An individual position in social hierarchy is the key mechanism, how an individual could gain a priority access to more quality food resources, find an attractive sexual partner to reproduce and find territory with low predation risk and maintain its fitness in total. Personality is supposed to be an important factor how an individuals keep their positions in social groups. The consistent individual variability in aggressive behaviour is closely related to the expression of melanin-based colouration and testosterone levels in blood. More explorative, aggressive, bolder and darker-coloured individuals are supposed to achieve higher dominance rank in social structures. But this prediction was barely tested. The main aims of this diploma thesis were: to find consistent individual variability in social and non-social context to confirm personality traits and to define connection between an individual variability in agonistic and explorative behaviour and melanin-based colouration in relation to social rank in experimental group of domestic pigeons (Columba liva f. domestica) under human care. Unfortunately, personality could not be defined and even more, there was no significant correlation between dominant position in social hierarchy and individual behavioural variability in social and non-social...

Acoustic and electromagnetic scattering from arbitrarily shaped structures can be numerically characterized by solving various surface integral equations (SIEs). One of the most effective techniques to solve SIEs is the Nyström method. Compared to other existing methods,the Nyström method is easier to implement especially when the geometrical discretization is non-conforming and higher-order representations of the geometry and unknowns are desired. However,singularities of the Green’s function are more difficult to”manage”since they are not ”smoothened” through the use of a testing function. This dissertation describes purely numerical schemes to account for different orders of singularities that appear in acoustic and electromagnetic SIEs when they are solved by a high-order Nyström method utilizing a mesh of curved discretization elements. These schemes make use of two sets of basis functions to smoothen singular integrals: the grid robust high-order Lagrange and the high-order Silvester-Lagrange interpolation basis functions. Numerical results comparing the convergence of two schemes are presented. Moreover, an extremely scalable implementation of fast multipole method (FMM) is developed to efficiently (and iteratively) solve the linear system resulting from the discretization of the acoustic SIEs by the Nyström method. The implementation results in O(N log N) complexity for high-frequency scattering problems. This FMM-accelerated solver can handle N =2 billion on a 200,000-core Cray XC40 with 85% strong scaling efficiency. Iterative solvers are often ineffective for ill-conditioned problems. Thus, a fast direct (LU)solver,which makes use of low-rank matrix approximations,is also developed. This solver relies on tile low rank (TLR) data compression format, as implemented in the hierarchical computations on many corearchitectures (HiCMA) library. This requires to taskify the underlying SIE kernels to expose fine-grained computations. The resulting asynchronous execution permit to weaken the artifactual synchronization points,while mitigating the overhead of data motion. We compare the obtained performance results of our TLRLU factorization against the state-of-the-art dense factorizations on shared memory systems. We achieve up to a fourfold performance speedup on a 3D acoustic problem with up to 150 K unknowns in double complex precision arithmetics.

Moving to a pass/fail curriculum has generally been associated with decreased levels of stress and increased medical student well-being. However, not much research has been done to identify the specific effect of retaining class rank in a pass/fail curriculum and how this might affect student stress levels. The purpose of the current study was to fill in current research gaps in this area and to provide further insight into some of the factors that contribute to medical student burnout. The study was carried out using the Medical Student Well-Being Index (MSWBI), a self-reported survey that evaluates medical student fatigue, depression, burnout, anxiety/stress, and mental/physical QOL on a weighted and unweighted basis. Additionally, a set of add-on questions developed by the research team were distributed to participants along with the MSWBI. These questions asked the students to determine whether the change to a pass/fail curriculum increased, decreased, or did not change their perceived stress levels and to identify the major sources of their perceived stress. Participants were full-time medical students enrolled at ETSU Quillen College of Medicine from the Fall 2019 to Spring 2020 terms. They were divided by graduation year and asked to complete the MSWBI and IRB-approved add-on questions once per school year during this period. The number of add-on question respondents from each class reporting an increased or unchanged level of stress since switching to a pass-fail system encompassed 62.6% of all respondents. The most common reason provided by respondents for either increased or unchanged levels of stress after switching to a pass/fail curriculum was the continued reporting of class rank. This work will be useful in determining the true sources of student stress within the medical education system. While a pass/fail curriculum may reduce medical students’ perceived stress, this data indicates that class rank remains burdensome for many. Understanding the underlying factors that influence poor medical student well-being can lead to better targeted interventions.

The objective of this thesis is to analyse theconnection between test results and field claims of ECUs (electronic controlunits) at Scania in order to improve the acceptance criteria and evaluatesoftware testing strategies. The connection is examined through computation ofdifferent measures of dependencies such as the Pearson’s correlation, Spearman’srank correlation and Kendall’s tau. The correlations are computed from testresults in different ECU projects and considered in a predictive model based onlogistic regression. Numerical results indicate a weak connection between testresults and field claims. This is partly due to insufficient number of ECUprojects and the lack of traceability of field claims and test results. Themain conclusion confirms the present software testing strategy. Continuoussoftware release and testing results in a lower field claim and thus a betterproduct.

