Search results for "Performance."
showing 10 items of 4178 documents
Probing the origin of cosmic-rays with extremely high energy neutrinos using the IceCube Observatory
2013
We have searched for extremely high energy neutrinos using data taken with the IceCube detector between May 2010 and May 2012. Two neutrino induced particle shower events with energies around 1 PeV were observed, as reported previously. In this work, we investigate whether these events could originate from cosmogenic neutrinos produced in the interactions of ultra-high energy cosmic-rays with ambient photons while propagating through intergalactic space. Exploiting IceCube's large exposure for extremely high energy neutrinos and the lack of observed events above 100 PeV, we can rule out the corresponding models at more than 90% confidence level. The model independent quasi-differential 90% …
AnySeq: A High Performance Sequence Alignment Library based on Partial Evaluation
2020
Sequence alignments are fundamental to bioinformatics which has resulted in a variety of optimized implementations. Unfortunately, the vast majority of them are hand-tuned and specific to certain architectures and execution models. This not only makes them challenging to understand and extend, but also difficult to port to other platforms. We present AnySeq - a novel library for computing different types of pairwise alignments of DNA sequences. Our approach combines high performance with an intuitively understandable implementation, which is achieved through the concept of partial evaluation. Using the AnyDSL compiler framework, AnySeq enables the compilation of algorithmic variants that ar…
ASR performance prediction on unseen broadcast programs using convolutional neural networks
2018
In this paper, we address a relatively new task: prediction of ASR performance on unseen broadcast programs. We first propose an heterogenous French corpus dedicated to this task. Two prediction approaches are compared: a state-of-the-art performance prediction based on regression (engineered features) and a new strategy based on convolutional neural networks (learnt features). We particularly focus on the combination of both textual (ASR transcription) and signal inputs. While the joint use of textual and signal features did not work for the regression baseline, the combination of inputs for CNNs leads to the best WER prediction performance. We also show that our CNN prediction remarkably …
Analyzing Learned Representations of a Deep ASR Performance Prediction Model
2018
This paper addresses a relatively new task: prediction of ASR performance on unseen broadcast programs. In a previous paper, we presented an ASR performance prediction system using CNNs that encode both text (ASR transcript) and speech, in order to predict word error rate. This work is dedicated to the analysis of speech signal embeddings and text embeddings learnt by the CNN while training our prediction model. We try to better understand which information is captured by the deep model and its relation with different conditioning factors. It is shown that hidden layers convey a clear signal about speech style, accent and broadcast type. We then try to leverage these 3 types of information …
Sparsity-Driven Digital Terrain Model Extraction
2020
We here introduce an automatic Digital Terrain Model (DTM) extraction method. The proposed sparsity-driven DTM extractor (SD-DTM) takes a high-resolution Digital Surface Model (DSM) as an input and constructs a high-resolution DTM using the variational framework. To obtain an accurate DTM, an iterative approach is proposed for the minimization of the target variational cost function. Accuracy of the SD-DTM is shown in a real-world DSM data set. We show the efficiency and effectiveness of the approach both visually and quantitatively via residual plots in illustrative terrain types.
Quantum algorithms for formula evaluation
2010
We survey the recent sequence of algorithms for evaluating Boolean formulas consisting of NAND gates.
Metastable memristive lines for signal transmission and information processing applications
2016
Traditional studies of memristive devices have mainly focused on their applications in nonvolatile information storage and information processing. Here, we demonstrate that the third fundamental component of information technologies-the transfer of information-can also be employed with memristive devices. For this purpose, we introduce a metastable memristive circuit. Combining metastable memristive circuits into a line, one obtains an architecture capable of transferring a signal edge from one space location to another. We emphasize that the suggested metastable memristive lines employ only resistive circuit components. Moreover, their networks (for example, Y-connected lines) have an info…
Alignment-free Genomic Analysis via a Big Data Spark Platform
2021
Abstract Motivation Alignment-free distance and similarity functions (AF functions, for short) are a well-established alternative to pairwise and multiple sequence alignments for many genomic, metagenomic and epigenomic tasks. Due to data-intensive applications, the computation of AF functions is a Big Data problem, with the recent literature indicating that the development of fast and scalable algorithms computing AF functions is a high-priority task. Somewhat surprisingly, despite the increasing popularity of Big Data technologies in computational biology, the development of a Big Data platform for those tasks has not been pursued, possibly due to its complexity. Results We fill this impo…
Statistical Performance Analysis of a Fast Super-Resolution Technique Using Noisy Translations.
2014
It is well known that the registration process is a key step for super-resolution reconstruction. In this work, we propose to use a piezoelectric system that is easily adaptable on all microscopes and telescopes for controlling accurately their motion (down to nanometers) and therefore acquiring multiple images of the same scene at different controlled positions. Then a fast super-resolution algorithm \cite{eh01} can be used for efficient super-resolution reconstruction. In this case, the optimal use of $r^2$ images for a resolution enhancement factor $r$ is generally not enough to obtain satisfying results due to the random inaccuracy of the positioning system. Thus we propose to take seve…
What happens when software developers are (un)happy
2017
The growing literature on affect among software developers mostly reports on the linkage between happiness, software quality, and developer productivity. Understanding happiness and unhappiness in all its components -- positive and negative emotions and moods -- is an attractive and important endeavor. Scholars in industrial and organizational psychology have suggested that understanding happiness and unhappiness could lead to cost-effective ways of enhancing working conditions, job performance, and to limiting the occurrence of psychological disorders. Our comprehension of the consequences of (un)happiness among developers is still too shallow, being mainly expressed in terms of developmen…