Elad Liebman

Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia

High‐throughput omics have proven invaluable in studying human disease, and yet day‐to‐day clinical practice still relies on physiological, non‐omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, we studied a mouse model of diet‐induced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. We find that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue‐specific manner—treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non‐restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a diseases omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.

A Phylogenetic Approach to Music Performance Analysis

Abstract This paper presents a novel algorithmic approach to music performance analysis. Previous attempts to use algorithmic tools in this field focused typically on tempo and dynamics alone. We base our analysis on ten different performance categories (such as bowing, vibrato and durations). We adapt phylogenetic analysis tools to resolve the inherent inconsistencies between these categories, and describe the relationships between performances. Taking samples from 29 different performances of two pieces from Bachs sonatas for solo violin, we construct a ‘phylogenetic’ tree, representing the relationship between those performances. The tree supports several interesting relations previously conjectured by the musicology community, such as the importance of date of birth and recording period in determining interpretative style. Our work also highlights some unexpected inter-connections between performers, and challenges previous assumptions regarding the significance of educational background and affiliation to the historically informed performance (HIP) style.

Bin-Based Estimation of the Amount of Effort for Embedded Software Development Projects with Support Vector Machines

In this paper we study a bin-based estimation method of the amount of effort associated with code development. We investigate the following 3 variants to define the bins: (1) the same amount of data in a bin (SVM same #), (2) the same range for each bin (SVM same range) and (3) the bins made by Ward’s method (SVM Ward). We carry out evaluation experiments to compare the accuracy of the proposed SVM models with that of the \(\varepsilon \)-SVR using Welch’s t-test and effect sizes. These results indicate that the methods SVM same # (1) and SVM Ward (3) can improve the accuracy of estimating the amount of effort in terms of the mean percentage of predictions that fall within 25 % of the actual value.

Fast and Precise Black and White Ball Detection for RoboCup Soccer

In 2016, UT Austin Villa claimed the Standard Platform League’s second place position at the RoboCup International Robot Soccer Competition in Leipzig, Germany as well as first place at both the RoboCup US Open in Brunswick, USA and the World RoboCup Conference in Beijing, China. This paper describes some of the key contributions that led to the team’s victories with a primary focus on our techniques for identifying and tracking black and white soccer balls. UT Austin Villa’s ball detection system was overhauled in order to transition from the league’s bright orange ball, used every year of the competition prior to 2016, to the truncated icosahedral pattern commonly associated with soccer balls.

UT Austin Villa: Project-Driven Research in AI and Robotics

UT Austin Villa is a robot soccer team that has competed in the annual RoboCup soccer competitions since 2003. The team has won several championships and has inspired research contributions spanning many topics in robotics and artificial intelligence. This article summarizes some of these research contributions and provides a snapshot into the current development status of the team. Educational uses of the teams code bases are also presented.

Representative Selection in Nonmetric Datasets

This study considers the problem of representative selection: choosing a subset of data points from a dataset that best represents its overall set of elements. This subset needs to inherently reflect the type of information contained in the entire set, while minimizing redundancy. For such purposes, clustering might seem like a natural approach. However, existing clustering methods are not ideally suited for representative selection, especially when dealing with nonmetric data, in which only a pairwise similarity measure exists. In this article we propose δ-medoids, a novel approach that can be viewed as an extension of the k-medoids algorithm and is specifically suited for sample representative selection from nonmetric data. We empirically validate δ-medoids in two domains: music analysis and motion analysis. We also show some theoretical bounds on the performance of δ-medoids and the hardness of representative selection in general.

Decision mechanisms underlying mood-congruent emotional classification

ABSTRACT There is great interest in understanding whether and how mood influences affective processing. Results in the literature have been mixed: some studies show mood-congruent processing but others do not. One limitation of previous work is that decision components for affective processing and responses biases are not dissociated. The present study explored the roles of affective processing and response biases using a drift-diffusion model (DDM) of simple choice. In two experiments, participants decided if words were emotionally positive or negative while listening to music that induced positive or negative mood. The behavioural results showed weak, inconsistent mood-congruency effects. In contrast, the DDM showed consistent effects that were selectively driven by an a-priori bias in response expectation, suggesting that music-induced mood influences expectations about the emotionality of upcoming stimuli, but not the emotionality of the stimuli themselves. Implications for future studies of emotional classification and mood are subsequently discussed.

Adaptation of Surrogate Tasks for Bipedal Walk Optimization

In many learning and optimization tasks, the sample cost of performing the task is prohibitively expensive or time consuming. Learning is instead often performed on a less expensive task that is believed to be a reasonable approximation or surrogate of the actual target task. This paper focuses on the challenging open problem of performing learning on an approximation of a true target task, while simultaneously adapting the surrogate task used for learning to be a better representation of the true target task. Our work is evaluated in the RoboCup 3D simulation environment where we attempt to learn configuration parameters for an omnidirectional walk engine used by humanoid soccer playing robots.