>Magister Scientiae - MSc / The groups we consider in this study belong to the class Xo of all finitely generated groups with finite commutator subgroups. We shall eventually narrow down to the groups of the form T)<lw zn for some nE N and some finite abelian group T. For a Xo-group H, we study the non-cancellation set, X(H), which is defined to be the set of all isomorphism classes of groups K such that H x Z ~ K x Z. For Xo-groups H, on X(H) there is an abelian group structure [38], defined in terms of embeddings of K into H, for groups K of which the isomorphism classes belong to X(H). If H is a nilpotent Xo-group, then the group X(H) is the same as the Hilton-Mislin (see [10]) genus group Q(H) of H. A number of calculations of such Hilton-Mislin genus groups can be found in the literature, and in particular there is a very nice calculation in article [11] of Hilton and Scevenels. The main aim of this thesis is to compute non-cancellation (or genus) groups of special types of .Xo-groups such as mentioned above. The groups in question can in fact be considered to be direct products of metacyclic groups, very much as in [11]. We shall make extensive use of the methods developed in [30] and employ computer algebra packages to compute determinants of endomorphisms of finite groups.

In this thesis, we present the local rank differences (LRD). These novel image features are invariant to lighting changes and are suitable for object detection in programmable hardware, such as FPGA. The performance of AdaBoost classifiers with the LRD was tested on a face detection dataset with results which are similar to the Haar-like features which are the state of the art in real-time object detection. These results together with the fact that the LRD are evaluated much faster in FPGA then the Haar-like features are very encouraging and suggest that the LRD may be a solution for future hardware object detectors. We also present a framework for experiments with boosting methods in computer vision. This framework is very flexible and, at the same time, offers high learning performance and a possibility for future parallelization. The framework is available as open source software and we hope that it will simplify work for other researchers.

No description available.

Nonstationary signals are widely observed in many real-world applications, e.g., radar, sonar, radio astronomy, communication, acoustics, and vibration applications. Joint time-frequency (TF) domain representations provide a time-varying spectrum for their analyses, discrimination, and classifications. Nonstationary signals commonly exhibit sparse occupancy in the TF domain. In this dissertation, we incorporate such sparsity to enable robust TF analysis in impaired observing environments. In practice, missing data samples frequently occur during signal reception due to various reasons, e.g., propagation fading, measurement obstruction, removal of impulsive noise or narrowband interference, and intentional undersampling. Missing data samples in the time domain lend themselves to be missing entries in the instantaneous autocorrelation function (IAF) and induce artifacts in the TF representation (TFR). Compared to random missing samples, a more realistic and more challenging problem is the existence of burst missing data samples. Unlike the effects of random missing samples, which cause the artifacts to be uniformly spread over the entire TF domain, the artifacts due to burst missing samples are highly localized around the true instantaneous frequencies, rendering extremely challenging TF analyses for which many existing methods become ineffective. In this dissertation, our objective is to develop novel signal processing techniques that offer effective TF analysis capability in the presence of burst missing samples. We propose two mutually related methods that recover missing entries in the IAF and reconstruct high-fidelity TFRs, which approach full-data results with negligible performance loss. In the first method, an IAF slice corresponding to the time or lag is converted to a Hankel matrix, and its missing entries are recovered via atomic norm minimization. The second method generalizes this approach to reduce the effects of TF crossterms. It considers an IAF patch, which is reformulated as a low-rank block Hankel matrix, and the annihilating filter-based approach is used to interpolate the IAF and recover the missing entries. Both methods are insensitive to signal magnitude differences. Furthermore, we develop a novel machine learning-based approach that offers crossterm-free TFRs with effective autoterm preservation. The superiority and usefulness of the proposed methods are demonstrated using simulated and real-world signals. / Electrical and Computer Engineering

In this thesis we address the multi-armed bandit (MAB) problem with stochastic rewards and correlated arms. Particularly, we investigate the case when the expected rewards are a Lipschitz function of the arm, and the learning to rank problem, as viewed from a MAB perspective. For the former, we derive a problem specific lower bound and propose both an asymptotically optimal algorithm (OSLB) and a (pareto)optimal, algorithm (POSLB). For the latter, we construct the regret lower bound and determine its closed form for some particular settings, as well as propose two asymptotically optimal algorithms PIE and PIE-C. For all algorithms mentioned above, we present performance analysis in the form of theoretical regret guarantees as well as numerical evaluation on artificial datasets as well as real-world datasets, in the case of PIE and PIE-C. / <p>QC 20160223</p>

Machine learning has revolutionized recommendation systems by employing ranking models for personalized item suggestions. However, the complexity of learning-to-rank (LTR) models poses challenges in understanding the underlying reasons contributing to the ranking outcomes. This lack of transparency raises concerns about potential errors, biases, and ethical implications. To address these issues, interpretable LTR models have emerged as a solution. Currently, the state-of-the-art for interpretable LTR models is led by generalized additive models (GAMs). However, ranking GAMs face limitations in terms of computational intensity and handling high-dimensional data. To overcome these drawbacks, post-hoc methods, including local interpretable modelagnostic explanations (LIME), have been proposed as potential alternatives. Nevertheless, a quantitative evaluation comparing post-hoc methods efficacy to state-of-the-art ranking GAMs remains largely unexplored. This study aims to investigate the capabilities and limitations of LIME in an attempt to approximate a complex ranking model using a surrogate model. The proposed methodology for this study is an experimental approach. The neural ranking GAM, trained on two benchmark information retrieval datasets, serves as the ground truth for evaluating LIME’s performance. The study adapts LIME in the context of ranking by translating the problem into a classification task and asses three different sampling strategies against the prevalence of imbalanced data and their influence on the correctness of LIME’s explanations. The findings of this study contribute to understanding the limitations of LIME in the context of ranking. It analyzes the low similarity between the explanations of LIME and those generated by the ranking model, highlighting the need to develop more robust sampling strategies specific to ranking. Additionally, the study emphasizes the importance of developing appropriate evaluation metrics for assessing the quality of explanations in ranking tasks.