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A computational Analysis on 5-Qubit Quantum Circuits Designed With Electron Spins in Silicon

IEEE NANOTECHNOLOGY MAGAZINE 2024年第6期18卷 11-20页

作者： Ryu, Junghee Ryu, Hoon Korea Inst Sci & Technol Informat Ctr Quantum Informat R&D Daejeon 34141 South Korea Univ Sci & Technol Div Quantum Informat Daejeon 34113 South Korea Kumoh Natl Inst Technol Dept Artificial Intelligence Engn Gumi 39177 Gyeongsangbuk D South Korea

We computationally investigate the programmability of quantum circuits that are manipulated with electron spins in semiconductor devices based on the silicon/silicon-germanium heterostructure. Using our multi-scale modeling approach whose workloads are fairly accelerated with a parallel computing, we design an electrically defined quantum dot structure that confines up to 5 electron spins with bias controls, and calculate the sets of electrical biases that serve as the standard operation modes that can initialize the system with weakly or strongly correlated spins. With the secured operation modes, we discuss how essential universal gates can be implemented, and demonstrate end-to-end operations of two different 5-qubit algorithm circuits. This work not only reveals the fundamental guidelines for designs of silicon quantum processors that may not be easy to be procured directly from existing commercial softwares or experiments, but presents the details of a practical method for a computational analysis of quantum information processing in silicon devices that would be hard to be conducted with ab-initio simulations.

关键词： Silicon devices Electrons Mathematical models Logic Quantum computing Electric potential Silicon germanium Periodic structures Electrodes computational modeling Quantum dots Quantum circuit Program processors Parallel processing

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computational markers of risky decision-making predict for relapse to alcohol

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EUROPEAN ARCHIVES OF PSYCHIATRY AND CLINICAL NEUROSCIENCE 2024年第2期274卷 353-362页

作者： Yuan, Wei Chen, Meng Wang, Duan-Wei Li, Qian-Hui Yin, Yuan-Yuan Li, Bin Wang, Hai-Rong Hu, Ji Gong, Yuan-Dong Yuan, Ti-Fei Yu, Tian-Gui Shandong Mental Hlth Ctr Dept Addict Med Jinan 250014 Peoples R China Liaoning Normal Univ Brain & Cognit Neurosci Res Ctr Dalian 116029 Peoples R China Sun Yat Sen Univ Affiliated Hosp 7 Div Gastroenterol Shenzhen 518107 Peoples R China Wenzhou Med Univ Sch Mental Hlth Wenzhou 325035 Peoples R China ShanghaiTech Univ Sch Life Sci & Technol Shanghai 201210 Peoples R China Shanghai Jiao Tong Univ Brain Hlth Inst Natl Ctr Mental Disorders Shanghai Mental Hlth CtrSch MedShanghai Key Lab Shanghai Peoples R China Nantong Univ Co Innovat Ctr Neuroregenerat Nantong Jiangsu Peoples R China

BackgroundRelapse remains the major challenge in treatment of alcohol use disorder (AUD). Aberrant decision-making has been found as important cognitive mechanism underlying relapse, but factors associated with relapse vulnerability are unclear. Here, we aim to identify potential computational markers of relapse vulnerability by investigating risky decision-making in individuals with ***-six healthy controls and fifty-two individuals with AUD were recruited for this study. The risk-taking propensity of these subjects was investigated using the balloon analog risk task (BART). After completion of clinical treatment, all individuals with AUD were followed up and divided into a non-relapse AUD group and a relapse AUD group according to their drinking *** risk-taking propensity differed significantly among healthy controls, the non-relapse AUD group, and the relapse AUD group, and was negatively associated with the duration of abstinence in individuals with AUD. Logistic regression models showed that risk-taking propensity, as measured by the computational model, was a valid predictor of alcohol relapse, and higher risk-taking propensity was associated with greater risk of relapse to *** study presents new insights into risk-taking measurement and identifies computational markers that provide prospective information for relapse to drink in individuals with AUD.

