Demo Data from EDBT 2026 / reviewers in the wild / expert
Vijay Srinivasan
dblp:77/6327
· DBLP profile ↗
32ranked-venue papers
10as first author
9since 2021 · last 2024
—ORCID · conflict
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 10 · 3 first-author · 1 since 2021Artificial intelligence and machine learning · 8 · 1 first-author · 7 since 2021Computer networks · 7 · 3 first-authorDatabases, data management, data science and information retrieval · 3 · 3 since 2021Applied, interdisciplinary, general and emerging computing · 3 · 1 first-author · 1 since 2021Systems, architecture and hardware · 2 · 1 first-authorHuman-computer interaction and ubiquitous computing · 2 · 2 first-authorTheory of computation · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
3 papers |
Language models and text generation · 42% Efficient and distributed learning · 27% Question answering and dialogue systems · 16% | |
| Databases, data mining, and information retrieval
2 papers |
Information retrieval · 93% Data mining · 7% | |
| Computer graphics and multimedia
7 papers |
Geometric modeling and processing · 56% Computational fabrication · 44% | |
| Human-computer interaction and pervasive computing
4 papers |
Ubiquitous computing and smart environments · 100% |
Topics — the 30 heaviest of 39, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Efficient and distributed learning
data selection |
0.8 | 1 | 2024 | AlpaGasus: Training a Better Alpaca with Fewer Data · ICLR 2024 |
Natural language and speech › Language models and text generation
instruction tuning |
0.8 | 1 | 2024 | AlpaGasus: Training a Better Alpaca with Fewer Data · ICLR 2024 |
Natural language and speech › Language models and text generation › large language model
large language model applications |
0.8 | 1 | 2024 | PAG-LLM: Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors · SIGIR 2024 |
Information retrieval › query understanding
intent detection |
0.8 | 1 | 2024 | PAG-LLM: Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors · SIGIR 2024 |
Information retrieval
query understanding |
0.8 | 1 | 2024 | PAG-LLM: Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors · SIGIR 2024 |
Natural language and speech › Question answering and dialogue systems › task-oriented dialogue
information-seeking dialogue |
0.6 | 1 | 2022 | ISEEQ: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval and Knowledge Graphs · AAAI 2022 |
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge graph |
0.6 | 1 | 2022 | ISEEQ: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval and Knowledge Graphs · AAAI 2022 |
Ubiquitous computing and smart environments
smart home |
0.3 | 2 | 2013 | FixtureFinder: discovering the existence of electrical and water fixtures · IPSN 2013 The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010 |
Machine learning › Efficient and distributed learning
data-efficient learning |
0.2 | 1 | 2024 | AlpaGasus: Training a Better Alpaca with Fewer Data · ICLR 2024 |
Geometric modeling and processing › computer-aided design › product data exchange
CAD data exchange |
0.2 | 1 | 2014 | Report from a 2013 ASME panel on geometric interoperability for advanced manufacturing · Comput. Aided Des. 2014 |
Ubiquitous computing and smart environments › smart home
smart thermostat |
0.1 | 1 | 2010 | The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010 |
Cyber-physical and IoT security › iot privacy
smart home privacy |
0.1 | 1 | 2008 | Protecting your daily in-home activity information from a wireless snooping attack · UbiComp 2008 |
Computational science and engineering › materials science
materials science simulation |
0.1 | 1 | 2016 | The new frontiers in computational modeling of material structures · Comput. Aided Des. 2016 |
Data mining › pattern mining › frequent pattern mining
co-occurrence pattern mining |
0.1 | 1 | 2014 | MobileMiner: mining your frequent patterns on your phone · UbiComp 2014 |
Data mining
pattern mining |
0.1 | 1 | 2014 | MobileMiner: mining your frequent patterns on your phone · UbiComp 2014 |
Geometric modeling and processing
computer-aided design |
0.1 | 1 | 2014 | Report from a 2013 ASME panel on geometric interoperability for advanced manufacturing · Comput. Aided Des. 2014 |
