Article
Computer Science, Information Systems
Zhihan Fang, Yu Yang, Guang Yang, Yikuan Xian, Fan Zhang, Desheng Zhang
Summary: CellSense is a human mobility recovery system that utilizes cellular network signaling data to improve existing mobility models. By integrating collective and individual mobility patterns, CellSense achieved a 35.3% improvement over state-of-the-art models.
PROCEEDINGS OF THE ACM ON INTERACTIVE MOBILE WEARABLE AND UBIQUITOUS TECHNOLOGIES-IMWUT
(2021)
Article
Physics, Multidisciplinary
Yifan He, Chen Zhao, An Zeng
Summary: This paper uses 4G communication data of 1.7 million users in Shijiazhuang, a city in northern China, to investigate user mobility patterns and analyze the importance of locations in the city. The study reveals that most users prefer to live and work within the same district and identifies important locations using the weighted PageRank algorithm.
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
(2022)
Article
Green & Sustainable Science & Technology
Peng Guo, Yanling Sun, Qiyi Chen, Junrong Li, Zifei Liu
Summary: This study utilizes taxi GPS data to analyze the impact of rainfall on residents' travels in urban areas. The results show significant changes in taxi flow and its spatial and temporal distribution pattern on rainy days, which affects transportation supply and demand. These findings may provide useful references for formulating urban transport policies that can adapt to different weather conditions.
Article
Biochemical Research Methods
Giacomo Baruzzo, Giulia Cesaro, Barbara Di Camillo
Summary: scSeqComm is a computational method for identifying and quantifying intercellular and intracellular signaling from scRNA-seq data, while providing functional characterization of cellular communication. The method enhances the reliability and robustness of the results by considering both intercellular and intracellular signaling evidence.
Article
Computer Science, Artificial Intelligence
Fernando Terroso-Saenz, Raul Flores, Andres Munoz
Summary: This paper explores the feasibility and effectiveness of using Twitter data to predict human mobility. By combining Twitter data with government open data sources and using machine learning models for prediction, the results show that Twitter data have considerable value in predicting large-scale human mobility.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Urban Studies
Jinzhou Cao, Qingquan Li, Wei Tu, Qili Gao, Rui Cao, Chen Zhong
Summary: This study uses massive-scale mobile phone tracking data to extract individual travel patterns, build urban mobility networks, and propose network properties to characterize mobility networks, demonstrating the impact of individual travel pattern heterogeneity on urban mobility patterns.
Article
Engineering, Civil
Yanchen Wang, Fei Yang, Li He, Haode Liu, Li Tan, Cheng Wang
Summary: This study proposes a travel mode identification model based on the gated recurrent unit (GRU) neural network, which can accurately identify four traffic modes: walking, bicycle, car, and bus. Cellular signaling data and GPS data were collected in cooperation with the operator, and the performance of the proposed method was verified and compared with other popular methods using the collected dataset as ground-truth data. The results show that the GRU-based method outperforms other machine learning algorithms, with a precision, recall, and F score of 90.5%. This study provides insights for the future application and development of cellular signaling-based travel information collection technology for residents.
JOURNAL OF ADVANCED TRANSPORTATION
(2023)
Article
Mathematics, Interdisciplinary Applications
Frank Schlosser, Dirk Brockmann
Summary: The method proposed in this study utilizes geolocated movement data of affected individuals to determine outbreak origins, accurately identifying true outbreak locations even in scenarios with multiple outbreak sources. It offers a reliable and accurate out-of-the-box approach to identify outbreak locations in the initial phase of an outbreak.
