The present study examined the factor structure of the Penn State Worry Questionnaire (PSWQ), the Student Worry Questionnaire-30 (SWQ-30), the Anxious Thoughts Inventory (AnTI), the Ruminative Responses Scale (RRS), and the Rumination-Reflection Questionnaire (RRQ). The present study also examined the measurement invariance between men (n = 186) and women (n = 316) in a university sample. Different models for each measure were identified through a review of the literature, and the models were examined through confirmatory factor analyses. The best-fitting models for each measure were retained for subsequent model modification to improve fit and for invariance testing across gender. The results of the confirmatory factor analyses and subsequent exploratory models provided general support for invariance in the configural models, but only the RRQ displayed measurement, scalar, and latent mean structure invariance. Results of the bootstrapped regression analyses indicated that summated scores derived from the exploratory models demonstrated different relationships between anxiety and depression in men and women. Frequency of worry, metaworry, and general anxiety symptoms significantly contributed to prediction of anxiety in men, whereas metaworry, social worry, lethargy, general anxiety symptoms, and health worries predicted anxiety in women. Social worry, metaworry, recrimination, and reflection (negatively) contributed to prediction of depression in men, whereas social worry, metaworry, lethargy, general anxiety symptoms, and social adequacy concerns predicted depression in women.

It is important to establish the role of age in face-processing since perceived-age is a dimension that may be used to encode faces within memory. While previous research has demonstrated faces can be categorised by age, a question that has not been addressed is how well people are able to do so. This study identifies the extent to which people can categorise faces on the basis of age and also explores the nature of the visual information used for this. The evidence suggests that age-perception is much more complex than has been previously suggested. Using realistic faces as stimuli, it becomes apparent that people are adept at using a wide variety of cues to age. Overall, this demonstrates that we have a sophisticated understanding of the changes that occur through ageing, that we can use with a high degree of subtlety and accuracy. Given the robust nature of information about age and the ability to which it can be used to differentiate faces, age must be influential at encoding. However, the ability to determine and encode a face's physical properties at one point in time can not be a full explanation for the way faces are represented simply because those physical properties do not stay the same over time. The ageing face can therefore be used as a tool to gain greater insight into what facial information is utilised for individual recognition. This was investigated using a recognition paradigm where the individual faces were of different ages to those initially presented and hence displayed different physical properties. The evidence shows that recognition despite age-induced changes is possible; this implies that there is not a one to one mapping between the physical properties at encoding and those that the memory system operates on to accomplish recognition.

The Main Injector Neutrino Oscillation Search (MINOS) is a long baseline experiment that was built for studying the neutrino oscillation phenomena. The MINOS experiment uses high intensity muon neutrino and antineutrino beams created by Neutrinos at the Main Injector facility (NuMI) at the Fermi National Accelerator Laboratory (Fermilab). Neutrino interactions are recorded by two sampling steel-scintillator tracking calorimeters: 0.98 kton Near Detector at Fermilab, IL and 5.4 kton Far Detector at the Soudan Underground Laboratory, MN. These two detectors are functionally identical, which helps to reduce the systematic uncertainties in the muon neutrino and antineutrino disappearance measurements. The Near Detector, located 1.04 km from the neutrino production target, is used to measure the initial beam composition and neutrino energy proximal to the neutrino source. The collected data at the Near Detector is then used to predict energy spectrum in the Far Detector. By comparing this prediction to collected data at the Far Detector, which is 735 km away from the target, it enables a measurement of a set of parameters that govern the neutrino oscillation phenomenon. The flexibility of the NuMI beam configuration and the magnetization of the MINOS detectors facilitate the identification of v[subscript mu] and v̄[subscript mu] charged-current interactions on an event-by-event basis. This enables one to measure neutrino and antineutrino oscillation parameters independently and therefore allows us to test the CPT symmetry in the lepton sector. To enhance the sensitivity of the oscillation parameters measurement, a number of techniques have been implemented. Event classification, shower energy estimation and energy resolution bin fitting, which are described in this dissertation, are three of these techniques. Moreover, the most stringent constraints on oscillation parameters can be achieved by combining multiple data sets. This dissertation reports the measurement of antineutrino oscillation parameters using the complete MINOS accelerator and atmospheric data set of charged-current v̄[subscript mu] events. / text

No description available.

College student and Amazon's Mechanical TURK (MTURK) samples are regularly utilized in trauma research. Recent literature, however, has criticized these samples for not being generalizable to the general U.S. population. Measurement invariance (MI) using confirmatory factor analyses (CFA), is rarely utilized in trauma research, even though the analysis can determine whether groups are invariant across factor structure, factor loadings, item intercepts, and residual error variances on a given measure of PTSD symptom severity. The purpose of this study was to determine whether college student (n = 255) and MTURK (n = 316) samples are invariant on the PCL-5. Model fit indices indicated the 7-factor Hybrid model was the best fitting model, but the 6-factor anhedonia model was the most parsimonious model. Both models demonstrated equivalence in factor structures (configural invariance), factor loadings (metric invariance), intercepts (scalar invariance), and residuals (strict invariance), indicating MTURK and college student samples are similar in regards to PTSD symptom severity. These findings provide evidence that these groups can be combined in future studies to increase sample size for trauma research. Only the Anhedonia factor exhibited mean differences between groups, which may be related to true differences between college students and MTURK survey-takers. Thus, there is further evidence that the findings from trauma studies using these populations are generalizable to each other.

