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534 results on '"Lo, Eric"'

1. DEX: Scalable Range Indexing on Disaggregated Memory [Extended Version]

Author

Lu, Baotong, Huang, Kaisong, Liang, Chieh-Jan Mike, Wang, Tianzheng, and Lo, Eric

Subjects
Computer Science - Databases, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Memory disaggregation can potentially allow memory-optimized range indexes such as B+-trees to scale beyond one machine while attaining high hardware utilization and low cost. Designing scalable indexes on disaggregated memory, however, is challenging due to rudimentary caching, unprincipled offloading and excessive inconsistency among servers. This paper proposes DEX, a new scalable B+-tree for memory disaggregation. DEX includes a set of techniques to reduce remote accesses, including logical partitioning, lightweight caching and cost-aware offloading. Our evaluation shows that DEX can outperform the state-of-the-art by 1.7--56.3X, and the advantage remains under various setups, such as cache size and skewness., Comment: 16 pages; To appear at VLDB 2024

Published
2024

2. Biathlon: Harnessing Model Resilience for Accelerating ML Inference Pipelines

Author

Chang, Chaokun, Lo, Eric, and Ye, Chunxiao

Subjects
Computer Science - Databases, Computer Science - Machine Learning
Abstract

Machine learning inference pipelines commonly encountered in data science and industries often require real-time responsiveness due to their user-facing nature. However, meeting this requirement becomes particularly challenging when certain input features require aggregating a large volume of data online. Recent literature on interpretable machine learning reveals that most machine learning models exhibit a notable degree of resilience to variations in input. This suggests that machine learning models can effectively accommodate approximate input features with minimal discernible impact on accuracy. In this paper, we introduce Biathlon, a novel ML serving system that leverages the inherent resilience of models and determines the optimal degree of approximation for each aggregation feature. This approach enables maximum speedup while ensuring a guaranteed bound on accuracy loss. We evaluate Biathlon on real pipelines from both industry applications and data science competitions, demonstrating its ability to meet real-time latency requirements by achieving 5.3x to 16.6x speedup with almost no accuracy loss.

Published
2024

3. Real-World Image Variation by Aligning Diffusion Inversion Chain

Author

Zhang, Yuechen, Xing, Jinbo, Lo, Eric, and Jia, Jiaya

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Recent diffusion model advancements have enabled high-fidelity images to be generated using text prompts. However, a domain gap exists between generated images and real-world images, which poses a challenge in generating high-quality variations of real-world images. Our investigation uncovers that this domain gap originates from a latents' distribution gap in different diffusion processes. To address this issue, we propose a novel inference pipeline called Real-world Image Variation by ALignment (RIVAL) that utilizes diffusion models to generate image variations from a single image exemplar. Our pipeline enhances the generation quality of image variations by aligning the image generation process to the source image's inversion chain. Specifically, we demonstrate that step-wise latent distribution alignment is essential for generating high-quality variations. To attain this, we design a cross-image self-attention injection for feature interaction and a step-wise distribution normalization to align the latent features. Incorporating these alignment processes into a diffusion model allows RIVAL to generate high-quality image variations without further parameter optimization. Our experimental results demonstrate that our proposed approach outperforms existing methods concerning semantic similarity and perceptual quality. This generalized inference pipeline can be easily applied to other diffusion-based generation tasks, such as image-conditioned text-to-image generation and stylization., Comment: NuerIPS 2023 Spotlight. 21 pages; Code: https://github.com/dvlab-research/RIVAL/ Project page: https://rival-diff.github.io/

Published
2023

4. Knock Out 2PC with Practicality Intact: a High-performance and General Distributed Transaction Protocol (Technical Report)

Author

Lai, Ziliang, Fan, Hua, Zhou, Wenchao, Ma, Zhanfeng, Peng, Xiang, Li, Feifei, and Lo, Eric

Subjects
Computer Science - Databases
Abstract

Two-phase-commit (2PC) has been widely adopted for distributed transaction processing, but it also jeopardizes throughput by introducing two rounds of network communications and two durable log writes to a transaction's critical path. Despite the various proposals that eliminate 2PC such as deterministic database and access localization, 2PC remains the de facto standard since the alternatives often lack generality (e.g., requiring workloads without branches based on query results). In this paper, we present Primo, a distributed transaction protocol that supports a more general set of workloads without 2PC. Primo features write-conflict-free concurrency control that guarantees once a transaction enters the commit phase, no concurrency conflict (e.g., deadlock) would occur when installing the write-set -- hence the prepare phase is no longer needed to account for any potential conflict from any partition. In addition, Primo further optimizes the transaction path using asynchronous group commit. With that, the durability delay is also taken off the transaction's critical path. Empirical results on Primo are encouraging -- in YCSB and TPC-C, Primo attains 1.42x to 8.25x higher throughput than state-of-the-art general protocols including Sundial and COCO, while having similar latency as COCO which also employs group commit., Comment: Technical Report

Published
2023

5. When Private Blockchain Meets Deterministic Database

Author

Lai, Ziliang, Liu, Chris, and Lo, Eric

Subjects
Computer Science - Databases
Abstract

Private blockchain as a replicated transactional system shares many commonalities with distributed database. However, the intimacy between private blockchain and deterministic database has never been studied. In essence, private blockchain and deterministic database both ensure replica consistency by determinism. In this paper, we present a comprehensive analysis to uncover the connections between private blockchain and deterministic database. While private blockchains have started to pursue deterministic transaction executions recently, deterministic databases have already studied deterministic concurrency control protocols for almost a decade. This motivates us to propose Harmony, a novel deterministic concurrency control protocol designed for blockchain use. We use Harmony to build a new relational blockchain, namely HarmonyBC, which features low abort rates, hotspot resiliency, and inter-block parallelism, all of which are especially important to disk-oriented blockchain. Empirical results on Smallbank, YCSB, and TPC-C show that HarmonyBC offers 2.0x to 3.5x throughput better than the state-of-the-art private blockchains.

Published
2022

6. ByteStore: Hybrid Layouts for Main-Memory Column Stores

Author

Zhang, Pengfei, Feng, Ziqiang, Lo, Eric, and Qin, Hailin

Subjects
Computer Science - Databases
Abstract

The performance of main memory column stores highly depends on the scan and lookup operations on the base column layouts. Existing column-stores adopt a homogeneous column layout, leading to sub-optimal performance on real workloads since different columns possess different data characteristics. In this paper, we propose ByteStore, a column store that uses different storage layouts for different columns. We first present a novel data-conscious column layout, PP-VBS (Prefix-Preserving Variable Byte Slice). PP-VBS exploits data skew to accelerate scans without sacrificing lookup performance. Then, we present an experiment-driven column layout advisor to select individual column layouts for a workload. Extensive experiments on real data show that ByteStore outperforms homogeneous storage engines by up to 5.2X., Comment: 13 pages, 15 figures

Published
2022

7. Are Updatable Learned Indexes Ready?

Author

Wongkham, Chaichon, Lu, Baotong, Liu, Chris, Zhong, Zhicong, Lo, Eric, and Wang, Tianzheng

Subjects
Computer Science - Databases
Abstract

Recently, numerous promising results have shown that updatable learned indexes can perform better than traditional indexes with much lower memory space consumption. But it is unknown how these learned indexes compare against each other and against the traditional ones under realistic workloads with changing data distributions and concurrency levels. This makes practitioners still wary about how these new indexes would actually behave in practice. To fill this gap, this paper conducts the first comprehensive evaluation on updatable learned indexes. Our evaluation uses ten real datasets and various workloads to challenge learned indexes in three aspects: performance, memory space efficiency and robustness. Based on the results, we give a series of takeaways that can guide the future development and deployment of learned indexes.

