Search results for " data"
showing 10 items of 7516 documents
Optimization of Linearized Belief Propagation for Distributed Detection
2020
In this paper, we investigate distributed inference schemes, over binary-valued Markov random fields, which are realized by the belief propagation (BP) algorithm. We first show that a decision variable obtained by the BP algorithm in a network of distributed agents can be approximated by a linear fusion of all the local log-likelihood ratios. The proposed approach clarifies how the BP algorithm works, simplifies the statistical analysis of its behavior, and enables us to develop a performance optimization framework for the BP-based distributed inference systems. Next, we propose a blind learning-adaptation scheme to optimize the system performance when there is no information available a pr…
Modeling and Mitigating Errors in Belief Propagation for Distributed Detection
2021
We study the behavior of the belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts a distributed multidimensional hypothesis test over binary random variables. The joint statistical behavior of the sensor observations is modeled by a Markov random field whose parameters are used to build the BP messages exchanged between the sensing nodes. Through linearization of the BP message-update rule, we analyze the behavior of the resulting erroneous decision variables and derive closed-form relationships that describe the impact of stochastic errors on the performance of the BP algorithm. We then develop a decentralized distribute…
Data obstacles and privacy concerns in artificial intelligence initiatives
2021
To become and remain competitive, many companies (especially large ones) are considering capitalising on data-based technologies, such as Artificial Intelligence (AI). However, whether these companies are structurally ready in terms of data collection and management remains unknown. This chapter discusses how privacy issues and reforms, such as the General Data and Protection Regulation (GDPR), affect companies’ AI initiatives and processes. For this purpose, we reviewed the relevant literature and collected empirical data using in-depth interviews with AI and data industry experts in five countries. Our main findings indicated that companies are lacking sound data collection and management…
Analyse multirésolution pour la recherche et l'indexation d'images par le contenu dans les bases de données images - Application à la base d'images p…
2005
Recent content-based image retrieval systems offer an interactive visual browsing of images databases. These methods perform a classification of images (offline) into a search tree for users browsing (online). This approach shows three main problems:1) The size of decriptor vector (n>100) makes distance computing sensitive to dimensionality curse,2) Having many different kinds of attributes into descriptor vector does not help classification,3) In general, classification does not take in consideration users' search context. In this work, we propose a method based on building hierarchical signatures having small increasing sizes, this allows to take users' search context into consideration. …
Review of Particle Physics
2020
The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, …
Using Differential Geometry for Sparse High-Dimensional Risk Regression Models
2023
With the introduction of high-throughput technologies in clinical and epidemiological studies, the need for inferential tools that are able to deal with fat data-structures, i.e., relatively small number of observations compared to the number of features, is becoming more prominent. In this paper we propose an extension of the dgLARS method to high-dimensional risk regression models. The main idea of the proposed method is to use the differential geometric structure of the partial likelihood function in order to select the optimal subset of covariates.
Recruitments in Finnish universities: practicing strategic or pathetic HRM?
2016
Recruitment is a core instrument in the academic labour market. This article takes the perspective of the organisation − here, the university − on recruitment. Universities’ personnel policies and practises are shifting from legally oriented personnel administration to more strategic human resource management (HRM). In Nordic countries, this shift is partly driven by the changing status of higher education institutions from state-governed bureaus to more autonomous institutions. This article provides insight into this transition, using Finland as a case example of higher education systems that have undergone drastic reform, moving from a civil servant model to autonomous personnel policy. D…
The formation, properties and impact of secondary organic aerosol: Current and emerging issues
2009
Hallquist, M. Wenger, J. C. Baltensperger, U. Rudich, Y. Simpson, D. Claeys, M. Dommen, J. Donahue, N. M. George, C. Goldstein, A. H. Hamilton, J. F. Herrmann, H. Hoffmann, T. Iinuma, Y. Jang, M. Jenkin, M. E. Jimenez, J. L. Kiendler-Scharr, A. Maenhaut, W. McFiggans, G. Mentel, Th. F. Monod, A. Prevot, A. S. H. Seinfeld, J. H. Surratt, J. D. Szmigielski, R. Wildt, J.; Secondary organic aerosol (SOA) accounts for a significant fraction of ambient tropospheric aerosol and a detailed knowledge of the formation, properties and transformation of SOA is therefore required to evaluate its impact on atmospheric processes, climate and human health. The chemical and physical processes associated wit…
Farm-Scale Crop Yield Prediction from Multi-Temporal Data Using Deep Hybrid Neural Networks
2021
Farm-scale crop yield prediction is a natural development of sustainable agriculture, producing a rich amount of food without depleting and polluting environmental resources. Recent studies on crop yield production are limited to regional-scale predictions. The regional-scale crop yield predictions usually face challenges in capturing local yield variations based on farm management decisions and the condition of the field. For this research, we identified the need to create a large and reusable farm-scale crop yield production dataset, which could provide precise farm-scale ground-truth prediction targets. Therefore, we utilise multi-temporal data, such as Sentinel-2 satellite images, weath…
Antifungal activity improved by coproduction of cyclodextrins and anabaenolysins in Cyanobacteria
2015
Department of Chemistry, Nanoscience Center, University of Jyväskylä, FI-40014, Jyväskylä, Finland Cyclodextrins are cyclic oligosaccharides widely used in the pharmaceutical industry to improve drug delivery and to increase the solubility of hydrophobic compounds. Anabaenolysins are lipopeptides produced by cyanobacteria with potent lytic activity in cholesterolcontaining membranes. Here, we identified the 23- To 24-kb gene clusters responsible for the production of the lipopeptide anabaenolysin. The hybrid nonribosomal peptide synthetase and polyketide synthase biosynthetic gene cluster is encoded in the genomes of three anabaenolysin-producing strains of Anabaena.We detected previously u…