134,466 research outputs found
An investigation into adaptive power reduction techniques for neural hardware
No full textIn light of the growing applicability of Artificial Neural Network (ANN) in the signal processing field [1] and the present thrust of the semiconductor industry towards low-power SOCs for mobile devices [2], the power consumption of ANN hardware has become a very important implementation issue. Adaptability is a powerful and useful feature of neural networks. All current approaches for low-power ANN hardware techniques are ‘non-adaptive’ with respect to the power consumption of the network (i.e. power-reduction is not an objective of the adaptation/learning process). In the research work presented in this thesis, investigations on possible adaptive power reduction techniques have been carried out, which attempt to exploit the adaptability of neural networks in order to reduce the power consumption. Three separate approaches for such adaptive power reduction are proposed: adaptation of size, adaptation of network weights and adaptation of calculation precision. Initial case studies exhibit promising results with significant power reduction
Modi: myth and the man
No full textNarendra Modi reacted to the killing of 20 Indian army personnel by China’s People’s Liberation Army by reassuring the country that “Nobody has intruded into our border... nor have our posts been captured.” The posturing around ‘no Chinese incursion’ continues till date. But many commentators suggest that the de facto shift in the region — with the Galwan Valley now under effective control of China — implies that India is giving up its claim on this crucial territory and that this will be the end of Modi’s credentials as a ‘strong leader’.
We, however, argue that far from weakening Modi’s ‘strongman’, ‘nationalist’ credentials, the recent developments are consistent with Modi’s political strategy and are likely to bolster his success.
Why do we think that Modi’s China gamble will pay off
Behavioral Simulation of Biological Neuron Systems in SystemC
No full textThe investigation of neuron structures is an incredibly difficult and complex task that yields relatively low rewards in terms of information from biological forms (either animals or tissue). The structures and connectivity of even the simplest invertebrates are almost impossible to establish with standard laboratory techniques. Recent work has shown how a simplified behavioural approach to modeling neurons can allow “virtual” experiments to be carried out that map the behaviour of a simulated structure onto a hypothetical biological one, with correlation of behaviour rather than underlying connectivity. The problems with such approaches are twofold. The first is the difficulty of simulating realistic aggregates efficiently, and the second is making sense of the results. In this paper we describe a method of modeling neuron aggregates using SystemC (a language developed for hardware design), and also a design interface to enable structures and connection maps to be developed, with simulations carried out leading to animated visualization of the result
Quantum non-Markovianity elusive to interventions
No full textThe non-Markovian nature of open quantum dynamics lies in the structure of multitime correlations, which are accessible by means of interventions. Here, by examining multitime correlations, we show that it is possible to engineer non-Markovian systems with only long-term memory but seemingly no short-term memory, so that their non-Markovianity is completely nondetectable by any interventions up to an arbitrarily large time. Our results raise the question about the assessability of non-Markovianity: in principle, non-Markovian effects that are perfectly elusive to interventions may emerge at much later times
A retrospective cohort study comparing a novel, spherical, resorbable particle against five established embolic agents for uterine fibroid embolisation
No full textAIM: To evaluate the effectiveness of a novel, resorbable, spherical embolic agent compared with other established agents, by studying percentage fibroid infarction (the best indicator of long-term symptom improvement) in patients undergoing uterine fibroid embolisation (UFE). MATERIALS AND METHODS: This retrospective cohort study examined six different embolic agents used for fibroid embolisation, including a new gelatin-based, fully resorbable, spherical agent. The primary effectiveness outcomes were magnetic resonance imaging (MRI)-determined dominant fibroid infarct percentage (DF%) and all fibroid percentage infarct (AF%) at 3 months post-embolisation. MRI-determined uterine artery patency rate was the secondary outcome. Chi-squared test (χ
2), relative risk (RR) calculation (primary outcomes), and analysis of variance (ANOVA) (secondary outcome) were the statistical tests employed. RESULTS: One hundred and twenty patients were treated with six embolic agents (20 consecutive patients per group, overall mean age 44.8±6.4, initial uterine volume 570±472 ml, dominant fibroid volume 249±324 ml). Fibroid infarctrates were similar between the cohorts with no significant difference between the new gelatin-based resorbable particle and other embolics in either DF% (χ
2=3.92, p=0.56) or AF% (χ
2=2.83, p=0.73). Complete DF% RR=1.07 (0.90–1.27) and AF% RR=1.09 (0.85–1.41) suggest non-inferiority of the resorbable particle (d=0.67, p<0.05). A favourable uterine artery patency rate was demonstrated for the resorbable particle compared with gelatin slurry (82.5% versus 27.5%, p<0.001 after Bonferroni adjustment). CONCLUSIONS: This new gelatin-based, fully resorbable particle is an effective embolic agent for fibroid embolisation and achieves an infarct rate non-inferior to established embolics.
