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A I I E Transactions 2024年第3期56卷

作者： Yaren Bilge Kaya Kayse Lee Maass Geri L. Dimas Renata Konrad Andrew C. Trapp Meredith Dank a Mechanical and Industrial Engineering Northeastern University Boston MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USA c WPI Business School Worcester Polytechnic Institute Worcester MA USA b Data Science Program Worcester Polytechnic Institute Worcester MA USAc WPI Business School Worcester Polytechnic Institute Worcester MA USA d New York University New York City NY USA

Recent estimates indicate that there are over a million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services such as physical and mental healthcare is an effective response to the vulnerability of RHY towards being trafficked, the number of youth experiencing homelessness exceeds the capacity of available housing resources in most US communities. We undertake a RHY-informed, systematic, and data-driven approach to project the collective capacity required by service providers to adequately meet the needs of RHY in New York City, including those most at risk of being trafficked. Our approach involves an integer linear programming model that extends the multiple multidimensional knapsack problem and is informed by partnerships with key stakeholders. The mathematical model allows for time-dependent allocation and capacity expansion, while incorporating stochastic youth arrivals and length of stays, services provided in a periodic fashion, and service delivery time windows. Our RHY and service provider-centered approach is an important step toward meeting the actual, rather than presumed, survival needs of vulnerable youth.

关键词： Human trafficking homelessness capacity expansion mixed-integer linear programming homeless shelters

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programming Variational Quantum Circuits with Quantum-Train Agent

Programming Variational Quantum Circuits with Quantum-Train ...

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Quantum Communications, Networking, and Computing (QCNC), International Conference on

作者： Chen-Yu Liu Samuel Yen-Chi Chen Kuan-Cheng Chen Wei-Jia Huang Yen-Jui Chang Graduate Institute of Applied Physics National Taiwan University Taipei Taiwan Hon Hai (Foxconn) Research Institute Taipei Taiwan Wells Fargo New York NY USA Department of Electrical and Electronic Engineering Imperial College London London UK Centre for Quantum Engineering Science and Technology (QuEST) Imperial College London London UK Quantum Information Center Chung Yuan Christian University Taoyuan City Taiwan Master Program in Intelligent Computing and Big Data Chung Yuan Christian University Taoyuan City Taiwan

ISBN: (数字)9798331531591

ISBN: (纸本)9798331531607

This study introduces the Quantum-Train Quantum Fast Weight programmer (QT-QFWP) framework, enabling efficient and scalable programming of variational quantum circuits (VQCs) through quantum-driven parameter updates for the classical slow programmer controlling the fast programmer VQC. By optimizing quantum and classical parameter management, QT-QFWP significantly reduces parameters (by 70–90%) compared to Quantum Long Short-Term Memory (QLSTM) and Quantum Fast Weight programmer (QFWP) while maintaining accuracy. Benchmarking on time-series tasks—including Damped Simple Harmonic Motion (SHM), NARMA5, and Simulated Gravitational Waves (GW)—demonstrates superior efficiency and predictive accuracy. QT-QFWP is particularly advantageous for near-term quantum systems, addressing qubit and gate fidelity constraints, enhancing VQC deployment in time-sensitive applications, and expanding quantum computing’s role in machine learning.

关键词： Quantum system Accuracy Computational modeling Qubit Neural networks programming Predictive models Quantum circuit Physics Long short term memory

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data science in science: Special Issue on data science in the Brain sciences

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data science in science 2023年第1期2卷

作者： Euan, Carolina Fiecas, Mark B. Ombao, Hernando Matteson, David S. Department of Mathematics and Statistics Lancaster University United Kingdom Division of Biostatistics University of Minnesota United States Statistics Program King Abdullah University of Science and Technology Saudi Arabia Department of Statistics and Data Science Cornell University United States

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Recent trend of metal promoter role for CO₂ hydrogenation to C₁ and C₂₊ products

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South African Journal of Chemical Engineering 2023年 44卷 14-30页