关键词： Alcohol relapse Risk-taking Balloon analog risk task computational modeling

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A computational modeling of student cognitive processes in science education

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COMPUTERS & EDUCATION 2014年第0期79卷 116-125页

作者： Lamb, Richard L. Vallett, David B. Akmal, Tariq Baldwin, Kathryn Washington State Univ Pullman WA 99164 USA Univ Nevada Las Vegas Las Vegas NV USA

The purpose of this paper is to explain and document the creation of a computational model in the form of an Artificial Neural Network (ANN) capable of simulating student cognition. Specifically, the model simulates students' cognition as they complete activities within a science classroom. This study also seeks to examine the effects, as evidenced in the ANN, of an intervention designed to develop increased levels of critical thinking related to science skills. This model is based on the identification of cognitive attributes and integration of two advanced measurement frameworks: cognitive diagnostics and Item Response Theory. Both frameworks examine student response patterns, providing initial inputs for the ANN portion of the model. Once initial task response patterns are identified, they are parameterized and presented to the ANN. The ANN within this study is the foundational component of a computational model based upon the interaction of multiple, connected, adaptive processing elements know as cognitive attributes. These cognitive attributes process student responses to cognitive tasks within science tasks. Using the Student Task and Cognition Model (STAC-M), the study authors simulated a cognitive training intervention using a randomized control trial design of 100,000 students. Results of the simulation suggest that it is possible to increase levels of student success using a targeted cognitive attribute approach and that computational modeling provides a means to test educational theory for future education research. The paper also discusses limitations of the use of this computational model within education and the possible future directions for educators and researchers. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： computational modeling Cognitive processing Science education Critical reasoning Serious educational games

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computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets

IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY

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IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2024年 5卷 611-620页

作者： Sharafutdinov, Konstantin Fritsch, Sebastian Johannes Iravani, Mina Ghalati, Pejman Farhadi Saffaran, Sina Bates, Declan G. Hardman, Jonathan G. Polzin, Richard Mayer, Hannah Marx, Gernot Bickenbach, Johannes Schuppert, Andreas Rhein Westfal TH Aachen Inst Computat Biomed D-52062 Aachen Germany Rhein Westfal TH Aachen Joint Res Ctr Computat Biomed D-52062 Aachen Germany SMITH Consortium German Med Informat Initiat D-04103 Leipzig Germany Univ Hosp RWTH Aachen Dept Intens Care Med D-52056 Aachen Germany Forschungszentrum Juelich Juelich Supercomp Ctr D-52428 Julich Germany Univ Warwick Sch Engn Coventry CV4 7AL England Univ Nottingham Sch Med Nottingham NG7 2RD England Bayer AG Syst Pharmacol & Med D-51368 Leverkusen Germany

Goal: Machine learning (ML) technologies that leverage large-scale patient data are promising tools predicting disease evolution in individual patients. However, the limited generalizability of ML models developed on single-center datasets, and their unproven performance in real-world settings, remain significant constraints to their widespread adoption in clinical practice. One approach to tackle this issue is to base learning on large multi-center datasets. However, such heterogeneous datasets can introduce further biases driven by data origin, as data structures and patient cohorts may differ between hospitals. Methods: In this paper, we demonstrate how mechanistic virtual patient (VP) modeling can be used to capture specific features of patients' states and dynamics, while reducing biases introduced by heterogeneous datasets. We show how VP modeling can be used for data augmentation through identification of individualized model parameters approximating disease states of patients with suspected acute respiratory distress syndrome (ARDS) from observational data of mixed origin. We compare the results of an unsupervised learning method (clustering) in two cases: where the learning is based on original patient data and on data derived in the matching procedure of the VP model to real patient data. Results: More robust cluster configurations were observed in clustering using the model-derived data. VP model-based clustering also reduced biases introduced by the inclusion of data from different hospitals and was able to discover an additional cluster with significant ARDS enrichment. Conclusions: Our results indicate that mechanistic VP modeling can be used to significantly reduce biases introduced by learning from heterogeneous datasets and to allow improved discovery of patient cohorts driven exclusively by medical conditions.