Privacy and data protection
on-device data processing |
0.1 | 1 | 2014 | MobileMiner: mining your frequent patterns on your phone · UbiComp 2014 |
Internet of things and sensor networks › wireless sensor network
sensor fusion |
0.0 | 1 | 2013 | FixtureFinder: discovering the existence of electrical and water fixtures · IPSN 2013 |
Internet of things and sensor networks
wireless sensor network |
0.0 | 1 | 2011 | The hitchhiker's guide to successful residential sensing deployments · SenSys 2011 |
High-performance computing › supercomputing
bluegene/l |
0.0 | 1 | 2002 | An overview of the BlueGene/L Supercomputer · SC 2002 |
High-performance computing
supercomputing |
0.0 | 1 | 2002 | An overview of the BlueGene/L Supercomputer · SC 2002 |
Integrated circuit design
system-on-chip |
0.0 | 1 | 2002 | An overview of the BlueGene/L Supercomputer · SC 2002 |
Energy systems and smart grids › building energy management
home energy management |
0.0 | 1 | 2010 | The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010 |
Energy systems and smart grids › building energy management
HVAC control |
0.0 | 1 | 2010 | The smart thermostat: using occupancy sensors to save energy in homes · SenSys 2010 |
Geometric modeling and processing › computer-aided design
data exchange |
0.0 | 1 | 2008 | Standardizing the specification, verification, and exchange of product geometry: Research, status and trends · Comput. Aided Des. 2008 |
Network security
traffic analysis |
0.0 | 1 | 2008 | Protecting your daily in-home activity information from a wireless snooping attack · UbiComp 2008 |
Geometric modeling and processing › computer-aided design
tolerance analysis |
0.0 | 1 | 1997 | Kinematic tolerance analysis · Comput. Aided Des. 1997 |
Routing and switching
multicast routing |
0.0 | 1 | 1997 | Improved Neural Heuristics for Multicast Routing · IEEE J. Sel. Areas Commun. 1997 |
Routing and switching › multicast routing › multicast tree construction
steiner tree |
0.0 | 1 | 1997 | Improved Neural Heuristics for Multicast Routing · IEEE J. Sel. Areas Commun. 1997 |
Computational geometry › shape analysis
medial axis transform |
0.0 | 1 | 1992 | Automatic mesh generation using the symmetric axis transformation of polygonal domains · Proc. IEEE 1992 |
Methods — techniques the papers use, named apart from their topics
large language model · 2.3paraphrasing · 1.5data filtering · 0.8reinforcement learning · 0.6meta-information retrieval · 0.6generative adversarial network · 0.6frequent pattern mining · 0.6pattern mining · 0.3data fusion · 0.3deployment study · 0.2occupancy sensing · 0.2traffic analysis · 0.1eavesdropping · 0.1system architecture design · 0.0performance scaling studies · 0.0random neural network · 0.0kinematic analysis · 0.0heuristic · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Explicit over Implict: Explicit Diversity Conditions for Effective Question Answer GenerationabstractQuestion Answer Generation (QAG) is an effective data augmentation technique to improve the accuracy of question answering systems, especially in low-resource domains. While recent pretrained and large language model-based QAG methods have made substantial progress, they face the critical issue of redundant QA pair generation, affecting downstream QA systems. Implicit diversity techniques such as sampling and diverse beam search are proven effective solutions but often yield smaller diversity. We present explicit diversity conditions for QAG, focusing on spatial aspects, question types, and entities, substantially increasing diversity in QA generation. Our work emphasizes the need of explicit diversity conditions for generating diverse question-answer synthetic data by showing significant improvements in downstream QA task over existing implicit diversity techniques. In particular, generated QA pairs from explicit diversity conditions result in an average 4.1% exact match and 4.5% F1 improvement over implicit sampling techniques on SQuAD-DU. Our work emphasizes the need for explicit diversity conditions even more in low-resource datasets (SubjQA), where average QA performance improvements are ~12% EM. Vikas Yadav, Hyuk Joon Kwon, Vijay Srinivasan, Hongxia Jin |
