A fingerprint matching algorithm with a novel set of matching parameters based on core points and triangular descriptors is proposed to discover rarity in fingerprints. The algorithm uses a mathematical and statistical approach to discover rare features in fingerprints which provides scientific validation for both ten-print and latent fingerprint evidence. A feature is considered rare if it is statistically uncommon; that is, the rare feature should be unique among N (N>100) randomly sampled prints. A rare feature in a fingerprint has higher discriminatory power when it is identified in a print (latent or otherwise). In the case of latent fingerprint matching, the enhanced discriminatory power from the rare features can help in delivering a confident court judgment. In addition to mining the rare features, a parallel algorithm for fingerprint matching on GPUs is also proposed to reduce the run-time of fingerprint matching on larger databases. Results show that 1) matching algorithm is useful in eliminating false matches. 2) each of the 30 fingerprints randomly selected to mine rare features have a small set of highly distinctive statistically rare features some of whose occurrence is one in 1000 fingerprints. 3) the parallel algorithm implemented on GPUs for larger databases is around 40 times faster than the sequential algorithm. / Master of Science

Individuals with Social Anxiety Disorder (SAD) are typically perceived by others as shy, submissive, or risk-averse. However, recent work has identified an understudied subtype within SAD characterized by high levels of anger and high-risk or novelty-seeking impulsive behaviors. Interestingly, this subtype bears conceptual similarities with prior accounts of vulnerable narcissism. For example, both are associated with concerns regarding self-presentation and how they are perceived by others. The angry-impulsive subtype and vulnerable narcissism may further share similar etiologic origins and similar associations with self-reported anger, impulsivity, and anxiety-related characteristics. However, despite these key similarities no prior work has systematically evaluated the common and potentially distinguishing factors within and between these conceptually similar but diagnostically distinct groups. For example, cognitive features such as fear of negative evaluation and interpersonal rivalry could be distinguishing features of SAD and vulnerable narcissism, although the utility of these distinguishing features to clarify the differential diagnosis remains unknown. Accordingly, the purpose of this study was to utilize a person-centered analytic approach (latent profile analysis; LPA) to empirically establish whether vulnerable narcissistic traits exist within high anger, risk-prone individuals who are also socially anxious, or alternatively whether specific features of each disorder can be used to disambiguate them empirically. Results of this work supported the existence of the angry impulsive socially anxious subtype and supported a relationship between that group and vulnerable narcissistic traits. These findings have implications for treatment selection among affected individuals and may further clarify why prior work evaluating interventions for adults with SAD and angry impulsive features has been met with only limited success. / M.S. / Individuals with Social Anxiety Disorder (SAD) are typically perceived by others as shy, submissive, or risk-averse. However, recent work has identified an understudied subtype within SAD characterized by high levels of anger and high-risk or novelty-seeking impulsive behaviors. Interestingly, this subtype has conceptual similarities with descriptions of vulnerable narcissism. For example, both are associated with concerns regarding self-presentation and how they are perceived by others. The angry-impulsive subtype and vulnerable narcissism may further share similar origins and similar associations with self-reported anger, impulsivity, and anxiety-related characteristics. However, despite these key similarities no prior work has evaluated the common and potentially distinctive factors within and between these distinct groups. For example, features such as fear of negative evaluation and interpersonal rivalry could be distinguishing features of SAD and vulnerable narcissism, although the utility of these differentiating features to clarify the differential diagnosis is unknown. Accordingly, the purpose of this study was to utilize a person-centered analytic approach (latent profile analysis; LPA) to establish whether vulnerable narcissistic traits exist within high anger, risk-prone individuals who are also socially anxious, or whether specific features of each disorder can be used to differentiate them. Results of this work supported the existence of the angry impulsive socially anxious subtype and supported a relationship between that group and vulnerable narcissistic traits. These findings have implications for treatment selection among affected individuals and may further clarify why prior work evaluating interventions for adults with SAD and angry impulsive features has been met with only limited success.

Artificial music generation is a rapidly developing field focused on the complex task of creating neural networks that can produce realistic-sounding music. Generating music is very difficult; components like long and short term structure present time complexity, which can be difficult for neural networks to capture. Additionally, the acoustics of musical features like harmonies and chords, as well as timbre and instrumentation require complex representations for a network to accurately generate them. Various techniques for both music representation and network architecture have been used in the past decade to address these challenges in music generation. The focus of this thesis extends beyond generating music to the challenge of controlling and/or conditioning that generation. Conditional generation involves an additional piece or pieces of information which are input to the generator and constrain aspects of the results. Conditioning can be used to specify a tempo for the generated song, increase the density of notes, or even change the genre. Latent walking is one of the most popular techniques in conditional image generation, but its effectiveness on music-domain generation is largely unexplored. This paper focuses on latent walking techniques for conditioning the music generation network MuseGAN and examines the impact of this conditioning on the generated music. / Master of Science / Artificial music generation is a rapidly developing field focused on the complex task of creating neural networks that can produce realistic-sounding music. Beyond simply generating music lies the challenge of controlling or conditioning that generation. Conditional generation can be used to specify a tempo for the generated song, increase the density of notes, or even change the genre. Latent walking is one of the most popular techniques in conditional image generation, but its effectiveness on music-domain generation is largely unexplored, especially for generative adversarial networks (GANs). This paper focuses on latent walking techniques for conditioning the music generation network MuseGAN and examines the impact and effectiveness of this conditioning on the generated music.

