Consistent Answers Research Articles

Computing Range Consistent Answers to Aggregation Queries via Rewriting

Computing Range Consistent Answers to Aggregation Queries via Rewriting

Assessment of Large Language Models (LLMs) in decision-making support for gynecologic oncology

ObjectiveThis study investigated the ability of Large Language Models (LLMs) to provide accurate and consistent answers by focusing on their performance in complex gynecologic cancer cases. BackgroundLLMs are advancing rapidly and require a thorough evaluation to ensure that they can be safely and effectively used in clinical decision-making. Such evaluations are essential for confirming LLM reliability and accuracy in supporting medical professionals in casework. Study designWe assessed three prominent LLMs—ChatGPT-4 (CG-4), Gemini Advanced (GemAdv), and Copilot—evaluating their accuracy, consistency, and overall performance. Fifteen clinical vignettes of varying difficulty and five open-ended questions based on real patient cases were used. The responses were coded, randomized, and evaluated blindly by six expert gynecologic oncologists using a 5-point Likert scale for relevance, clarity, depth, focus, and coherence. ResultsGemAdv demonstrated superior accuracy (81.87 %) compared to both CG-4 (61.60 %) and Copilot (70.67 %) across all difficulty levels. GemAdv consistently provided correct answers more frequently (>60 % every day during the testing period). Although CG-4 showed a slight advantage in adhering to the National Comprehensive Cancer Network (NCCN) treatment guidelines, GemAdv excelled in the depth and focus of the answers provided, which are crucial aspects of clinical decision-making. ConclusionLLMs, especially GemAdv, show potential in supporting clinical practice by providing accurate, consistent, and relevant information for gynecologic cancer. However, further refinement is needed for more complex scenarios. This study highlights the promise of LLMs in gynecologic oncology, emphasizing the need for ongoing development and rigorous evaluation to maximize their clinical utility and reliability.

The Influence of Social Networks on the Social Development of Children

The development of the computer industry and the internet, social networks have experienced diverse applications. They are necessary tools for work, information and communication. However, their application and dependence increasingly includes children. Social networks such as Facebook, Twitter, Myspace, Flicker, Viber, Internet, Google, YouTube and others are the most famous and used means of communication between children. They are an intermediary between the two talking parties. They replace verbal, direct communication by sending messages, images, files as codes of indirect communication. Socialization as a process starts from the family, as the primary environment from which the basic postulates of the process are drawn, but as a secondary environment, the wider circle of family, relatives, friends, school are considered. The main goal of this paper is to point out the negative emergent forms of interpersonal communication, as a consequence of the overall rapid technical-technological development and the process of globalization, with a more detailed elaboration of the impact of social networks on the process of socialization among children. The paper will more specifically elaborate this problem through a theoretical presentation and through a specifically conducted research. The theoretical part will include the basic chapters that will contain data drawn from a wide variety of foreign and domestic literature. In my paper I will give a more detailed description of the research process that I will carry out in order to prove the validity and logical foundation of all my theses presented in this paper. It will cover the methodology of the research, the way in which the collected data will be processed, the results obtained, as well as the interpretation of the findings and the conclusions reached. For the comprehensiveness of the topic, in the paper I will try to give a consistent answer to all questions related to this problem.

Making Sense of Racial Membership

AbstractWhich individuals belong to a racial group, and what determines membership? I argue that questions about membership are importantly different from questions regarding the ontology of racial categories and invite a distinctive line of inquiry. But there's a formidable challenge to providing determinate and consistent answers to membership questions: Many phenomena documented in sociology and psychology generate reasonable and systematic disagreement about membership that cannot be adjudicated by an influential variety of social constructionism. While the puzzle of racial identity remains, we emerge with a new appreciation for how racial membership is a distinctive part of it.

