Cross-Modal Localization via Sparsity

Cross-modal analysis is a natural progression beyond processing of single-source signals. Simultaneous processing of two sources can reveal information that is unavailable when handling the sources separately. Indeed, human and animal perception, computer vision, weather forecasting, and various other scientific and technological fields can benefit from such a paradigm. A particular cross-modal problem is localization: out of the entire data array originating from one source, localize the components that best correlate with the other. For example, auditory and visual data sampled from a scene can be used to localize visual events associated with the sound track. In this paper we present a rigorous analysis of fundamental problems associated with the localization task. We then develop an approach that leads efficiently to a unique, high definition localization outcome. Our method is based on canonical correlation analysis (CCA), where inherent ill-posedness is removed by exploiting sparsity of cross-modal events. We apply our approach to localization of audio-visual events. The proposed algorithm grasps such dynamic audio-visual events with high spatial resolution. The algorithm effectively detects the pixels that are associated with sound, while filtering out other dynamic pixels, overcoming substantial visual distractions and audio noise. The algorithm is simple and efficient thanks to its reliance on linear programming, while being free of user-defined parameters     

Space-dependent color gamut mapping: a variational approach.

Gamut mapping deals with the need to adjust a color image to fit into the constrained color gamut of a given rendering medium. A typical use for this tool is the reproduction of a color image prior to its printing, such that it exploits best the given printer/medium color gamut, namely the colors the printer can produce on the given medium. Most of the classical gamut mapping methods involve a pixel-by-pixel mapping and ignore the spatial color configuration. Recently proposed spatial-dependent approaches for gamut mapping are either based on heuristic assumptions or involve a high computational cost. In this paper, we present a new variational approach for space-dependent gamut mapping. Our treatment starts with the presentation of a new measure for the problem, closely related to a recent measure proposed for Retinex. We also link our method to recent measures that attempt to couple spectral and spatial perceptual measures. It is shown that the gamut mapping problem leads to a quadratic programming formulation, guaranteed to have a unique solution if the gamut of the target device is convex. An efficient numerical solution is proposed with promising results.     

Representation of change in controlled medical terminologies

Computer-based systems that support health care require large controlled terminologies to manage names and meanings of data elements. These terminologies are not static, because change in health care is inevitable. To share data and applications in health care, we need standards not only for terminologies and concept representation, but also for representing change. To develop a principled approach to managing change, we analyze the requirements of controlled medical terminologies and consider features that frame knowledge-representation systems have to offer. Based on our analysis, we present a concept model, a set of change operations, and a change-documentation model that may be appropriate for controlled terminologies in health care. We are currently implementing our modeling approach within a computational architecture.     

Behavioral Neuroscience in the Era of Genomics: Tools and Lessons for Analyzing High-Dimensional Datasets

Behavioral neuroscience underwent a technology-driven revolution with the emergence of machine-vision and machine-learning technologies. These technological advances facilitated the generation of high-resolution, high-throughput capture and analysis of complex behaviors. Therefore, behavioral neuroscience is becoming a data-rich field. While behavioral researchers use advanced computational tools to analyze the resulting datasets, the search for robust and standardized analysis tools is still ongoing. At the same time, the field of genomics exploded with a plethora of technologies which enabled the generation of massive datasets. This growth of genomics data drove the emergence of powerful computational approaches to analyze these data. Here, we discuss the composition of a large behavioral dataset, and the differences and similarities between behavioral and genomics data. We then give examples of genomics-related tools that might be of use for behavioral analysis and discuss concepts that might emerge when considering the two fields together.     

A probabilistic alternative to regression suites

Automated regression suites are essential in developing large applications, while maintaining reasonable quality and timetables. The main argument against the automation of regression suites, in addition to the cost of creation and maintenance, is the observation that if you run the same test many times, it becomes increasingly less likely to find bugs. To alleviate such problems, a new regression suite practice, using random test generators to create regression suites on-the-fly, is becoming more common. In this practice, instead of maintaining tests, we generate test suites on-the-fly by choosing several specifications and generating a number of tests from each specification.     

Role of perfectionism and personality disorder features in response to brief treatment for depression.

Using data from the Treatment of Depression Collaborative Research Program (TDCRP), the authors compared the role of patients' perfectionism and features of personality disorder (PD) in the outcome of brief treatment for depression. Data were extracted as to patients' intake levels of symptoms; perfectionism; and PD features, measured as continuous variables, as well as their symptoms at termination; their contribution to the therapeutic alliance; and their satisfaction with social relations. Poorer therapeutic outcome was demonstrated for patients with elevated levels of perfectionism and odd-eccentric and depressive PD features. Patients' contribution to therapeutic alliance and satisfaction with social relations were predicted by perfectionism but not by PD features. Results highlight the central role played by patients' personality in the course of brief treatment for depression. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Depressive symptoms erode self-esteem in severe mental illness: A three-wave, cross-lagged study.

Vulnerability, scar, and reciprocal-relations models of depressive symptoms and self-esteem were compared among people with severe mental illness (SMI; N=260) participating in a partnership-based intervention study. Assessments were conducted at baseline, midway through the intervention (after 4 months), and at termination (after 9 months). Cross-lagged, structural equation modeling analyses revealed that participants' baseline depressive symptoms predicted a decrease in self-esteem in the first 4 months but not in the subsequent 5 months of participation. Exploratory regression analyses indicated that improved social functioning buffered this deleterious effect of depressive symptoms. These findings, which are consistent with the scar model, highlight the fragile nature of the self and the importance of social functioning in recovery from SMI. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Mixed anaclitic-introjective psychopathology in treatment-resistant inpatients undergoing psychoanalytic psychotherapy.

Utilizing data from the Riggs-Yale Project, 45 male and 45 female 18-29-year-old treatment-resistant inpatients undergoing intensive psychoanalytically oriented treatment were studied. Twenty-seven mixed-type anaclitic-introjective inpatients were compared with 29 "pure" anaclitic and 34 "pure" introjective inpatients. At intake, mixed-type inpatients were more clinically impaired (i.e., were more symptomatic, cognitively impaired, and thought disordered) and more vulnerable (i.e., less accurate object representations and more frequently used maladaptive defense mechanisms) in comparison with clearly defined anaclitic and introjective patients. Mixed-type patients, however, improved significantly more in the course of psychoanalytically oriented treatment, in terms of clinical functioning (i.e., symptoms, cognitive functioning) and psychological vulnerability (i.e., utilization of more adaptive defense mechanisms). (PsycINFO Database Record (c) 2010 APA, all rights reserved)