LREC/COLING | 3 |
| 2024 | AlpaGasus: Training a Better Alpaca with Fewer DataabstractLarge language models~(LLMs) strengthen instruction-following capability through instruction-finetuning (IFT) on supervised instruction/response data. However, widely used IFT datasets (e.g., Alpaca's 52k data) surprisingly contain many low-quality instances with incorrect or irrelevant responses, which are misleading and detrimental to IFT. In this paper, we propose a simple and effective data selection strategy that automatically identifies and removes low-quality data using a strong LLM (e.g., ChatGPT). To this end, we introduce Alpagasus, which is finetuned on only 9k high-quality data filtered from the 52k Alpaca data. Alpagasus significantly outperforms the original Alpaca as evaluated by GPT-4 on multiple test sets and the controlled human study. Its 13B variant matches $>90\%$ performance of its teacher LLM (i.e., Text-Davinci-003) on test tasks. It also provides 5.7x faster training, reducing the training time for a 7B variant from 80 minutes (for Alpaca) to 14 minutes \footnote{We apply IFT for the same number of epochs as Alpaca(7B) but on fewer data, using 4$\times$NVIDIA A100 (80GB) GPUs and following the original Alpaca setting and hyperparameters.}. In the experiment, we also demonstrate that our method can work not only for machine-generated datasets but also for human-written datasets. Overall, Alpagasus demonstrates a novel data-centric IFT paradigm that can be generally applied to instruction-tuning data, leading to faster training and better instruction-following models. Lichang Chen, Kalpa Gunaratna, Vikas Yadav, Vijay Srinivasan, Tianyi Zhou 0001, Heng Huang 0001, Hongxia Jin |
ICLR | 8 |
| 2024 | Backdooring Instruction-Tuned Large Language Models with Virtual Prompt InjectionabstractJun Yan, Vikas Yadav, Shiyang Li, Lichang Chen, Zheng Tang, Hai Wang, Vijay Srinivasan, Xiang Ren, Hongxia Jin. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024. Vikas Yadav, Lichang Chen, Vijay Srinivasan, Hongxia Jin |
NAACL-HLT | 7 |
| 2024 | PAG-LLM: Paraphrase and Aggregate with Large Language Models for Minimizing Intent Classification Errors
Vikas Yadav, Vijay Srinivasan |
SIGIR | 3 |
| 2023 | Explainable and Accurate Natural Language Understanding for Voice Assistants and BeyondabstractJoint intent detection and slot filling, which is also termed as joint NLU (Natural Language Understanding) is invaluable for smart voice assistants. Recent advancements in this area have been heavily focusing on improving accuracy using various techniques. Explainability is undoubtedly an important aspect for deep learning-based models including joint NLU models. Without explainability, their decisions are opaque to the outside world and hence, have tendency to lack user trust. Therefore to bridge this gap, we transform the full joint NLU model to be 'inherently' explainable at granular levels without compromising on accuracy. Further, as we enable the full joint NLU model explainable, we show that our extension can be successfully used in other general classification tasks. We demonstrate this using sentiment analysis and named entity recognition. Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin |
CIKM | 2 |
| 2022 | ISEEQ: Information Seeking Question Generation Using Dynamic Meta-Information Retrieval and Knowledge GraphsabstractConversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy the users' needs. If realized, such a CIS system has far-reaching benefits in the real world; for example, CIS systems can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end-user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query. Firstly, ISEEQ uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the knowledge-enriched query to retrieve relevant context passages to ask coherent ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep generative-adversarial reinforcement learning-based approach for generating ISQs. We show that ISEEQ can generate high-quality ISQs to promote the development of CIS agents. ISEEQ significantly outperforms comparable baselines on five ISQ evaluation metrics across four datasets having user queries from diverse domains. Further, we argue that ISEEQ is transferable across domains for generating ISQs, as it shows the acceptable performance when trained and tested on different pairs of domains. A qualitative human evaluation confirms that ISEEQ generated ISQs are comparable in quality to human-generated questions, and it outperformed the best comparable baseline. Manas Gaur, Kalpa Gunaratna, Vijay Srinivasan, Hongxia Jin |