Article
Chemistry, Multidisciplinary
Jingbo Song, Qiuhua Yi, Haoran Gao, Buyu Wang, Xiangjie Kong
Summary: Point of interest (POI) recommendation is essential in location-based social networks, providing personalized travel experiences. However, current methods neglect urban crowds' regular travel patterns. This paper proposes an HMRec algorithm based on human mobility patterns to address this issue, achieving a 3% improvement on average compared to baseline models.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Emanuel Lima, Ana Aguiar, Paulo Carvalho, Aline Carneiro Viana
Summary: This study proposes using human mobility to inform offloading tasks during commuting. By analyzing granular mobility datasets from two cities, the study extracts Offloading Regions (ORs) and characterizes them based on offloading opportunity metrics. The results show that a significant portion of travel time is spent inside the extracted ORs. Predicting the next OR can improve offloading orchestration, but current models have poor predictive performance. The study highlights the need for offloading systems to adopt hybrid strategies and characterizes the trade-off between mobility predictability and offloading opportunities.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2022)
Article
Chemistry, Multidisciplinary
Victor G. Desyatkin, William B. Martin, Ali E. Aliev, Nathaniel E. Chapman, Alexandre F. Fonseca, Douglas S. Galvao, Ericka Roy Miller, Kevin H. Stone, Zhong Wang, Dante Zakhidov, F. Ted Limpoco, Sarah R. Almahdali, Shane M. Parker, Ray H. Baughman, Valentin O. Rodionov
Summary: In this study, multilayer gamma-Graphyne was synthesized through crystallization-assisted irreversible cross-coupling polymerization and comprehensively characterized. Experimental results showed that gamma-Graphyne is a 0.48 eV band gap semiconductor with a specific crystal structure. The reported methodology is scalable and applicable to other allotropes of the graphyne family.
JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
(2022)
Article
Engineering, Civil
Xiao Zhang, Qilin Wang, Ziming Ye, Haochao Ying, Dongxiao Yu
Summary: The advancement of smart wearable devices and location-based smart services has enabled a new paradigm for smart human mobility prediction. However, federated human mobility prediction suffers from the challenge of data heterogeneity and data scarcity. In this paper, a federated representation learning framework called FR-HMP is proposed to overcome these obstacles. Experimental results demonstrate the advantages of FR-HMP over state-of-the-art methods on two real-world HMP datasets.
IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Urban Studies
Rui Xin, Tinghua Ai, Linfang Ding, Ruoxin Zhu, Liqiu Meng
Summary: This study proposes a multiscale geospatial network framework to analyze the impact of the pandemic on bike-sharing data. The findings show that in New York City, with the development of the pandemic, the riding flow and its spatiotemporal distribution pattern changed significantly, which had a series of effects on the use and management of bikes. These results provide important references for travel planning, bike dispatching, and traffic management.
Article
Urban Studies
Qi-Li Gao, Yang Yue, Chen Zhong, Jinzhou Cao, Wei Tu, Qing-Quan Li
Summary: This study introduces people-based activity space approaches to measure activity disparities between public transit riders and private car users. Using vehicle plate recognition data and public transit smart card data, activities were anonymously identified and individual activity spaces were characterized by six primary activity features. The study found that, compared to transit riders, people who use cars accessed more activities within a larger activity space and enjoyed higher travel efficiency. A comprehensive indicator was derived from the primary activity features to quantify activity disparities at the zone level. The study also found that public transport facilities and location factors played essential roles in determining modality-associated gaps in access to urban activity opportunities.
Article
Chemistry, Analytical
Mohammad Al Shinwan, Laith Abualigah, Trong-Dinh Huy, Ahmed Younes Shdefat, Maryam Altalhi, Chulsoo Kim, Shaker El-Sappagh, Mohamed Abd Elaziz, Kyung Sup Kwak
Summary: This paper proposes a partially distributed architecture for 5G networks, achieving an optimal flat network architecture by decoupling the control plane and data plane. Simulation results validate the improved performance of this architecture in terms of attachment, data delivery, and mobility procedures compared to the legacy architecture.
Article
Computer Science, Hardware & Architecture
Teague Tomesh, Margaret Martonosi
Summary: Codesign has been an integral part of computer architecture, with end-user applications and new computational hardware shaping its design and capabilities. Quantum computing similarly relies on codesign approaches, especially in its resource constrained early days.
