| Sep 15th, 2023 | |
| Query 1: all publications | |
| Expected: Listing all the publications | |
| Actual: only some selected publications, missing some publications | |
| document_content: # Publication | |
| title=Jupiter: a networked computing architecture” | |
| venue=UCC Companion 2021: 28:1-28:8 | |
| authors=['P Ghosh', 'Q Nguyen', 'P Sakulkar', 'J Tran', 'A Knezevic', 'J Wang', 'Z Lin', 'B Krishnamachari', 'M Annavaram', 'S Avestimehr'] | |
| abstract=Modern latency-sensitive applications such as real-time multi-camera video analytics require networked computing to meet the time constraints. We present Jupiter, an open-source networked computing system that inputs a Directed Acyclic Graph (DAG)-based computational task graph to efficiently distribute the tasks among a set of networked compute nodes and orchestrates the execution thereafter. This Kubernetes container-orchestration-based system includes a range of profilers: network profilers, resource profilers, and execution time profilers; to support both centralized and decentralized scheduling algorithms. While centralized scheduling algorithms with global knowledge have been popular among the grid/cloud computing community, we argue that a distributed scheduling approach is better suited for networked computing due to lower communication and computation overhead in the face of network dynamics. We propose a new class of distributed scheduling algorithms called WAVE and show that despite using more localized knowledge, the WAVE algorithm can match the performance of a well-known centralized scheduling algorithm called Heterogeneous Earliest Finish Time (HEFT). To this, we present a set of real-world experiments on two separate testbeds: (1) a worldwide network of 90 cloud computers across eight cities and (2) a cluster of 30 Raspberry pi nodes. | |
| # Information | |
| links.pdf=/static/public/papers/JUPITER.pdf | |
| links.semantic_scholar=https://www.semanticscholar.org/paper/ef3485930a0eca1cd98b8dd7dfc7a919501ec06c | |
| type=Conference Papers | |
| year=2021 | |
| paper_id=bb6a0958 | |
| ss_title=Jupiter: a networked computing architecture | |
| ss_authors=[{'authorId': '49934897', 'name': 'Pradipta Ghosh'}, {'authorId': '145628959', 'name': 'Quynh Nguyen'}, {'authorId': '2254069', 'name': 'Pranav Sakulkar'}, {'authorId': '2073070356', 'name': 'Aleksandra Knezevic'}, {'authorId': '40553305', 'name': 'Jason A. Tran'}, {'authorId': '2109016686', 'name': 'Jiatong Wang'}, {'authorId': '46268272', 'name': 'Zhifeng Lin'}, {'authorId': '1701475', 'name': 'B. Krishnamachari'}, {'authorId': '145599558', 'name': 'M. Annavaram'}, {'authorId': '5877233', 'name': 'A. Avestimehr'}] | |
| ss_venue=UCC Companion | |
| ss_year=2019 | |
| ss_abstract=Modern latency-sensitive applications such as real-time multi-camera video analytics require networked computing to meet the time constraints. We present Jupiter, an open-source networked computing system that inputs a Directed Acyclic Graph (DAG)-based computational task graph to efficiently distribute the tasks among a set of networked compute nodes and orchestrates the execution thereafter. This Kubernetes container-orchestration-based system includes a range of profilers: network profilers, resource profilers, and execution time profilers; to support both centralized and decentralized scheduling algorithms. While centralized scheduling algorithms with global knowledge have been popular among the grid/cloud computing community, we argue that a distributed scheduling approach is better suited for networked computing due to lower communication and computation overhead in the face of network dynamics. We propose a new class of distributed scheduling algorithms called WAVE and show that despite using more localized knowledge, the WAVE algorithm can match the performance of a well-known centralized scheduling algorithm called Heterogeneous Earliest Finish Time (HEFT). To this, we present a set of real-world experiments on two separate testbeds: (1) a worldwide network of 90 cloud computers across eight cities and (2) a cluster of 30 Raspberry pi nodes. | |