作者： Sholeha, Novia Amalia Holilah, Holilah Bahruji, Hasliza Ayub, Athirah Widiastuti, Nurul Ediati, Ratna Jalil, Aishah Abdul Ulfa, Maria Masruchin, Nanang Nugraha, Reva Edra Prasetyoko, Didik College of Vocational Studies Bogor Agricultural University (IPB University) Jalan Kumbang No. 14 Bogor 16151 Indonesia Research Center for Biomass and Bioproducts National Research and Innovation Agency of Indonesia (BRIN) Cibinong 16911 Indonesia Department Chemistry Faculty of Science and Data Analytics Institut Teknologi Sepuluh Nopember Kampus ITS Keputih Surabaya Sukolilo 60111 Indonesia Centre of Advanced Material and Energy Science Universiti Brunei Darussalam Jalan Tungku Link BE 1410 Brunei Darussalam Department of Chemical Engineering Faculty of Chemical and Energy Engineering Universiti Teknologi Malaysia 81310 UTM Skudai Johor Johor Bahru Malaysia Centre of Hydrogen Energy Institute of Future Energy Universiti Teknologi Malaysia Skudai 81310 UTM Skudai Johor Johor Bahru Malaysia Chemistry Education Study Program Faculty of Teacher Training and Education Sebelas Maret University Jl. Ir. Sutami 36A Surakarta 57126 Indonesia Research Collaboration Center for Biomass and Biorefinery between BRIN and Universitas Padjadjaran Jatinangor 45363 Indonesia Department of Chemical Engineering Faculty of Engineering Universitas Pembangunan Nasional “Veteran” Jawa Timur East Java Surabaya 60294 Indonesia

CO2 hydrogenation as sustainable route for generation of value-added carbon feedstock is identified as green pathway for mitigation of greenhouse gasses emission. CO2 methanation is one of the promising solutions, whi... 详细信息

CO₂ hydrogenation as sustainable route for generation of value-added carbon feedstock is identified as green pathway for mitigation of greenhouse gasses emission. CO₂ methanation is one of the promising solutions, which only requires reactions at atmospheric pressure while utilizing metal catalysts to overcome kinetic limitations. Metal catalysts can be promoted to alter reducibities, CO₂ adsorption capacities, and H_2[sbnd]CO₂ dissociation potential. The role of metal promoter such as rare earth elements (Ce, Mn, Co, and La) and alkali and alkali earth metal (Li, Na, Ca, and K) will be discussed within the scope of CO₂ methanation. The aspect of catalysts modification towards hydrogen dissociation potential and surface oxygen vacancy will be emphasized to enhance selectivity for methane. Another pathway for CO₂ hydrogenation is via further conversion into longer chain molecules such as olefin and ethanol. The benefit of metal promoter will be discussed in this review on the effect towards promoting C[sbnd]C coupling reaction for producing longer chain alcohol and light olefin. The strategies to develop active catalysts for the coupling reaction of C[sbnd]C will be emphasized with the promoter introduction. For the hydrogenation of CO₂ to longer chain molecules, the main metal catalysts Ni, Pd, Rh, and Co, and their modification with promoter such as Ga, Cu, and alkali metal Na, K will be discussed. A critical analysis of the CO₂ methanation mechanism and further C[sbnd]C reaction to longer chain molecules will be discussed, particularly the effect of metal promoters to stabilize the intermediate and maneuver the catalytic reaction pathway into the desired products. © 2023 The Author(s)

关键词： C1 products C2+ products CO2 Hydrogenation Metal promoter

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SETS of LOW CORRELATION SEQUENCES from CYCLOTOMY

arXiv

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arXiv 2021年

作者： Castello, Jonathan M. Katz, Daniel J. King, Jacob M. Olavarrieta, Alain The Department of Mathematics California State University Northridge United States The Data Science Program George Washington University WashingtonDC United States The Department of Mathematics Las Positas College LivermoreCA United States

Low correlation (finite length) sequences are used in communications and remote sensing. One seeks codebooks of sequences in which each sequence has low aperiodic autocorrelation at all nonzero shifts, and each pair of distinct sequences has low aperiodic crosscorrelation at all shifts. An overall criterion of codebook quality is the demerit factor, which normalizes all sequences to unit Euclidean norm, sums the squared magnitudes of all the correlations between every pair of sequences in the codebook (including sequences with themselves to cover autocorrelations), and divides by the square of the number of sequences in the codebook. This demerit factor is expected to be 1 + 1/N - 1/(N) for a codebook of N randomly selected binary sequences of length , but we want demerit factors much closer to the absolute minimum value of 1. For each N such that there is an N × N Hadamard matrix, we use cyclotomy to construct an infinite family of codebooks of binary sequences, in which each codebook has N - 1 sequences of length p, where p runs through the primes with N | p - 1. As p tends to infinity, the demerit factor of the codebooks tends to 1 + 1/(6(N - 1)), and the maximum magnitude of the undesirable correlations (crosscorrelations between distinct sequences and off-peak autocorrelations) is less than a small constant times √p log(p). This construction also generalizes to nonbinary sequences. Copyright © 2021, The Authors. All rights reserved.

