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2019


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Prototyping Micro- and Nano-Optics with Focused Ion Beam Lithography

Keskinbora, K.

SL48, pages: 46, SPIE.Spotlight, SPIE Press, Bellingham, WA, 2019 (book)

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DOI [BibTex]

2019


DOI [BibTex]


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Methods of forming dry adhesive structures

Sitti, M., Murphy, M., Aksak, B.

September 2015, US Patent 9,120,953 (patent)

Abstract
Methods of forming dry adhesives including a method of making a dry adhesive including applying a liquid polymer to the second end of the stem, molding the liquid polymer on the stem in a mold, wherein the mold includes a recess having a cross-sectional area that is less than a cross-sectional area of the second end of the stem, curing the liquid polymer in the mold to form a tip at the second end of the stem, wherein the tip includes a second layer stem; corresponding to the recess in the mold, and removing the tip from the mold after the liquid polymer cures.

pi

[BibTex]

[BibTex]


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Micro-fiber arrays with tip coating and transfer method for preparing same

Sitti, M., Washburn, N. R., Glass, P. S., Chung, H.

July 2015, US Patent 9,079,215 (patent)

Abstract
Present invention describes a patterned and coated micro- and nano-scale fibers elastomeric material for enhanced adhesion in wet or dry environments. A multi-step fabrication process including optical lithography, micromolding, polymer synthesis, dipping, stamping, and photopolymerization is described to produce uniform arrays of micron-scale fibers with mushroom-shaped tips coated with a thin layer of an intrinsically adhesive synthetic polymer, such as lightly crosslinked p(DMA-co-MEA).

pi

[BibTex]

[BibTex]


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Dry adhesives and methods for making dry adhesives

Sitti, M., Murphy, M., Aksak, B.

March 2015, US Patent App. 14/625,162 (patent)

Abstract
Dry adhesives and methods for forming dry adhesives. A method of forming a dry adhesive structure on a substrate, comprises: forming a template backing layer of energy sensitive material on the substrate; forming a template layer of energy sensitive material on the template backing layer; exposing the template layer to a predetermined pattern of energy; removing a portion of the template layer related to the predetermined pattern of energy, and leaving a template structure formed from energy sensitive material and connected to the substrate via the template backing layer.

pi

[BibTex]

[BibTex]

2006


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Semi-Supervised Learning

Chapelle, O., Schölkopf, B., Zien, A.

pages: 508, Adaptive computation and machine learning, MIT Press, Cambridge, MA, USA, September 2006 (book)

Abstract
In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in which no label data are given). Interest in SSL has increased in recent years, particularly because of application domains in which unlabeled data are plentiful, such as images, text, and bioinformatics. This first comprehensive overview of SSL presents state-of-the-art algorithms, a taxonomy of the field, selected applications, benchmark experiments, and perspectives on ongoing and future research. Semi-Supervised Learning first presents the key assumptions and ideas underlying the field: smoothness, cluster or low-density separation, manifold structure, and transduction. The core of the book is the presentation of SSL methods, organized according to algorithmic strategies. After an examination of generative models, the book describes algorithms that implement the low-density separation assumption, graph-based methods, and algorithms that perform two-step learning. The book then discusses SSL applications and offers guidelines for SSL practitioners by analyzing the results of extensive benchmark experiments. Finally, the book looks at interesting directions for SSL research. The book closes with a discussion of the relationship between semi-supervised learning and transduction.

ei

Web [BibTex]

2006


Web [BibTex]


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MR/PET Attenuation Correction

Hofmann, M., Schölkopf, B., Steinke, F., Pichler, B.

Max-Planck-Gesellschaft, Biologische Kybernetik, July 2006 (patent)

ei

[BibTex]

[BibTex]


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Gaussian Processes for Machine Learning

Rasmussen, CE., Williams, CKI.

pages: 248, Adaptive Computation and Machine Learning, MIT Press, Cambridge, MA, USA, January 2006 (book)

Abstract
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

ei

Web [BibTex]

Web [BibTex]