| ss_paper_id=ef3485930a0eca1cd98b8dd7dfc7a919501ec06c | |
| Sure! Here are the details of all the publications mentioned in the input text: | |
| 1. Publication: | |
| - Title: Jupiter: a networked computing architecture | |
| - Venue: UCC Companion 2021: 28:1-28:8 | |
| - Authors: P Ghosh, Q Nguyen, P Sakulkar, J Tran, A Knezevic, J Wang, Z Lin, B Krishnamachari, M Annavaram, S Avestimehr | |
| - Abstract: Modern latency-sensitive applications such as real-time multi-camera video analytics require networked computing to meet the time constraints. We present Jupiter, an open-source networked computing system that inputs a Directed Acyclic Graph (DAG)-based computational task graph to efficiently distribute the tasks among a set of networked compute nodes and orchestrates the execution thereafter. This Kubernetes container-orchestration-based system includes a range of profilers: network profilers, resource profilers, and execution time prof | |
| Hi! What question do you have for ANGR? press 0 to exit | |
| Query 2: math-related publications | |
| Expected: Solving Math Word Problems Concerning Systems of Equations with GPT-3 | |
| Actual: fits the expected (only search by titles but not sure if any other publication contents are also related) | |
| document_content: # Publication | |
| title=Solving Math Word Problems Concerning Systems of Equations with GPT-3 | |
| venue=Thirteenth AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-23) to be held February 11-12, 2023. | |
| authors=['M Zong', 'B Krishnamachari'] | |
| abstract=None | |
| # Information | |
| links.pdf=/static/public/papers/Solving_Math_Word_Problems_with_GPT3-2022.pdf | |
| type=Conference Papers | |
| year=2023 | |
| paper_id=42a8fad3 | |
| Query 3: all authors | |
| Expected: only author names | |
| Actual: publication names also included, and some authors are not included, such as E. Ondula who publicated “Using Reinforcement Learning for Operating Educational Campuses Safely during a Pandemic (Student Abstract)“, AAAI 2022. | |
| document_content: # Publication | |
| title=Jupiter: a networked computing architecture” | |
| venue=UCC Companion 2021: 28:1-28:8 | |
| authors=['P Ghosh', 'Q Nguyen', 'P Sakulkar', 'J Tran', 'A Knezevic', 'J Wang', 'Z Lin', 'B Krishnamachari', 'M Annavaram', 'S Avestimehr'] | |
| abstract=Modern latency-sensitive applications such as real-time multi-camera video analytics require networked computing to meet the time constraints. We present Jupiter, an open-source networked computing system that inputs a Directed Acyclic Graph (DAG)-based computational task graph to efficiently distribute the tasks among a set of networked compute nodes and orchestrates the execution thereafter. This Kubernetes container-orchestration-based system includes a range of profilers: network profilers, resource profilers, and execution time profilers; to support both centralized and decentralized scheduling algorithms. While centralized scheduling algorithms with global knowledge have been popular among the grid/cloud computing community, we argue that a distributed scheduling approach is better suited for networked computing due to lower communication and computation overhead in the face of network dynamics. We propose a new class of distributed scheduling algorithms called WAVE and show that despite using more localized knowledge, the WAVE algorithm can match the performance of a well-known centralized scheduling algorithm called Heterogeneous Earliest Finish Time (HEFT). To this, we present a set of real-world experiments on two separate testbeds: (1) a worldwide network of 90 cloud computers across eight cities and (2) a cluster of 30 Raspberry pi nodes. | |
