricular myocytes from failing KI mice displayed significantly reduced Ca transient amplitudes and lack of ICa stimulation. Conversely, direct pharmacological stimulation of ICa using Bay K8644 rescued Ca transients in AngII‐treated KI myocytes and contractile function in failing KI mice in vivo. Conclusions Oxidative activation of PKARIα with subsequent stimulation of ICa preserves cardiac function in the setting of acute and chronic oxidative stress

Assessment of the Influence of Hydrogen Share on Performance, Combustion, and Emissions in a Four-Stroke Gasoline Engine

This study aims to develop a one-dimensional model to investigate the effect of hydrogen share in gasoline fuel on the performance, combustion, and exhaust emissions of a gasoline direct-injection engine. Iso-octane was used as a reference fuel to compare performance, combustion, and emission parameters. The model was developed using commercial GT-Suite and ANSYS software. The simulation results using GT-Suite were validated with the published data and ANSYS results. The hydrogen fractions were varied from 0% to 11.09% to validate the simulation results with the published results. The investigation continued with three higher hydrogen fractions (15%, 20% and 25%) to study the performance, combustion, emissions, and sustainability parameters. Compared to neat gasoline, hydrogen-shared fuels show a maximum 2% higher exergy efficiency, 51% higher exergy and 42% energy rates while reducing carbon dioxide (CO2) emissions by 51% with a penalty of nitrogen oxide emissions (NOx) by 62% at an excess ratio of 1.3. Other novel findings, including higher sustainability indices, lower depletion potentials, and lower unitary cost indices with higher-fraction hydrogen fuels, suggest that they are environmentally and economically sustainable. In the second part of this study, the NOx formation mechanism and its associated factors, including in-cylinder temperature, heat transfer rate, cumulative heat release, and burned rate, were confirmed and compared with gasoline and neat ethylene.This research work was supported in part by the Kuwait Foundation for the Advancement of Sciences, Kuwait, under Grant CR19-45EM-01; in part by Central Queensland University, Australia, under Grant RSH/5221. The publication of this article was funded by Qatar National Library

Dried Blood Spots (DBS): A suitable alternative to using whole blood samples for diagnostic testing of visceral leishmaniasis in the post-elimination era.

BackgroundSerum or whole blood collection, processing, transport and storage still present significant challenges in low resource settings where mass surveillance is required to sustain disease elimination. Therefore, in this study, we explored the diagnostic efficacy of dried blood spots (DBS) as a minimally invasive and potentially cost-effective alternative sampling technique to whole blood sampling procedures for subsequent detection of Leishmania donovani antibodies or DNA.Methodology and principal findingsArchived serum, DNA samples from whole blood of visceral leishmaniasis (VL) cases and healthy controls, and DBS from corresponding cases and controls, were used. Both molecular and serological assays were optimized to detect L. donovani antibodies or DNA in DBS elute and results were compared against those obtained with whole blood. Serological assays (both rK28 ELISA and rK39 ELISA) of DBS samples showed sensitivity and specificity of 100% and had excellent agreement with results from whole blood samples (kappa value ranged from 0.98-1). Bland-Altman analysis of OD values from rK28-ELISA with DBS elute and patients' serum showed an excellent agreement (ICC = 0.9) whereas a good agreement (ICC = 0.8) was observed in the case of rK39-ELISA. However, qPCR and RPA of DBS samples had a diminished sensitivity of 76% and 68%, respectively, and poor agreement was observed with the whole blood samples.ConclusionOur results demonstrate that DBS offer excellent diagnostic efficiency for serological assays and represent a viable alternative to whole blood sampling procedures