Thesis advisor: Thomas O'Hare / The Behavior and Symptom Identification Scale 24 (BASIS-24) is a psychiatric outcome measure used for inpatient and outpatient populations. This 24-item measure comprises six subscales: depression/functioning; interpersonal relationships; self-harm; emotional lability; psychosis; and substance abuse. Earlier studies examined the reliability and validity of the BASIS-24, but none empirically examined its factor structure across gender. The purpose of this study was therefore to assess the construct validity of the BASIS-24 six-factor model and find evidence of configural, metric, strong and strict factorial invariance across gender. The sample consisted of 1398 psychiatric inpatients that completed BASIS-24 at admission and discharge at 11 facilities nation-wide. Confirmatory factor analyses were used to test measurement invariance of the BASIS-24 six-factor model across males and females. The single confirmatory factor analysis showed the original six-factor model of BASIS-24 provided an acceptable fit to the male sample at admission (RMSEA=0.058, SRMR=0.070, CFI=0.975, NNFI=0.971 and GFI=0.977) and at discharge (RMSEA=0.059, SRMR=0 .078, CFI=0.977, NNFI=0.972, and GFI=0.969). The goodness-of-fit indices for the female group at admission (RMSEA=0.055, SRMR=0.067, CFI=0.980, NNFI=0.976, and GFI=0.983), and at discharge (RMSEA=0.055, SRMR=0.079, CFI=0.98, NNFI=0.977, and GFI=0.971) also revealed that the six factor model fit reasonably well to the data. The goodness-of-fit indices between the unconstrained and constrained models showed that all four multi-group models were equivalent for both male and female samples at admission and discharge in terms of goodness-of-fit examined through the &#916;CFI and that all of them show an acceptable fit to the data. The decrease in CFI was <0.008 for admission sample and <0.003 for discharge sample and both fell below the 0.01 cut-off. This indicates that the configural, metric, as well as the strong and strict factorial invariance of BASIS-24 exist across males and females. The two important contributions of the present study are: 1) BASIS-24 can be used as a reliable and valid symptom measurement tool in assessing psychiatric inpatient populations which can compare quantitative differences in the magnitude of patient symptoms and functioning across genders; 2) the current study provides an example of useful statistical methodology for examining specific questions related to factorial invariance of the BASIS-24 instrument across gender. Implications of social work practice and research are discussed. / Thesis (PhD) — Boston College, 2008. / Submitted to: Boston College. Graduate School of Social Work. / Discipline: Social Work.

In the further development of the string theory, one needs to understand 3 or 4-dimensional volume minimizing subvarieties in 7 or 8-dimensional manifolds. As one example, one would like to understand 4-dimensional volume minimizing cycles in a torus T8. The Cayley calibration form can be used to find all volume minimizing cycles in each homology class of T8. In order to apply the Cayley form to 8-dimensional tori, we need to understand the finite symmetry of the Cayley form, which has a continuous symmetry group Spin(7). We have found one finite symmetry group of order eight generated by three elements. We have also studied the symmetry groups of tori based on the results of H.S.M. Coxeter, and have had a simple description of the four crystallographic groups in O(8). They can be used to classify all finite symmetry groups of the Cayley form.

The face inversion effect has been widely documented as an effect of the uniqueness of face processing. Using a computational model, we show that the face inversion effect is a byproduct of expertise with respect to the face object class. In simulations using HMAX, a hierarchical, shape based model, we show that the magnitude of the inversion effect is a function of the specificity of the representation. Using many, sharply tuned units, an ``expert'' has a large inversion effect. On the other hand, if fewer, broadly tuned units are used, the expertise is lost, and this ``novice'' has a small inversion effect. As the size of the inversion effect is a product of the representation, not the object class, given the right training we can create experts and novices in any object class. Using the same representations as with faces, we create experts and novices for cars. We also measure the feasibility of a view-based model for recognition of rotated objects using HMAX. Using faces, we show that transfer of learning to novel views is possible. Given only one training view, the view-based model can recognize a face at a new orientation via interpolation from the views to which it had been tuned. Although the model can generalize well to upright faces, inverted faces yield poor performance because the features change differently under rotation.

This paper sketches a hypothetical cortical architecture for visual 3D object recognition based on a recent computational model. The view-centered scheme relies on modules for learning from examples, such as Hyperbf-like networks. Such models capture a class of explanations we call Memory-Based Models (MBM) that contains sparse population coding, memory-based recognition, and codebooks of prototypes. Unlike the sigmoidal units of some artificial neural networks, the units of MBMs are consistent with the description of cortical neurons. We describe how an example of MBM may be realized in terms of cortical circuitry and biophysical mechanisms, consistent with psychophysical and physiological data.

What does the spike of a sensory neuron mean? This is a fundamental question in computational neuroscience. Conventional approaches provide an answer based on correlation between spike pattern and the stimulus that caused it. However, these approaches do not satisfactorily explain how the brain, which does not have direct knowledge of the world or the stimuli, can achieve this task. This thesis frames the problem in terms of a task for a simulated agent and provides a solution based on an approach which regards action as necessary for acquiring the meaning of neural spikes. This approach differs from some others in that it proposes a new criterion called the sensory invariance criterion, which can be used to associate meaning to spike patterns in terms of action sequences the agent generates. This criterion forms the basis of the Sensory Invariance Driven Action (SIDA) framework presented in this thesis. This framework is implemented in a reinforcement learning agent and the results indicate that the agent can successfully learn to associate meaning to the sensor activity in terms of specific actions which reflect the properties of the stimulus. Further behavioral experiments on the agent show that this framework allows the agent to learn the meaning of complex (spatiotemporal) spike patterns. The successful learning exhibited by the agent raises hopes that SIDA can be used to build agents with natural semantics.