Information Set Research Articles

Modeling Rationality: Toward Better Performance Against Unknown Agents in Sequential Games.

Opponent modeling is necessary for autonomous agents to capture the intents of others during strategic interactions. Most previous works assume that they can access enough interaction history to build the model. However, it may not be realistic. To solve this problem, we present a novel rationality-consistent opponent modeling (ROM) method for games with imperfect information. In our approach, a game-theoretical concept of consistence about rationality is proposed to take advantage of the characteristic of imperfect information sequential games that rational behavior at disjoint information sets is correlated through anticipated opponent's behavior. With the correlation between different information sets, agents could infer the opponents' strategies at information sets correlated to observed behavior. To exploit the correlation, ROM attempts to conduct reasoning from the opponent's perspective and rationalize its past behavior. In this way, ROM acquires the ability to better adapt to different opponents and achieves a more accurate opponent model with insufficient observation history, which is verified by experiments in different settings. A heuristic adaptation approach is also applied in ROM, which updates the opponent model in an online manner and significantly reduces the computation cost. We evaluate ROM in both a grid world game and a poker game. Compared with other opponent modeling methods, ROM shows better performance and has more accurate predictions in both games against different types of opponents with limited action interactions. Experimental results also show that ROM's time cost is significantly reduced through heuristic adaptation.

The Optimal Stopping Problem under a Random Horizon

This paper considers a pair (F,τ), where F is a filtration representing the “public” flow of information that is available to all agents over time, and τ is a random time that might not be an F-stopping time. This setting covers the case of a credit risk framework, where τ models the default time of a firm or client, and the setting of life insurance, where τ is the death time of an agent. It is clear that random times cannot be observed before their occurrence. Thus, the larger filtration, G, which incorporates F and makes τ observable, results from the progressive enlargement of F with τ. For this informational setting, governed by G, we analyze the optimal stopping problem in three main directions. The first direction consists of characterizing the existence of the solution to this problem in terms of F-observable processes. The second direction lies in deriving the mathematical structures of the value process of this control problem, while the third direction singles out the associated optimal stopping problem under F. These three aspects allow us to deeply quantify how τ impacts the optimal stopping problem and are also vital for studying reflected backward stochastic differential equations that arise naturally from pricing and hedging of vulnerable claims.

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy.

Does Goodwill-Related Critical Audit Matters Disclosure Influence Firms’ Financial Reporting Decisions? Evidence from Goodwill Impairment

SUMMARY We investigate whether the new critical audit matters (CAMs) disclosure requirement for auditors under the PCAOB Standard AS 3101 affects audited firms’ financial reporting decisions in the context of goodwill impairment recognition. We argue that an auditor's intense engagement with management and the audit committee in the CAMs determination and evaluation process improves management's information sets. Specifically, we report that the propensity to recognize goodwill impairment loss increases for firms with goodwill-related CAM disclosure relative to those without. This result is more pronounced when the likelihood of expected impairment is higher, suggesting that CAMs reporting lowers managers’ discretion in delaying impairment loss recognition. Additionally, we find that goodwill-related CAM disclosure increases the length and use of uncertain words in goodwill-related footnote disclosure by management but has no significant impact on audit costs. Overall, these findings suggest that adopting CAMs requirements can reduce management's discretion in financial reporting decisions. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: M41; M42.

New Proposed Classification of Difficulty in Laparoscopic Cholecystectomy.

Background: Difficult laparoscopic cholecystectomy (LC) has been challenging for surgeons. Randhawa's system used operative time, complications, and conversion to define three difficulty grades. However, using fixed numbers of operative time as dividers among three groups might not be applicable universally. This study aimed to propose new classification with more flexible parameters. Methods: This retrospective cohort study was conducted with patients who underwent LC because of gallstone-related diseases between January 2017 and December 2021 at Thammasat University Hospital. The exclusion criteria were (1) emergent LC for acute cholecystitis, (2) other procedures performed in the same setting of LC, (3) incomplete information, and (4) LC converted to open cholecystectomy. Patients were categorized into three groups using Randhawa's classification. Thereafter, new classification using mean and standard deviation was applied to reclassify patients into three new groups. The comparison between two grading results was performed to prove the advantage of new classification. Results: Total of 523 patients who underwent LC were included with median age 59.3 years old and 60.8% female. By Randhawa classification, proportions of easy, difficult, and very difficult groups were 39%, 53.7%, and 7.3%, respectively. Then, the new operative-time dividers among three groups were changed from 60 and 120 minutes to mean and mean + 2SD, respectively. Reclassified three difficult groups were 38.9%, 57.1%, and 4%. The comparison demonstrated new classification as more flexible and more compatible with each individual surgeon. Conclusions: New surgeon-referenced grading system of difficult LC included surgeon's factors, not only unfavorable operative findings. This classification should be more flexible than the previous criterion-referenced one. Thai Clinical Trials Registry at https://www.thaiclinicaltrials.org with Number TCTR20220426003.

