Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Data Mining - Concepts and Techniques.

Data Mining Practical Machine Learning Tools and Techniques

The author's preface gives an outline: "This book is about weakconvergence methods in metric spaces, with applications sufficient to show their power and utility. The Introduction motivates the definitions and indicates how the theory will yield solutions to problems arising outside it. Chapter 1 sets out the basic general theorems, which are then specialized in Chapter 2 to the space C[0, l ] of continuous functions on the unit interval and in Chapter 3 to the space D [0, 1 ] of functions with discontinuities of the first kind. The results of the first three chapters are used in Chapter 4 to derive a variety of limit theorems for dependent sequences of random variables. " The book develops and expands on Donsker's 1951 and 1952 papers on the invariance principle and empirical distributions. The basic random variables remain real-valued although, of course, measures on C[0, l ] and D[0, l ] are vitally used. Within this framework, there are various possibilities for a different and apparently better treatment of the material. More of the general theory of weak convergence of probabilities on separable metric spaces would be useful. Metrizability of the convergence is not brought up until late in the Appendix. The close relation of the Prokhorov metric and a metric for convergence in probability is (hence) not mentioned (see V. Strassen, Ann. Math. Statist. 36 (1965), 423-439; the reviewer, ibid. 39 (1968), 1563-1572). This relation would illuminate and organize such results as Theorems 4.1, 4.2 and 4.4 which give isolated, ad hoc connections between weak convergence of measures and nearness in probability. In the middle of p. 16, it should be noted that C*(S) consists of signed measures which need only be finitely additive if 5 is not compact. On p. 239, where the author twice speaks of separable subsets having nonmeasurable cardinal, he means "discrete" rather than "separable." Theorem 1.4 is Ulam's theorem that a Borel probability on a complete separable metric space is tight. Theorem 1 of Appendix 3 weakens completeness to topological completeness. After mentioning that probabilities on the rationals are tight, the author says it is an

Convergence of probability measures

IEEE Transactions on Pattern Analysis and Machine Intelligence

Face recognition performance degrades when images are captured in difficult environments and non-ideal imaging conditions, for example in surveillance video. Heterogenous face recognition applications that require matching of images across varying distances, resolution, and visible and infrared imaging bands pose additional challenges. Deep-learning techniques have been employed to improve unconstrained face recognition performance, but they require a very large amount of data. There is a distinct need for publicly-available facial video datasets, collected with the consent of the subjects, that can be used to overcome the challenge of performing facial recognition in multi-domain, heterogenous scenarios. This paper presents a multi-domain face image dataset comprised of ground truth and operational images and video captured using a variety of cameras. These include fixed and body-worn cameras capable of imaging at visible, short-mid-, and long-wave infrared wavelengths, including images captured at distances up to 500m. Over 161 hours of video were collected from 251 subjects. This paper provides a description of the data collection, post processing, and baseline matching performance of this unique dataset.

IARPA Janus Benchmark Multi-Domain Face

In this paper a novel cross-device text-independent speaker verification architecture is proposed. Majority of the state-of-the-art deep architectures that are used for speaker verification tasks consider Mel-frequency cepstral coefficients. In contrast, our proposed Siamese convolutional neural network architecture uses Mel-frequency spectrogram coefficients to benefit from the dependency of the adjacent spectro-temporal features. Moreover, although spectro-temporal features have proved to be highly reliable in speaker verification models, they only represent some aspects of short-term acoustic level traits of the speaker’s voice. However, the human voice consists of several linguistic levels such as acoustic, lexicon, prosody, and phonetics, that can be utilized in speaker verification models. To compensate for these inherited shortcomings in spectro-temporal features, we propose to enhance the proposed Siamese convolutional neural network architecture by deploying a multilayer perceptron network to incorporate the prosodic, jitter, and shimmer features. The proposed end-to-end verification architecture performs feature extraction and verification simultaneously. This proposed architecture displays significant improvement over classical signal processing approaches and deep algorithms for forensic cross-device speaker verification.

Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification

Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering coregistered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera; attached to a microscope at varying objective powers and illumination intensity. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyper-spectral data cube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting.

/pdf/imaging-of-blood-cells-based-on-snapshot-hyper-spectral-56unucj8zg.pdf

Imaging of blood cells based on snapshot Hyper-Spectral Imaging systems

Closed-loop MEMS control enables mechanical microsystems to adapt to the demands of the environment which they are actuating opening a new window of opportunity for future MEMS applications. Planar diffractive optical microsystems have the potential to enable the integrated optical interrogation of MEMS microstructure position fully decoupled from the means of mechanical actuation which is central to realization of feedback control. This paper presents the results of initial research evaluating through-wafer optical microsystems for MEMS integrated optical monitoring. Positional monitoring results obtained from a 1.3 micrometer wavelength through- wafer free-space optical probe of a lateral comb resonator fabricated using the Multi-User MEMS Process Service (MUMPS) are presented. Given the availability of positional information via probe signal feedback, a simulation of the application of nonlinear sliding control is presented illustrating position control of the lateral comb resonator structure.© (1999) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Through-wafer interrogation of microstructure motion for MEMS feedback control

Accelerated Publication: Direct fabrication of two-dimensional photonic crystal structures in silicon using positive and negative Hydrogen Silsesquioxane (HSQ) patterns

Jeremy Dawson

Papers

IARPA Janus Benchmark Multi-Domain Face

Prosodic-Enhanced Siamese Convolutional Neural Networks for Cross-Device Text-Independent Speaker Verification

Imaging of blood cells based on snapshot Hyper-Spectral Imaging systems

Through-wafer interrogation of microstructure motion for MEMS feedback control

Accelerated Publication: Direct fabrication of two-dimensional photonic crystal structures in silicon using positive and negative Hydrogen Silsesquioxane (HSQ) patterns