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Local random potentials of high differentiability to model the Landscape
No full textWe generate random functions locally via a novel generalization of Dyson Brownian motion, such that the functions are in a desired differentiability class C-k, while ensuring that the Hessian is a member of the Gaussian orthogonal ensemble (other ensembles might be chosen if desired). Potentials in such higher differentiability classes (k >= 2) are required/desirable to model string theoretical landscapes, for instance to compute cosmological perturbations (e.g., k = 2 for the power-spectrum) or to search for minima (e.g., suitable de Sitter vacua for our universe). Since potentials are created locally, numerical studies become feasible even if the dimension of field space is large (D similar to 100). In addition to the theoretical prescription, we provide some numerical examples to highlight properties of such potentials; concrete cosmological applications will be discussed in companion publications
Maternal antecedents to adolescent girls’ neural regulation of emotion
No full textCurrent research on adolescent brain development has uncovered individual differences in patterns of functional connectivity during the regulation of emotions, reflecting differences in psychological and emotional functioning. The purpose of this study was to identify possible contributors to these individual differences by investigating the role of maternal emotional resources, in the form of adult attachment and emotional awareness. Participants included 35 adolescent girls (M age = 15.51, SD = 0.37) who completed an implicit emotion regulation task (Lieberman et al., 2007) during an fMRI scan following 9th grade. Mothers reported on the quality of their adult attachment when youth were in 3rd and 4th grades and reported on their emotional awareness when youth were in 4th and 5th grades. We found that higher levels of maternal anxious attachment and lower levels of maternal emotional awareness were significantly correlated with more positive (i.e., ineffective) amygdala-right ventrolateral prefrontal cortex (rVLPFC) connectivity. Further, path analysis revealed that there was an indirect effect of maternal anxious attachment on adolescent functional connectivity through maternal emotional awareness. These results suggest that exposure to compromised maternal emotional resources in childhood may be linked to the development of ineffective neural processing of emotions, highlighting one pathway for the intergenerational transmission of disrupted emotion processing.Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-12-01The student, Haina Modi, accepted the attached license on 2018-11-19 at 09:33.The student, Haina Modi, submitted this Thesis for approval on 2018-11-19 at 09:42.This Thesis was approved for publication on 2018-11-26 at 11:59.DSpace SAF Submission Ingestion Package generated from Vireo submission #13098 on 2019-02-07 at 14:17:36Made available in DSpace on 2019-02-07T20:35:58Z (GMT). No. of bitstreams: 2
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Previous issue date: 2018-11-26Embargo set by: Seth Robbins for item 109821
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Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 109821
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Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemEmbargo set by: Seth Robbins for item 109821
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Reason: Author requested U of Illinois access only (OA after 2yrs) in Vireo ETD systemU of I Only Restriction Lifted for Item 109821 on 2021-02-08T10:15:29Z
MeSH term explosion and author rank improve expert recommendations
No full textInformation overload is an often-cited phenomenon that reduces the productivity, efficiency and efficacy of scientists. One challenge for scientists is to find appropriate collaborators in their research. The literature describes various solutions to the problem of expertise location, but most current approaches do not appear to be very suitable for expert recommendations in biomedical research. In this study, we present the development and initial evaluation of a vector space model-based algorithm to calculate researcher similarity using four inputs: 1) MeSH terms of publications; 2) MeSH terms and author rank; 3) exploded MeSH terms; and 4) exploded MeSH terms and author rank. We developed and evaluated the algorithm using a data set of 17,525 authors and their 22,542 papers. On average, our algorithms correctly predicted 2.5 of the top 5/10 coauthors of individual scientists. Exploded MeSH and author rank outperformed all other algorithms in accuracy, followed closely by MeSH and author rank. Our results show that the accuracy of MeSH term-based matching can be enhanced with other metadata such as author rank
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Architettura del luogo e dell'ego : modi per integrare il paesaggio, modi per dis-intengrarlo
No full textConfronto fra linguaggi territoriali locali e forme e modi d’ uso del suolo incongruenti che compromettono i valori identitati e produttivi dei contesti metropolitanii
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