Published
2022
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8. Rebalanced Siamese Contrastive Mining for Long-Tailed Recognition

Author

Zhong, Zhisheng, Cui, Jiequan, Li, Zeming, Lo, Eric, Sun, Jian, and Jia, Jiaya

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Deep neural networks perform poorly on heavily class-imbalanced datasets. Given the promising performance of contrastive learning, we propose Rebalanced Siamese Contrastive Mining (ResCom) to tackle imbalanced recognition. Based on the mathematical analysis and simulation results, we claim that supervised contrastive learning suffers a dual class-imbalance problem at both the original batch and Siamese batch levels, which is more serious than long-tailed classification learning. In this paper, at the original batch level, we introduce a class-balanced supervised contrastive loss to assign adaptive weights for different classes. At the Siamese batch level, we present a class-balanced queue, which maintains the same number of keys for all classes. Furthermore, we note that the imbalanced contrastive loss gradient with respect to the contrastive logits can be decoupled into the positives and negatives, and easy positives and easy negatives will make the contrastive gradient vanish. We propose supervised hard positive and negative pairs mining to pick up informative pairs for contrastive computation and improve representation learning. Finally, to approximately maximize the mutual information between the two views, we propose Siamese Balanced Softmax and joint it with the contrastive loss for one-stage training. Extensive experiments demonstrate that ResCom outperforms the previous methods by large margins on multiple long-tailed recognition benchmarks. Our code and models are made publicly available at: https://github.com/dvlab-research/ResCom., Comment: Tech report

Published
2022

11. Early Career Teachers Thriving in the Everyday Challenges of Teaching: Understanding the Relationship between Professional Competence and Teacher Buoyancy

Author

Tang, Sylvia Yee Fan, Lo, Eric Siu Chung, Yeh, Fang-Yin, and Cheng, May May Hung

Abstract

Purpose: This study examined the relationship between early career teachers' (ECTs') perceived professional competence (PC) and teacher buoyancy (TB) and the contribution of such dynamic interaction between ECTs' perceived PC and TB to their thriving in the face of everyday teaching challenges. Design/methodology/approach: The study adopted a concurrent mixed-methods research design. A total of 218 ECTs taking a postgraduate, part-time initial teacher education programme completed two quantitative measures: Professional Competence Questionnaire and Teacher Buoyancy Scale -- Extended Version. Structural Equation Modelling (SEM) analysis was conducted to examine the relationship between PC and TB. Qualitative data via semi-structured interviews were collected from 14 survey respondents and were interpreted through a case study approach. Findings: The quantitative findings showed--(1) Competence in classroom teaching predicts the personal and contextual dimensions of teacher buoyancy; and (2) Competence to work in schools predicts the personal dimension of teacher buoyancy. The qualitative findings showed two cases that exemplified how the dynamic interaction between ECTs' perceived PC and the personal and contextual dimensions of TB supported their development of teaching expertise. Originality/value: The paper provides empirical findings on the relationship between PC and TB. It highlights ECTs' perceived Competence to work in schools as goal alignment and engagement with school policy as a crucial facilitating condition that develops ECTs' capacity to face daily challenges and engenders their thriving in terms of development of teaching expertise.

Published
2023
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13. APEX: A High-Performance Learned Index on Persistent Memory

Author

Lu, Baotong, Ding, Jialin, Lo, Eric, Minhas, Umar Farooq, and Wang, Tianzheng

Subjects
Computer Science - Databases
Abstract

The recently released persistent memory (PM) offers high performance, persistence, and is cheaper than DRAM. This opens up new possibilities for indexes that operate and persist data directly on the memory bus. Recent learned indexes exploit data distribution and have shown great potential for some workloads. However, none support persistence or instant recovery, and existing PM-based indexes typically evolve B+-trees without considering learned indexes. This paper proposes APEX, a new PM-optimized learned index that offers high performance, persistence, concurrency, and instant recovery. APEX is based on ALEX, a state-of-the-art updatable learned index, to combine and adapt the best of past PM optimizations and learned indexes, allowing it to reduce PM accesses while still exploiting machine learning. Our evaluation on Intel DCPMM shows that APEX can perform up to ~15x better than existing PM indexes and can recover from failures in ~42ms., Comment: To appear at VLDB 2022 (PVLDB Vol. 15 Issue 3)

Published
2021
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14. Saguaro: An Edge Computing-Enabled Hierarchical Permissioned Blockchain

Author

Amiri, Mohammad Javad, Lai, Ziliang, Patel, Liana, Loo, Boon Thau, Lo, Eric, and Zhou, Wenchao

Subjects
Computer Science - Databases, Computer Science - Networking and Internet Architecture
Abstract

We present Saguaro, a permissioned blockchain system designed specifically for edge computing networks. Saguaro leverages the hierarchical structure of edge computing networks to reduce the overhead of wide-area communication by presenting several techniques. First, Saguaro proposes coordinator-based and optimistic protocols to process cross-domain transactions with low latency where the lowest common ancestor of the involved domains coordinates the protocol or detects inconsistency. Second, data are collected over hierarchy enabling higher-level domains to aggregate their sub-domain data. Finally, transactions initiated by mobile edge devices are processed without relying on high-level fog and cloud servers. Our experimental results across a wide range of workloads demonstrate the scalability of Saguaro in supporting a range of cross-domain and mobile transactions.

Published
2021

15. Dash: Scalable Hashing on Persistent Memory

Author

Lu, Baotong, Hao, Xiangpeng, Wang, Tianzheng, and Lo, Eric

Subjects
Computer Science - Databases
Abstract

Byte-addressable persistent memory (PM) brings hash tables the potential of low latency, cheap persistence and instant recovery. The recent advent of Intel Optane DC Persistent Memory Modules (DCPMM) further accelerates this trend. Many new hash table designs have been proposed, but most of them were based on emulation and perform sub-optimally on real PM. They were also piece-wise and partial solutions that side-step many important properties, in particular good scalability, high load factor and instant recovery. We present Dash, a holistic approach to building dynamic and scalable hash tables on real PM hardware with all the aforementioned properties. Based on Dash, we adapted two popular dynamic hashing schemes (extendible hashing and linear hashing). On a 24-core machine with Intel Optane DCPMM, we show that compared to state-of-the-art, Dash-enabled hash tables can achieve up to ~3.9X higher performance with up to over 90% load factor and an instant recovery time of 57ms regardless of data size., Comment: To appear at VLDB 2020 (PVLDB Vol. 13 Issue 8)

