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A metamodel to represent agile software development projects 22

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A metamodel to represent agile software development projects

22nd Ibero-American Conference on Software engineering, CIbSE 2019

Agile Software Development is an important paradigm in the software engineering community and a significant movement in the software industry. According to Version One Survey 2018[28], 97% of the surveyed organizations are using some aspect of agile practices. However, the adoption of agile practices is not a straightforward task and there are many open questions related to agile methods that the academic community is trying to answer. One of the challenges of academic research related to Agile Software Development is the lack of metamodels to support the representation of agile projects. A preliminary investigation conducted in this study revealed that existing software development metamodels focus on representing elements related to software process definition and neglect aspects related to software project execution such as Iterations, Deliveries and Task Instances. This study aims at investigating how existing software development metamodels support the representation of agile software development projects and how they can be extended to fill possible gaps. To achieve this goal, we first identified a set of concepts, entities, and information related to agile projects. Then, we investigated how existing software development metamodels represent the identified elements. Finally, we proposed a metamodel, that we named Agile Software Development Project Metamodel (ASDPM), which contains the elements necessary to represent agile software development projects. © XXII Ibero-American Conference on Software engineering, CIbSE 2019. All rights reserved.

关键词： Software design

Dynamical phases of short-term memory mechanisms in RNNs

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arXiv 2025年

作者： Kurtkaya, Bariscan Dinc, Fatih Yuksekgonul, Mert Blanco-Pozo, Marta Cirakman, Ege Schnitzer, Mark Yemez, Yucel Tanaka, Hidenori Yuan, Peng Miolane, Nina CNC Program Stanford University Stanford United States KUIS AI Center Department of Computer Engineering Koc University Turkey Geometric Intelligence Lab UC Santa Barbara Santa Barbara United States Kavli Institute for Theoretical Physics UC Santa Barbara Santa Barbara United States Stanford University Stanford United States James H. Clark Center for Biomedical Engineering & Sciences Stanford University Stanford United States Howard Hughes Medical Institute Stanford University Stanford United States Phi Lab NTT Research Sunnyvale United States Center for Brain Science Harvard University Cambridge United States State Key Laboratory of Medical Neurobiology Institute for Translational Brain Research MOE Frontiers Center for Brain Science Fudan University China Research Institute of Intelligent Complex System Fudan University China

Short-term memory is essential for cognitive processing, yet our understanding of its neural mechanisms remains unclear. A key focus in neuroscience has been the study of sequential activity patterns, where neurons fire one after another within large networks, to explain how information is maintained. While recurrent connections were shown to drive sequential dynamics, a mechanistic understanding of this process still remains unknown. In this work, we first introduce two unique mechanisms that can subserve short-term memory: slow-point manifolds generating direct sequences or limit cycles providing temporally localized approximations. Then, through analytical models, we identify fundamental properties that govern the selection of these mechanisms, i.e., we derive theoretical scaling laws for critical learning rates as a function of the delay period length, beyond which no learning is possible. We empirically verify these observations by training and evaluating more than 35,000 recurrent neural networks (RNNs) that we will publicly release upon publication. Overall, our work provides new insights into short-term memory mechanisms and proposes experimentally testable predictions for systems neuroscience. © 2025, CC BY.

关键词： Long short-term memory

Pale Red Dot: A Large, Robust Architecture for Human Settlements on Mars

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Pale Red Dot: A Large, Robust Architecture for Human Settlem...

Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2023

作者： Lordos, George Hoying, Madelyn McKinney, Lanie Webb, Alisa Lin, Yuying Martin, Estelle Beilstein, John Miller, Duncan Villegas, David Sepulveda, Francisco AlSadah, Yousif Iyer, Divya O’Neill, Cormac Patel, Palak Garcia, Ignacio Arzuaga Azzouz, H. Chan, Wing Lam Eyre, Ezra Gleason, Dane Romero, Elizabeth Arias, Liliana Orozco, Omar Gentgen, Chloe Latyshev, Kir Hoffman, Jeffrey de Weck, Olivier Ikinci, Meltem Lordos, Alexandros Aeronautics and Astronautics Massachusetts Institute of Technology 33-409 CambridgeMA02139 United States Massachusetts Institute of Technology Harvard-MIT Health Sciences and Technology Program and MIT Aeronautics and Astronautics 33-409 CambridgeMA02139 United States Massachusetts Institute of Technology CambridgeMA02139 United States Aeronautics and Astronautics Massachusetts Institute of Technology 77 Mass Ave. 33-409 CambridgeMA02139 United States System Design and Management Massachusetts Institute of Technology 77 Mass Ave. E40-300 CambridgeMA02139 United States Mechanical Engineering Massachusetts Institute of Technology 77 Mass Ave. 33-409 CambridgeMA02139 United States Civil and Environmental Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Electrical Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Nuclear Science and Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Computer Science and Engineering Massachusetts Institute of Technology 77 Mass Ave. CambridgeMA02139 United States Massachusetts Institute of Technology 77 Mass Ave. 37-219 CambridgeMA02139 United States Massachusetts Institute of Technology 77 Mass Ave. 33-410 CambridgeMA02139 United States Behavioral Economics University of Cyprus Nicosia Cyprus Department of Psychology University of Cyprus Nicosia Cyprus

ISBN: (数字)9781624107054

ISBN: (纸本)9781624107054

We propose a ten-yearMars mission to establish Earth-independence and support efficient, safe, and sustainable science and exploration goals. The campaign begins with a series of precursor missions, starting with the Artemis program, followed by robotic missions to performsite surveys and selection, de-risk technologies, and to start stockpiling in-situ water and propellant. Robotic Cargo Starships would depart first in 2035 to deliver and deploy approximately 5,800 tons of habitats, nuclear microreactors, farmmodules, manufacturing and ISRU systems. In 2040, two Crew Starships carrying 36 persons would land onMars to establish two close-proximity villages. The lifecycle cost is estimated at $81 billion, with a peak annual cost of $6.6 billion. To meet RASC-AL competition requirements, intended to motivate the exploration of highly self-reliant architecture concepts, resupply from Earth was constrained to only 5 metric tons every two years and minimum surface endurance was set at 7 years. The risks to human life and to critical systems increase exponentially over a minimally-resupplied, 7-10 year mission toMars. Our approach focused on identifying capabilities to mitigate known and unknown risks. Using a high-fidelity crew time model, we found that large architectures, with their economies of scale and productivity gains from specialization, are essential to robustly support the necessary capabilities. The model also shows that small 7-year missions of 12 persons or less cannot provide value in terms of free surface traverse time for science and exploration. Given the necessity of a large, complex architecture, we then sought out efficiencies and synergies (value engineering) in order to make the mission feasible and affordable. In the process, we identified more benefits that are enabled only by large missions, including the creation of a true community on Mars with sufficient social complexity for humans to thrive, and the geopolitically compelling option to inclu

关键词： Value engineering

Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon Coupling in Hybrid Organic-Inorganic Perovskites

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Nanoslot-Induced Ultrastrong Phonon-Photon and Phonon-Phonon...

Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Dasom Kim Jin Hou Geon Lee Ayush Agrawal Sunghwan Kim Hao Zhang Di Bao Andrey Baydin Wenjing WU Fuyang Tay Shengxi Huang Daivd Hagenmüller Elbert E. M. Chia Dai-Sik Kim Minah Seo Aditya D. Mohite Junichiro Kono Applied Physics Graduate Program Smalley-Curl Institute Rice University Houston TX USA Department of Electrical and Computer Engineering Rice University Houston TX USA Department of Materials Science and NanoEngineering Rice University Houston TX USA Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Physics and Astronomy Seoul National University Seoul Republic of Korea Department of Chemical and Biomolecular Engineering Rice University Houston TX USA Department of Physics Long-wavelength Nanotechnology Laboratory Quantum Photonics Institute Ulsan National Institute of Science and Technology (UNIST) Ulsan Republic of Korea Division of Physics and Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Smalley-Curl institute Rice University Houston TX USA CESQ-ISIS (UMR 7006) Universite de Strasbourg and CNRS Strasbourg France KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Department of Physics and Astronomy Rice University Houston TX USA

