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Jan 28, 2019
Symbolic Computation

We have written a toy implementation666Available online: https://github.com/ThibautVerron/SignatureMoller in Magma (Magma, ) of the algorithm, with the F5, Singular and 1-singular criteria. We give experimental data related to the computation of Grö…

Signature-based Möller's algorithm for strong Gröbner bases over PI…

Signature-based algorithms are the latest and most efficient approach as of today to compute Gr\"obner bases for polynomial systems over fields. Rece…
Aug 09, 2018
Machine Learning

Compressed Sensing Using Binary Matrices of Nearly Optimal Dimensions

In this paper, we study the problem of compressed sensing using binary measurement matrices, and $\ell_1$-norm minimization (basis pursuit) as the re…
Apr 15, 2017

Separation of pulsar signals from noise with supervised machine learn…

We evaluate the performance of four different machine learning (ML) algorithms: an Artificial Neural Network Multi-Layer Perceptron (ANN MLP ), Adabo…
Nov 21, 2015
Data Structures and Algorithms

A Simple Algorithm For Replacement Paths Problem

Let G=(V,E)(|V|=n and |E|=m) be an undirected graph with positive edge weights. Let P_{G}(s, t) be a shortest s-t path in G. Let l be the number of e…
Dec 16, 2015
Social and Information Networks
Physics and Society

Subgraph Similarity Search in Large Graphs

One of the major challenges in applications related to social networks, computational biology, collaboration networks etc., is to efficiently search …
May 29, 2014
Materials Science

High Pressure Structural Stability, and Optical Properties of Scheeli…

\emph{Ab-initio} calculations were performed on the scheelite type MGeO$_4$ (M = Hf, and Zr) compounds which find wide range of applications such as …
Jul 19, 2018
Multimedia

Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory…

HTTP based adaptive video streaming has become a popular choice of streaming due to the reliable transmission and the flexibility offered to adapt to…
Jun 19, 2016
Machine Learning

In this paper we have shown that the ℓ1-norm is the convex relaxation of two commonly used group sparsity indices. Therefore ℓ1-norm minimization can be used for recovering group sparse vectors, and not just for recovering conventionally sparse vect…

Tight Performance Bounds for Compressed Sensing With Conventional and…

In this paper, we study the problem of recovering a group sparse vector from a small number of linear measurements. In the past the common approach h…
Feb 06, 2019
Machine Learning

We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architecture is viewed as a Structural Causal Model, and a methodology to comput…

Neural Network Attributions: A Causal Perspective

We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such).…
Jan 09, 2018
Information Theory

Linear Codes for Broadcasting with Noisy Side Information

We consider network coding for a noiseless broadcast channel where each receiver demands a subset of messages available at the transmitter and is equ…
Dec 01, 2016
Computer Vision

Sync-DRAW: Automatic Video Generation using Deep Recurrent Attentive …

This paper introduces a novel approach for generating videos called Synchronized Deep Recurrent Attentive Writer (Sync-DRAW). Sync-DRAW can also perf…
Jan 11, 2018
Information Theory

On Locally Decodable Index Codes

Index coding achieves bandwidth savings by jointly encoding the messages demanded by all the clients in a broadcast channel. The encoding is performe…
Jun 13, 2018
Signal Processing

Novel Light Weight Compressed Data Aggregation Using Sparse Measureme…

Optimal data aggregation aimed at maximizing IoT network lifetime by minimizing constrained on-board resource utilization continues to be a challengi…
Oct 24, 2012

Determining neutrino mass hierarchy from electron disappearance at a …

Recent measurements of large theta_{13} by Daya Bay and RENO reactor experiments have opened up the possibility of determining the neutrino mass hier…
Jul 28, 2019
Machine Learning
Computer Vision

Charting the Right Manifold: Manifold Mixup for Few-shot Learning

Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled examples. A rece…
May 13, 2018

Baryogenesis via Leptogenesis from Asymmetric Dark Matter and radia…

We propose an extension of the standard model (SM) by including a dark sector comprising of heavy right-handed neutrinos, a singlet scalar and a sing…
Dec 30, 2016
Social and Information Networks

Community-based Outlier Detection for Edge-attributed Graphs

The study of networks has emerged in diverse disciplines as a means of analyzing complex relationship data. Beyond graph analysis tasks like graph qu…
Oct 04, 2019
Computer Vision
Quantitative Methods

Active Learning with Weak Supervision for Cost-Effective Panicle De…

Panicle density of cereal crops such as wheat and sorghum is one of the main components for plant breeders and agronomists in understanding the yield…
Oct 10, 2019
Logic in Computer Science
Artificial Intelligence

Reflections on "Incremental Cardinality Constraints for MaxSAT"

To celebrate the first 25 years of the International Conference on Principles and Practice of Constraint Programming (CP) the editors invited the aut…
Feb 06, 2018

On Some Universal Features of the Holographic Quantum Complexity of B…

We perform a comparative study of the time dependence of the holographic quantum complexity of some space like singular bulk gravitational background…
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