Controlled query evaluation in description logics through consistent query answering

Controlled Query Evaluation (CQE) is a framework for the protection of confidential data, where a policy given in terms of logic formulae indicates which information must be kept private. Functions called censors filter query answering so that no answers are returned that may lead a user to infer data protected by the policy. The preferred censors, called optimal censors, are the ones that conceal only what is necessary, thus maximizing the returned answers. Typically, given a policy over a data or knowledge base, several optimal censors exist.Our research on CQE is based on the following intuition: confidential data are those that violate the logical assertions specifying the policy, and thus censoring them in query answering is similar to processing queries in the presence of inconsistent data as studied in Consistent Query Answering (CQA). In this paper, we investigate the relationship between CQE and CQA in the context of Description Logic ontologies. We borrow the idea from CQA that query answering is a form of skeptical reasoning that takes into account all possible optimal censors. This approach leads to a revised notion of CQE, which allows us to avoid making an arbitrary choice on the censor to be selected, as done by previous research on the topic.We then study the data complexity of query answering in our CQE framework, for conjunctive queries issued over ontologies specified in the popular Description Logics DL-LiteR and EL⊥. In our analysis, we consider some variants of the censor language, which is the language used by the censor to enforce the policy. Whereas the problem is in general intractable for simple censor languages, we show that for DL-LiteR ontologies it is first-order rewritable, and thus in AC0 in data complexity, for the most expressive censor language we propose.

Evaluating the effectiveness of transcranial direct current stimulation on improving cognitive function in bipolar patients

BackgroundBipolar disorder is classified as one of the most debilitating mental diseases. Although patients receive medical treatment, cognitive deficits remain in the euthymic phase in many patients. It is hypothesized that brain electrical stimulation protocols that increase cognitive function may increase the quality of life. Therefore, this study aimed to evaluate the effectiveness of transcranial direct current stimulation in improving cognitive function in bipolar patients in the euthymic phase. Methods & MaterialsIn a double-blind, sham-controlled- clinical trial, 46 euthymic bipolar patients diagnosed based on DSM-V were randomly divided into the two intervention and control groups. tDCS was applied for the intervention group using an anode electrode at the left dorsolateral-peripheral cortex (F3) and the cathode at the right dorsolateral-frontal (F4) (based on the 10/20 system) at 2 mA for 20 min. The Strop Color and Word Test, The Rey Auditory Verbal Learning Test (RAVLT), and The Wechsler Adult Intelligence Scale (WAISR) were evaluated before, at the end of the third and the seventh weeks of the intervention. Statistical analysis was performed using SPSS v 16. ResultsThe scores of immediate and delayed auditory memory and visual memory in the intervention group, unlike the control group, improved significantly after treatment. Moreover, Wechsler's working memory score in the intervention group improved significantly after treatment. Consistent and inconsistent answers scores were also significantly improved in the intervention group compared with the controls. ConclusionThe present study indicated that tDCS might be an efficient and safe method for improving cognitive function in euthymic bipolar patients.

The performance of large language model powered chatbots compared to oncology physicians on colorectal cancer queries.

Large language model (LLM)-powered chatbots have become increasingly prevalent in healthcare, while their capacity in oncology remains largely unknown. To evaluate the performance of LLM-powered chatbots compared to oncology physicians in addressing to colorectal cancer queries. This study was conducted between August 13, 2023, and January 5, 2024. A total of 150 questions were designed, and each question was submitted three times to eight chatbots: ChatGPT-3.5, ChatGPT-4, ChatGPT-4 Turbo, Doctor GPT, Llama-2-70B, Mixtral-8x7B, Bard, and Claude 2.1. No feedback was provided to these chatbots. The questions were also answered by nine oncology physicians, including three residents, three fellows, and three attendings. Each answer was scored based on its consistency with guidelines, with a score of 1 for consistent answers and 0 for inconsistent answers. The total score for each question was based on the number of corrected answers, ranging from 0 to 3. The accuracy and scores of the chatbots were compared to those of the physicians. Claude 2.1 demonstrated the highest accuracy, with an average accuracy of 82.67%, followed by Doctor GPT at 80.45%, ChatGPT-4 Turbo at 78.44%, ChatGPT-4 at 78%, Mixtral-8x7B at 73.33%, Bard at 70%, ChatGPT-3.5 at 64.89%, and Llama-2-70B at 61.78%. Claude 2.1 outperformed residents, fellows, and attendings. Doctor GPT outperformed residents and fellows. Additionally, Mixtral-8x7B outperformed residents. In terms of scores, Claude 2.1 outperformed residents and fellows. Doctor GPT, ChatGPT-4 Turbo and ChatGPT-4 outperformed residents. This study shows that LLM-powered chatbots can provide more accurate medical information compared to oncology physicians.