关键词： Data models computational modeling Hospitals Optimization Lung Biological system modeling Diseases ARDS computational simulation dataset bias machine learning virtual patients

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Evaluating Climate and Community Health Care Infection Risks through computational Predesign

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TECHNOLOGY-ARCHITECTURE + DESIGN 2024年第1期8卷 70-82页

作者： Platt, Lisa Sundahl Mahmoudi, Ali Univ Florida Coll Design Construct & Planning Gainesville FL 32611 USA

This study highlights the value of computational modeling in health care infection prevention planning, especially in response to climate-induced community risk factors. It investigates the relationship between Socioecological modeling features and Principal Component Analysis derived parameters in a Decision Tree classification model, predicting regional risk levels of Clostridioides difficile Infection (CDI) and Methicillin-resistant Staphylococcus aureus health care-associated infections (MRSA) Hospital Acquired Infections (HAI). The research confirms that climate-related factors, such as housing instability and medical infrastructure strain, impact regional infectivity risks, influencing health care environment resilience. It emphasizes the link between Clostridioides difficile and MRSA HAI, highlighting the role of Built Environment Vulnerability. These findings have vital implications for hospital predesign, material selection, layout, and hand sanitization device choices, and underscore optimizing patient flow in health care environment planning.

关键词： Architectural Programming Infection Prevention computational modeling

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computational Approaches for Predicting Preterm Birth and Newborn Outcomes

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CLINICS IN PERINATOLOGY 2024年第2期51卷 461-473页

作者： Seong, David Espinosa, Camilo Aghaeepour, Nima Laboratory, Aghaeepour Stanford Univ Immunol Program Sch Med 300 Pasteur DrGrant S280 Stanford CA 94305 USA Stanford Univ Med Scientist Training Program Sch Med 300 Pasteur DrGrant S280 Stanford CA 94305 USA Stanford Univ Dept Microbiol & Immunol Sch Med 300 Pasteur DrGrant S280 Stanford CA 94305 USA Stanford Univ Sch Med Dept Anesthesiol Perioperat & Pain Med 300 Pasteur Dr Stanford CA 94305 USA Stanford Univ Dept Pediat Sch Med 300 Pasteur DrGrant S280 Stanford CA 94305 USA Stanford Univ Dept Biomed Data Sci 300 Pasteur DrGrant S280 Stanford CA 94305 USA Stanford Univ Dept Pathol Sch Med 300 Pasteur DrGrant S280 Stanford CA 94305 USA 300 Pasteur Dr Grant S280 Stanford CA 94305 USA

Prematurity, currently defined as birth before 37 weeks of gestation, encompasses a heterogenous population of infants. The exclusive reliance on GA and birth weight as the principal measures of prematurity fails to account for neonates who, despite being born well before the arbitrary 37-week demarcation, do not manifest any adverse health complications. Here, we have presented studies that have utilized a combination of EHR, biological, and SDOH data to generate more holistic and accurate Acknowledgments The authors listed here are part of the Aghaeepour Laboratory that contributed to the writing of this manuscript: Eloise Berson, Dipro Chakraborty, Alan L. Chang, Will Haberkorn, Natasha Harrison, Debapriya Hazra, Tomin James, Yeasul Kim, Ivana Marić, Samson Mataraso, Neshat Mohammadi, Thanaphong Phongpreecha, S Momsen Reincke, Jonathan Reiss, Geetha Saarunya, Sayane Shome, Nolan Shu, Yuqi Tan, Feng Xie, and Lei Xue from Stanford University. Disclosure The authors have nothing to disclose.