Published
2020
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16. Top-K Deep Video Analytics: A Probabilistic Approach

Author

Lai, Ziliang, Han, Chenxia, Liu, Chris, Zhang, Pengfei, Lo, Eric, and Kao, Ben

Subjects
Computer Science - Databases
Abstract

The impressive accuracy of deep neural networks (DNNs) has created great demands on practical analytics over video data. Although efficient and accurate, the latest video analytic systems have not supported analytics beyond selection and aggregation queries. In data analytics, Top-K is a very important analytical operation that enables analysts to focus on the most important entities. In this paper, we present Everest, the first system that supports efficient and accurate Top-K video analytics. Everest ranks and identifies the most interesting frames/moments from videos with probabilistic guarantees. Everest is a system built with a careful synthesis of deep computer vision models, uncertain data management, and Top-K query processing. Evaluations on real-world videos and the latest Visual Road benchmark show that Everest achieves between 14.3x to 20.6x higher efficiency than baseline approaches with high result accuracy, Comment: 14 pages, 9 figures, 8 tables

Published
2020

17. High Performance Depthwise and Pointwise Convolutions on Mobile Devices

Author

Zhang, Pengfei, Lo, Eric, and Lu, Baotong

Subjects
Computer Science - Distributed, Parallel, and Cluster Computing, Computer Science - Performance
Abstract

Lightweight convolutional neural networks (e.g., MobileNets) are specifically designed to carry out inference directly on mobile devices. Among the various lightweight models, depthwise convolution (DWConv) and pointwise convolution (PWConv) are their key operations. In this paper, we observe that the existing implementations of DWConv and PWConv are not well utilizing the ARM processors in the mobile devices, and exhibit lots of cache misses under multi-core and poor data reuse at register level. We propose techniques to re-optimize the implementations of DWConv and PWConv based on ARM architecture. Experimental results show that our implementation can respectively achieve a speedup of up to 5.5x and 2.1x against TVM (Chen et al. 2018) on DWConv and PWConv., Comment: 8 pages, Thirty-Four AAAI conference on Artificial Intelligence

Published
2020

19. Patterns of oral anticoagulant use and outcomes in Asian patients with atrial fibrillation: a post-hoc analysis from the GLORIA-AF Registry