Is ageing undesirable? An ethical analysis

The technical possibilities of biomedicine open up the opportunity to intervene in ageing itself with the aim of mitigating, reducing or eliminating it. However, before undertaking these changes or rejecting...

The Algorithmic Assignment of Incentive Schemes

The assignment of individuals with different observable characteristics to different treatments is a central question in designing optimal policies. We study this question in the context of increasing workers’ performance via targeted incentives using machine learning algorithms with worker demographics, personality traits, and preferences as input. Running two large-scale experiments, we show that (i) performance can be predicted by accurately measured worker characteristics, (ii) a machine learning algorithm can detect heterogeneity in responses to different schemes, (iii) a targeted assignment of schemes to individuals increases performance significantly above the level of the single best scheme, and (iv) algorithmic assignment is more effective for workers who have a high likelihood to repeatedly interact with the employer or who provide more consistent survey answers. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy [Grant EXC 2126/1-390838866]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03362 .

Combined Approximations for Uniform Operational Consistent Query Answering

Operational consistent query answering (CQA) is a recent framework for CQA based on revised definitions of repairs, which are built by applying a sequence of operations (e.g., fact deletions) starting from an inconsistent database until we reach a database that is consistent w.r.t. the given set of constraints. It has been recently shown that there is an efficient approximation for computing the percentage of repairs that entail a given query when we focus on primary keys, conjunctive queries, and assuming the query is fixed (i.e., in data complexity). However, it has been left open whether such an approximation exists when the query is part of the input (i.e., in combined complexity). We show that this is the case when we focus on self-join-free conjunctive queries of bounded generelized hypertreewidth. We also show that it is unlikely that efficient approximation schemes exist once we give up one of the adopted syntactic restrictions, i.e., self-join-freeness or bounding the generelized hypertreewidth. Towards the desired approximation, we introduce a counting complexity class, called SpanTL, show that each problem in it admits an efficient approximation scheme by using a recent approximability result about tree automata, and then place the problem of interest in SpanTL.

Navigating the landscape of parameter identifiability methods: A workflow recommendation for model development.

In pharmacometric modeling, it is often important to know whether the data is sufficiently rich to identify the parameters of a proposed model. While it may be possible to assess this based on the results of a model fit, it is often difficult to disentangle identifiability issues from other model fitting and numerical problems. Furthermore, it can be of value to ascertain identifiability beforehand. This paper compares four methods for parameter identifiability, namely Differential Algebra for Identifiability of SYstems (DAISY), the sensitivity matrix method (SMM), Aliasing, and the Fisher information matrix method (FIMM). We discuss the characteristics of the methods and apply them to a set of applications, consisting of frequently used PK model structures, with suitable dosing regimens and sampling times. These applications were selected to validate the methods and demonstrate their usefulness. While traditional identifiability analysis provides a categorical result [PLoS One, 6, 2011, e27755; CPT Pharmacometrics Syst Pharmacol, 8, 2019, 259; Bioinformatics, 30, 2014, 1440], we argue that in practice a continuous scale better reflects the limitations on the data and is more informative. The methods were generally consistent in their evaluation of the applications. The Fisher information matrix method seemed to provide the most consistent answers. All methods provided information on the parameters that were unidentifiable. Some of the results were unexpected, indicating identifiability issues where none were foreseen, but could be explained upon further analysis. This illustrated the usefulness of identifiability assessment.

