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We present a comparative study of the accuracy and precision of correlation function methods and full-field inference in cosmological data analysis. To do so, we examine a Bayesian hierarchical model that predicts log-normal fields and their two-point correlation function. Although a simplified analytic model, the log-normal model produces fields that share many of the essential characteristics of the present-day non-Gaussian cosmological density fields. We use three different statistical techniques: (i) a standard likelihood-based analysis of the two-point correlation function; (ii) a likelihood-free (simulation-based) analysis of the two-point correlation function; (iii) a field-level analysis, made possible by the more sophisticated data assimilation technique. We find that (a) standard assumptions made to write down a likelihood for correlation functions can cause significant biases, a problem that is alleviated with simulation-based inference; and (b) analysing the entire field offers considerable advantages over correlation functions, through higher accuracy, higher precision, or both. The gains depend on the degree of non-Gaussianity, but in all cases, including for weak non-Gaussianity, the advantage of analysing the full field is substantial., Comment: 6+8 pages, 4+5 figures. Matches MNRAS Letters published version. Appendices provide supplementary information, including calculations of Fisher matrices. Our code and data are publicly available at https://github.com/florent-leclercq/correlations_vs_field

Working memory (WM) refers to a set of processes that makes task-relevant information accessible to higher-level cognitive processes. Recent work suggests WM is supported by a variety of information gating, updating, and removal processes, which ensure only task-relevant information occupies WM. Current neurocomputational theory suggests WM gating is accomplished via ‘go/no-go’ signalling in basal ganglia-thalamus-prefrontal cortex pathways, but is less clear about other subprocesses and brain structures known to play a role in WM. We review recent efforts to identify the neural basis of WM subprocesses using the recently developed reference-back task as a benchmark measure of WM subprocesses. Targets for future research using the methods of model-based cognitive neuroscience and novel extensions to the reference-back task are suggested.

Video calling or voice calling through the use of the internet is a very common thing in today's world. Nevertheless, users have to pay charges directly or indirectly, tolerating all the delays even if you use the facility within the same network or sub-network. Over 30 million US households used Voice over IP (VoIP) until the end of 2010. Specialist found that 31.4% of the household used VoIP as the main and only home phone line by 2012. Users up till now have multiplied by a huge factor. There are several drawbacks of using video calling over the internet within a small network such as delay of packets (travel to the main server outside network and reenter the same network again), direct or indirect costs, users need to be always connected online and efficiency and speed of the user's system vary according to connection quality and bandwidth. In this project, we implemented a WebRTC enabled web application which provides free peer to peer video calling and conferencing tool for any private organization on top of their existing private network.

Neuroscientists seek efficient solutions for deciphering the sophisticated unknowns of the brain. Effective development of complicated brain-related tools is the focal point of research in neuroscience and neurotechnology. Thanks to today's technological advancements, the physical development of high-density and high-resolution neural interfaces has been made possible. This is where the critical bottleneck in receiving the expected functionality from such devices shifts to transferring, processing, and subsequently analyzing the massive neurophysiological extra-cellular data recorded. To respond to this inevitable concern, a spectrum of neuronal signal processing techniques have been proposed to extract task-related informative content of the signals conveying neuronal activities, and eliminate the irrelevant contents. Such techniques provide powerful tools for a wide range of neuroscience research, from low-level perception to high-level cognition. Data transformations are among the most efficient processing techniques that serve this purpose by properly changing the data representation. Mapping the data from its original domain (i.e., the time-space domain) to a new representational domain, data transformations change the viewing angle of observing the informative content of the data. This paper reviews the employment of data transformations in order to process neuronal signals and their three key applications, including spike detection, spike sorting, and data compression.

DES Collaboration: M. Gatti et al., We present a cosmological analysis using the second and third moments of the weak lensing mass (convergence) maps from the first three years of data (Y3) data of the Dark Energy Survey. The survey spans an effective area of 4139 square degrees and uses the images of over 100 million galaxies to reconstruct the convergence field. The second moment of the convergence as a function of smoothing scale contains information similar to standard shear 2-point statistics. The third moment, or the skewness, contains additional non-Gaussian information. The data is analyzed in the context of the ΛCDM model, varying five cosmological parameters and 19 nuisance parameters modeling astrophysical and measurement systematics. Our modeling of the observables is completely analytical, and has been tested with simulations in our previous methodology study. We obtain a 1.7% measurement of the amplitude of fluctuations parameter S8≡σ8(Ωm/0.3)0.5=0.784±0.013. The measurements are shown to be internally consistent across redshift bins, angular scales, and between second and third moments. In particular, the measured third moment is consistent with the expectation of gravitational clustering under the ΛCDM model. The addition of the third moment improves the constraints on S8 and Ωm by ∼15% and ∼25% compared to an analysis that only uses second moments. We compare our results with Planck constraints from the cosmic microwave background, finding a 2.2–2.8σ tension in the full parameter space, depending on the combination of moments considered. The third moment, independently, is in 2.8σ tension with Planck, and thus provides a cross-check on the analyses of 2-point correlations., Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Fundação Carlos Chagas Filho de Amparo a Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Científico e Tecnológico and the Ministerio da Ciência, Tecnologia e Inovação, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energeticas, Medioambientales y Tecnológicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgenössische Technische Hochschule (ETH) Zürich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ciencies de l’Espai (IEEC/CSIC), the Institut de Física d’Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universität München and the associated Excellence Cluster Universe, the University of Michigan, NSF’s NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF’s NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grants No. AST-1138766 and No. AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under Grants No. ESP2017-89838, No. PGC2018-094773, No. PGC2018-102021, No. SEV-2016-0588, No. SEV-2016-0597, and No. MDM-2015-0509, some of which include ERDF funds from the European Union. I. F. A. E. is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union’s Seventh Framework Program (FP7/2007-2013) including ERC Grant agreements No. 240672, No. 291329, and No. 306478. We acknowledge support from the Brazilian Instituto Nacional de Ciência e Tecnologia (INCT) do e-Universo (CNPq Grant No. 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DEAC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.