| # Information | |
| links.pdf=/static/public/papers/JUPITER.pdf | |
| links.semantic_scholar=https://www.semanticscholar.org/paper/ef3485930a0eca1cd98b8dd7dfc7a919501ec06c | |
| type=Conference Papers | |
| year=2021 | |
| paper_id=bb6a0958 | |
| ss_title=Jupiter: a networked computing architecture | |
| ss_authors=[{'authorId': '49934897', 'name': 'Pradipta Ghosh'}, {'authorId': '145628959', 'name': 'Quynh Nguyen'}, {'authorId': '2254069', 'name': 'Pranav Sakulkar'}, {'authorId': '2073070356', 'name': 'Aleksandra Knezevic'}, {'authorId': '40553305', 'name': 'Jason A. Tran'}, {'authorId': '2109016686', 'name': 'Jiatong Wang'}, {'authorId': '46268272', 'name': 'Zhifeng Lin'}, {'authorId': '1701475', 'name': 'B. Krishnamachari'}, {'authorId': '145599558', 'name': 'M. Annavaram'}, {'authorId': '5877233', 'name': 'A. Avestimehr'}] | |
| ss_venue=UCC Companion | |
| ss_year=2019 | |
| ss_abstract=Modern latency-sensitive applications such as real-time multi-camera video analytics require networked computing to meet the time constraints. We present Jupiter, an open-source networked computing system that inputs a Directed Acyclic Graph (DAG)-based computational task graph to efficiently distribute the tasks among a set of networked compute nodes and orchestrates the execution thereafter. This Kubernetes container-orchestration-based system includes a range of profilers: network profilers, resource profilers, and execution time profilers; to support both centralized and decentralized scheduling algorithms. While centralized scheduling algorithms with global knowledge have been popular among the grid/cloud computing community, we argue that a distributed scheduling approach is better suited for networked computing due to lower communication and computation overhead in the face of network dynamics. We propose a new class of distributed scheduling algorithms called WAVE and show that despite using more localized knowledge, the WAVE algorithm can match the performance of a well-known centralized scheduling algorithm called Heterogeneous Earliest Finish Time (HEFT). To this, we present a set of real-world experiments on two separate testbeds: (1) a worldwide network of 90 cloud computers across eight cities and (2) a cluster of 30 Raspberry pi nodes. | |
| ss_paper_id=ef3485930a0eca1cd98b8dd7dfc7a919501ec06c | |
| The authors of the publication are: | |
| 1. P Ghosh | |
| 2. Q Nguyen | |
| 3. P Sakulkar | |
| 4. J Tran | |
| 5. A Knezevic | |
| 6. J Wang | |
| 7. Z Lin | |
| 8. B Krishnamachari | |
| 9. M Annavaram | |
| 10. S Avestimehr | |
| Query 4: What publications does B Krishnamachari contribute to | |
| Expected: | |
| "Search and Rescue on the Line", | |
| "Solving Math Word Problems Concerning Systems of Equations with GPT-3", | |
| "Graph Convolutional Network-based Scheduler for Distributing Computation in the Internet of Robotic Things", | |
| "Characterizing ML training performance at the tactical edge", | |
| "Optimal Trading on a Dynamic Curve Automated Market Maker", | |
| "Using Reinforcement Learning for Operating Educational Campuses Safely during a Pandemic (Student Abstract)", | |
| "\u201cMulti-objective network synthesis for dispersed computing in tactical environments\u201d", | |
| "\u201cIntelligent Communication over Realistic Wireless Networks in Multi-Agent Cooperative Games\u201d", | |
| "DEFER: Distributed Edge Inference for Deep Neural Networks", | |
| "Network Synthesis for Tactical Environments: Scenario, Challenges, and Opportunities", | |
| "Neural Networks for DDoS Attack Detection using an Enhanced Urban IoT Dataset", | |
| "Trip Planning for Autonomous Vehicles with Wireless Data Transfer Needs Using Reinforcement Learning", | |
| "\u201cLearning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning\u201d", | |