Perception of burnout syndrome among nurses providing nursing care in the emergency room at a hospital in Riau Province

The demands to nurses in the Emergency Room (ER) can lead to stress, potentially causing Burnout Syndrome. This may diminish the effectiveness of the nursing care provided to patients. This study aimed to explore the experience of Burnout Syndrome among nurses in the ER using a descriptive qualitative research design. Ten nurses from the ER participated in the study. All participants provided consistent answers, leading researchers to conclude data saturation through purposive sampling. In-depth interviews with a semi-structured format were conducted for data collection, and the Collaizi technique was employed for analysis. Findings revealed that nurses exhibited signs of Burnout Syndrome, such as physical, emotional, and mental exhaustion, while providing patient care, attributing this to the substantial workload.

Application of Web-Based Certainty Factor Method To Developo An Employee Job Stress Diagnosis System To Improve Employee Performance

This research aims to find out how to build an expert system for diagnosing work stress in employees using the certainty factor method. The data obtained was obtained in the following ways: Literature study, interviews, and questionnaires The population in this research is all employees. The sample for this research was 100 employees. The system development method used consists of requirements plan, system design, and implementation. Based on the research results, it was found that the expert system for diagnosing employee work stress uses the certainty factor method, which is easy for users to understand in presenting questions and results by employees, and the system can process data on symptoms, causes, and solutions because employees only select symptoms and do not enter them. symptom values that make users less understanding. This expert system has consistent answers. If you try to select the same symptoms, the results will be the same and in accordance with the expert's results.

Belnap-Dunn Logic and Query Answering in Inconsistent Databases with Null Values

This paper concerns an expansion of first-order Belnap-Dunn logic, called BD^{\supset,F}_{\bot}, and an application of this logic in the area of relational database theory. The notion of a relational database, the notion of a query applicable to a relational database, and several notions of an answer to a query with respect to a relational database are considered from the perspective of this logic, taking into account that a database may be an inconsistent database and/or a database with null values. The chosen perspective enables among other things the definition of a notion of a consistent answer to a query with respect to a possibly inconsistent database without resort to database repairs. For each of the notions of an answer considered, being an answer to a query with respect to a database of the kind considered is decidable.

The roles of e-government in combating corruption: evidence from European countries

Purpose This study aims to analyze the effects of e-government on corruption prevalence by using a sample of 29 European countries over the period 2012–2019. Design/methodology/approach This paper uses the panel corrected standard errors (PCSE) model to mitigate the problems of cross-sectional dependence. The PCSE model is also considered to reexamine the findings when the presence of heteroscedasticity, fixed effects and endogeneity issues are taken into account. The theoretical model incorporates one-year-lagged explanatory variables to deal with endogeneity. The autoregressive distributed lag method using the dynamic fixed effects estimator is chosen to deal with the time and country-fixed effects in the effort to measure the short- and long-run effects of e-government more precisely. Findings The results indicate that e-government plays a critical role in improving the population’s perception of corruption. Furthermore, e-government appears to have an effect in the short run. Notably, the estimation results show that there is a nonlinear relationship between e-government, especially user centricity and key enablers and the corruption perception index in the U-shaped curve. Practical implications The short-run and nonlinear effects of e-government on corruption prevalence suggest that the fight against corruption requires countries to pursue a consistent and continuous improvement and development of the e-government system. Originality/value The authors contribute to the literature by providing a consistent and precise answer to this relationship in the case of European countries. Another contribution of the work is to use diverse indicators to reflect e-government in a typical country, which helps us confirm the reliability and robustness of the findings.

Evaluation of semantic relations impact in query expansion-based retrieval systems

With the increasing demand of intelligent systems capable of operating in different contexts (e.g. users on the move) the correct interpretation of the user-need by such systems has become crucial to give consistent answers to the user questions. The most effective applications addressing such task are in the fields of natural language processing and semantic expansion of terms. These techniques are aimed at estimating the goal of an input query reformulating it as an intent, commonly relying on textual resources built exploiting different semantic relations like synonymy, antonymy and many others. The aim of this paper is to generate such resources using the labels of a given taxonomy as source of information. The obtained resources are integrated into a plain classifier for reformulating a set of input queries as intents and tracking the effect of each relation, in order to quantify the impact of each semantic relation on the classification. As an extension to this, the best tradeoff between improvement and noise introduction when combining such relations is evaluated. The assessment is made generating the resources and their combinations and using them for tuning the classifier which is used to reformulate the user questions as labels. The evaluation employs a wide and varied taxonomy as a use-case, exploiting its labels as basis for the semantic expansion and producing several corpora with the purpose of enhancing the pseudo-queries estimation.