Let string S[1..n] be parsed into z phrases by the Lempel-Ziv algorithm. The corresponding compression algorithm encodes S in O(z) space, but it does not support random access to S. We introduce a data structure, the block tree, that represents S in O(z log(n/z)) space and extracts any symbol of S in time O(log(n/z)), among other space-time tradeoffs. The structure also supports other queries that are useful for building compressed data structures on top of S. Further, block trees can be built in linear time and in a scalable manner. Our experiments show that block trees offer relevant space-time tradeoffs compared to other compressed string representations for highly repetitive strings. (C) 2020 Elsevier Inc. All rights reserved.

Many cosmological models have only a finite number of parameters of interest, but a very expensive data-generating process and an intractable likelihood function. We address the problem of performing likelihood-free Bayesian inference from such black-box simulation-based models, under the constraint of a very limited simulation budget (typically a few thousand). To do so, we adopt an approach based on the likelihood of an alternative parametric model. Conventional approaches to approximate Bayesian computation such as likelihood-free rejection sampling are impractical for the considered problem, due to the lack of knowledge about how the parameters affect the discrepancy between observed and simulated data. As a response, we make use of a strategy previously developed in the machine learning literature (Bayesian optimisation for likelihood-free inference, BOLFI), which combines Gaussian process regression of the discrepancy to build a surrogate surface with Bayesian optimisation to actively acquire training data. We extend the method by deriving an acquisition function tailored for the purpose of minimising the expected uncertainty in the approximate posterior density, in the parametric approach. The resulting algorithm is applied to the problems of summarising Gaussian signals and inferring cosmological parameters from the Joint Lightcurve Analysis supernovae data. We show that the number of required simulations is reduced by several orders of magnitude, and that the proposed acquisition function produces more accurate posterior approximations, as compared to common strategies., Comment: 16+9 pages, 12 figures. Matches PRD published version after minor modifications

Design productivity is essential for high-performance application development involving accelerators. Low level hardware description languages such as Verilog and VHDL are widely used to design FPGA accelerators, however, they require significant expertise and considerable design efforts. Recent advances in high-level synthesis have brought forward tools that relieve the burden of FPGA application development but the achieved performance results can not approximate designs made using low-level languages. In this paper we compare different FPGA implementations of gzip. All of them implement the same system architecture using different languages. This allows us to compare Verilog, OpenCL and MaxJ design productivity. First, we illustrate several conceptional advantages of the MaxJ language and its platform over OpenCL. Next we show on the example of our gzip implementation how an engineer without previous MaxJ experience can quickly develop and optimize a real, complex application. The gzip design in MaxJ presented here took only one man-month to develop and achieved better performance than the related work created in Verilog and OpenCL.

The maskless lithography technology requires the management and transmission of exposure data at very high data rates. To meet these requirements an effective representation of the data is necessary which leads to the need for lossless data compression. In this paper, common standard compression techniques are compared regarding their applicability and a new lossless compression approach and its hardware implementation is presented. Furthermore, the trade-off between the buffer size as an essential part of the compression scheme and the compression ratio is examined. The problem of DC balance related to optical transmission is explicitly addressed and being solved by a DC-balanced coding. The aim was to develop a cost-optimized method for compressing and decompressing lithography data in real-time and being suitable for circuit integration. The results including a proposed integration in a maskless lithography system are presented here.

A power and area efficient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case of severe mental diseases, require heavy surgery and long term hospitalization periods. In this work, the circuit design and the signal processing algorithm dovetail in order to allow real-time neuronal signal monitoring. Two main features must be met on the circuit level to facilitate the acceptance of the implant from the human body: small area and low power consumption. The presented work proposes a new compression scheme based on the Learning-Based Compressive Subsampling approach, which allows an area reduction with respect to recent published works, while allowing high signal reconstruction quality within low power requirements. The proposed method implements on-the-fly compression coefficients generation, which does not require large static memories. This new fully digital architecture handles the data compression of each individual neuronal acquisition channel with an area of 200 x 190 mu m in 0.18 mu m, CMOS technology, and a power dissipation of only 1.15 mu W.