Author

Abban, Dzifa Wosornu, Abdul, Nasser, Abud, Atilio Marcelo, Adams, Fran, Addala, Srinivas, Adragão, Pedro, Ageno, Walter, Aggarwal, Rajesh, Agosti, Sergio, Agostoni, Piergiuseppe, Aguilar, Francisco, Linares, Julio Aguilar, Aguinaga, Luis, Ahmed, Jameel, Aiello, Allessandro, Ainsworth, Paul, Aiub, Jorge Roberto, Al-Dallow, Raed, Alderson, Lisa, Aldrete Velasco, Jorge Antonio, Alexopoulos, Dimitrios, Manterola, Fernando Alfonso, Aliyar, Pareed, Alonso, David, Alves da Costa, Fernando Augusto, Amado, José, Amara, Walid, Amelot, Mathieu, Amjadi, Nima, Ammirati, Fabrizio, Andrade, Marianna, Andrawis, Nabil, Annoni, Giorgio, Ansalone, Gerardo, Ariani, M.Kevin, Arias, Juan Carlos, Armero, Sébastien, Arora, Chander, Aslam, Muhammad Shakil, Asselman, M., Audouin, Philippe, Augenbraun, Charles, Aydin, S., Ayryanova, Ivaneta, Aziz, Emad, Backes, Luciano Marcelo, Badings, E., Bagni, Ermentina, Baker, Seth H., Bala, Richard, Baldi, Antonio, Bando, Shigenobu, Banerjee, Subhash, Bank, Alan, Esquivias, Gonzalo Barón, Barr, Craig, Bartlett, Maria, Kes, Vanja Basic, Baula, Giovanni, Behrens, Steffen, Bell, Alan, Benedetti, Raffaella, Mazuecos, Juan Benezet, Benhalima, Bouziane, Bergler-Klein, Jutta, Berneau, Jean-Baptiste, Bernstein, Richard A., Berrospi, Percy, Berti, Sergio, Berz, Andrea, Best, Elizabeth, Bettencourt, Paulo, Betzu, Robert, Bhagwat, Ravi, Bhatta, Luna, Biscione, Francesco, Bisignani, Giovanni, Black, Toby, Bloch, Michael J., Bloom, Stephen, Blumberg, Edwin, Bo, Mario, Bøhmer, Ellen, Bollmann, Andreas, Bongiorni, Maria Grazia, Boriani, Giuseppe, Boswijk, D.J., Bott, Jochen, Bottacchi, Edo, Kalan, Marica Bracic, Bradman, Drew, Brautigam, Donald, Breton, Nicolas, Brouwers, P.J.A.M., Browne, Kevin, Cortada, Jordi Bruguera, Bruni, A., Brunschwig, Claude, Buathier, Hervé, Buhl, Aurélie, Bullinga, John, Cabrera, Jose Walter, Caccavo, Alberto, Cai, Shanglang, Caine, Sarah, Calò, Leonardo, Calvi, Valeria, Sánchez, Mauricio Camarillo, Candeias, Rui, Capuano, Vincenzo, Capucci, Alessandro, Caputo, Ronald, Rizo, Tatiana Cárdenas, Cardona, Francisco, Carlos da Costa Darrieux, Francisco, Duarte Vera, Yan Carlos, Carolei, Antonio, Carreño, Susana, Carvalho, Paula, Cary, Susanna, Casu, Gavino, Cavallini, Claudio, Cayla, Guillaume, Celentano, Aldo, Cha, Tae-Joon, Cha, Kwang Soo, Chae, Jei Keon, Chalamidas, Kathrine, Challappa, Krishnan, Chand, Sunil Prakash, Chandrashekar, Harinath, Chartier, Ludovic, Chatterjee, Kausik, Chavez Ayala, Carlos Antero, Cheema, Aamir, Cheema, Amjad, Chen, Lin, Chen, Shih-Ann, Chen, Jyh Hong, Chiang, Fu-Tien, Chiarella, Francesco, Chih-Chan, Lin, Cho, Yong Keun, Choi, Jong-Il, Choi, Dong Ju, Chouinard, Guy, Hoi-Fan Chow, Danny, Chrysos, Dimitrios, Chumakova, Galina, José Roberto, Eduardo Julián, Valenzuela, Chuquiure, Nica, Nicoleta Cindea, Cislowski, David J., Clay, Anthony, Clifford, Piers, Cohen, Andrew, Cohen, Michael, Cohen, Serge, Colivicchi, Furio, Collins, Ronan, Colonna, Paolo, Compton, Steve, Connolly, Derek, Conti, Alberto, Buenostro, Gabriel Contreras, Coodley, Gregg, Cooper, Martin, Coronel, Julian, Corso, Giovanni, Sales, Juan Cosín, Cottin, Yves, Covalesky, John, Cracan, Aurel, Crea, Filippo, Crean, Peter, Crenshaw, James, Cullen, Tina, Darius, Harald, Dary, Patrick, Dascotte, Olivier, Dauber, Ira, Davalos, Vicente, Davies, Ruth, Davis, Gershan, Davy, Jean-Marc, Dayer, Mark, De Biasio, Marzia, De Bonis, Silvana, De Caterina, Raffaele, De Franceschi, Teresiano, de Groot, J.R., De Horta, José, De La Briolle, Axel, Topete, Gilberto de la Pena, Vicenzo de Paola, Angelo Amato, de Souza, Weimar, de Veer, A., De Wolf, Luc, Decoulx, Eric, Deepak, Sasalu, Defaye, Pascal, Del-Carpio Munoz, Freddy, Brkljacic, Diana Delic, Deumite, N. Joseph, Di Legge, Silvia, Diemberger, Igor, Dietz, Denise, Dionísio, Pedro, Dong, Qiang, Rossi dos Santos, Fabio, Dotcheva, Elena, Doukky, Rami, D'Souza, Anthony, Dubrey, Simon, Ducrocq, Xavier, Dupljakov, Dmitry, Duque, Mauricio, Dutta, Dipankar, Duvilla, Nathalie, Duygun, A., Dziewas, Rainer, Eaton, Charles B., Eaves, William, Ebels-Tuinbeek; Clifford Ehrlich, L.A., Eichinger-Hasenauer, Sabine, Eisenberg, Steven J., El Jabali, Adnan, El Shahawy, Mahfouz, Hernandes, Mauro Esteves, Izal, Ana Etxeberria, Evonich, Rudolph, III, Evseeva, Oksana, Ezhov, Andrey, Fahmy, Raed, Fang, Quan, Farsad, Ramin, Fauchier, Laurent, Favale, Stefano, Fayard, Maxime, Fedele, Jose Luis, Fedele, Francesco, Fedorishina, Olga, Fera, Steven R., Gomes Ferreira, Luis Gustavo, Ferreira, Jorge, Ferri, Claudio, Ferrier, Anna, Ferro, Hugo, Finsen, Alexandra, First, Brian, Fischer, Stuart, Fonseca, Catarina, Almeida, Luísa Fonseca, Forman, Steven, Frandsen, Brad, French, William, Friedman, Keith, Friese, Athena, Fruntelata, Ana Gabriela, Fujii, Shigeru, Fumagalli, Stefano, Fundamenski, Marta, Furukawa, Yutaka, Gabelmann, Matthias, Gabra, Nashwa, Gadsbøll, Niels, Galinier, Michel, Gammelgaard, Anders, Ganeshkumar, Priya, Gans, Christopher, Quintana, Antonio Garcia, Gartenlaub, Olivier, Gaspardone, Achille, Genz, Conrad, Georger, Frédéric, Georges, Jean-Louis, Georgeson, Steven, Giedrimas, Evaldas, Gierba, Mariusz, Ortega, Ignacio Gil, Gillespie, Eve, Giniger, Alberto, Giudici, Michael C., Gkotsis, Alexandros, Glotzer, Taya V., Gmehling, Joachim, Gniot, Jacek, Goethals, Peter, Goldbarg, Seth, Goldberg, Ronald, Goldmann, Britta, Golitsyn, Sergey, Gómez, Silvia, Mesa, Juan Gomez, Gonzalez, Vicente Bertomeu, Gonzalez