AAAI | 3 |
| 2022 | A New Concept of Knowledge based Question Answering (KBQA) System for Multi-hop ReasoningabstractYu Wang, [email protected] [email protected], Hongxia Jin. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022. Yu Wang 0091, Vijay Srinivasan, Hongxia Jin |
NAACL-HLT | 2 |
| 2021 | Using Neighborhood Context to Improve Information Extraction from Visual Documents Captured on Mobile PhonesabstractInformation Extraction from visual documents enables convenient and intelligent assistance to end users. We present a Neighborhood-based Information Extraction (NIE) approach that uses contextual language models and pays attention to the local neighborhood context in the visual documents to improve information extraction accuracy. We collect two different visual document datasets and show that our approach outperforms the state-of-the-art global context-based IE technique. In fact, NIE outperforms existing approaches in both small and large model sizes. Our on-device implementation of NIE on a mobile platform that generally requires small models showcases NIE's usefulness in practical real-world applications. Kalpa Gunaratna, Vijay Srinivasan, Sandeep Nama, Hongxia Jin |
CIKM | 2 |
| 2021 | Recent Industrial Roadmaps to Enable Smart Manufacturing of BiopharmaceuticalsabstractBiopharmaceutical manufacturing is an evolving industry with great potential to improve public health, but laden with technical and operational challenges. It has many features that are similar to other smart manufacturing industries, but it also has some unique features that are specific to the biological domain in which it operates. Both types of features have been carefully explored in recent industrial roadmaps to guide the research, development, and manufacturing of biopharmaceuticals. These roadmaps explicitly identify “fully automated facilities” and “knowledge management” as two of the critical enabling capabilities that will drive the biopharmaceutical industry in the next decade. This article describes these roadmaps to identify the emerging opportunities and future directions in this knowledge-intensive manufacturing industry. It also serves as a “call for action” for collaboration among key stakeholders, including the robotics and automation community. Sheng Lin-Gibson, Vijay Srinivasan |
IEEE Trans Autom. Sci. Eng. | 2 |
| 2017 | Guest Editorial A Remarkable Resurgence of Artificial Intelligence and Its Impact on Automation and Autonomyabstract“After many false starts,” declared the Economist magazine in a June 2016 Special Report,1“artificial intelligence (AI) has taken off.” This special report examined “how AI, associated with hubris and disappointment since its earliest days, has suddenly become the hottest field in technology.” The report also heralded the return of themachinery question, first posed nearly 200 years ago in the context of industrial revolution, on the impact of such disruptive technologies on the different classes of society. K. C. Morris 0001, Craig Schlenoff, Vijay Srinivasan |
IEEE Trans Autom. Sci. Eng. | 3 |
| 2016 | The new frontiers in computational modeling of material structures
William C. Regli, Jarek Rossignac, Vadim Shapiro, Vijay Srinivasan |
Comput. Aided Des. | 4 |