In order to deliver statistical and qualitative backing to latent fingerprint evidence, algorithms are proposed (1) to perform fingerprint matching to aid in quality assessment, and (2) to discover statistically rare features or patterns in fingerprints. These features would help establish an objective minimum-quality baseline for latent prints as well as aid in the latent examination process in making a matching comparison. The proposed methodologies use minutiae triplet-based features in a hierarchical fashion, where not only minutia points are used, but ridge information is used to help establish relations between minutiae. Results show (1) that our triplet-based descriptor is useful in eliminating false matches in the matching algorithm, and (2) that a set of distinctive features can be found that have sufficient discriminatory power to aid in quality assessment. / Master of Science

No / Almost all studies in health research control or investigate socioeconomic position (SEP) as exposure or confounder. Different measures of SEP capture different aspects of the underlying construct, so efficient methodologies to combine them are needed. SEP and ethnicity are strongly associated, however not all measures of SEP may be appropriate for all ethnic groups. Methods We used latent class analysis (LCA) to define subgroups of women with similar SEP profiles using 19 measures of SEP. Data from 11,326 women were used, from eight different ethnic groups but with the majority from White British (40%) or Pakistani (45%) s, who were recruited during pregnancy to the Born in Bradford birth cohort study. Results Five distinct SEP subclasses were identified in the LCA: (i) "Least socioeconomically deprived and most educated" (20%); (ii) "Employed and not materially deprived" (19%); (iii) "Employed and no access to money" (16%); (iv) "Benefits and not materially deprived" (29%) and (v) "Most economically deprived" (16%). Based on the magnitude of the point estimates, the strongest associations were that compared to White British women, Pakistani and Bangladeshi women were more likely to belong to groups: (iv) "benefits and not materially deprived" (relative risk ratio (95% CI): 5.24 (4.44, 6.19) and 3.44 (2.37, 5.00), respectively) or (v) most deprived group (2.36 (1.96, 2.84) and 3.35 (2.21, 5.06) respectively) compared to the least deprived class. White Other women were more than twice as likely to be in the (iv) "benefits and not materially deprived group" compared to White British women and all ethnic groups, other than the Mixed group, were less likely to be in the (iii) "employed and not materially deprived" group than White British women. Conclusions LCA allows different aspects of an individual’s SEP to be considered in one multidimensional indicator, which can then be integrated in epidemiological analyses. Ethnicity is strongly associated with these identified subgroups. Findings from this study suggest a careful use of SEP measures in health research, especially when looking at different ethnic groups. Further replication of these findings is needed in other populations.

Half of the students who begin college do not complete a degree or certificate. The odds of completing a degree are decreased if a student has a low socio-economic status (SES), is the first in a family to attend college (first-generation), attends multiple institutions, stops out multiple times, reduces credit loads over time, performs poorly in major-specific coursework, has competing family obligations, and experiences financial difficulties. Stopping out of college does not always indicate that a student is no longer interested in pursuing an education; it can be an indication of a barrier or several barriers faced. Institutions can benefit themselves and students by utilizing person-centered statistical methods to re-engage students they have lost, particularly those near the end of their degree plan. Using demographic, academic, and financial variables, this study applied latent class analysis (LCA) to explore subgroups of seniors who have stopped out of a public four-year Tier One research intuition before graduating with a four-year degree. The findings indicated a six-class model was the best fitting model. Similar to previous research, academic and financial variables were key determinants of the latent classes. This paper demonstrates how the results of an LCA can assist institutions in the decisions around intervention strategies and resource allocations.

Objectives: Adherence to psychotropic medication is poor among individuals with bipolar disorder (BD). To understand treatment experiences and associated adherence among these individuals, we developed a novel construct of Clinical Net Benefit (CNB) using psychiatric symptoms, adverse effects and overall functioning assessments. We tested whether adherence differed across classes of CNB, whether individuals transitioned between classes over time, and whether these transitions were differentially associated with adherence. Methods: Data come from individuals aged 18+ during five years of the Systematic Treatment Enhancement Program for Bipolar Disorder (STEP-BD). Latent class analysis identified groups of CNB. Latent transition analysis determined probabilities of transitioning between classes over time. Adherence was defined as taking 75%+ of medications as prescribed. Associations between CNB and adherence were tested using multiple logistic regression adjusting for sociodemographic characteristics. Results: Five classes of CNB were identified during the first two years (high, moderately high, moderate, moderately low, low), and four classes (removing moderately high) during the last three years. Adherence did not differ across classes or time points. Medication regimens differed by class; those with higher CNB taking fewer medications had lower odds of adherence while those with lower CNB taking more medications had higher odds of adherence compared with monotherapy. Probability of transitioning from higher to lower CNB, and lower to higher CNB was greatest over time. Conclusions: CNB is heterogeneous in individuals treated for BD, and movement between classes is not uncommon. Understanding why individuals adhere despite suboptimal CNB may provide novel insights into aspects influencing adherence.