Hermosillo, Jesus Antonio, González López, Víctor Manuel, Gorka, Hervé, Gornick, Charles, Gorog, Diana, Gottipaty, Venkat, Goube, Pascal, Goudevenos, Ioannis, Graham, Brett, Greer, G. Stephen, Gremmler, Uwe, Grena, Paul G., Grond, Martin, Gronda, Edoardo, Grönefeld, Gerian, Gu, Xiang, Torres Torres, Ivett Guadalupe, Guardigli, Gabriele, Guevara, Carolina, Guignier, Alexandre, Gulizia, Michele, Gumbley, Michael, Günther, Albrecht, Ha, Andrew, Hahalis, Georgios, Hakas, Joseph, Hall, Christian, Han, Bing, Han, Seongwook, Hargrove, Joe, Hargroves, David, Harris, Kenneth B., Haruna, Tetsuya, Hayek, Emil, Healey, Jeff, Hearne, Steven, Heffernan, Michael, Heggelund, Geir, Heijmeriks, J.A., Hemels, Maarten, Hendriks, I., Henein, Sam, Her, Sung-Ho, Hermany, Paul, Hernández Del Río, Jorge Eduardo, Higashino, Yorihiko, Hill, Michael, Hisadome, Tetsuo, Hishida, Eiji, Hoffer, Etienne, Hoghton, Matthew, Hong, Kui, Hong, Suk keun, Horbach, Stevie, Horiuchi, Masataka, Hou, Yinglong, Hsing, Jeff, Huang, Chi-Hung, Huckins, David, Hughes, kathy, Huizinga, A., Hulsman, E.L., Hung, Kuo-Chun, Hwang, Gyo-Seung, Ikpoh, Margaret, Imberti, Davide, Ince, Hüseyin, Indolfi, Ciro, Inoue, Shujiro, Irles, Didier, Iseki, Harukazu, Israel, C. Noah, Iteld, Bruce, Iyer, Venkat, Jackson-Voyzey, Ewart, Jaffrani, Naseem, Jäger, Frank, James, Martin, Jang, Sung-Won, Jaramillo, Nicolas, Jarmukli, Nabil, Jeanfreau, Robert J., Jenkins, Ronald D., Sánchez, Carlos Jerjes, Jimenez, Javier, Jobe, Robert, Joen-Jakobsen, Tomas, Jones, Nicholas, Moura Jorge, Jose Carlos, Jouve, Bernard, Jung, Byung Chun, Jung, Kyung Tae, Jung, Werner, Kachkovskiy, Mikhail, Kafkala, Krystallenia, Kalinina, Larisa, Kallmünzer, Bernd, Kamali, Farzan, Kamo, Takehiro, Kampus, Priit, Kashou, Hisham, Kastrup, Andreas, Katsivas, Apostolos, Kaufman, Elizabeth, Kawai, Kazuya, Kawajiri, Kenji, Kazmierski, John F., Keeling, P., Kerr Saraiva, José Francisco, Ketova, Galina, Khaira, Ajit Singh, Khripun, Aleksey, Kim, Doo-Il, Kim, Young Hoon, Kim, Nam Ho, Kim, Dae Kyeong, Kim, Jeong Su, Kim, June Soo, Kim, Ki Seok, Kim, Jin bae, Kinova, Elena, Klein, Alexander, Kmetzo, James J., Kneller, G. Larsen, Knezevic, Aleksandar, Angela Koh, Su Mei, Koide, Shunichi, Kollias, Anastasios, Kooistra, J.A., Koons, Jay, Koschutnik, Martin, Kostis, William J., Kovacic, Dragan, Kowalczyk, Jacek, Koziolova, Natalya, Kraft, Peter, Kragten, Johannes A., Krantz, Mori, Krause, Lars, Krenning, B.J., Krikke, F., Kromhout, Z., Krysiak, Waldemar, Kumar, Priya, Kümler, Thomas, Kuniss, Malte, Kuo, Jen-Yuan, Küppers, Achim, Karla Kurrelmeyer, Kwak, Choong Hwan, Laboulle, Bénédicte, Labovitz, Arthur, Lai, Wen Ter, Lam, Andy, Lam, Yat Yin, Zanetti, Fernando Lanas, Landau, Charles, Landini, Giancarlo, Figueiredo, Estêvão Lanna, Larsen, Torben, Lavandier, Karine, LeBlanc, Jessica, Lee, Moon Hyoung, Lee, Chang-Hoon, Lehman, John, Leitão, Ana, Lellouche, Nicolas, Lelonek, Malgorzata, Lenarczyk, Radoslaw, Lenderink, T., González, Salvador León, Leong-Sit, Peter, Leschke, Matthias, Ley, Nicolas, Li, Zhanquan, Li, Xiaodong, Li, Weihua, Li, Xiaoming, Lichy, Christhoh, Lieber, Ira, Limon Rodriguez, Ramon Horacio, Lin, Hailong, Lip, Gregory Y.H., Liu, Feng, Liu, Hengliang, Esperon, Guillermo Llamas, Navarro, Nassip Llerena, Lo, Eric, Lokshyn, Sergiy, López, Amador, López-Sendón, José Luís, Lorga Filho, Adalberto Menezes, Lorraine, Richard S., Luengas, Carlos Alberto, Luke, Robert, Luo, Ming, Lupovitch, Steven, Lyrer, Philippe, Ma, Changsheng, Ma, Genshan, Madariaga, Irene, Maeno, Koji, Magnin, Dominique, Maid, Gustavo, Mainigi, Sumeet K., Makaritsis, Konstantinos, Malhotra, Rohit, Manning, Rickey, Manolis, Athanasios, Manrique Hurtado, Helard Andres, Mantas, Ioannis, Jattin, Fernando Manzur, Maqueda, Vicky, Marchionni, Niccolo, Ortuno, Francisco Marin, Santana, Antonio Martín, Martinez, Jorge, Maskova, Petra, Hernandez, Norberto Matadamas, Matsuda, Katsuhiro, Maurer, Tillmann, Mauro, Ciro, May, Erik, Mayer, Nolan, McClure, John, McCormack, Terry, McGarity, William, McIntyre, Hugh, McLaurin, Brent, Medina Palomino, Feliz Alvaro, Melandri, Francesco, Meno, Hiroshi, Menzies, Dhananjai, Mercader, Marco, Meyer, Christian, Meyer, Beat J., Miarka, Jacek, Mibach, Frank, Michalski, Dominik, Michel, Patrik, Chreih, Rami Mihail, Mikdadi, Ghiath, Mikus, Milan, Milicic, Davor, Militaru, Constantin, Minaie, Sedi, Minescu, Bogdan, Mintale, Iveta, Mirault, Tristan, Mirro, Michael J., Mistry, Dinesh, Miu, Nicoleta Violeta, Miyamoto, Naomasa, Moccetti, Tiziano, Mohammed, Akber, Nor, Azlisham Mohd, Mollerus, Michael, Molon, Giulio, Mondillo, Sergio, Moniz, Patrícia, Mont, Lluis, Montagud, Vicente, Montaña, Oscar, Monti, Cristina, Moretti, Luciano, Mori, Kiyoo, Moriarty, Andrew, Morka, Jacek, Moschini, Luigi, Moschos, Nikitas, Mügge, Andreas, Mulhearn, Thomas J., Muresan, Carmen, Muriago, Michela, Musial, Wlodzimierz, Musser, Carl W., Musumeci, Francesco, Nageh, Thuraia, Nakagawa, Hidemitsu, Nakamura, Yuichiro, Nakayama, Toru, Nam, Gi-Byoung, Nanna, Michele, Natarajan, Indira, Nayak, Hemal M., Naydenov, Stefan, Nazlić, Jurica, Cristian Nechita, Alexandru, Nechvatal, Libor, Negron, Sandra Adela, Neiman, James, Neuenschwander, Fernando Carvalho, Neves, David, Neykova, Anna, Miguel, Ricardo Nicolás, Nijmeh, George, Nizov, Alexey, Campos, Rodrigo Noronha, Nossan, Janko, Novikova, Tatiana, Nowalany-Kozielska, Ewa, Nsah, Emmanuel, Nunez Fragoso, Juan