Article
Computer Science, Hardware & Architecture
Aninda Manocha, Tyler Sorensen, Esin Tureci, Opeoluwa Matthews, Juan L. Aragon, Margaret Martonosi
Summary: GraphAttack is a hardware-software data supply approach that accelerates graph applications on in-order multicore architectures, significantly increasing memory-level parallelism to mitigate latency bottlenecks and achieving speedup and energy efficiency gains across various graph application domains.
ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION
(2021)
Editorial Material
Computer Science, Hardware & Architecture
Anindya Banerjee, Sankar Basu, Erik Brunvand, Pinaki Mazumder, Branka Cleaveland, Gurdip Singh, Margaret Martonosi, Fernanda Pembleton
Article
Computer Science, Hardware & Architecture
Prakash Murali, Dripto M. Debroy, Kenneth R. Brown, Margaret Martonosi
Summary: Trapped ions (TIs) are a leading candidate for building Noisy Intermediate-Scale Quantum (NISQ) hardware. A modular architecture named Quantum Charge Coupled Device (QCCD) has been proposed to achieve 50-100 qubit TI devices. Extensive architectural studies have been performed to evaluate the design choices and provide recommendations for highly reliable and performant application executions. The insights from these studies have the potential to influence quantum computing hardware in the near future.
COMMUNICATIONS OF THE ACM
(2022)
Article
Computer Science, Information Systems
David Binkley, Leon Moonen, Sibren Isaacman
Summary: Predicting vulnerable source code helps developers focus on the parts of the code that need closer examination. In this study, function names are used as semantic cues to predict vulnerable functions, aided by a frequency-based algorithm. The transparency of the algorithm allows for a better understanding of the deep neural network's decision-making process, and the effectiveness of the approach has been demonstrated in empirical evaluations.
INFORMATION AND SOFTWARE TECHNOLOGY
(2022)
Editorial Material
Computer Science, Hardware & Architecture
Manish Parashar, Amy Friedlander, Erwin Gianchandani, Margaret Martonosi
COMMUNICATIONS OF THE ACM
(2022)
Article
Computer Science, Hardware & Architecture
Aninda Manocha, Juan L. Aragon, Margaret Martonosi
Summary: Despite the challenges posed by indirect accesses to vertex property data in graph analytic kernels, Graphfire introduces a flexible memory hierarchy approach that learns access patterns and optimizes fetch, insertion, and replacement accordingly, achieving significant speedups.
IEEE TRANSACTIONS ON COMPUTERS
(2023)
Proceedings Paper
Computer Science, Hardware & Architecture
Marcelo Orenes-Vera, Aninda Manocha, Jonathan Balkind, Fei Gao, Juan L. Aragon, David Wentzlaff, Margaret Martonosi
Summary: Modern computing systems employ heterogeneity and specialization to meet performance targets, but memory latency remains a challenge for certain applications. This paper presents a system implementation of latency tolerance hardware called MAPLE, which achieves significant speedups without requiring modifications to the memory hierarchy or processor tiles. MAPLE allows for asynchronous execution of long-latency memory accesses, avoiding stalls and enabling greater memory parallelism.
PROCEEDINGS OF THE 2022 THE 49TH ANNUAL INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE (ISCA '22)
(2022)
Proceedings Paper
Computer Science, Hardware & Architecture
Teague Tomesh, Pranav Gokhale, Victory Omole, Gokul Subramanian Ravi, Kaitlin N. Smith, Joshua Viszlai, Xin-Chuan Wu, Nikos Hardavellas, Margaret R. Martonosi, Frederic T. Chong
Summary: The emergence of quantum computers has led to speculation about their revolutionary changes. However, the variety of architectures used in quantum computing makes it difficult to measure and compare performance. SupermarQ is introduced as a scalable, hardware-agnostic quantum benchmark suite that addresses this issue by using application-level metrics to measure performance.
