A Virtual SIMD Machine Approach for Abstracting Heterogeneous Multicore Processors

The heterogeneous design of multi-core processors, such as the Cell processor, introduced new challenges in porting high-level languages. Our project is developing tools that hide the underlying details of the Cell processor and eases parallel programming. We present a Virtual SIMD machine (VSM) paradigm that can be used to parallelize array expression automatically. The novelty is the use of a virtual SIMD machine model to completely hide the underlying details required for programming the Cell processor. The VSM paradigm can also be used to develop an automatic parallelizing compiler for the Cell Broadband Engine (Cell BE). In this paper we give an overview of the VSM interface and present preliminary results that show the performance of our VSM and its behavior on multiple accelerator cores using basic arrays operations

Is it Time to Swish? Comparing Deep Learning Activation Functions Across NLP tasks

Activation functions play a crucial role in neural networks because they are the nonlinearities which have been attributed to the success story of deep learning. One of the currently most popular activation functions is ReLU, but several competitors have recently been proposed or 'discovered', including LReLU functions and swish. While most works compare newly proposed activation functions on few tasks (usually from image classification) and against few competitors (usually ReLU), we perform the first large-scale comparison of 21 activation functions across eight different NLP tasks. We find that a largely unknown activation function performs most stably across all tasks, the so-called penalized tanh function. We also show that it can successfully replace the sigmoid and tanh gates in LSTM cells, leading to a 2 percentage point (pp) improvement over the standard choices on a challenging NLP task.Comment: Published at EMNLP 201

The Large Contraction Principle and Existence of Periodic Solutions for Infinite Delay Volterra Difference Equations

In this article, we establish sufficient conditions for the existence of periodic solutions of a nonlinear infinite delay Volterra difference equation. (See paper for equation.) We employ a Krasnosel’skii type fixed point theorem, originally proved by Burton. The primary sufficient condition is not verifiable in terms of the parameters of the difference equation, and so we provide three applications in which the primary sufficient condition is verified

Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis

Automatically summarizing radiology reports into a concise impression can reduce the manual burden of clinicians and improve the consistency of reporting. Previous work aimed to enhance content selection and factuality through guided abstractive summarization. However, two key issues persist. First, current methods heavily rely on domain-specific resources to extract the guidance signal, limiting their transferability to domains and languages where those resources are unavailable. Second, while automatic metrics like ROUGE show progress, we lack a good understanding of the errors and failure modes in this task. To bridge these gaps, we first propose a domain-agnostic guidance signal in form of variable-length extractive summaries. Our empirical results on two English benchmarks demonstrate that this guidance signal improves upon unguided summarization while being competitive with domain-specific methods. Additionally, we run an expert evaluation of four systems according to a taxonomy of 11 fine-grained errors. We find that the most pressing differences between automatic summaries and those of radiologists relate to content selection including omissions (up to 52%) and additions (up to 57%). We hypothesize that latent reporting factors and corpus-level inconsistencies may limit models to reliably learn content selection from the available data, presenting promising directions for future work.Comment: Accepted at INLG202

Gradient-based dimension reduction of multivariate vector-valued functions

Multivariate functions encountered in high-dimensional uncertainty quantification problems often vary most strongly along a few dominant directions in the input parameter space. We propose a gradient-based method for detecting these directions and using them to construct ridge approximations of such functions, in the case where the functions are vector-valued (e.g., taking values in $\mathbb{R}^n$). The methodology consists of minimizing an upper bound on the approximation error, obtained by subspace Poincar\'e inequalities. We provide a thorough mathematical analysis in the case where the parameter space is equipped with a Gaussian probability measure. The resulting method generalizes the notion of active subspaces associated with scalar-valued functions. A numerical illustration shows that using gradients of the function yields effective dimension reduction. We also show how the choice of norm on the codomain of the function has an impact on the function's low-dimensional approximation

Expression of interleukin-1 (IL-1) ligands system in the most common endometriosis-associated ovarian cancer subtypes

<p>Abstract</p> <p>Objectives</p> <p>Endometrioid carcinoma of the ovary is one of the most types of epithelial ovarian cancer associated to endometrioisis. Endometrioid tumors as well as endometriotic implants are characterized by the presence of epithelial cells, stromal cells, or a combination of booth, that resemble the endometrial cells, suggesting a possible endometrial origin of these tumors. Pro-inflammatory cytokines, including interleukin-1 (IL-1) have been reported to be involved in both endometriosis and ovarian carcinogenesis. The major objective of this study was to determine the level expression of IL-1 ligands system (IL-1α, IL-1β and IL-1RA) in the most common subtypes of ovarian cancer cells compared to endometrial cells.</p> <p>Methods</p> <p>We used primary endometrial cells, endometrial cell line RL-952 and different subtypes of epithelial ovarian cancer cell lines including TOV-112D (endometrioid), TOV-21G (clear cell) and OV-90 (serous). Immunofluorescence and real-time PCR analysis were used respectively for detecting IL-1 ligands at the levels of cell-associated protein and mRNA. Soluble IL-1 ligands were analyzed by ELISA.</p> <p>Results</p> <p>We demonstrated that IL-1 ligands were expressed by all endometriosis-associated ovarian cancer subtypes and endometrial cells. In contrast to other cancer ovarian cells, endometrioid cells exhibit a specific decrease of cell-associated IL-1RA expression and its soluble secretion.</p> <p>Conclusion</p> <p>Endometrioid ovarian cancer exhibits an alteration in the expression of IL-1RA, a key protector against tumorogenic effects of IL-1. This alteration evokes the same alteration observed in endometriotic cells in previous studies. This suggests a possible link between the endometrium, the tissue ectopic endometriosis and endometrioid ovarian cancer.</p