Carlos, Nurgalieva, Svetlana, Nuyens, Dieter, Nyvad, Ole, Odin de Los Rios Ibarra, Manuel, O'Donnell, Philip, O'Donnell, Martin, Oh, Seil, Oh, Yong Seog, Oh, Dongjin, O'Hara, Gilles, Oikonomou, Kostas, Olivares, Claudia, Oliver, Richard, Ruiz, Rafael Olvera, Olympios, Christoforos, omaszuk-Kazberuk, Anna, Asensi, Joaquín Osca, Jose, eena Padayattil, Padilla Padilla, Francisco Gerardo, Rios, Victoria Padilla, Pajes, Giuseppe, Pandey, Shekhar, Paparella, Gaetano, Paris, F., Park, Hyung Wook, Park, Jong Sung, Parthenakis, Fragkiskos, Passamonti, Enrico, Patel, Rajesh J., Patel, Jaydutt, Patel, Mehool, Patrick, Janice, Jimenez, Ricardo Pavón, Paz, Analía, Pengo, Vittorio, Pentz, William, Pérez, Beatriz, Pérez Ríos, Alma Minerva, Pérez-Cabezas, Alejandro, Perlman, Richard, Persic, Viktor, Perticone, Francesco, Peters, Terri K., Petkar, Sanjiv, Pezo, Luis Felipe, Pflücke, Christian, Pham, David N., Phillips, Roland T., Phlaum, Stephen, Pieters, Denis, Pineau, Julien, Pinter, Arnold, Pinto, Fausto, Pisters, R., Pivac, Nediljko, Pocanic, Darko, Podoleanu, Cristian, Politano, Alessandro, Poljakovic, Zdravka, Pollock, Stewart, Garcéa, Jose Polo, Poppert, Holger, Porcu, Maurizio, Reino, Antonio Pose, Prasad, Neeraj, Précoma, Dalton Bertolim, Prelle, Alessandro, Prodafikas, John, Protasov, Konstantin, Pye, Maurice, Qiu, Zhaohui, Quedillac, Jean-Michel, Raev, Dimitar, Raffo Grado, Carlos Antonio, Rahimi, Sidiqullah, Raisaro, Arturo, Rama, Bhola, Ramos, Ricardo, Ranieri, Maria, Raposo, Nuno, Rashba, Eric, Rauch-Kroehnert, Ursula, Reddy, Ramakota, Renda, Giulia, Reza, Shabbir, Ria, Luigi, Richter, Dimitrios, Rickli, Hans, Rieker, Werner, Vera, Tomas Ripolil, Ritt, Luiz Eduardo, Roberts, Douglas, Briones, Ignacio Rodriguez, Rodriguez Escudero, Aldo Edwin, Pascual, Carlos Rodríguez, Roman, Mark, Romeo, Francesco, Ronner, E., Roux, Jean-Francois, Rozkova, Nadezda, Rubacek, Miroslav, Rubalcava, Frank, Russo, Andrea M., Rutgers, Matthieu Pierre, Rybak, Karin, Said, Samir, Sakamoto, Tamotsu, Salacata, Abraham, Salem, Adrien, Bodes, Rafael Salguero, Saltzman, Marco A., Salvioni, Alessandro, Vallejo, Gregorio Sanchez, Fernández, Marcelo Sanmartín, Saporito, Wladmir Faustino, Sarikonda, Kesari, Sasaoka, Taishi, Sati, Hamdi, Savelieva, Irina, Scala, Pierre-Jean, Schellinger, Peter, Scherr, Carlos, Schmitz, Lisa, Schmitz, Karl-Heinz, Schmitz, Bettina, Schnabel, Teresa, Schnupp, Steffen, Schoeniger, Peter, Schön, Norbert, Schwimmbeck, Peter, Seamark, Clare, Searles, Greg, Seidl, Karl-Heinz, Seidman, Barry, Sek, Jaroslaw, Sekaran, Lakshmanan, Serrati, Carlo, Shah, Neerav, Shah, Vinay, Shah, Anil, Shah, Shujahat, Sharma, Vijay Kumar, Shaw, Louise, Sheikh, Khalid H., Shimizu, Naruhito, Shimomura, Hideki, Shin, Dong-Gu, Shin, Eun-Seok, Shite, Junya, Sibilio, Gerolamo, Silver, Frank, Sime, Iveta, Simmers, Tim A., Singh, Narendra, Siostrzonek, Peter, Smadja, Didier, Smith, David W., Snitman, Marcelo, Filho, Dario Sobral, Soda, Hassan, Sofley, Carl, Sokal, Adam, Oi Yan, Yannie Soo, Sotolongo, Rodolfo, Ferreira de Souza, Olga, Sparby, Jon Arne, Spinar, Jindrich, Sprigings, David, Spyropoulos, Alex C., Stakos, Dimitrios, Steinwender, Clemens, Stergiou, Georgios, Stiell, Ian, Stoddard, Marcus, Stoikov, Anastas, Streb, Witold, Styliadis, Ioannis, Su, Guohai, Su, Xi, Sudnik, Wanda, Sukles, Kai, Sun, Xiaofei, Swart, H., Szavits-Nossan, Janko, Taggeselle, Jens, Takagi, Yuichiro, Singh Takhar, Amrit Pal, Tamm, Angelika, Tanaka, Katsumi, Tanawuttiwat, Tanyanan, Tang, Sherman, Tang, Aylmer, Tarsi, Giovanni, Tassinari, Tiziana, Tayal, Ashis, Tayebjee, Muzahir, Berg, J.M. ten, Tesloianu, Dan, The, Salem H.K., Thomas, Dierk, Timsit, Serge, Tobaru, Tetsuya, Tomasik, Andrzej R., Torosoff, Mikhail, Touze, Emmanuel, Trendafilova, Elina, Tsai, W. Kevin, Tse, Hung Fat, Tsutsui, Hiroshi, Tu, Tian Ming, Tuininga, Ype, Turakhia, Minang, Turk, Samir, Turner, Wayne, Tveit, Arnljot, Tytus, Richard, Valadão, C., van Bergen, P.F.M.M., van de Borne, Philippe, van den Berg, B.J., van der Zwaan, C., Van Eck, M., Vanacker, Peter, Vasilev, Dimo, Vasilikos, Vasileios, Vasilyev, Maxim, Veerareddy, Srikar, Miño; Asok Venkataraman, Mario Vega, Verdecchia, Paolo, Versaci, Francesco, Vester, Ernst Günter, Vial, Hubert, Victory, Jason, Villamil, Alejandro, Vincent, Marc, Vlastaris, Anthony, Dahl, Jürgen vom, Vora, Kishor, Vranian, Robert B., Wakefield, Paul, Wang, Ningfu, Wang, Mingsheng, Wang, Xinhua, Wang, Feng, Wang, Tian, Warner, Alberta L., Watanabe, Kouki, Wei, Jeanne, Weimar, Christian, Weiner, Stanislav, Weinrich, Renate, Wen, Ming-Shien, Wiemer, Marcus, Wiggers, Preben, Wilke, Andreas, Williams, David, Williams, Marcus L., Witzenbichler, Bernhard, Wong, Brian, Lawrence Wong, Ka Sing, Wozakowska-Kaplon, Beata, Wu, Shulin, Wu, Richard C., Wunderlich, Silke, Wyatt, Nell, Wylie, John (Jack), Xu, Yong, Xu, Xiangdong, Yamanoue, Hiroki, Yamashita, Takeshi, Bryan Yan, Ping Yen, Yang, Tianlun, Yao, Jing, Yeh, Kuo-Ho, Yin, Wei Hsian, Yotov, Yoto, Zahn, Ralf, Zarich, Stuart, Zenin, Sergei, Zeuthen, Elisabeth Louise, Zhang, Huanyi, Zhang, Donghui, Zhang, Xingwei, Zhang, Ping, Zhang, Jun, Zhao, Shui Ping, Zhao, Yujie, Zhao, Zhichen, Zheng, Yang, Zhou, Jing, Zimmermann, Sergio, Zini, Andrea, Zizzo, Steven, Zong, Wenxia, Zukerman, L. Steven, Romiti, Giulio Francesco, Corica, Bernadette, Proietti, Marco, Mei, Davide Antonio, Frydenlund, Juliane, Bisson, Arnaud, Olshansky, Brian, Chan, Yi-Hsin, Huisman, Menno V., and Chao, Tze-Fan