关键词： Preterm birth computational modeling Multimodal Neonatal outcomes

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Analyzing Foam Injection Dynamics in Porous Media: A Combined computational and Experimental Approach

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Defect and Diffusion Forum 2024年 435卷 47-57页

作者： Werneck, Yan Barbosa Motta, Luciana Paixão Bastos, Flávia de Dos Santos, Rodrigo Weber Federal University of Juiz de Fora - Juiz de Fora MG Brazil

The study of foam injection dynamics into porous media holds numerous applications, with its primary use being the optimization of the oil extraction process. Additionally, it finds utility in various other domains, including soil decontamination techniques. This study focuses on evaluating foam injection through low-cost experiments and computational models. The model integrates a classic two phaseimmiscible fluid flow in a porous media framework, augmented by a mobility reduction factor to simulate foam’s impact on gas phase mobility. Simple experiments were conducted to assess water displacement using both gas and gas-foam injection methods. Results revealed that while gas injection tends to form preferential paths rapidly, leading to gas disruption, foam injection generates a more compact front, enhancing sweep efficiency by mitigating preferential path formation. The computational model successfully reproduced the experimental findings qualitatively. This research introduces a cost-effective experiment suitable for didactic purposes, illustrating complex concepts and underscoring the complementary nature of experimental and computational methodologies in advancing engineering techniques. © 2024 Trans Tech Publications Ltd, All Rights Reserved.

关键词： computational modeling Foam Dynamics Oil Extraction Reservoir Simulation

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Reinforcement learning and working memory in mood disorders: A computational analysis in a developmental transdiagnostic sample

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JOURNAL OF AFFECTIVE DISORDERS 2024年 344卷 423-431页

作者： Cheng, Ziwei Moser, Amelia D. Jones, Matt Kaiser, Roselinde H. Univ Colorado Boulder Dept Psychol & Neurosci Boulder CO USA Univ Colorado Boulder Inst Cognit Sci Boulder CO USA Univ Colorado Boulder Res Affect Disorders & Dev Lab Muenzinger D244USB 345 Boulder CO 80309 USA

Background: Mood disorders commonly onset during adolescence and young adulthood and are conceptually and empirically related to reinforcement learning abnormalities. However, the nature of abnormalities associated with acute symptom severity versus lifetime diagnosis remains unclear, and prior research has often failed to disentangle working memory from reward processes. Methods: The present sample (N = 220) included adolescents and young adults with a lifetime history of unipolar disorders (n = 127), bipolar disorders (n = 28), or no history of psychopathology (n = 62), and varying severity of mood symptoms. Analyses fitted a reinforcement learning and working memory model to an instrumental learning task that varied working memory load, and tested associations between model parameters and diagnoses or current ***: Current severity of manic or anhedonic symptoms negatively correlated with task performance. Participants reporting higher severity of current anhedonia, or with lifetime unipolar or bipolar disorders, showed lower reward learning rates. Participants reporting higher severity of current manic symptoms showed faster working memory decay and reduced use of working memory. Limitations: computational parameters should be interpreted in the task environment (a deterministic reward learning paradigm), and developmental population. Future work should test replication in other paradigms and ***: Results indicate abnormalities in reinforcement learning processes that either scale with current symptom severity, or correspond with lifetime mood diagnoses. Findings may have implications for understanding reward processing anomalies related to state-like (current symptom) or trait-like (lifetime diagnosis) aspects of mood disorders.

关键词： Reward learning Executive functions Mood disorders computational modeling

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Impact of Selective Posterior Cruciate Ligament Fiber Release on Femoral Rollback in Cruciate-Retaining Total Knee Arthroplasty: A computational Study

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JOURNAL OF ARTHROPLASTY 2024年第8期39卷 S345-S352页

作者： Pourmodheji, Reza Chalmers, Brian P. Debbi, Eytan M. Long, William J. Wright, Timothy M. Westrich, Geoffrey H. Mayman, David J. Imhauser, Carl W. Sculco, Peter K. Kahlenberg, Cynthia A. Hosp Special Surg Dept Biomech New York NY USA Hosp Special Surg Adult Reconstruct & Joint Replacement New York NY USA