Pengaruh Rasio Keuangan terhadap Stock Price: Perusahaan yang Terindeks di JII70

The capital market can play two very crucial roles in the country's economy, namely the role of economy and finance. For these two roles, some parties act as investors and as issuers. Seeing the existence of parties as investors, of course, there will be hope to profit, where the size of the return obtained depends on the company's stock price. However, the company's stock price continues to change every period because of influencing factors such as return on assets (ROA), debt on equity ratio (DER), current ratio (CR), and so on. In previous studies, these factors have not provided consistent answers on whether they affect stock prices. Therefore, to find out the actual results, this research was conducted. The object used is a company indexed in JII70. The selection of this object is due to the lack of research using Sharia objects. JII is a type of index in which there are 70 types of Sharia stocks with high liquidity listed on the IDX, Panel regression analysis is the analysis method applied in this study, where the study results show that ROA can significantly positively influence the stock price. In contrast, DER and CR cannot substantially affect the stock price

On a problem of Angelo Bella

The main result of this note is the following theorem.If X is any Hausdorff space withκ=Fˆ(X)⋅μˆ(X)thenL(X<κ)≤ϱ(κ).Here Fˆ(X) is the smallest cardinal φ so that |S|<φ for any set S that is free in X and μˆ(X) is the smallest cardinal μ so that, for every set S that is free in X, any open cover of S‾ has a subcover of size <μ. Moreover, X<κ is the G<κ-modification of X and ϱ(κ)=min⁡{ϱ:ϱ<κ=ϱ}.As a corollary we obtain that if X is a linearly Lindelöf regular space of countable tightness then L(Xδ)≤c, provided that c=2<c. This yields a consistent affirmative answer to a question of Angelo Bella.

CONTRIBUTION TO THE DEBATE ON THE QUOAD USUM AGREEMENT

The paper aims to propose a general and unified concept of the most optimal approach to the quoad usum agreement. In view of the significant discrepancies regarding the evaluation of the quoad usum contract both in the literature and in the jurisprudence, the weaknesses and inconsistencies of proposals incompatible with the suggested model have also been demonstrated. In the concept presented, consistent answers were proposed to the questions of the content of the concluded agreement, its nature (obligatory or in rem) and its characteristics (an act of management or an act modifying the content of the co-owners rights). The settlement of these issues made it possible to answer questions concerning the stability of the legal relationship created by the agreement, the transferability of rights and obligations from the agreement to the acquirer of the share (in inter vivos transactions and in connection with the death of a co-owner) and the possibility of termination or modification of the content of the quoad usum agreement.

LinCQA: Faster Consistent Query Answering with Linear Time Guarantees

Most data analytical pipelines often encounter the problem of querying inconsistent data that violate pre-determined integrity constraints. Data cleaning is an extensively studied paradigm that singles out a consistent repair of the inconsistent data. Consistent query answering (CQA) is an alternative approach to data cleaning that asks for all tuples guaranteed to be returned by a given query on all (in most cases, exponentially many) repairs of the inconsistent data. In this paper, we identify a class of acyclic select-project-join (SPJ) queries for which CQA can be solved via SQL rewriting with a linear time guarantee. Our rewriting method can be viewed as a generalization of Yannakakis' algorithm for acyclic joins to the inconsistent setting. We present LinCQA, a system that takes as input any query in our class and outputs rewritings in both SQL and non-recursive Datalog with negation. We show that LinCQA often outperforms the existing CQA systems on both synthetic and real-world workloads, and in some cases, by orders of magnitude.