Max-affine regression via first-order methods

We consider regression of a max-affine model that produces a piecewise linear model by combining affine models via the max function. The max-affine model ubiquitously arises in applications in signal processing and statistics including multiclass classification, auction problems, and convex regression. It also generalizes phase retrieval and learning rectifier linear unit activation functions. We present a non-asymptotic convergence analysis of gradient descent (GD) and mini-batch stochastic gradient descent (SGD) for max-affine regression when the model is observed at random locations following the sub-Gaussianity and an anti-concentration with additive sub-Gaussian noise. Under these assumptions, a suitably initialized GD and SGD converge linearly to a neighborhood of the ground truth specified by the corresponding error bound. We provide numerical results that corroborate the theoretical finding. Importantly, SGD not only converges faster in run time with fewer observations than alternating minimization and GD in the noiseless scenario but also outperforms them in low-sample scenarios with noise

Analysis of Thin Film Parylene-Metal-Parylene Device Based on Mechanical Tensile Strength Measurement

International audienceThis paper presents an FEM analysis and experiment of parylene-metal-parylene flexible substrate for implantable medical devices. Tensile strength measurement of the parylene-metal-parylene has been carried out and corresponding FEM modeling and simulation has been done to understand its mechanical behaviour. Besides, frequently encountered metal delamination on parylene substrate has been studied based on cohesive zone model of interface between the two materials

Catechin-capped gold nanoparticles: green synthesis, characterization, and catalytic activity toward 4-nitrophenol reduction

An eco-friendly approach is described for the green synthesis of gold nanoparticles using catechin as a reducing and capping agent. The reaction occurred at room temperature within 1 h without the use of any external energy and an excellent yield (99%) was obtained, as determined by inductively coupled plasma mass spectrometry. Various shapes of gold nanoparticles with an estimated diameter of 16.6 nm were green-synthesized. Notably, the capping of freshly synthesized gold nanoparticles by catechin was clearly visualized with the aid of microscopic techniques, including high-resolution transmission electron microscopy, atomic force microscopy, and field emission scanning electron microscopy. Strong peaks in the X-ray diffraction pattern of the as-prepared gold nanoparticles confirmed their crystalline nature. The catalytic activity of the as-prepared gold nanoparticles was observed in the reduction of 4-nitrophenol to 4-aminophenol in the presence of NaBH(4). The results suggest that the newly prepared gold nanoparticles have potential uses in catalysis

Design and Evaluation of Techniques to Utilize Implicit Rating Data in Complex Information Systems.

Research in personalization, including recommender systems, focuses on applications such as in online shopping malls and simple information systems. These systems consider user profile and item information obtained from data explicitly entered by users - where it is possible to classify items involved and to make personalization based on a direct mapping from user or user group to item or item group. However, in complex, dynamic, and professional information systems, such as Digital Libraries, additional capabilities are needed to achieve personalization to support their distinctive features: large numbers of digital objects, dynamic updates, sparse rating data, biased rating data on specific items, and challenges in getting explicit rating data from users. In this report, we present techniques for collecting, storing, processing, and utilizing implicit rating data of Digital Libraries for analysis and decision support. We present our pilot study to find virtual user groups using implicit rating data. We demonstrate the effectiveness of implicit rating data for characterizing users and finding virtual user communities, through statistical hypothesis testing. Further, we describe a visual data mining tool named VUDM (Visual User model Data Mining tool) that utilizes implicit rating data. We provide the results of formative evaluation of VUDM and discuss the problems raised and plans for further studies

Improving Detection Performance of Ionospheric Disturbances due to Earthquake by Optimization of Sequential Measurement Combination

Energy generated from earthquake (EQ) is transferred to the ionosphere and results in co-seismic ionospheric disturbances (CID). CID can be observed in the ionospheric combination using L1, L2 frequency carrier phase. As ionospheric trend due to normal conditions such as elevation angle of satellites is generally larger than disturbances, a proper measure is required to extract disturbance signals. Derivative, or sequential combination, is a simple and effective way to remove the normal trend in the ionospheric delay. When using derivative, however, disturbance signals can often be obscured by noise due to its small amplitude. In order to reduce the noise while preserving the time rate of data, and thus to improve signal-to-noise ratio (SNR), we designed a new derivative method using optimization under a couple of assumptions. With simulation data, it is found that N, the number of epochs used for sequential combination, turned out to be the best when N=160 with maximum SNR. Finally, the proposed algorithm’s SNR was compared to that of the previous study which also used derivative method. 120~260% improvements were observed for the proposed method compared to the conventional method