Background: Partial or total release of the posterior cruciate ligament (PCL) is often performed intraoperatively in cruciate-retaining total knee arthroplasty (CR-TKA) to alleviate excessive femoral rollback. However, the effect of the release of selected fibers of the PCL on femoral rollback in CR-TKA is not well understood. Therefore, we used a computational model to quantify the effect of selective PCL fiber releases on femoral rollback in CR-TKA. Methods: computational models of 9 cadaveric knees (age: 63 years, range 47 to 79) were virtually implanted with a CR-TKA. Passive flexion was simulated with the PCL retained and after serially releasing each individual fiber of the PCL, starting with the one located most anteriorly and laterally on the femoral notch and finishing with the one located most posteriorly on the medial femoral condyle. The experiment was repeated after releasing only the central PCL fiber. The femoral rollback of each condyle was defined as the anterior-posterior distance between tibiofemoral contact points at 0 degrees and 90 degrees of flexion. Results: Release of the central PCL fiber in combination with the anterolateral (AL) fibers, reduced femoral rollback a median of 1.5 [0.8, 2.1] mm (P = .01) medially and by 2.0 [1.2, 2.5] mm (P = .04) laterally. Releasing the central fiber alone reduced the rollback by 0.7 [0.4, 1.1] mm (P < .01) medially and by 1.0 [0.5, 1.1] mm (P < .01) laterally, accounting for 47 and 50% of the reduction when released in combination with the AL fibers. Conclusions: Releasing the central fibers of the PCL had the largest impact on reducing femoral rollback, either alone or in combination with the release of the entire AL bundle. Thus, our findings provide clinical guidance regarding the regions of the PCL that surgeons should target to reduce femoral rollback in CR-TKA. (c) 2024 Elsevier Inc. All rights reserved.

关键词： total knee arthroplasty cruciate-retaining femoral rollback posterior cruciate ligament selective release computational modeling

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Enhanced decision-making in nicotine dependent individuals who abstain: A computational analysis using Hierarchical Drift Diffusion modeling

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DRUG AND ALCOHOL DEPENDENCE 2023年第1期250卷 110890-110890页

作者： Biernacki, Kathryn Molokotos, Elena Han, Chungmin Dillon, Daniel G. Leventhal, Adam M. Janes, Amy C. NIDA NIH Baltimore MD 21224 USA Suffolk Univ Boston MA 02116 USA CBTeam Lexington MA 02421 USA McLean Hosp Ctr Depress Anxiety & Stress Res Belmont MA 02478 USA Harvard Med Sch Boston MA 02115 USA Univ Southern Calif Inst Addict Sci Los Angeles CA 90033 USA

Background: Variability in decision-making capacity and reward responsiveness may underlie differences in the ability to abstain from smoking. computational modeling of choice behavior, as with the Hierarchical Drift Diffusion Model (HDDM), can help dissociate reward responsiveness from underlying components of decision -making. Here we used the HDDM to identify which decision-making or reward-related parameters, extracted from data acquired in a reward processing task, contributed to the ability of people who smoke that are not seeking treatment to abstain from cigarettes during a laboratory task. Methods: 80 adults who smoke cigarettes completed the Probabilistic Reward Task (PRT) -a signal detection task with a differential reinforcement schedule -following smoking as usual, and the Relapse Analogue Task (RAT) -a task in which participants could earn money for delaying smoking up to 50 min -after a period of overnight abstinence. Two cohorts were defined by the RAT;those who waited either 0-min (n=36) or the full 50-min (n=44) before smoking. Results: PRT signal detection metrics indicated all subjects learned the task contingencies, with no differences in response bias or discriminability between the two groups. However, HDDM analyses indicated faster drift rates in 50-min vs. 0-min waiters. Conclusions: Relative to those who did not abstain, computational modeling indicated that people who abstained from smoking for 50 min showed faster evidence accumulation during reward-based decision-making. These results highlight the importance of decision-making mechanisms to smoking abstinence, and suggest that focusing on the evidence accumulation process may yield new targets for treatment.

关键词： Decision-making Nicotine computational modeling Reward response Drift diffusion

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