Methods for harnessing vibrational states are desired for phonon-based technologies. We realized ultrastrong coupling of two phonon modes in perovskite materials induced by ultrastrong coupling with a common photonic ... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Couplings Phonons Laser modes Perovskites Electro-optical waveguides Photonics

Plug-and-play stability for intracortical brain-computer interfaces: a one-year demonstration of seamless brain-to-text communication 23

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Plug-and-play stability for intracortical brain-computer int...

Proceedings of the 37th International Conference on Neural Information Processing systems

作者： Chaofei Fan Nick Hahn Foram Kamdar Donald Avansino Guy H. Wilson Leigh Hochberg Krishna V. Shenoy Jaimie M. Henderson Francis R. Willett Department of Computer Science Stanford University Department of Neurosurgery Stanford University Howard Hughes Medical Institute at Stanford University Department of Neuroscience Stanford University School of Engineering and Carney Institute for Brain Science Brown University and VA RR & D Center for Neurorestoration and Neurotechnology VA Providence Healthcare System and Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School Bio-X Program Stanford University and Department of Neurobiology Stanford University and Department of Bioengineering Stanford University and Wu Tsai Neurosciences Institute Stanford University and Howard Hughes Medical Institute at Stanford University and Department of Electrical Engineering Stanford University Department of Neurosurgery Stanford University and Wu Tsai Neurosciences Institute Stanford University

Intracortical brain-computer interfaces (iBCIs) have shown promise for restoring rapid communication to people with neurological disorders such as amyotrophic lateral sclerosis (ALS). However, to maintain high performance over time, iBCIs typically need frequent recalibration to combat changes in the neural recordings that accrue over days. This requires iBCI users to stop using the iBCI and engage in supervised data collection, making the iBCI system hard to use. In this paper, we propose a method that enables self-recalibration of communication iBCIs without interrupting the user. Our method leverages large language models (LMs) to automatically correct errors in iBCI outputs. The self-recalibration process uses these corrected outputs ("pseudo-labels") to continually update the iBCI decoder online. Over a period of more than one year (403 days), we evaluated our Continual Online Recalibration with Pseudo-labels (CORP) framework with one clinical trial participant. CORP achieved a stable decoding accuracy of 93.84% in an online handwriting iBCI task, significantly outperforming other baseline methods. Notably, this is the longest-running iBCI stability demonstration involving a human participant. Our results provide the first evidence for long-term stabilization of a plug-and-play, high-performance communication iBCI, addressing a major barrier for the clinical translation of iBCIs.

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Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology

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arXiv 2024年

作者： Remedios, Lucas W. Bao, Shunxing Remedios, Samuel W. Lee, Ho Hin Cai, Leon Y. Li, Thomas Deng, Ruining Newlin, Nancy R. Saunders, Adam M. Cui, Can Li, Jia Liu, Qi Lau, Ken S. Roland, Joseph T. Washington, Mary K. Coburn, Lori A. Wilson, Keith T. Huo, Yuankai Landman, Bennett A. Vanderbilt University Department of Computer Science Nashville United States Vanderbilt University Department of Electrical and Computer Engineering Nashville United States Johns Hopkins University Department of Computer Science Baltimore United States National Institutes of Health Department of Radiology and Imaging Sciences Bethesda United States Vanderbilt University Department of Biomedical Engineering Nashville United States Vanderbilt University Medical Center Department of Biostatistics Nashville United States Vanderbilt University Medical Center Center for Quantitative Sciences Nashville United States Vanderbilt University Medical Center Epithelial Biology Center Nashville United States Vanderbilt University School of Medicine Department of Cell and Developmental Biology Nashville United States Vanderbilt University Medical Center Division of Gastroenterology Hepatology and Nutrition Department of Medicine Nashville United States Vanderbilt University Medical Center Vanderbilt Center for Mucosal Inflammation and Cancer Nashville United States Vanderbilt University School of Medicine Program in Cancer Biology Nashville United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Medical Center Department of Pathology Microbiology and Immunology NashvilleTN United States