Published
2023
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21. Self-administered intranasal etripamil using a symptom-prompted, repeat-dose regimen for atrioventricular-nodal-dependent supraventricular tachycardia (RAPID): a multicentre, randomised trial

Author

Ip, James E, Jones, Alonzo, Sr, Stambler, Bruce S, Schleinkofer, David E, Winters, Stephen, Lam, Wilson, Goldstein, Robert, Dor, Isaac Dor, Talwar, Sandeep, O'Neill, Padraig G, Koren, Michael J, Beinart, Sean C, Ambati, Srivani, Mazer, Sean P, Kinn, Robert M, Roy, Karine, Manshadi, Ramin, Kuk, Richard, Verma, Aditya, Phelan, Timothy, Stoian, Alexandru A, Ellenbogen, Kenneth, Jayachandran, J Vijay, Connelly, Terence P, Daccarett, Marcos, Gandhi, Gaurang, Mittal, Suneet, Abdel-Wahab, Amir, Augostini, Ralph, Ip, John H, Sorrentino, Denise, Roux, Jean-Francois, Brar, Ramandeep, Khoo, Clarence, Bennett, Matthew, Lo, Eric, Coutu, Benoit, Sterns, Laurence, Greer, Gerald S, Gujja, Pradeep, Gianfagna, Robert A, Swarup, Vijendra, Sogade, Felix, Wilton, Stephen, Ruisi, Christopher, Barbera, Saverio, Banchs, Javier E., Korley, Victoria, Schulze, Christopher, Damle, Roger, Healey, Jeffrey Sean, Pandey, A Shekhar, Anderson, Jeffrey L, Venkatachalam, K L, Noseworthy, Peter, Mondésert, Blandine, Verma, Atul, Friars, Douglas G, Kambur, Thomas R, Lockwood, Evan, Meininger, Glenn, Olsovsky, Greg, Jones Sr, Alonzo, Greer, Gerald, Mehta, Rohit, Akbar, Saleem, Salman, Malik, Rama, Bhola, Arora, Ramesh, Shah, Dhirenkumar, Shah, Rakesh, Cammarata, Michael, Owens, Andrew, Ludington, Katherine, Wharton, Marcus, Bagheri, Michael, Khan, Mohammed, Carr, Kenneth Warren, Siddique, Sultan, Parvathaneni, Sunthosh, Devabhaktuni, Subodh, Mouhaffel, Assad, Mark, George, Di Biase, Luigi, Rangappa, Sunil, Morton, Jared, Yarmohammadi, Hirad, Karim, Amin, Martinez Fleites, Aldo, Nero, Thomas, Shafiq, Qaiser, Pandey, Shekhar, Khaykin, Yaariv, Joza, Jacqueline, Gosselin, Gilbert, Skanes, Allen, Vyselaar, John, Savard, Daniel, Heidbuchel, Hein, Xhaet, Olivier, Catez, Emmanuel, Vijgen, Johan, Godart, Pascal, Casado, Ruben, Mairesse, Georges, Rossenbacker, Tom, Haemers, Peter, Hoffer, Etienne, Hausman, Pierre, Vertes, Andras, Duray, Gabor, Szolnoki, Erzsebet, Csanadi, Zoltan, Lakatos, Ferenc, Alings, Marco, Dorman, Reinhart, Tuininga, Y S, Jansen, W, Velthuis, Sebastiaan, van Bemmel, B, Bech, Gerhard Jan Willem, Pisters, Ron, Valk, Suzanne, Romer, Tjeerd, Luermans, Justin, Beelen, Driek, van Eck, Martijn, Houtgraaf, Jaco, Vet, Thijs, Shellings, Dirk, Oosterhof, T, Miekus, Pawel, Bebenek, Waldemar, Bielecka-Dabrowa, Agata, Gniot, Jacek, Zmuda, Witold, Kasprzak, Michal, Balak, Wojciech, Ptaszynski, Pawel, Nowak, Jacek, Prokopczuk, Janusz, Przybylski, Andrzej, Wozniak-Skowerska, Iwona, Derejko, Pawel, Czarnecka, Danuta, Janas, Adam, Merino Llorens, Jose Luis, Tercedor, Luis, Iniguez Romo, Andres, Ruiz Granell, Ricardo, Gonzalez Juanatey, Jose Ramon, Pascual-Figal, Domingo, Martinez-Selles, Manuel, Anguera Camos, Ignacio, Ibanez Criado, Alicia, Matrinez Basterra, Javier, Izquierdo, Alvaro, Rivas Gandara, Nuria, Alegret Colome, Josep M, Perez Diez, Diego, Martínez Hervás, Miguel Ángel, Medina, Maria, Segura Saint-Gerons, Jose María, Pedrote Leal, Alonso, Fernandez Lozano, Ignacio, Quesada, Aurelio, Guerra Ramos, Jose, Sarrias, Axel, Ramos Maqueda, Javier, Mansourati, Jacques, Defaye, Pascal, Guedon-Moreau, Laurence, Milhem, Antoine, De Guillebon, Maxime, Chevalier, Philippe, Badoz, Marc, Kriatselis, Charalampos, Simonis, Gregor, Lewalter, Thorsten, Zarse, Markus, Wilke, Andreas, Kraemer, Fabian, Al-Zoebi, Ayham, Camm, A John, Dorian, Paul, Kowey, Peter R, Merino, Jose L, Piccini, Jonathan P, Pokorney, Sean D, Sager, Philip T, Wharton, J Marcus, Bharucha, David B, Plat, Francis, Shardonofsky, Silvia, and Chen, Michael

Published
2023
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24. The Geisel Library, the Brutalist Architecture of William Pereira

Author

Bartolomei, Cristiana, Kuester, Falko, Lo, Eric, Morganti, Caterina, Rissolo, Dominique, Solari, Giovanni, Series Editor, Chen, Sheng-Hong, Series Editor, di Prisco, Marco, Series Editor, Vayas, Ioannis, Series Editor, Bartolomei, Cristiana, editor, Ippolito, Alfonso, editor, and Vizioli, Simone Helena Tanoue, editor

Published
2022
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31. Towards Self-Tuning Parameter Servers

Author

Liu, Chris, Zhang, Pengfei, Tang, Bo, Shen, Hang, Zhu, Lei, Lai, Ziliang, and Lo, Eric

Subjects
Computer Science - Databases
Abstract

Recent years, many applications have been driven advances by the use of Machine Learning (ML). Nowadays, it is common to see industrial-strength machine learning jobs that involve millions of model parameters, terabytes of training data, and weeks of training. Good efficiency, i.e., fast completion time of running a specific ML job, therefore, is a key feature of a successful ML system. While the completion time of a long-running ML job is determined by the time required to reach model convergence, practically that is also largely influenced by the values of various system settings. In this paper, we contribute techniques towards building self-tuning parameter servers. Parameter Server (PS) is a popular system architecture for large-scale machine learning systems; and by self-tuning we mean while a long-running ML job is iteratively training the expert-suggested model, the system is also iteratively learning which system setting is more efficient for that job and applies it online. While our techniques are general enough to various PS-style ML systems, we have prototyped our techniques on top of TensorFlow. Experiments show that our techniques can reduce the completion times of a variety of long-running TensorFlow jobs from 1.4x to 18x., Comment: 13 pages

Published
2018

32. Decentralized Search on Decentralized Web

Author

Lai, Ziliang, Liu, Chris, Lo, Eric, Kao, Ben, and Yiu, Siu-Ming

Subjects
Computer Science - Information Retrieval, Computer Science - Cryptography and Security, Computer Science - Distributed, Parallel, and Cluster Computing
Abstract

Decentralized Web, or DWeb, is envisioned as a promising future of the Web. Being decentralized, there are no dedicated web servers in DWeb; Devices that retrieve web contents also serve their cached data to peer devices with straight privacy-preserving mechanisms. The fact that contents in DWeb are distributed, replicated, and decentralized lead to a number of key advantages over the conventional web. These include better resiliency against network partitioning and distributed-denial-of-service attacks (DDoS), and better browsing experiences in terms of shorter latency and higher throughput. Moreover, DWeb provides tamper-proof contents because each content piece is uniquely identified by a cryptographic hash. DWeb also clicks well with future Internet architectures, such as Named Data Networking (NDN).Search engines have been an inseparable element of the Web. Contemporary ("Web 2.0") search engines, however, provide centralized services. They are thus subject to DDoS attacks, insider threat, and ethical issues like search bias and censorship. As the web moves from being centralized to being decentralized, search engines ought to follow. We propose QueenBee, a decentralized search engine for DWeb. QueenBee is so named because worker bees and honeycomb are a common metaphor for distributed architectures, with the queen being the one that holds the colony together. QueenBee aims to revolutionize the search engine business model by offering incentives to both content providers and peers that participate in QueenBee's page indexing and ranking operations.