Evaluation of patient health outcomes of a student-run free clinic in East Harlem

BackgroundMost United States medical schools have affiliated student-run free clinics, but the quality of services provided in such contexts compared to national metrics is unknown. This study determines whether a student-run, attending-supervised free clinic servicing a low-income and minority race patient population in New York City can meet national metrics of care.MethodsThrough chart review from January 1, 2020 to December 31, 2020, patient outcomes and service utilization in the Healthcare Effectiveness Data and Information Set were examined and compared to national rates of patients using Medicaid HMO or Medicare. Patients are ≥ 21 years of age, residents of East Harlem, and ineligible for health insurance because of legal residency requirements. The majority identify as Hispanic and speak Spanish as their primary language. All patients who were seen in the clinic during the 2020 calendar year were included. The primary study outcome is the number of Healthcare Effectiveness Data and Information Set measures in which patients, seen in a student-run free clinic, meet or exceed national comparisons.ResultsThe healthcare outcomes of 238 patients, mean age 47.8 years and 54.6% female, were examined in 18 Healthcare Effectiveness Data and Information Set measures. The student-run free clinic met or exceeded national metrics in 16 out of 18 categories.ConclusionsThe student-run free clinic met or exceeded the national standard of care according to national metrics. Evidence-based priorities have been clarified for future improvement. Other student-run free clinics should similarly evaluate the quality of their services.

Assessing the accuracy of race-and-ethnicity data in the Outcome and Assessment Information Set.

Limitations in the quality of race-and-ethnicity information in Medicare's data systems constrain efforts to assess disparities in care among older Americans. Using demographic information from standardized patient assessments may be an efficient way to enhance the accuracy and completeness of race-and-ethnicity information in Medicare's data systems, but it is critical to first establish the accuracy of these data as they may be prone to inaccurate observer-reported or third-party-based information. This study evaluates the accuracy of patient-level race-and-ethnicity information included in the Outcome and Assessment Information Set (OASIS) submitted by home health agencies. We compared 2017-2022 OASIS-D race-and-ethnicity data to gold-standard self-reported information from the Medicare Consumer Assessment of Healthcare Providers and Systems® survey in a matched sample of 304,804 people with Medicare coverage. We also compared OASIS data to indirect estimates of race-and-ethnicity generated using the Medicare Bayesian Improved Surname and Geocoding (MBISG) 2.1.1 method and to existing Centers for Medicare & Medicaid Services (CMS) administrative records. Compared with existing CMS administrative data, OASIS data are far more accurate for Hispanic, Asian American and Native Hawaiian or other Pacific Islander, and White race-and-ethnicity; slightly less accurate for American Indian or Alaska Native race-and-ethnicity; and similarly accurate for Black race-and-ethnicity. However, MBISG 2.1.1 accuracy exceeds that of both OASIS and CMS administrative data for every racial-and-ethnic category. Patterns of inconsistent reporting of racial-and-ethnic information among people for whom there were multiple observations in the OASIS and Consumer Assessment of Healthcare Providers and Systems (CAHPS) datasets suggest that some of the inaccuracies in OASIS data may result from observation-based reporting that lessens correspondence with self-reported data. When health record data on race-and-ethnicity includes observer-reported information, it can be less accurate than both true self-report and a high-performing imputation approach. Efforts are needed to encourage collection of true self-reported data and explicit record-level data on the source of race-and-ethnicity information.

Characterizing changes to older adults' care transition patterns from hospital to home care in the initial year of COVID-19.

