scispace - formally typeset
Search or ask a question
Institution

Helsinki Institute for Information Technology

FacilityEspoo, Finland
About: Helsinki Institute for Information Technology is a facility organization based out in Espoo, Finland. It is known for research contribution in the topics: Population & Bayesian network. The organization has 630 authors who have published 1962 publications receiving 63426 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: The model unifies previous extensions to independent component analysis such as subspace and topographic models and provides new evidence that localized, oriented, phase-invariant features reflect the statistical properties of natural image patches.
Abstract: We consider a hierarchical two-layer model of natural signals in which both layers are learned from the data. Estimation is accomplished by score matching, a recently proposed estimation principle for energy-based models. If the first-layer outputs are squared and the second-layer weights are constrained to be nonnegative, the model learns responses similar to complex cells in primary visual cortex from natural images. The second layer pools a small number of features with similar orientation and frequency, but differing in spatial phase. For speech data, we obtain analogous results. The model unifies previous extensions to independent component analysis such as subspace and topographic models and provides new evidence that localized, oriented, phase-invariant features reflect the statistical properties of natural image patches.

40 citations

Book ChapterDOI
13 Jun 2012
TL;DR: The preliminary results from an experiment investigating the perceived intensity of modulated friction created by electrostatic force indicate that there are significant correlations between intensity perception and signal amplitude and the highest sensitivity was found at a frequency of 80 Hz.
Abstract: We describe the preliminary results from an experiment investigating the perceived intensity of modulated friction created by electrostatic force, or electrovibration. A prototype experimental system was created to evaluate user perception of sinusoidal electrovibration stimuli on a flat surface emulating a touch screen interface. We introduce a fixed 6-point Effect Strength Subjective Index (ESSI) as a measure of generic sensation intensity, and compare it with an open magnitude scale. The results of the experiment indicate that there are significant correlations between intensity perception and signal amplitude, and the highest sensitivity was found at a frequency of 80 Hz. The subjective results show that the users perceived the electrovibration stimuli as pleasant and a useful means of feedback for touchscreens.

39 citations

Proceedings ArticleDOI
01 Dec 2016
TL;DR: In this paper, the redundancy in a large number of candidate sets obtained by independently generated random projections is exploited to reduce the number of expensive exact distance evaluations, which leads to a reduced memory footprint and fast index construction.
Abstract: Efficient index structures for fast approximate nearest neighbor queries are required in many applications such as recommendation systems. In high-dimensional spaces, many conventional methods suffer from excessive usage of memory and slow response times. We propose a method where multiple random projection trees are combined by a novel voting scheme. The key idea is to exploit the redundancy in a large number of candidate sets obtained by independently generated random projections in order to reduce the number of expensive exact distance evaluations. The method is straightforward to implement using sparse projections which leads to a reduced memory footprint and fast index construction. Furthermore, it enables grouping of the required computations into big matrix multiplications, which leads to additional savings due to cache effects and low-level parallelization. We demonstrate by extensive experiments on a wide variety of data sets that the method is faster than existing partitioning tree or hashing based approaches, making it the fastest available technique on high accuracy levels.

39 citations

Journal ArticleDOI
04 May 2016-PLOS ONE
TL;DR: The present results demonstrate a negativity bias exists for social media messages in media multitasking; however, this effect does not amplify the overall detrimental effects ofMedia multitasking.
Abstract: Television viewers' attention is increasingly more often divided between television and "second screens", for example when viewing television broadcasts and following their related social media discussion on a tablet computer. The attentional costs of such multitasking may vary depending on the ebb and flow of the social media channel, such as its emotional contents. In the present study, we tested the hypothesis that negative social media messages would draw more attention than similar positive messages. Specifically, news broadcasts were presented in isolation and with simultaneous positive or negative Twitter messages on a tablet to 38 participants in a controlled experiment. Recognition memory, gaze tracking, cardiac responses, and self-reports were used as attentional indices. The presence of any tweets on the tablet decreased attention to the news broadcasts. As expected, negative tweets drew longer viewing times and elicited more attention to themselves than positive tweets. Negative tweets did not, however, decrease attention to the news broadcasts. Taken together, the present results demonstrate a negativity bias exists for social media messages in media multitasking; however, this effect does not amplify the overall detrimental effects of media multitasking.

39 citations


Authors

Showing all 632 results

NameH-indexPapersCitations
Dimitri P. Bertsekas9433285939
Olli Kallioniemi9035342021
Heikki Mannila7229526500
Jukka Corander6641117220
Jaakko Kangasjärvi6214617096
Aapo Hyvärinen6130144146
Samuel Kaski5852214180
Nadarajah Asokan5832711947
Aristides Gionis5829219300
Hannu Toivonen5619219316
Nicola Zamboni5312811397
Jorma Rissanen5215122720
Tero Aittokallio522718689
Juha Veijola5226119588
Juho Hamari5117616631
Network Information
Related Institutions (5)
Google
39.8K papers, 2.1M citations

93% related

Microsoft
86.9K papers, 4.1M citations

93% related

Carnegie Mellon University
104.3K papers, 5.9M citations

91% related

Facebook
10.9K papers, 570.1K citations

91% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20231
20224
202185
202097
2019140
2018127