Published
2018

34. Lessons on monitoring mangroves

Author

Hsu, Astrid J, Lo, Eric, Dorian, John, Qi, Kathy, Costa, Matthew T, and Guerrero Martinez, Benigno

Published
2019

36. InferSpark: Statistical Inference at Scale

Author

Zhao, Zhuoyue, Pei, Jialing, Lo, Eric, Zhu, Kenny Q., and Liu, Chris

Subjects
Computer Science - Databases
Abstract

The Apache Spark stack has enabled fast large-scale data processing. Despite a rich library of statistical models and inference algorithms, it does not give domain users the ability to develop their own models. The emergence of probabilistic programming languages has showed the promise of developing sophisticated probabilistic models in a succinct and programmatic way. These frameworks have the potential of automatically generating inference algorithms for the user defined models and answering various statistical queries about the model. It is a perfect time to unite these two great directions to produce a programmable big data analysis framework. We thus propose, InferSpark, a probabilistic programming framework on top of Apache Spark. Efficient statistical inference can be easily implemented on this framework and inference process can leverage the distributed main memory processing power of Spark. This framework makes statistical inference on big data possible and speed up the penetration of probabilistic programming into the data engineering domain., Comment: 13 pages, 22 figures

Published
2017

39. Development of a Psychometric Measure of the Propensity to Consciously Control and Monitor Speech Production

Author

Lo, Eric Siu-Chung, Wong, Andus Wing-Kuen, Tse, Andy Choi-Yeung, Ma, Estella Pui-Man, Whitehill, Tara L., and Masters, Rich S. W.

Abstract

Purpose: A speech-specific reinvestment scale (SSRS) is a psychometric measure of the propensity to consciously control and monitor speech production. This study develops and validates an SSRS as well as examines its relationship with speech performance with the moderating effects of trait social anxieties (i.e., social interaction anxiety, public speaking anxiety, and social phobia). Method: Scale development involves the following stages: (a) initial item generation based on relevant literature, (b) item evaluation through cognitive interviews with 24 healthy respondents, (c) scale reliability and validity tests using cross-sectional survey data from 498 healthy respondents, and (d) test--retest reliability assessment using longitudinal survey data from 185 healthy respondents. Respondents' speech performance is quantified using speech examination scores. Hierarchical moderated regression analyses are conducted to examine the moderating effects of trait social anxieties. Results: The validated SSRS comprises 35 items, which can be categorized into four subdimensions, namely, speech movement self-consciousness, public consciousness of speech content, speech manner, and speech movement. Results show that respondents with low trait social anxieties indicate a generally positive relationship between public consciousness of speech movement and speech performance, whereas respondents with high trait social anxieties exhibit a nonsignificant relationship. Conclusions: SSRS offers a reliable and valid method for assessing the predisposition for conscious speech control and monitoring, which plays a role in speech performance and is moderated by an individual's level of trait social anxiety. SSRS is a potential assessment tool for speech-language pathologists to evaluate the impacts of conscious speech control and monitoring on individuals with speech impairment.

Published
2020
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41. FIGURE 2 from High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance

Author

Saouaf, Rola, primary, Xie, Yibin, primary, Kim, Sungjin, primary, Raphael, Yaniv, primary, Nguyen, Christopher, primary, Luthringer, Daniel, primary, Daskivich, Timothy J., primary, Lo, Eric, primary, Tighiouart, Mourad, primary, Li, Debiao, primary, and Kim, Hyung L., primary

Published
2024
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42. TABLE 1 from High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance

Author

Saouaf, Rola, primary, Xie, Yibin, primary, Kim, Sungjin, primary, Raphael, Yaniv, primary, Nguyen, Christopher, primary, Luthringer, Daniel, primary, Daskivich, Timothy J., primary, Lo, Eric, primary, Tighiouart, Mourad, primary, Li, Debiao, primary, and Kim, Hyung L., primary

Published
2024
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43. TABLE 2 from High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance

Author

Saouaf, Rola, primary, Xie, Yibin, primary, Kim, Sungjin, primary, Raphael, Yaniv, primary, Nguyen, Christopher, primary, Luthringer, Daniel, primary, Daskivich, Timothy J., primary, Lo, Eric, primary, Tighiouart, Mourad, primary, Li, Debiao, primary, and Kim, Hyung L., primary

Published
2024
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44. High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance

Author

Saouaf, Rola, primary, Xie, Yibin, additional, Kim, Sungjin, additional, Raphael, Yaniv, additional, Nguyen, Christopher, additional, Luthringer, Daniel, additional, Daskivich, Timothy J., additional, Lo, Eric, additional, Tighiouart, Mourad, additional, Li, Debiao, additional, and Kim, Hyung L., additional

Published
2024
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45. Supplementary Tables S1, S2 from High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance

Author

Saouaf, Rola, primary, Xie, Yibin, primary, Kim, Sungjin, primary, Raphael, Yaniv, primary, Nguyen, Christopher, primary, Luthringer, Daniel, primary, Daskivich, Timothy J., primary, Lo, Eric, primary, Tighiouart, Mourad, primary, Li, Debiao, primary, and Kim, Hyung L., primary

Published
2024
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46. TABLE 4 from High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance

Author

Saouaf, Rola, primary, Xie, Yibin, primary, Kim, Sungjin, primary, Raphael, Yaniv, primary, Nguyen, Christopher, primary, Luthringer, Daniel, primary, Daskivich, Timothy J., primary, Lo, Eric, primary, Tighiouart, Mourad, primary, Li, Debiao, primary, and Kim, Hyung L., primary

Published
2024
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47. Data from High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance

Author

Saouaf, Rola, primary, Xie, Yibin, primary, Kim, Sungjin, primary, Raphael, Yaniv, primary, Nguyen, Christopher, primary, Luthringer, Daniel, primary, Daskivich, Timothy J., primary, Lo, Eric, primary, Tighiouart, Mourad, primary, Li, Debiao, primary, and Kim, Hyung L., primary

Published
2024
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48. TABLE 3 from High-resolution Diffusion-weighted Imaging to Detect Changes in Tumor Size and ADC, and Predict Adverse Biopsy Histology during Prostate Cancer Active Surveillance

Author

Saouaf, Rola, primary, Xie, Yibin, primary, Kim, Sungjin, primary, Raphael, Yaniv, primary, Nguyen, Christopher, primary, Luthringer, Daniel, primary, Daskivich, Timothy J., primary, Lo, Eric, primary, Tighiouart, Mourad, primary, Li, Debiao, primary, and Kim, Hyung L., primary

Published
2024
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