Skilled home healthcare (HH) provided in-person care to older adults during the COVID-19 pandemic, yet little is known about the pandemic's impact on HH care transition patterns. We investigated pandemic impact on (1) HH service volume; (2) population characteristics; and (3) care transition patterns for older adults receiving HH services after hospital or skilled nursing facility (SNF) discharge. Retrospective, cohort, comparative study of recently hospitalized older adults (≥ 65 years) receiving HH services after hospital or SNF discharge at two large HH agencies in Baltimore and New York City (NYC) 1-year pre- and 1-year post-pandemic onset. We used the Outcome and Assessment Information Set (OASIS) and service use records to examine HH utilization, patient characteristics, visit timeliness, medication issues, and 30-day emergency department (ED) visit and rehospitalization. Across sites, admissions to HH declined by 23% in the pandemic's first year. Compared to the year prior, older adults receiving HH services during the first year of the pandemic were more likely to be younger, have worse mental, respiratory, and functional status in some areas, and be assessed by HH providers as having higher risk of rehospitalization. Thirty-day rehospitalization rates were lower during the first year of the pandemic. COVID-positive HH patients had lower odds of 30-day ED visit or rehospitalization. At the NYC site, extended duration between discharge and first HH visit was associated with reduced 30-day ED visit or rehospitalization. HH patient characteristics and utilization were distinct in Baltimore versus NYC in the initial year of the COVID-19 pandemic. Study findings suggest some older adults who needed HH may not have received it, since the decrease in HH services occurred as SNF use decreased nationally. Findings demonstrate the importance of understanding HH agency responsiveness during public health emergencies to ensure older adults' access to care.

Data Acquiring System for Gas Turbine Engine’s Dynamic Performance; Build and Validate

ABSTRACT Gas Turbine Engines (GTE) have the highest power-to-weight ratio among Internal Combustion Engines (ICE). Its modularity and ability to utilize various types of fuel make it highly recommended in power plants, naval transportation, and, of course, the most equipped in aviation. The lack of GTEs’ real data is increasing a recognized need for collecting informative real data sets from GTEs for modeling, monitoring, and fault diagnosis and isolation (FDI). In the present article, a robust versatile data acquisition measuring system has been built to collect real-time data from an axial turboshaft GTE. The system facilitates connectivity and integrity by means of two wiring harnesses connecting the data acquisition card (DAQ), the engine’s control unit, and the operator’s control console. Mobility is guaranteed using a wireless connection between DAQ and a customized programmable LabVIEW interface application. Many experiments have been conducted in different operating conditions to collect the engine and controller’s data in different regimes. The measured signals have been filtered using software-implemented filters and transformed into the right measurement quantities. The experiment data have been analyzed for different regimes, and the milestone documented readings were compared with the measured ones to validate the accuracy and precision of the measuring system. The system is valid for collecting informative data sets that are usable for modeling the engine’s dynamic performance and fault detection.

What can analyst forecasts tell us about imperfect information?

We used two imperfect information models, taking analyst as representatives of informed investors, quantifying the potential information constraints of the financial market and different companies, and found that China's financial market has more serious information friction than the United States, which means that people update the information set every seven to eight months on average or people's weight on new information is only 0.37. At the same time, we also found evidence consistent with cognitive constraints or rational inattention theory, that the more volatile a company's and industry performance, the slower analysts are to respond to that company's information.

Open-loop and closed-loop local and remote stochastic nonzero-sum game with inconsistent information structure

In this paper, the open-loop and closed-loop local and remote stochastic nonzero-sum game (LRSNG) problem is investigated. Different from previous works, the stochastic nonzero-sum game problem under consideration is essentially a special class of two-person nonzero-sum game problem, in which the information sets accessed by the two players are inconsistent. More specifically, both the local player and the remote player are involved in the system dynamics, and the information sets obtained by the two players are different, and each player is designed to minimise its own cost function. For the considered LRSNG problem, both the open-loop and closed-loop Nash equilibrium are derived. The contributions of this paper are given as follows. Firstly, the open-loop optimal Nash equilibrium is derived, which is determined in terms of the solution to the forward and backward stochastic difference equations (FBSDEs). Furthermore, by using the orthogonal decomposition method and the completing square method, the feedback representation of the optimal Nash equilibrium is derived for the first time. Finally, the effectiveness of our results is verified by a numerical example.

Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework

ABSTRACT: Plant-allergenic proteins (PAPs) have the potential to induce allergic reactions in certain individuals. While these proteins are generally innocuous for the majority of people, they can elicit an immune response in those with particular sensitivities. Thus, screening and prioritizing the allergenic potential of plant proteins is indispensable for the development of diagnostic tools, therapeutic interventions or medications to treat allergic reactions. However, investigating the allergenic potential of plant proteins based on experimental methods is costly and labour-intensive. Therefore, we develop StackPAP, a three-layer stacking ensemble framework for accurate large-scale identification of PAPs. In StackPAP, at the first layer, we conducted a comprehensive analysis of an extensive set of feature descriptors. Subsequently, we selected and fused five potential sequence-based feature descriptors, including amphiphilic pseudo-amino acid composition, dipeptide deviation from expected mean, amino acid composition, pseudo amino acid composition and dipeptide composition. Additionally, we applied an efficient genetic algorithm (GA-SAR) to determine informative feature sets. In the second layer, 12 powerful machine learning (ML) methods, in combination with all the informative feature sets, were employed to construct a pool of base classifiers. Finally, 13 potential base classifiers were selected using the GA-SAR method and combined to develop the final meta-classifier. Our experimental results revealed the promising prediction performance of StackPAP, with an accuracy, Matthew’s correlation coefficient and AUC of 0.984, 0.969 and 0.993, respectively, as judged by the independent test dataset. In conclusion, both cross-validation and independent test results indicated the superior performance of StackPAP compared with several ML-based classifiers. To accelerate the identification of the allergenicity of plant proteins, we developed a user-friendly web server for StackPAP (https://pmlabqsar.pythonanywhere.com/StackPAP). We anticipate that StackPAP will be an efficient and useful tool for rapidly screening PAPs from a vast number of plant proteins. Communicated by Ramaswamy H. Sarma

Clinician Perception of Likelihood of Death in the Next Year Is Associated With 1-Year Mortality and Hospice Use Among Older Adults Receiving Home Health Care.

Background: Given the complex care needs of older adults receiving home health care (HHC), it is important for HHC clinicians to identify those with limited prognosis who may benefit from a transition to hospice care. Objectives: To assess the association between HHC clinician-identified likelihood of death and (1) 1-year mortality, and (2) hospice use. Methods: Prospective cohort study from the National Health and Aging Trends Study (NHATS) waves 2011-2018, linked to the Outcomes and Assessment Information Set (OASIS) HHC assessment and Medicare data among 915 community-dwelling NHATS respondents. HHC clinician-identified likelihood of death/decline was determined using OASIS item M1034. Multivariable logistic regression was used to assess the association between clinician-identified likelihood of death/decline and 1-year mortality and hospice use. Results: HHC clinicians identified 42% of the sample as at increased risk of decline or death. One year mortality was 22.3% (n = 548), and 15.88% (n = 303) used hospice within 12 months of HHC. HHC clinician-perceived likelihood of death/decline was associated with greater odds of 1-year mortality (odds ratio [OR], 6.57; confidence interval (95% CI), 2.56-16.90) and was associated with greater likelihood of hospice use (OR, 1.61; 95% CI, 1.00-2.62). Conclusion: HHC clinician perception of patients' risk of death or decline is associated with 1-year mortality. A better understanding of HHC patients at high risk for mortality can facilitate improved care planning and identification of homebound older adults who may benefit from hospice.

If You’re Happy, Then You Know It: The Logic of Happiness . . . and Sadness

The article proposes a formal semantics of happiness and sadness modalities in the imperfect information setting. It shows that these modalities are not definable through each other and gives a sound and complete axiomatization of their properties.

Correction of single error bursts beyond the code correction capability using information sets

Correction of single error bursts beyond the code correction capability using information sets

Functional Status Associations With Treatment Receipt and Outcomes Among Older Adults Newly Diagnosed With Multiple Myeloma.

Multiple myeloma (MM) is a prevalent hematologic malignancy in older adults, who often experience physical disability, increased health care usage, and reduced treatment tolerance. Home health (HH) services are frequently used by this group, but the relationship between disability, HH use, and MM treatment receipt is unclear. This study examines the connections between disability, treatment receipt, and survival outcomes in older adults with newly diagnosed MM using a nationwide data set. The SEER-Medicare data set was used to identify adults aged 66 years and older diagnosed with MM from 2010 to 2017, who used HH services the year before diagnosis. Disability was assessed with the Outcome and Assessment Information Set, using a composite score derived from items related to ability to complete activities of daily living. Mortality, therapy receipt, and health care utilization patterns were evaluated. Of 37,280 older adults with MM, 6,850 (18.2%) used HH services before diagnosis. Moderate disability at HH assessment resulted in similar MM-directed therapy receipt as mild disability, with comparable health care usage after diagnosis to severe disability. HH users had a higher comorbidity burden and higher mortality (adjusted risk ratio for 3-year mortality: 1.59 [95% CI, 1.55 to 1.64]). Severe functional disability before diagnosis was strongly related to postdiagnosis mortality. Among older adults with MM receiving HH services, disability is a predictor of early mortality. Moderately disabled individuals undergo similar therapy intensity as the mildly disabled but experience increased acute care utilization. Previous HH use could identify patients with MM requiring intensive support during therapy initiation.

