University of Colorado Colorado Springs

About: University of Colorado Colorado Springs is a education organization based out in Colorado Springs, Colorado, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 6664 authors who have published 10872 publications receiving 323416 citations. The organization is also known as: UCCS & University of Colorado at Colorado Springs.

The importance of isometric maximum strength and peak rate-of-force development in sprint cycling.

Abstract: This study was designed to investigate the relationship of whole-body maximum strength to variables potentially associated with track sprint-cycling success. These variables included body composition, power measures, coach's rank, and sprint-cycling times. The study was carried out in 2 parts. The first part (n = 30) served as a pilot for the second part (n = 20). Subjects for both parts ranged from international-caliber sprint cyclists to local-level cyclists. Maximum strength was measured using an isometric midthigh pull (IPF). Explosive strength was measured as the peak rate-of-force development (IPRFD) from the isometric force-time curve. Peak power was estimated from countermovement (CMJPP) and static vertical jumps (SJPP) and measured by modified Wingate tests. Athletes were ranked by the U.S. national cycling coach (part 1). Sprint times (from a standing start) were measured using timing gates placed at 25, 82.5, 165, 247.5, and 330 m of an outdoor velodrome (part 2). Maximum strength (both absolute and body-mass corrected) and explosive strength were shown to be strongly correlated with jump and Wingate power. Additionally, maximum strength was strongly correlated with both coach's rank (parts 1 and 2) and sprint cycling times (part 2). The results suggest that larger, stronger sprint cyclists have an advantage in producing power and are generally faster sprint cyclists.

Summarizing Microblogs Automatically

Abstract: In this paper, we focus on a recent Web trend called microblogging, and in particular a site called Twitter. The content of such a site is an extraordinarily large number of small textual messages, posted by millions of users, at random or in response to perceived events or situations. We have developed an algorithm that takes a trending phrase or any phrase specified by a user, collects a large number of posts containing the phrase, and provides an automatically created summary of the posts related to the term. We present examples of summaries we produce along with initial evaluation.

Orientational coupling amplification in ferroelectric nematic colloids.

Abstract: We investigated the physical properties of low concentration ferroelectric nematic colloids, using calorimetry, optical methods, infrared spectroscopy, and capacitance studies. The resulting homogeneous colloids possess a significantly amplified nematic orientational coupling. We find that the nematic orientation coupling increases by approximately 10% for particle concentrations of 0.2%. A manifestation of the increased orientational order is that the clearing temperature of a nematic colloid increases by up to 40 degrees C compared to the pure liquid crystal host. A theoretical model is proposed in which the ferroelectric particles induce local dipoles whose effective interaction is proportional to the square of the orientational order parameter.

Towards Open World Recognition

Abstract: With the of advent rich classification models and high computational power visual recognition systems have found many operational applications. Recognition in the real world poses multiple challenges that are not apparent in controlled lab environments. The datasets are dynamic and novel categories must be continuously detected and then added. At prediction time, a trained system has to deal with myriad unseen categories. Operational systems require minimum down time, even to learn. To handle these operational issues, we present the problem of Open World recognition and formally define it. We prove that thresholding sums of monotonically decreasing functions of distances in linearly transformed feature space can balance "open space risk" and empirical risk. Our theory extends existing algorithms for open world recognition. We present a protocol for evaluation of open world recognition systems. We present the Nearest Non-Outlier (NNO) algorithm which evolves model efficiently, adding object categories incrementally while detecting outliers and managing open space risk. We perform experiments on the ImageNet dataset with 1.2M+ images to validate the effectiveness of our method on large scale visual recognition tasks. NNO consistently yields superior results on open world recognition.

AUGEM: automatically generate high performance dense linear algebra kernels on x86 CPUs

Abstract: Basic Liner algebra subprograms (BLAS) is a fundamental library in scientific computing. In this paper, we present a template-based optimization framework, AUGEM, which can automatically generate fully optimized assembly code for several dense linear algebra (DLA) kernels, such as GEMM, GEMV, AXPY and DOT, on varying multi-core CPUs without requiring any manual interference from developers. In particular, based on domain-specific knowledge about algorithms of the DLA kernels, we use a collection of parameterized code templates to formulate a number of commonly occurring instruction sequences within the optimized low-level C code of these DLA kernels. Then, our framework uses a specialized low-level C optimizer to identify instruction sequences that match the pre-defined code templates and thereby translates them into extremely efficient SSE/AVX instructions. The DLA kernels generated by our template-based approach surpass the implementations of Intel MKL and AMD ACML BLAS libraries, on both Intel Sandy Bridge and AMD Piledriver processors.