Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Recognizing arbitrary multi-character text in unconstrained natural photographs is a hard problem. In this paper, we address an equally hard sub-problem in this domain viz. recognizing arbitrary multi-digit numbers from Street View imagery. Traditional approaches to solve this problem typically separate out the localization, segmentation, and recognition steps. In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels. We employ the DistBelief implementation of deep neural networks in order to train large, distributed neural networks on high quality images. We find that the performance of this approach increases with the depth of the convolutional network, with the best performance occurring in the deepest architecture we trained, with eleven hidden layers. We evaluate this approach on the publicly available SVHN dataset and achieve over $96\%$ accuracy in recognizing complete street numbers. We show that on a per-digit recognition task, we improve upon the state-of-the-art, achieving $97.84\%$ accuracy. We also evaluate this approach on an even more challenging dataset generated from Street View imagery containing several tens of millions of street number annotations and achieve over $90\%$ accuracy. To further explore the applicability of the proposed system to broader text recognition tasks, we apply it to synthetic distorted text from reCAPTCHA. reCAPTCHA is one of the most secure reverse turing tests that uses distorted text to distinguish humans from bots. We report a $99.8\%$ accuracy on the hardest category of reCAPTCHA. Our evaluations on both tasks indicate that at specific operating thresholds, the performance of the proposed system is comparable to, and in some cases exceeds, that of human operators

TensorFlow Doing HPC

TensorFlow is a popular emerging open-source programming framework supporting the execution of distributed applications on heterogeneous hardware. While TensorFlow has been initially designed for developing Machine Learning (ML) applications, in fact TensorFlow aims at supporting the development of a much broader range of application kinds that are outside the ML domain and can possibly include HPC applications. However, very few experiments have been conducted to evaluate TensorFlow performance when running HPC workloads on supercomputers. This work addresses this lack by designing four traditional HPC benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG) solver and Fast Fourier Transform (FFT). We analyze their performance on two supercomputers with accelerators and evaluate the potential of TensorFlow for developing HPC applications. Our tests show that TensorFlow can fully take advantage of high performance networks and accelerators on supercomputers. Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical communication bandwidth on our testing platform. We find an approximately 2x, 1.7x and 1.8x performance improvement when increasing the number of GPUs from two to four in the matrix-matrix multiply, CG and FFT applications respectively. All our performance results demonstrate that TensorFlow has high potential of emerging also as HPC programming framework for heterogeneous supercomputers.Comment: Accepted for publication at The Ninth International Workshop on Accelerators and Hybrid Exascale Systems (AsHES'19

Linear Transformation with Given Eigenvectors

Drag the locators to specify two eigenvectors and their corresponding eigenvalues (taken from the length of the vectors). The image shows how a linear transformation, uniquely determined by these eigenvector/eigenvalue pairs, transforms points on the unit circleComponente Curricular::Educação Superior::Ciências Exatas e da Terra::Matemátic

Multidimensional Scaling

Classical (metric) multidimensional scaling (MDS) tries to find points that have a given set of pairwise distances. When no set of points satisfies distance constraints, MDS finds the best solution in the least squares sense-sum of squared errors (SSE) is minimized. Sliders specify desired distances between the points, that is, a slider labelled 3 4gives the desired distance between points 3 and 4. The darkness of the line between each pair of points reflects how closely the actual distance meets the specified goal (lighter means better correspondence)Componente Curricular::Educação Superior::Ciências Exatas e da Terra::Matemátic

Multidimensional Scaling

Classical (metric) multidimensional scaling (MDS) tries to find points that have a given set of pairwise distances. When no set of points satisfies distance constraints, MDS finds the best solution in the least squares sense-sum of squared errors (SSE) is minimized. Sliders specify desired distances between the points, that is, a slider labelled 3 4gives the desired distance between points 3 and 4. The darkness of the line between each pair of points reflects how closely the actual distance meets the specified goal (lighter means better correspondence)Componente Curricular::Educação Superior::Ciências Exatas e da Terra::Matemátic

Training Conditional Random Fields via Gradient Tree Boosting

Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such applications as part-of-speech tagging, text-to-speech mapping, protein and DNA sequence analysis, and information extraction from web pages. However, existing learning algorithms are slow, particularly in problems with large numbers of potential input features. This paper describes a new method..

Hand Recognition Using Geometric Classifiers

Introduction Biometric recognition systems find applications in security systems with varying requirements. While finger printing and iris based systems work well for high security applications, they are not as suitable for medium and low security applications because of privacy concerns. Hand Geometry based verification systems find more acceptance because hand geometry is not considered distinctive enough to establish a positive identity. Hand geometry recognition systems may provide three kinds of services. Verification, classification and identification. For verification the user provides her identity along with the hand geometry and the system verifies her identity. For classification the user does not provide any identity information but is known to be legitimate. For identification the user does not provide any identity information other than the hand geometry and may be an intruder. The system tries to identify the individual or deny access. Previous work Jain et.al. develo

Knowns and Unknowns about CAR-T Cell Dysfunction

Immunotherapy using chimeric antigen receptor (CAR) T cells is a promising option for cancer treatment. However, T cells and CAR-T cells frequently become dysfunctional in cancer, where numerous evasion mechanisms impair antitumor immunity. Cancer frequently exploits intrinsic T cell dysfunction mechanisms that evolved for the purpose of defending against autoimmunity. T cell exhaustion is the most studied type of T cell dysfunction. It is characterized by impaired proliferation and cytokine secretion and is often misdefined solely by the expression of the inhibitory receptors. Another type of dysfunction is T cell senescence, which occurs when T cells permanently arrest their cell cycle and proliferation while retaining cytotoxic capability. The first section of this review provides a broad overview of T cell dysfunctional states, including exhaustion and senescence; the second section is focused on the impact of T cell dysfunction on the CAR-T therapeutic potential. Finally, we discuss the recent efforts to mitigate CAR-T cell exhaustion, with an emphasis on epigenetic and transcriptional modulation