Search · four archives
Search · four archives
18 papers · ranked by Valyu relevance
Genqiang Wu, Yeping He, Jingzheng Wu, Xianyao Xia
How to achieve differential privacy in the distributed setting, where the dataset is distributed among the distrustful parties, is an important problem. We consider in what condition can a protocol inherit the differential privacy property of a function it computes. The heart of the problem is the secure multiparty…
Iqra Altaf Gillani, Pooja Vyavahare, Amitabha Bagchi
We study in-network computation on general network topologies. Specifically, we are given the description of a function, and a network with distinct nodes at which the operands of the function are made available, and a designated sink where the computed value of the function is to be consumed. We want to compute the…
Artur Czumaj, Peter Davies-Peck, Merav Parter
In this paper, we study the power and limitations of component-stable algorithms in the low-space model of massively parallel computation (MPC). Recently Ghaffari, Kuhn and Uitto (FOCS 2019) introduced the class of component-stable low-space MPC algorithms, which are, informally, those algorithms for which the outputs…
Matthias Volk, Borzoo Bonakdarpour, Joost-Pieter Katoen, Saba Aflaki
Randomization is a key concept in distributed computing to tackle impossibility results. This also holds for self-stabilization in anonymous networks where coin flips are often used to break symmetry. Although the use of randomization in self-stabilizing algorithms is rather common, it is unclear what the optimal coin…
Linqi Song, Sundara Rajan Srinivasavaradhan, Christina Fragouli
—In wireless distributed computing, networked nodes perform intermediate computations over data placed in their memory and exchange these intermediate values to calculate function values. In this paper we consider an asymmetric setting where each node has access to a random subset of the data, i.e., we cannot control…
Derya Malak, Mohammad Reza Deylam Salehi, Berksan Serbetci, Petros Elia + 2 more
'Petros Elia' 'Chintha Tellambura' 'Jun Chen'] The work here studies the communication cost for a multi-server multi-task distributed computation framework, as well as for a broad class of functions and data statistics. Considering the framework where a user seeks the computation of multiple complex (conceivably…
Ali Khalesi, Sajad Daei, Marios Kountouris, Petros Elia
—The multi-user linearly-separable distributed computing problem is considered here, in which N servers help to compute the real-valued functions requested by K users, where each function can be written as a linear combination of up to L (generally non-linear) subfunctions. Each server computes a fraction γ of the…
Nicholas Woolsey, Rong‐Rong Chen, Mingyue Ji
—Coded distributed computing introduced by Li et al. in 2015 is an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce. In particular, Li et al. show that increasing the computation load in the Map phase by a factor of r can create…
Yongcheng Yang, Yifei Huang, Xiaohuan Qin, Shenglian Lu + 3 more
'Yanlin Geng' 'Youlong Wu' 'Ling Liu'] Coded distributed computing (CDC) is a powerful approach to reduce the communication overhead in distributed computing frameworks by utilizing coding techniques. In this paper, we focus on the CDC problem in $(H,L)$-combination networks, where H APs act as intermediate pivots and…
Yann Garniron, Thomas Applencourt, Kevin Gasperich, Anouar Benali + 15 more
Quantum Package is an open-source programming environment for quantum chemistry specially designed for wave function methods. Its main goal is the development of determinant-driven selected configuration interaction (sCI) methods and multi-reference second-order perturbation theory (PT2). The determinant-driven…
Yann Garniron, Thomas Applencourt, Kevin Gasperich, Anouar Benali + 15 more
Quantum Package is an open-source programming environment for quantum chemistry specially designed for wave function methods. Its main goal is the development of determinant-driven selected configuration interaction (sCI) methods and multi-reference second-order perturbation theory (PT2). The determinant-driven…
Abdullah Al Nahid, Linda Serafin, Nicholas Mancuso
In many applications, from statistical inference to machine learning, calculating the trace of a matrix is a fundamental operation, yet may be infeasible due to memory constraints. Stochastic trace estimation offers a practical solution by using randomized matrix-vector products to obtain accurate, unbiased estimates…
Pier Paolo Poier, Louis Lagardère, Jean-Philip Piquemal
We propose a new strategy to solve the Tkatchenko-Scheffler Many-Body Dispersion (MBD) model’s equations. Our approach overcomes the original O(N**3) computational complexity that limits its applicability to large molecular systems within thecontext of O(N) Density Functional Theory (DFT). First, in order to generate…
Pier Paolo Poir, Louis Lagardère, Jean-Philip Piquemal
We propose a new strategy to solve the Tkatchenko-Scheffler Many-Body Dispersion (MBD) model’s equations. Our approach overcomes the original O(N**3) computational complexity that limits its applicability to large molecular systems within thecontext of O(N) Density Functional Theory (DFT). First, in order to generate…
J Kyle Medley, Shaik Asifullah, Joseph Hellerstein, Herbert M Sauro
Mechanistic kinetic models of biological pathways are an important tool for understanding biological systems. Constructing kinetic models requires fitting the parameters to experimental data. However, parameter fitting on these models is a non–convex, non–linear optimization problem. Many algorithms have been proposed…
Konstantinos T. Konstantinidis, Aditya Ramamoorthy
—Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on different servers. The servers also need to exchange intermediate values to complete the computation. Experimental evidence suggests that this…
Haotian Li
Machine learning and deep learning are novel and trending approaches to solving real-world scientific problems. Graph machine learning is dedicated to performing learning methods, such as graph neural networks, on non-Euclidean data such as graphs. Molecules, with their natural graph structures, could be analyzed by…
Noah Lewis, Harshvardhan Gazula, Sergey M. Plis, Vince D. Calhoun
In this age of big data, large data stores allow researchers to compose robust models that are accurate and informative. In many cases, the data are stored in separate locations requiring data transfer between local sites, which can cause various practical hurdles, such as privacy concerns or heavy network load. This…