Purpose: Cells are building blocks for human physiology;consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both health and disease. Hematoxylin and eosin (H&E) is the standard stain used in histological analysis of tissues. Microscope slides of H&E-stained tissues are widely available in both clinical and research settings. While H&E is ubiquitous and reveals tissue microanatomy, the classification and tracking of cell subtypes often requires expert knowledge and the use of specialized stains, such as immunofluorescence staining. To reduce both the manual annotation burden and reliance on specialized staining technologies, artificial intelligence has been proposed for the automatic classification of cell types on H&E slides. For example, the recent Colon Nucleus Identification and Classification (CoNIC) Challenge focused on labeling 6 cell types on imaging of H&E stains from the human colon. However, this is a very small fraction of the number of potential cell subtypes within the intestines. Specifically, the CoNIC Challenge was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). To approach this problem, we propose to use inter-modality learning to label previously un-labelable cell types on H&E. Approach: We take advantage of the cell classification information inherent in whole slide images (WSIs) of multiplexed immunofluorescence (MxIF) histology to create cell level annotations for 14 subclasses. We performed style transfer on the same MxIF tissues to synthesize realistic virtual H&E which we paired with the MxIF-derived cell subclassification labels. We evaluated the efficacy of using a supervised learning scheme where the input was realistic-quality virtual H&E and the labels were MxIF-derived cell subclasses. We assessed our model on a testing

关键词： Tissue

Nucleus subtype classification using inter-modality learning

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arXiv 2024年

作者： Remedios, Lucas W. Bao, Shunxing Remedios, Samuel W. Lee, Ho Hin Cai, Leon Y. Li, Thomas Deng, Ruining Cui, Can Li, Jia Liu, Qi Lau, Ken S. Roland, Joseph T. Washington, Mary K. Coburn, Lori A. Wilson, Keith T. Huo, Yuankai Landman, Bennett A. Vanderbilt University Department of Computer Science Nashville United States Vanderbilt University Department of Electrical and Computer Engineering Nashville United States Johns Hopkins University Department of Computer Science Baltimore United States National Institutes of Health Department of Radiology and Imaging Sciences Bethesda United States Vanderbilt University Department of Biomedical Engineering Nashville United States Vanderbilt University Medical Center Department of Biostatistics Nashville United States Vanderbilt University Medical Center Center for Quantitative Sciences Nashville United States Vanderbilt University Medical Center Epithelial Biology Center Nashville United States Vanderbilt University School of Medicine Department of Cell and Developmental Biology Nashville United States Vanderbilt University Medical Center Division of Gastroenterology Hepatology and Nutrition Department of Medicine Nashville United States Vanderbilt University Medical Center Vanderbilt Center for Mucosal Inflammation and Cancer Nashville United States Vanderbilt University School of Medicine Program in Cancer Biology Nashville United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Medical Center Department of Pathology Microbiology and Immunology NashvilleTN United States

Understanding the way cells communicate, co-locate, and interrelate is essential to understanding human physiology. Hematoxylin and eosin (H&E) staining is ubiquitously available both for clinical studies and research. The Colon Nucleus Identification and Classification (CoNIC) Challenge has recently innovated on robust artificial intelligence labeling of six cell types on H&E stains of the colon. However, this is a very small fraction of the number of potential cell classification types. Specifically, the CoNIC Challenge is unable to classify epithelial subtypes (progenitor, endocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), or connective subtypes (fibroblasts, stromal). In this paper, we propose to use inter-modality learning to label previously un-labelable cell types on virtual H&E. We leveraged multiplexed immunofluorescence (MxIF) histology imaging to identify 14 subclasses of cell types. We performed style transfer to synthesize virtual H&E from MxIF and transferred the higher density labels from MxIF to these virtual H&E images. We then evaluated the efficacy of learning in this approach. We identified helper T and progenitor nuclei with positive predictive values of 0.34 ± 0.15 (prevalence 0.03 ± 0.01) and 0.47 ± 0.1 (prevalence 0.07 ± 0.02) respectively on virtual H&E. This approach represents a promising step towards automating annotation in digital pathology. © 2024, CC BY.