| 2014 | MobileMiner: mining your frequent patterns on your phoneabstractSmartphones can collect considerable context data about the user, ranging from apps used to places visited. Frequent user patterns discovered from longitudinal, multi-modal context data could help personalize and improve overall user experience. Our long term goal is to develop novel middleware and algorithms to efficiently mine user behavior patterns entirely on the phone by utilizing idle processor cycles. Mining patterns on the mobile device provides better privacy guarantees to users, and reduces dependency on cloud connectivity. As an important step in this direction, we develop a novel general-purpose service called MobileMiner that runs on the phone and discovers frequent co-occurrence patterns indicating which context events frequently occur together. Using longitudinal context data collected from 106 users over 1--3 months, we show that MobileMiner efficiently generates patterns using limited phone resources. Further, we find interesting behavior patterns for individual users and across users, ranging from calling patterns to place visitation patterns. Finally, we show how our co-occurrence patterns can be used by developers to improve the phone UI for launching apps or calling contacts. Vijay Srinivasan, Saeed Moghaddam, Abhishek Mukherji, Kiran Rachuri, Chenren Xu, Emmanuel Munguia Tapia |
UbiComp | 1 |
| 2014 | Boe: Context-Aware Global Power Management for Mobile Devices Balancing Battery Outage and User ExperienceabstractEnergy conservation on mobile devices is now more important than ever due to the increasing benefits that smartphones and tablets provide to our daily life. However, most existing power management approaches either focus narrowly on a particular sub-system of the mobile device such as the sensor system, the LCD display, or the communication system, or use heuristic approaches to maximize energy efficiency at the cost of user experience. In this paper, we present Boe, a context-aware global power management scheme for mobile devices Balancing battery outage and user experience. To meet the mobile device's expected battery life while sacrificing end user experience as little as possible. Boe takes into account the users' phone usage patterns and activities to dynamically adjust the device's global power management policy to minimize outage time and maximize user experience. We demonstrate our proposed technique by controlling display brightness level and GPS sampling rate on smartphones. We evaluate our approach through real world smartphone data from 10 users over two months. Compared to the best fixed user experience policies, we show that: (i) Boe eliminates all frustrating battery outage events for light, moderate, and heavy phone users, and (ii) Boe improves user experience by 20% for light users, maintains the same user experience for moderate users, and degrades user experience by 23% for heavy smartphone users. Chenren Xu, Vijay Srinivasan, Yoshiya Hirase, Emmanuel Munguia Tapia, Yanyong Zhang |
MASS | 2 |
| 2014 | Geometric interoperability via queries
Christoph M. Hoffmann, Vadim Shapiro, Vijay Srinivasan |
Comput. Aided Des. | 3 |
| 2014 | Report from a 2013 ASME panel on geometric interoperability for advanced manufacturing
Vadim Shapiro, Vijay Srinivasan |
Comput. Aided Des. | 2 |
| 2013 | FixtureFinder: discovering the existence of electrical and water fixturesabstractThe monitoring of electrical and water fixtures in the home is being applied for a variety of "smart home" applications, such as recognizing activities of daily living (ADLs) or conserving energy or water usage. Fixture monitoring techniques generally fall into two categories: fixture recognition and fixture disaggregation. However, existing techniques require users to explicitly identify each individual fixture, either by placing a sensor on it or by manually creating training data for it. In this paper, we present a new fixture discovery system that automatically infers the existence of electrical and water fixtures in the home. We call the system FixtureFinder. The basic idea is to use data fusion between the smart meters and other sensors or infrastructure already in the home, such as the home security or automation system, and to find repeating patterns in the fused data stream. To evaluate FixtureFinder, we deployed the system into 4 different homes for 7-10 days of data collection. Our results show that FixtureFinder is able to identify and differentiate major light and water fixtures in less than 10 days, including multiple copies of light bulbs and sinks that have identical power/water profiles. Vijay Srinivasan, John A. Stankovic, Kamin Whitehouse |
IPSN | 1 |