关键词： Binary sequences

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Insights on the influence of natural co-contaminants on the photocatalytic performance of biochar supported p-n heterojunction photocatalyst in an aqueous media

Cleaner Water

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Cleaner Water 2025年 4卷

作者： Saptarshi Ghosh Manoranjan Sahu Aerosol and Nanoparticle Technology Laboratory Environmental Science and Engineering Department Indian Institute of Technology Bombay Mumbai 400076 India Inter-Disciplinary Program in Climate Studies Indian Institute of Technology Bombay Mumbai 400076 India Centre for Machine Intelligence and Data Science Indian Institute of Technology Bombay Mumbai 400076 India

In last few decades, photocatalysis have been extensively studied in a simulated wastewater under laboratory conditions to degrade wide range of organic pollutants. To transfer this technology into a large scale, several approaches have been made focusing on developing novel photocatalyst for maximizing the generation of reactive species and efficient photocatalytic reactor for maximizing photon usage. However, there are some fundamental issues exist with the presence of natural co-contaminants such as competitive ions and natural organic matter (NOM) which can significantly reduce efficiency of the photocatalytic process. Hence, this study investigated role of competitive ions and NOM on photocatalytic oxidation of DMP using p-n heterojunction photocatalyst (O 2 -TiO 2 /CuO) decorated on biochar. The study revealed that competitive ions showed quenching effect towards photogenerated charged pairs and significantly reduces degradation efficiency. Similarly, presence of NOM in small amount could enhance or does not have substantial impact on the removal efficiency of DMP. However, at higher NOM concentration, DMP removal dropped significantly by screening of photons, blocking of adsorption sites of the catalyst and scavenging of positive holes (h + ). In addition, Electron Spin Resonance (ESR) analysis was performed as an evidence for the generation of radicals and quenching experiment was conducted to identify the major contributing radical in the degradation process among multiple radicals generated from the photocatalysis. The findings revealed that among different reactive species OH • is the dominant species in the degradation process and O 2 - • has least contribution to the degradation of DMP.

关键词： Co-contaminants P-n heterojunction Photocatalysis Reactive Oxygen Species Scavengers

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Multimodal Whole Slide Foundation Model for Pathology

arXiv

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arXiv 2024年

作者： Ding, Tong Wagner, Sophia J. Song, Andrew H. Chen, Richard J. Lu, Ming Y. Zhang, Andrew Vaidya, Anurag J. Jaume, Guillaume Shaban, Muhammad Kim, Ahrong Williamson, Drew F.K. Chen, Bowen Almagro-Perez, Cristina Doucet, Paul Sahai, Sharifa Chen, Chengkuan Komura, Daisuke Kawabe, Akihiro Ishikawa, Shumpei Gerber, Georg Peng, Tingying Le, Long Phi Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States John A. Paulson School of Engineering and Applied Sciences Harvard University CambridgeMA United States Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Harvard Data Science Initiative Harvard University CambridgeMA United States Department of Systems Biology Harvard Medical School BostonMA United States Department of Preventive Medicine Graduate School of Medicine The University of Tokyo Tokyo Japan Division of Pathology National Cancer Center Exploratory Oncology Research & Clinical Trial Center Chiba Japan

The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation. The model is publicly accessible at https://***/mahmoodlab/TITAN © 2024, CC BY-NC-ND.

关键词： Self-supervised learning

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How Does ChatGPT Perform on the United States Medical Licensing Examination? The Implications of Large Language Models for Medical Education and Knowledge Assessment

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JMIR Medical Education 2023年 9卷 e45312页

作者： Gilson, Aidan Safranek, Conrad W. Huang, Thomas Socrates, Vimig Chi, Ling Taylor, Richard Andrew Chartash, David Section for Biomedical Informatics and Data Science Yale University School of Medicine New Haven CT United States Department of Emergency Medicine Yale University School of Medicine New Haven CT United States Program of Computational Biology and Bioinformatics Yale University New Haven CT United States School of Medicine University College Dublin National University of Ireland Dublin Ireland

Background: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. Objective: This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Methods: We used 2 sets of multiple-choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT’s performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. Results: Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT’s answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001

关键词： artificial intelligence chatbot ChatGPT conversational agent education technology generative pre-trained transformer GPT machine learning medical education MedQA natural language processing NLP