关键词： Cell culture

Plug-and-Play Stability for Intracortical Brain-computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication

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arXiv 2023年

作者： Fan, Chaofei Hahn, Nick Kamdar, Foram Avansino, Donald Wilson, Guy H. Hochberg, Leigh Shenoy, Krishna V. Henderson, Jaimie M. Willett, Francis R. Bio-X Program Stanford University United States Department of Neuroscience Stanford University United States Department of Neurosurgery Stanford University United States Department of Neurobiology Stanford University United States Department of Bioengineering Stanford University United States Department of Computer Science Stanford University United States Wu Tsai Neurosciences Institute Stanford University United States Howard Hughes Medical Institute Stanford University United States Department of Electrical Engineering Stanford University United States School of Engineering Carney Institute for Brain Science Brown University United States VA RR&D Center for Neurorestoration and Neurotechnology VA Providence Healthcare System United States Center for Neurotechnology and Neurorecovery Department of Neurology Massachusetts General Hospital Harvard Medical School United States

关键词： Brain computer interface

Technical debt and agile software development practices and processes: An industry practitioner survey

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arXiv 2021年

作者： Holvitie, Johannes Licorish, Sherlock A. Spínola, Rodrigo O. Hyrynsalmi, Sami MacDonell, Stephen G. Mendes, Thiago S. Buchan, Jim Leppänen, Ville Turku Centre for Computer Science Software Development Laboratory Turku Finland University of Turku Department of Information Technology Turku Finland University of Otago Department of Information Science Otago Dunedin New Zealand Salvador University Graduate Program in Systems and Computer Bahia Salvador Brazil Federal University of Bahia Fraunhofer Project Center for Software and System Engineering Salvador Brazil Tampere University of Technology Pori Campus Pori Finland Federal Institute of Bahia Information Technology Department Bahia Santo Amaro Brazil Auckland University of Technology School of Engineering Computer and Mathematical Sciences Auckland New Zealand

Context: Contemporary software development is typically conducted in dynamic, resource-scarce environments that are prone to the accumulation of technical debt. While this general phenomenon is acknowledged, what remains unknown is how technical debt specifically manifests in and affects software processes, and how the software development techniques employed accommodate or mitigate the presence of this debt. Objectives: We sought to draw on practitioner insights and experiences in order to classify the effects of agile method use on technical debt management, given the popularity and perceived success of agile methods. We explore the breadth of practitioners' knowledge about technical debt;how technical debt is manifested across the software process;and the perceived effects of common agile software development practices and processes on technical debt. In doing so, we address a research gap in technical debt knowledge and provide novel and actionable managerial recommendations. Method: We designed, tested and executed a multi-national survey questionnaire to address our objectives, receiving 184 responses from practitioners in Brazil, Finland, and New Zealand. Results: Our findings indicate that: 1) Practitioners are aware of technical debt, although, there was under utilization of the concept, 2) Technical debt commonly resides in legacy systems, however, concrete instances of technical debt are hard to conceptualize which makes it problematic to manage, 3) Queried agile practices and processes help to reduce technical debt;in particular, techniques that verify and maintain the structure and clarity of implemented artifacts (e.g., Coding standards and Refactoring) positively affect technical debt management. Conclusions: The fact that technical debt instances tend to have characteristics in common means that a systematic approach to its management is feasible. However, notwithstanding the positive effects of some agile practices on technical debt management, comp

关键词： Legacy systems