| 2013 | Recent advances in sharing standardized STEP composite structure design and manufacturing information
Keith A. Hunten, Allison Barnard Feeney, Vijay Srinivasan |
Comput. Aided Des. | 3 |
| 2011 | The hitchhiker's guide to successful residential sensing deploymentsabstractHomes are rich with information about people's energy consumption, medical health, and personal or family functions. In this paper, we present our experiences deploying large-scale residential sensing systems in over 20 homes. Deploying small-scale systems in homes can be deceptively easy, but in our deployments we encountered a phase transition in which deployment effort increases dramatically as residential deployments scale up in terms of 1) the number of nodes, 2) the length of time, and 3) the number of houses. In this paper, we distill our experiences down to a set of guidelines and design principles to help future deployments avoid the potential pitfalls of large-scale sensing in homes. Timothy W. Hnat, Vijay Srinivasan, Jiakang Lu, Tamim I. Sookoor, Raymond Dawson, John A. Stankovic, Kamin Whitehouse |
SenSys | 2 |
| 2011 | An integration framework for product lifecycle management
Vijay Srinivasan |
Comput. Aided Des. | 1 |
| 2010 | The smart thermostat: using occupancy sensors to save energy in homesabstractHeating, ventilation and cooling (HVAC) is the largest source of residential energy consumption. In this paper, we demonstrate how to use cheap and simple sensing technology to automatically sense occupancy and sleep patterns in a home, and how to use these patterns to save energy by automatically turning off the home's HVAC system. We call this approach the smart thermostat. We evaluate this approach by deploying sensors in 8 homes and comparing the expected energy usage of our algorithm against existing approaches. We demonstrate that our approach will achieve a 28% energy saving on average, at a cost of approximately $25 in sensors. In comparison, a commercially-available baseline approach that uses similar sensors saves only 6.8% energy on average, and actually increases energy consumption in 4 of the 8 households. Jiakang Lu, Tamim I. Sookoor, Vijay Srinivasan, Brian Holben, John A. Stankovic, Eric Field, Kamin Whitehouse |
SenSys | 3 |
| 2008 | Protecting your daily in-home activity information from a wireless snooping attackabstractIn this paper, we first present a new privacy leak in residential wireless ubiquitous computing systems, and then we propose guidelines for designing future systems to prevent this problem. We show that we can observe private activities in the home such as cooking, showering, toileting, and sleeping by eavesdropping on the wireless transmissions of sensors in a home, even when all of the transmissions are encrypted. We call this the Fingerprint and Timing-based Snooping (FATS) attack. This attack can already be carried out on millions of homes today, and may become more important as ubiquitous computing environments such as smart homes and assisted living facilities become more prevalent. In this paper, we demonstrate and evaluate the FATS attack on eight different homes containing wireless sensors. We also propose and evaluate a set of privacy preserving design guidelines for future wireless ubiquitous systems and show how these guidelines can be used in a hybrid fashion to prevent against the FATS attack with low implementation costs. Vijay Srinivasan, John A. Stankovic, Kamin Whitehouse |
UbiComp | 1 |
| 2008 | Standardizing the specification, verification, and exchange of product geometry: Research, status and trends
Vijay Srinivasan |
Comput. Aided Des. | 1 |