La réfrigération solaire est une alternative à la production de froid à partir de machines à compression mécanique de vapeur dont l’alimentation est électrique. Parmi les technologies envisageables, le couplage d’une machine de réfrigération thermoacoustique avec un concentrateur solaire et un stockage frigorifique par chaleur latente apparait comme une option intéressante. Cette thèse introduit la problématique du sujet et présente les différentes technologies envisageables pour la conception d’un réfrigérateur thermoacoustique solaire. Ensuite, pour répondre au problème, le prototype expérimental qui a été conçu et fabriqué est présenté. Une méthode de modélisation transitoire au niveau système du prototype est proposée. Enfin les résultats obtenus par les simulations dynamiques sont discutés à travers l’analyse du comportement transitoire de l’ensemble du procédé et des performances associées. / Solar refrigeration is an alternative to electrically driven vapor compression cycle for refrigeration. Among the solar refrigeration technologies, the coupling of a heat driven thermoacoustic refrigerator with a solar concentrator and a cold latent energy storage system seems to be a promising technology. This thesis introduces the issue of the subject and analyzes the different available technologies to design a solar driven thermoacoustic refrigerator. Then, to address the problem, the prototype that has been designed and built, is presented. A lumped model is introduced to describe the transient behavior of the prototype. Finally, simulation results are presented and discussed in terms of dynamic behavior and performance analysis.

Background: Cannabis use (CU) and disorders (CUD) are highly prevalent among adolescents and young adults. We aim to identify clinically meaningful latent classes of users of cannabis and other illegal substances with distinct problem profiles. Methods: N= 3021 community subjects aged 14–24 at baseline were followed-up over a period ranging up to 10 years. Substance use (SU) and disorders (SUD) were assessed with the DSM-IV/M-CIDI. Latent class analysis (LCA) was conducted with a subset of N= 1089 subjects with repeated illegal SU. The variables entered in the LCA were CU-related problems, CUD, other SUD, and other mental disorders. Results: Four latent classes were identified: “Unproblematic CU” (class 1: 59.2%), “Primary alcohol use disorders” (class 2: 14.4%), “Delinquent cannabis/alcohol DSM-IV-abuse” (class 3: 17.9%), “CUD with multiple problems” (class 4: 8.5%). Range and level of CU-related problems were highest in classes 3 and 4. Comorbidity with other mental disorders was highest in classes 2 and 4. The probability of alcohol disorders and unmet treatment needs was considerable in classes 2–4. Conclusion: While the majority of subjects with repeated illegal SU did not experience notable problems over the 10-year period, a large minority (40.8%) experienced problematic outcomes, distinguished by clinically meaningful profiles. The data underline the need for specifically tailored interventions for adolescents with problematic CU and highlight the potentially important role of alcohol and other mental disorders.

Information from the TRMM-CSH and TRMM-2A12 datasets is used to examine the four-dimensional latent heating (LH) structures over the Asian monsoon region between 1998 and 2006. High sea surface temperatures, ocean-land contrasts and complex terrain produce large precipitation and atmospheric heating rates whose spatial and temporal characteristics are relatively undocumented. Analyses identify interannual and intraseasonal LH variations, with a large fraction of the interannual variability induced by internal intraseasonal variability. Also, the analyses identify a spatial dipole of LH anomalies between the equatorial Indian Ocean and the Bay of Bengal regions occurring during the summer active and suppressed phases of the monsoon intraseasonal oscillation. Comparisons made between the TRMM-CSH and TRMM-2A12 datasets indicate significant differences in the shape of the vertical profile of LH. Comparison of TRMM-LH retrievals with sounding budget observations made during the South China Sea Monsoon experiment shows a high correspondence in the timing of positive LH episodes during rainy periods. Negative values of LH, associated with radiative cooling and with higher troposphere cooling from non-precipitating clouds, are not captured by any of the TRMM datasets. In summary, LH algorithms based on satellite information are capable of representing the spatial and temporal characteristics of the vertically integrated heating in the Asian monsoon region. The TRMM-CSH presents better performance than TRMM-2A12. However, the vertical distribution of atmospheric heating is not captured accurately throughout all different convective phases. It is suggested that satellite derived radiative heating/cooling products are needed to supplement the LH products in order to give an overall better depiction of atmospheric heating.