Partial wave analysis at BES III harnessing the power of GPUs

Partial wave analysis is a core tool in hadron spectroscopy. With the high statistics data available at facilities such as the Beijing Spectrometer III, this procedure becomes computationally very expensive. We have successfully implemented a framework for performing partial wave analysis on graphics processors. We discuss the implementation, the parallel computing frameworks employed and the performance achieved, with a focus on the recent transition to the OpenCL framework.Comment: 6 pages, 2 figures, prepared for the proceedings of Computing in High Energy Physics (CHEP) 201

Context-aware Synthesis for Video Frame Interpolation

Video frame interpolation algorithms typically estimate optical flow or its variations and then use it to guide the synthesis of an intermediate frame between two consecutive original frames. To handle challenges like occlusion, bidirectional flow between the two input frames is often estimated and used to warp and blend the input frames. However, how to effectively blend the two warped frames still remains a challenging problem. This paper presents a context-aware synthesis approach that warps not only the input frames but also their pixel-wise contextual information and uses them to interpolate a high-quality intermediate frame. Specifically, we first use a pre-trained neural network to extract per-pixel contextual information for input frames. We then employ a state-of-the-art optical flow algorithm to estimate bidirectional flow between them and pre-warp both input frames and their context maps. Finally, unlike common approaches that blend the pre-warped frames, our method feeds them and their context maps to a video frame synthesis neural network to produce the interpolated frame in a context-aware fashion. Our neural network is fully convolutional and is trained end to end. Our experiments show that our method can handle challenging scenarios such as occlusion and large motion and outperforms representative state-of-the-art approaches.Comment: CVPR 2018, http://graphics.cs.pdx.edu/project/ctxsy

Graphene: A Context-Preserving Open Information Extraction System

We introduce Graphene, an Open IE system whose goal is to generate accurate, meaningful and complete propositions that may facilitate a variety of downstream semantic applications. For this purpose, we transform syntactically complex input sentences into clean, compact structures in the form of core facts and accompanying contexts, while identifying the rhetorical relations that hold between them in order to maintain their semantic relationship. In that way, we preserve the context of the relational tuples extracted from a source sentence, generating a novel lightweight semantic representation for Open IE that enhances the expressiveness of the extracted propositions.Comment: 27th International Conference on Computational Linguistics (COLING 2018

Graphene: Semantically-Linked Propositions in Open Information Extraction

We present an Open Information Extraction (IE) approach that uses a two-layered transformation stage consisting of a clausal disembedding layer and a phrasal disembedding layer, together with rhetorical relation identification. In that way, we convert sentences that present a complex linguistic structure into simplified, syntactically sound sentences, from which we can extract propositions that are represented in a two-layered hierarchy in the form of core relational tuples and accompanying contextual information which are semantically linked via rhetorical relations. In a comparative evaluation, we demonstrate that our reference implementation Graphene outperforms state-of-the-art Open IE systems in the construction of correct n-ary predicate-argument structures. Moreover, we show that existing Open IE approaches can benefit from the transformation process of our framework.Comment: 27th International Conference on Computational Linguistics (COLING 2018

Monoclonal Antibodies as Investigative Tools in Onchocerciasis

The development of monoclonal antibodies against parasites can facilitate analysis of host-parasite interactions and can lead to the identification and characterization of antigens that induce protective responses in the immunized host. As diagnostic reagents for human and bovine onchocerciasis, monoclonal antibodies have been used to detect circulating antigens. So far only complex antigens have been used to generate monoclonal antibodies against filarial parasites. The use of such heterogeneous immunogens may result in interference or even in inhibition of the response to the desired parasite antigen(s). Epitopes that are similar or identical to determinants on host molecules or that are components of other infectious agents serve to complicate the generation and selection of monoclonal antibodies against Onchocerca volvulus antigens, and cross-reactivity can be due to monoclonal antibodies reacting with phosphorylcholine, which is present in many preparations of helminth antigens. Because of the host-dependent immunogenicity of filarial agents, reagents should be screened carefully for species specificity by use of a set of different helminth antigens. It is hoped that active collaboration among all investigators engaged in filariasis research will facilitate resolution of such difficultie

Video Frame Interpolation via Adaptive Separable Convolution

Standard video frame interpolation methods first estimate optical flow between input frames and then synthesize an intermediate frame guided by motion. Recent approaches merge these two steps into a single convolution process by convolving input frames with spatially adaptive kernels that account for motion and re-sampling simultaneously. These methods require large kernels to handle large motion, which limits the number of pixels whose kernels can be estimated at once due to the large memory demand. To address this problem, this paper formulates frame interpolation as local separable convolution over input frames using pairs of 1D kernels. Compared to regular 2D kernels, the 1D kernels require significantly fewer parameters to be estimated. Our method develops a deep fully convolutional neural network that takes two input frames and estimates pairs of 1D kernels for all pixels simultaneously. Since our method is able to estimate kernels and synthesizes the whole video frame at once, it allows for the incorporation of perceptual loss to train the neural network to produce visually pleasing frames. This deep neural network is trained end-to-end using widely available video data without any human annotation. Both qualitative and quantitative experiments show that our method provides a practical solution to high-quality video frame interpolation.Comment: ICCV 2017, http://graphics.cs.pdx.edu/project/sepconv

Methodological Background of Decision Rules and Feedback Tools for Outcomes Management in Psychotherapy

Systems to provide feedback regarding treatment progress have been recognized as a promising method for the early identification of patients at risk for treatment failure in outpatient psychotherapy. The feedback systems presented in this article rely on decision rules to contrast the actual treatment progress of an individual patient and his or her expected treatment response (ETR). Approaches to predict the ETR on the basis of patient intake characteristics and previous treatment progress can be classified into two broad classes: Rationally derived decision rules rely on the judgments of experts, who determine the amount of progress that a patient has to achieve for a given treatment session to be considered “on track.” Empirically derived decision rules are based on expected recovery curves derived from statistical models applied to aggregated psychotherapy outcomes data. Examples of each type of decision rule and of feedback systems based on such rules are presented and reviewed