| 2002 | An overview of the BlueGene/L SupercomputerabstractThis paper gives an overview of the BlueGene/L Supercomputer. This is a jointly funded research partnership between IBM and the Lawrence Livermore National Laboratory as part of the United States Department of Energy ASCI Advanced Architecture Research Program. Application performance and scaling studies have recently been initiated with partners at a number of academic and government institutions,including the San Diego Supercomputer Center and the California Institute of Technology. This massively parallel system of 65,536 nodes is based on a new architecture that exploits system-on-a-chip technology to deliver target peak processing power of 360 teraFLOPS (trillion floating-point operations per second). The machine is scheduled to be operational in the 2004-2005 time frame, at price/performance and power consumption/performance targets unobtainable with conventional architectures. Narasimha R. Adiga, Gheorghe Almási 0001, George S. Almási, Yariv Aridor, Rajkishore Barik, Daniel K. Beece, Ralph Bellofatto, Gyan Bhanot, Randy Bickford, Matthias A. Blumrich, Arthur A. Bright, José R. Brunheroto, Calin Cascaval, José G. Castaños, Waiman Chan, Luis Ceze, Paul Coteus, Siddhartha Chatterjee, Dong Chen 0005, George L.-T. Chiu, Thomas M. Cipolla, Paul Crumley, K. M. Desai, Alina Deutsch, Tamar Domany, Marc Boris Dombrowa, Wilm E. Donath, Maria Eleftheriou, C. Christopher Erway, J. Esch, Blake G. Fitch, Joseph Gagliano, Alan Gara, Rahul Garg 0001, Robert S. Germain, Mark Giampapa, Balaji Gopalsamy, John A. Gunnels, Manish Gupta 0002, Fred G. Gustavson, Shawn Hall, Ruud A. Haring, David F. Heidel, Philip Heidelberger, Lorraine M. Herger, Dirk Hoenicke, R. D. Jackson, T. Jamal-Eddine, Gerard V. Kopcsay, Elie Krevat, Manish P. Kurhekar, Alphonso P. Lanzetta, Derek Lieber, L. K. Liu, M. Lu, Mark P. Mendell, A. Misra, Yosef Moatti, Lawrence S. Mok, José E. Moreira, Ben J. Nathanson, Matthew Newton, Martin Ohmacht, Adam J. Oliner, Vinayaka Pandit, R. B. Pudota, Rick A. Rand, Richard D. Regan, Bradley Rubin, Albert E. Ruehli, Silvius Vasile Rus, Ramendra K. Sahoo, Alda Sanomiya, Eugen Schenfeld, M. Sharma, Edi Shmueli, Sarabjeet Singh, Peilin Song, Vijay Srinivasan, Burkhard D. Steinmacher-Burow, Karin Strauss, Christopher W. Surovic, Richard A. Swetz, Todd Takken, R. Brett Tremaine, Mickey Tsao, Arun R. Umamaheshwaran, P. Verma, Pavlos Vranas, T. J. Christopher Ward, Michael E. Wazlowski, W. Barrett, C. Engel, B. Drehmel, B. Hilgart, D. Hill, F. Kasemkhani, David J. Krolak, Chun-Tao Li 0001, Thomas A. Liebsch, James A. Marcella, A. Muff, A. Okomo, M. Rouse, A. Schram, M. Tubbs, G. Ulsh, Charles D. Wait, J. Wittrup, Myung Bae, Kenneth A. Dockser, Lynn Kissel, Mark K. Seager, Jeffrey S. Vetter, K. Yates |
SC | 79 |
| 1999 | A geometrical product specification language based on a classification of symmetry groupsabstractA relatively old and deeply entrenched engineering “language” universally used by draughtsmen for specifying product geometry is undergoing renovation for the modern information age. Part of the renovation process is to find a set of firm mathematical principles that can support much of the edifice that has served industry well thus far. This search has turned up a set of surprisingly powerful results based on an elegant classification of continuous subgroups of rigid motion. Among them is a compact classification of surface features and lower order kinematic pairs, their reduction to simpler geometric elements that preserve the classification and serve as datums, and a rationale for parameterizing the relative positioning of geometric objects. These results are now being enthusiastically embraced by the International Organization for Standardization (ISO) as part of the basic principles to define the next generation of a product geometry specification language. The paper tells this story. Vijay Srinivasan |
Comput. Aided Des. | 1 |
| 1997 | Kinematic tolerance analysis
Leo Joskowicz, Elisha Sacks, Vijay Srinivasan |
Comput. Aided Des. | 3 |
| 1997 | Improved Neural Heuristics for Multicast RoutingabstractFuture networks must be adequately equipped to handle multipoint communication in a fast and economical manner. Services requiring such support include desktop video conferencing, tele-classrooms, distributed database applications, etc. In networks employing the asynchronous transfer mode (ATM) technology, routing a multicast is achieved by constructing a minimum cost tree that spans the source and all the destinations. When the network is modeled as a weighted, undirected graph, the problem is that of finding a minimal Steiner tree for the graph, given a set of destinations. The problem is known to be NP-complete. Consequently, several heuristics exist which provide approximate solutions to the Steiner problem in networks, We show how the random neural network (RNN) can be used to significantly improve the quality of the Steiner trees delivered by the best available heuristics which are the minimum spanning tree heuristic and the average distance heuristic. We provide an empirical comparison and find that the heuristics which are modified using the neural network yield significantly improved trees. Erol Gelenbe, Anoop Ghanwani, Vijay Srinivasan |