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AI-driven 3D Spatial Transcriptomics

arXiv

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arXiv 2025年

作者： Almagro-Pérez, Cristina Song, Andrew H. Weishaupt, Luca Kim, Ahrong Jaume, Guillaume Williamson, Drew F.K. Hemker, Konstantin Lu, Ming Y. Singh, Kritika Chen, Bowen Le, Long Phi Baras, Alexander S. Jiang, Sizun Bashashati, Ali Liu, Jonathan T.C. Mahmood, Faisal Department of Pathology Mass General Brigham Harvard Medical School BostonMA United States Cancer Program Broad Institute of Harvard and MIT CambridgeMA United States Data Science Program Dana-Farber Cancer Institute BostonMA United States Harvard-MIT Division of Health Sciences and Technology Massachusetts Institute of Technology CambridgeMA United States Department of Pathology Pusan National University Busan Korea Republic of Department of Pathology and Laboratory Medicine Emory University School of Medicine AtlantaGA United States Department of Computer Science & Technology University of Cambridge Cambridge United Kingdom Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States Rutgers Robert Wood Johnson Medical School New BrunswickNJ United States Department of Pathology Johns Hopkins Hospital BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins Hospital BaltimoreMD United States Broad Institute of Harvard and MIT CambridgeMA United States Center for Virology and Vaccine Research Beth Israel Deaconess Medical Center Harvard Medical School BostonMA United States Department of Pathology Dana Farber Cancer Institute BostonMA United States Department of Pathology and Laboratory Medicine University of British Columbia VancouverBC Canada School of Biomedical Engineering University of British Columbia VancouverBC Canada Department of Mechanical Engineering Bioengineering Laboratory Medicine & Pathology University of Washington SeattleWA United States Harvard Data Science Initiative Harvard University CambridgeMA United States

A comprehensive three-dimensional (3D) map of tissue architecture and gene expression is crucial for illuminating the complexity and heterogeneity of tissues across diverse biomedical applications1. However, most spatial transcriptomics (ST) approaches remain limited to two-dimensional (2D) sections of tissue2–4. Although current 3D ST methods hold promise, they typically require extensive tissue sectioning, are complex, are not compatible with non-destructive 3D tissue imaging technologies, and often lack scalability5–8. Here, we present VOlumetrically Resolved Transcriptomics EXpression (VORTEX), an AI framework that leverages 3D tissue morphology and minimal 2D ST to predict volumetric 3D ST. By pretraining on diverse 3D morphology–transcriptomic pairs from heterogeneous tissue samples and then fine-tuning on minimal 2D ST data from a specific volume of interest, VORTEX learns both generic tissue-related and sample-specific morphological correlates of gene expression. This approach enables dense, high-throughput, and fast 3D ST, scaling seamlessly to large tissue volumes far beyond the reach of existing 3D ST techniques. By offering a cost-effective and minimally destructive route to obtaining volumetric molecular insights, we anticipate that VORTEX will accelerate biomarker discovery and our understanding of morphomolecular associations and cell states in complex tissues. Interactive 3D ST volumes can be viewed at https://***/. © 2025, CC BY-NC-ND.

关键词： Vortex flow

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Multimodal deep learning models utilizing chest X-ray and electronic health record data for predictive screening of acute heart failure in emergency department

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Computer Methods and programs in Biomedicine 2024年 255卷 108357页

作者： Lee, Chih-Kuo Chen, Ting-Li Wu, Jeng-En Liao, Min-Tsun Wang, Chiehhung Wang, Weichung Chou, Cheng-Ying Department of Internal Medicine National Taiwan University Hospital Hsin-Chu Branch No. 25 Ln. 442 Sec. 1 Jingguo Rd. Taiwan Hsinchu300 Taiwan Institute of Statistical Science Academia Sinica 128 Academia Rd. Nankang Taiwan Taipei11529 Taiwan Master Program in Statistics National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan Data Science Degree Program National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan Institute of Applied Mathematical Sciences National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan Department of Biomechatronics Engineering National Taiwan University 1 Sec. 4 Roosevelt Rd. Taiwan Taipei106 Taiwan

Background and Objectives: Ambiguity in diagnosing acute heart failure (AHF) leads to inappropriate treatment and potential side effects of rescue medications. To address this issue, this study aimed to use multimodality deep learning models combining chest X-ray (CXR) and electronic health record (EHR) data to screen patients with abnormal N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in emergency departments. Methods: Using the open-source dataset MIMIC-IV and MIMIC[sbnd]CXR, the study population consisted of 1,432 patients and 1,833 pairs of CXRs and EHRs. We processed the CXRs, extracted relevant features through lung-heart masks, and combined these with the vital signs at triage to predict corresponding NT-proBNP levels. Results: The proposed method achieved a 0.89 area under the receiver operating characteristic curve by fusing predictions from single-modality models of heart size ratio, radiomic features, CXR, and the region of interest in the CXR. The model can accurately predict dyspneic patients with abnormal NT-proBNP concentrations, allowing physicians to reduce the risks associated with inappropriate treatment. Conclusion: The study provided new image features related to AHF and offered insights into future research directions. Overall, these models have great potential to improve patient outcomes and reduce risks in emergency departments. © 2024 Elsevier B.V.

关键词： Image segmentation

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