Network Centrality and Dissent Voting by Directors on Boards

AbstractWe investigate how directors’ positions within board interlocking networks influence their monitoring behaviors from a social network perspective. We argue that the effectiveness of directors’ monitoring of a firm's management depends on their ability to overcome the information barrier and their motivation to develop a public reputation in the directorship market. We further contend that network centrality can supplement directors’ existing information set and facilitate reputation spillover, leading to an increase in the extent of their dissent on boards. We analyze the unique individual-director-level data of Chinese firms and find that directors occupying positions of greater centrality in the board interlock network are more likely to dissent. We then examine the underlying mechanisms of information and reputation through two moderators: firm transparency and media mention of a director. We also find that the effect of network centrality on dissent is weaker for independent directors. Our study advances the corporate governance literature by examining the micro-foundations of board monitoring and providing a social network perspective.

Data-driven autoencoder neural network for onboard BMS Lithium-ion battery degradation prediction

An autoencoder based neural network architecture, CD-Net, is proposed to predict Lithium-ion battery capacity degradation as a function of operation time as part of a battery management system. CD-Net's generalization performance on various LIB cell chemistry is tested. The incorporation of cell chemistry in CD-Net leads to an improvement in the overall battery capacity prediction accuracy of >2 % for LiNiMnCoO2 cells, >5 % for LiNiCoAlO2 cells, and >12 % for LiFePO4 cells when compared to the similar ML models that do not incorporate cell chemistry information. A comparison of onboard battery health prediction using CD-Net against support vector regression, Bayesian regression, and Gaussian process regression-based approaches shows that CD-Net has higher computational efficiency with <2 % of relative remaining useful life (RUL) prediction error in a no-cell chemistry information setting. In summary, our work presents a chemistry-independent neural network model tailored specifically for onboard BMS applications, showcasing notable predictive capabilities in the context of Lithium-ion battery health assessment.

Unlocking the potential: A systematic analysis of big data applications in Ghanaian academic libraries

Big data can be described as a collection of varying information sets that are expanding at exponential rates and which may be gathered by companies and analysed to extract valuable insights. The purpose of this systematic review was to explain the possible benefits and difficulties that big data may present for Ghanaian academic libraries. A federated search was conducted in several academic databases subscribed to by the Consortium of Academic and Research Libraries in Ghana (CARLIGH) on the terms big data and academic libraries. Relevant articles searched and retrieved were based on abstract and title and spanned the years 2001 to 2023. Of the total documents retrieved, 65 met the inclusion criteria set by the researchers. Key findings were that big data is useful for enhancing collections of academic libraries, developing personalized services, analysing user behaviour, and assisting in decision-making. To maximise gains, addressing issues including protecting user privacy, managing data, and shortage of skilled personnel is necessary. Academic libraries in Ghana are advised to collaborate with several important stakeholders on using big data while ensuring the proper setting is available and accessible. The study contributes to the literature on big data in academic libraries by integrating researchers’ scientific insights and recommendations. It also outlines the main implementation issues based on a thorough examination of recent relevant literature in the field. It is recommended that an effective data management plan and other strategies be implemented by academic libraries that seek to incorporate big data in their operations.

Investigating the Social Sustainability of Immersive Virtual Technologies in Higher Educational Institutions: Students’ Perceptions toward Metaverse Technology

The Metaverse technology (MVTECH) is an immersive virtual sphere where people interact with each other via avatars. The MVTECH promised to provide a number of potentials for various sectors including higher education. Despite the fact that MVTECH promotes virtual social interaction between people (e.g., university students), there is a lack of knowledge on what affects users’ perceptions regarding its social sustainability in HEIs, specifically in developing nations. Therefore, this research paper aims to determine the variables that affect the learners’ perceptions toward social sustainability (SS) of MVTECH in higher educational institutions (HEIs) in Jordan. A study model was formulated by integrating the core factors of the “unified theory of acceptance and use of technology” (UTAUT) (“performance expectancy, PE; effort expectancy, EE; social influence, SI; and facilitating conditions, FC”) with “perceived curiosity” (PC) and “extraversion” (EXT) factors. Both PC and EXT were included as context-related variables that may possibly contribute to enhancing the applicability of UTAUT to a wide range of information technologies and settings. Data were collected from 422 students enrolled in Jordanian universities based on an online survey. The analysis of the “structural equation modeling” (SEM) found that students’ perceptions toward Metaverse were significantly influenced by PE, FC, and EXT. Furthermore, the PC construct significantly affected the EE construct. However, EE and SI were revealed to have no significant impact on SS. Drawing from these results, the study makes a number of theoretical advances and clarifies a number of practical implications for those involved in the development, design, and decision-making processes that support the use of the Metaverse in HEIs.