IEEE J. Sel. Areas Commun. | 3 |
| 1996 | Block loss reduction in ATM networks
Vijay Srinivasan, Anoop Ghanwani, Erol Gelenbe |
Comput. Commun. | 1 |
| 1995 | A simulation study of schemes for block loss reduction in ATM networks using FEC and buffer managementabstractTraditional approaches to guaranteeing quality-of-service (QoS) in ATM networks have focused on performance metrics such as cell loss probability, end-to-end cell delay and delay jitter. However, the block loss rate is a more meaningful metric for applications such as medical imaging and real-time video, and for high-level protocols such as IP which will use ATM as the transport mechanism. A block is defined as a group of consecutive ATM cells. A block loss occurs when a single cell from a block is lost. We propose and evaluate, via extensive simulation, a technique for reducing block loss in ATM networks. Our method combines two well-studied approaches for minimizing the impact of information loss during transport over ATM networks which have been considered independently of each other in the past: priority-based cell discarding and forward error correction (FEC). We also study an enhanced buffer management algorithm which we call adaptive pushout (ADP) that accounts for correlations between cell losses. The ADP algorithm reduces block loss rates to near-optimal levels. We consider further performance enhancements to the ADP policy that deliver excellent performance even under very heavy load. Vijay Srinivasan, Anoop Ghanwani, Erol Gelenbe |
ICCCN | 1 |
| 1994 | Point Placement Algorithms for Delaunay Triangulation of Polygonal Domains
Lee R. Nackman, Vijay Srinivasan |
Algorithmica | 2 |
| 1992 | Automatic mesh generation using the symmetric axis transformation of polygonal domainsabstractAn automatic method for generating finite element meshes for multiply connected planar domains with polygonal boundaries (i.e. planar polygons with polygonal holes) is described. The symmetric axis transform is used to obtain a planar graph that partitions the given domain. This transformation may introduce edges in the graph that are too long or too short for generating good meshes. A silver processing algorithm, which transforms the graph into another graph devoid of such edges, is presented. Finally, additional modes are placed on the edges of the graph to obtain a triangulation, and this process is applied iteratively, yielding the final mesh. The method automatically increases the mesh density in regions of rapid change in shape and allows both global and local control of the mesh density. The method also admits the imposition of node compatibility constraints along domain boundaries, thus making the method suitable for meshing planar cell complexes (i.e multiple polygonal domains with shared boundaries in two-dimensional space) and for generating boundary elements for polyhedra in three-dimensional space.> Vijay Srinivasan, Lee R. Nackman, Jung-Mu Tang, Siavash N. Meshkat |
Proc. IEEE | 1 |
| 1991 | Bisectors of Linearly Separable Sets
Lee R. Nackman, Vijay Srinivasan |
Discret. Comput. Geom. | 2 |
| 1985 | Issues in conditional tolerances for CAD systemsabstractIncorporation of tolerance information in CAD systems is a major challenge facing the mechanical CAD/CAM community. True integration of CAD and CAM can be achieved only after we know how to specify functionally acceptable class of mechanical parts in the CAD systems so that they can be fabricated and inspected by the CAM systems. This paper explores the issues involved in specifying one special case of tolerances called conditional tolerances for CAD systems. Vijay Srinivasan, Rangarajan Jayaraman |
ICRA | 1 |