| "GCNScheduler: Scheduling distributed computing applications using graph convolutional networks", | |
| "\u201cRevealing a Hidden, Stable Spectral Structure of Urban Vehicular Traffic\u201d", | |
| "\u201cDataset: Large-scale Urban IoT Activity Data for DDoS Attack Emulation\u201d", | |
| "Jupiter: a networked computing architecture\u201d", | |
| "Course Scheduling to Minimize Student Wait Times For University Buildings During Epidemics", | |
| "\u201cSimulation-Based Analysis of COVID-19 Spread Through Classroom Transmission on a University Campus,\u201d", | |
| "A Decentralized Review System for Data Marketplaces", | |
| "DAISIM: A Computational Simulator for the MakerDAO Stablecoin", | |
| "Dynamic Curves for Decentralized Autonomous Cryptocurrency Exchanges", | |
| "Simulating the MakerDAO Stablecoin", | |
| "Dynamic Automated Market Makers for Decentralized Cryptocurrency Exchange", | |
| "CONTAIN: Privacy-oriented Contact Tracing Protocols for Epidemics", | |
| "Large-scale Urban IoT Activity Data for DDoS Attack Emulation", | |
| "TEAM: Trilateration for Exploration and Mapping with Robotic Networks" | |
| Actual: missing a lot publications | |
| document_content: # Publication | |
| title=https://anrg.usc.edu/www/papers/scheduling.pdf | |
| venue=IEEE BigData 2021: 4365-4370 | |
| authors=['A Hekmati', 'B Krishnamachari', 'M Mataric', 'Course Scheduling to Minimize Student Wait Times For University Buildings During Epidemics'] | |
| abstract=ABSTRACT Of the 17 years (1946–64) Jawaharlal Nehru was India's Prime Minister, his Congress Party government had a senior politician holding the Finance Ministry for only 5: Morarji Desai (1958–63). Otherwise, this crucial portfolio was held by a succession of experts; an illustration of consociational power sharing. This article is about two of those, C.D. Deshmukh and T.T. Krishnamachari, and some of their chequered governmental experiences. Through these, it seeks to cast a critical light on the changing contours of relations between and within the party and the government in the economic sphere during its decade of political dominance. | |
| # Information | |
| links.pdf=/static/public/papers/scheduling.pdf | |
| links.semantic_scholar=https://www.semanticscholar.org/paper/de91d2dbca72d5115198139618222094e0fa7cf0 | |
| type=Conference Papers | |
| year=2021 | |
| paper_id=0a9ed062 | |
| ss_title=Bearing ‘financial responsibility’ for the Government and the Party: C.D. Deshmukh (1950–56) & T.T. Krishnamachari (1956–58) | |
| ss_authors=[{'authorId': '90288069', 'name': 'R. Ankit'}] | |
| ss_venue= | |
| ss_year=2020 | |
| ss_abstract=ABSTRACT Of the 17 years (1946–64) Jawaharlal Nehru was India's Prime Minister, his Congress Party government had a senior politician holding the Finance Ministry for only 5: Morarji Desai (1958–63). Otherwise, this crucial portfolio was held by a succession of experts; an illustration of consociational power sharing. This article is about two of those, C.D. Deshmukh and T.T. Krishnamachari, and some of their chequered governmental experiences. Through these, it seeks to cast a critical light on the changing contours of relations between and within the party and the government in the economic sphere during its decade of political dominance. | |
| ss_paper_id=de91d2dbca72d5115198139618222094e0fa7cf0 | |
| Based on the given information, B Krishnamachari has contributed to the following publication: | |
| - Title: "Course Scheduling to Minimize Student Wait Times For University Buildings During Epidemics" | |
| - Authors: A Hekmati, B Krishnamachari, M Mataric | |
| - Venue: IEEE BigData 2021 | |
| - Pages: 4365-4370 | |
| - Year: 2021 | |
| - Publication Link: [here](https://anrg.usc.edu/www/papers/scheduling.pdf) | |
| Please note that this is the only publication listed for B Krishnamachari based on the provided information. | |