Model Complexity-Accuracy Trade-off for a Convolutional Neural Network

Convolutional Neural Networks(CNN) has had a great success in the recent past, because of the advent of faster GPUs and memory access. CNNs are really powerful as they learn the features from data in layers such that they exhibit the structure of the V-1 features of the human brain. A huge bottleneck, in this case, is that CNNs are very large and have a very high memory footprint, and hence they cannot be employed on devices with limited storage such as mobile phone, IoT etc. In this work, we study the model complexity versus accuracy trade-off on MNSIT dataset, and give a concrete framework for handling such a problem, given the worst case accuracy that a system can tolerate. In our work, we reduce the model complexity by 236 times, and memory footprint by 19.5 times compared to the base model while achieving worst case accuracy threshold

Performance of b-tagging algorithms at the CMS experiment with pp collision data at s\sqrt s=8 TeV

The identification of jets originating from b quarks is crucial both for the searches for new physics and for the measurement of standard model processes. The Compact Muon Solenoid (CMS) collaboration at the Large Hadron Collider (LHC) has developed a variety of algorithms to select b-quark jets based on variables such as the impact parameter of charged particle tracks, properties of reconstructed secondary vertices from heavy hadron decays, and the presence or absence of a lepton in the jet, or combinations thereof. Performance measurements of these b-jet identification algorithms are presented, using multijet and $t\overline{t}$ events recorded in proton-proton collision data at $\sqrt s$=8 TeV with the CMS detector during the LHC Run 1

Face Identification and Clustering

In this thesis, we study two problems based on clustering algorithms. In the first problem, we study the role of visual attributes using an agglomerative clustering algorithm to whittle down the search area where the number of classes is high to improve the performance of clustering. We observe that as we add more attributes, the clustering performance increases overall. In the second problem, we study the role of clustering in aggregating templates in a 1:N open set protocol using multi-shot video as a probe. We observe that by increasing the number of clusters, the performance increases with respect to the baseline and reaches a peak, after which increasing the number of clusters causes the performance to degrade. Experiments are conducted using recently introduced unconstrained IARPA Janus IJB-A, CS2, and CS3 face recognition datasets

Trading Away Wide Brands for Cheap Brands

Firms face competing needs to expand product variety and reduce production costs. Trade policy affects firm investments in product variety and production processes differently. Access to larger markets enables innovation to reduce costs. Although firm scale increases, foreign competition reduces markups. Firms react by narrowing their product varieties to recapture these lost markups. I provide a theory detailing this conflicting impact of trade policy and address welfare gains from trade. Accounting for firm heterogeneity, I show support for the theoretical predictions with firm-level innovation data from Thailand's manufacturing sector which experienced unilateral home tariff changes during 2003-2006.brands, trade, manufacturing, heterogeneous firms, Thailand

Gang Re-engagement Intentions among Incarcerated Serious Juvenile Offenders

Research examining the factors that precipitate gang membership has contributed substantially to our understanding of gangs and gang-related activity, yet we know little about the factors influencing intentions to re-join a gang after having being incarcerated. This study examines the relationship between gang characteristics, number of incarcerated friends, and family characteristics and gang re-engagement intentions, while controlling for ethnicity. Participants were 206 male serious juvenile offenders interviewed as part of the Pathways to Desistance Study. The model explained between 35% and 47% of variance in gang re-engagement intentions. However, only three variables made a unique statistically significant contribution to the model (punishment if gang rules are broken, importance of gang membership, and moral disengagement), with the strongest predictor being importance of gang membership. The results suggest that challenging young offenders’ perceptions about the importance of gang membership might be particularly effective in reducing gang re-engagement intentions after incarceration

Psychopathic Traits of Business and Psychology Students and their Relationship to Academic Success

The notion that high levels of psychopathic trait leads to career success in the business sector has become a popular point of theorising in recent years, with research providing support for the alleged overrepresentation of psychopathy in the financial sector, and the existence of a relationship between psychopathy and professional success. A cross-sectional design was employed to compare psychopathy scores of business and psychology students, as well as to examine the psychopathy-academic success relationship. Participates were 263 participants recruited from a UK university. Results revealed greater psychopathic traits in business students relative to psychology students on all four factors of psychopathy. Furthermore, hierarchical multiple regression indicated that the four psychopathy factors, gender, age, study hours, and course explain 14% of variance in grade outcome. Two variables made unique statistic contributions to the model with Antisocial Behaviour and gender (male) negatively related to grade outcome. Theoretical and practical implications of our findings are discussed