2022 IEEE INTERNATIONAL SYMPOSIUM ON HIGH-PERFORMANCE COMPUTER ARCHITECTURE (HPCA 2022)
(2022)
Proceedings Paper
Computer Science, Hardware & Architecture
Yipeng Huang, Steven Holtzen, Todd Millstein, Guy Van den Broeck, Margaret Martonosi
Summary: The paper introduces a quantum circuit simulation toolchain based on logical abstractions for simulating variational algorithms, offering greater efficiency and cost reduction when sampling from noisy circuits and noise-free shallow quantum circuits. The proposed approach encodes quantum amplitudes and noise probabilities in a probabilistic graphical model and compiles circuits to logical formulas supporting efficient repeated simulation. This toolchain is ideal for simulating near-term variational quantum algorithms and demonstrates a significant reduction in sampling cost compared to traditional methods for noise-free shallow quantum circuits.
ASPLOS XXVI: TWENTY-SIXTH INTERNATIONAL CONFERENCE ON ARCHITECTURAL SUPPORT FOR PROGRAMMING LANGUAGES AND OPERATING SYSTEMS
(2021)
Proceedings Paper
Computer Science, Hardware & Architecture
Wei Tang, Teague Tomesh, Martin Suchara, Jeffrey Larson, Margaret Martonosi
Summary: Quantum computing offers exponential speedups over classical computing, but current Noisy Intermediate-Scale Quantum (NISQ) devices face scalability challenges. CutQC is a scalable hybrid approach that enables evaluation of quantum circuits beyond the limits of classical or quantum computers alone, achieving higher fidelity with small quantum computers compared to large NISQ devices. This hybrid method allows users to leverage both classical and quantum computing resources for evaluating quantum programs far beyond the reach of either one alone.
ASPLOS XXVI: TWENTY-SIXTH INTERNATIONAL CONFERENCE ON ARCHITECTURAL SUPPORT FOR PROGRAMMING LANGUAGES AND OPERATING SYSTEMS
(2021)
Proceedings Paper
Computer Science, Artificial Intelligence
Teague Tomesh, Kaiwen Gui, Pranav Gokhale, Yunong Shi, Frederic T. Chong, Margaret Martonosi, Martin Suchara
Summary: This study introduces a new compilation strategy that simultaneously advances both accuracy and gate cancellation goals, promising significant improvements in Hamiltonian Simulation performance.
2021 INTERNATIONAL CONFERENCE ON REBOOTING COMPUTING (ICRC 2021)
(2021)
Proceedings Paper
Automation & Control Systems
Marcelo Orenes-Vera, Aninda Manocha, David Wentzlaff, Margaret Martonosi
Summary: AutoSVA proposes a framework to automatically generate FV testbenches for verifying the liveness and safety of control logic involved in module interactions, demonstrating its effectiveness and efficiency on deadlock-critical modules of widely-used open-source hardware projects.
2021 58TH ACM/IEEE DESIGN AUTOMATION CONFERENCE (DAC)
(2021)
Proceedings Paper
Computer Science, Hardware & Architecture
Lingling Lao, Prakash Murali, Margaret Martonosi, Dan Browne
Summary: The study aims to balance between application expressivity and calibration overhead in near-term quantum computing systems. By using numerical optimization, NuOp efficiently decomposes application operations into different hardware gate types. Results show that implementing 4-8 types of 2Q gates can achieve similar expressivity as a full continuous gate family while significantly reducing calibration overheads.
2021 ACM/IEEE 48TH ANNUAL INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE (ISCA 2021)
(2021)
Article
Computer Science, Interdisciplinary Applications
Michael Kane, Xun Jiang, Simon Urbanek
Summary: The conveyance of clinical trial explorations and analysis results from a statistician to a clinical investigator is crucial for drug development and clinical research. Automating the generation of documents allows statisticians to provide a comprehensive view of trial information and focus on trial development, while the use of listdown package facilitates collaboration between statisticians and clinicians for reproducible documents.
