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.