query: Probability Product Kernels

positive: Fast Kernels for String and Tree Matching In this paper we present a new algorithm suitable for matching discrete objects such as strings and trees in linear time, thus obviating dynamic programming with quadratic time complexity. Furthermore, prediction cost in many cases can be reduced to linear cost in the length of the sequence to be classified, regardless of the number of support vectors. This improvement on the currently available algorithms makes string kernels a viable alternative for the practitioner.

positive: A Kernel Between Sets of Vectors In various application domains, including image recognition, it is natural to represent each example as a set of vectors. With a base kernel we can implicitly map these vectors to a Hilbert space and fit a Gaussian distribution to the whole set using Kernel PCA. We define our kernel between examples as Bhattacharyya’s measure of affinity between such Gaussians. The resulting kernel is computable in closed form and enjoys many favorable properties, including graceful behavior under transformations, potentially justifying the vector set representation even in cases when more conventional representations also exist.

positive: Convolution Kernels on Discrete Structures We introduce a new method of constructing kernels on sets whose elements are discrete structures like strings, trees and graphs. The method can be applied iteratively to build a kernel on a innnite set from kernels involving generators of the set. The family of kernels generated generalizes the family of radial basis kernels. It can also be used to deene kernels in the form of joint Gibbs probability distributions. Kernels can be built from hidden Markov random elds, generalized regular expressions, pair-HMMs, or ANOVA de-compositions. Uses of the method lead to open problems involving the theory of innnitely divisible positive deenite functions. Fundamentals of this theory and the theory of reproducing kernel Hilbert spaces are reviewed and applied in establishing the validity of the method.

positive: The Spectrum Kernel: A String Kernel for SVM Protein Classification We introduce a new sequence-similarity kernel, the spectrum kernel, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. Our kernel is conceptually simple and efficient to compute and, in experiments on the SCOP database, performs well in comparison with state-of-the-art methods for homology detection. Moreover, our method produces an SVM classifier that allows linear time classification of test sequences. Our experiments provide evidence that string-based kernels, in conjunction with SVMs, could offer a viable and computationally efficient alternative to other methods of protein classification and homology detection.

positive: Convolution Kernels for Natural Language We describe the application of kernel methods to Natural Language Processing (NLP) problems. In many NLP tasks the objects being modeled are strings, trees, graphs or other discrete structures which require some mechanism to convert them into feature vectors. We describe kernels for various natural language structures, allowing rich, high dimensional representations of these structures. We show how a kernel over trees can be applied to parsing using the voted perceptron algorithm, and we give experimental results on the ATIS corpus of parse trees.

negative: Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy We describe a new iterative method for parameter estimation of Gaussian mixtures. The new method is based on a framework developed by Kivinen and Warmuth for supervised on-line learning. In contrast to gradient descent and EM, which estimate the mixture's covariance matrices, the proposed method estimates the inverses of the covariance matrices. Furthennore, the new parameter estimation procedure can be applied in both on-line and batch settings. We show experimentally that it is typically faster than EM, and usually requires about half as many iterations as EM.

negative: Self-organized computation with unreliable , memristive nanodevices Nanodevices have terrible properties for building Boolean logic systems: high defect rates, high variability, high death rates, drift, and (for the most part) only two terminals. Economical assembly requires that they be dynamical. We argue that strategies aimed at mitigating these limitations, such as defect avoidance/reconfiguration, or applying coding theory to circuit design, present severe scalability and reliability challenges. We instead propose to mitigate device shortcomings and exploit their dynamical character by building self-organizing, self-healing networks that implement massively parallel computations. The key idea is to exploit memristive nanodevice behavior to cheaply implement adaptive, recurrent networks, useful for complex pattern recognition problems. Pulse-based communication allows the designer to make trade-offs between power consumption and processing speed. Self-organization sidesteps the scalability issues of characterization, compilation and configuration. Network dynamics supplies a graceful response to device death. We present simulation results of such a network—a self-organized spatial filter array—that demonstrate its performance as a function of defects and device variation. (Some figures in this article are in colour only in the electronic version) 1. Nanoelectronics and computing paradigms Nanodevices are crummy1. High defect rates, high device variability, device ageing, and limitations on device complexity (e.g., two-terminal devices are much easier to build) are to be expected if we intend to mass produce nanoelectronic systems economically. Not only that, it is almost axiomatic among many researchers that such systems will be built from simple structures, such as crossbars, composed of nanodevices that must be configured to implement the desired functionality (Heath et al 1998, Williams and Kuekes 2000, Kuekes and Williams 2002, DeHon 2003, Stan et al 2003, Ziegler and Stan 2003, Snider et al 2004, DeHon 2005, Ma et al 2005, Snider 2005, Snider et al 2005, Strukov and Likharev 2005). So we are faced with the challenge of computing with devices that are not only crummy, but dynamical as well. Can reliable Boolean logic systems be built from such crummy devices? Yes, but we argue that at some point as 1 ‘Crummy’ was introduced into the technical lexicon by Moore and Shannon (1954). device dimensions scale down, the overhead and complexity become so costly that performance and density improvements will hit a barrier. In the next section we discuss two frequently proposed strategies for implementing Boolean logic with crummy, dynamical devices—reconfiguration and coding theory—and argue that each has severe scalability problems. This is not suggesting that logic at the nanoscale is not worth pursuing. It clearly is, and semiconductor manufacturers have the economic motivation to continue scaling down as aggressively as their profits permit. Rather we are suggesting that the ‘killer app’ for nanoelectronics lies elsewhere. An alternative computational paradigm—adaptive, recurrent networks—is computationally powerful and requires only two types of components, which we call ‘edges’ and ‘nodes’. Edge to node ratios are typically high, hundreds to thousands, and edges are, unfortunately, difficult to implement efficiently. This difficulty has made these networks extremely unpopular; software implementations are impossibly slow, and hardware implementations require far too much area. 0957-4484/07/365202+13$30.00 1 © 2007 IOP Publishing Ltd Printed in the UK Nanotechnology 18 (2007) 365202 G S Snider In this paper we propose using memristive nanodevices to implement edges, conventional analog and digital electronics to implement nodes, and pairs of bipolar pulses, called ‘spikes’, to implement communication. The tiny size of the nanodevices implementing edges would allow, for the first time, a practical hardware implementation of adaptive, recurrent networks. We suggest that such networks are a better architectural fit to nanoelectronics than Boolean logic circuits. They are robust in the presence of device variations and defects; they degrade gracefully as devices become defective over time, and can even ‘self-heal’ in response to internal change; they can be implemented with simple, crossbar-like structures. Just as importantly, they can be structured to self-organize their computations, sidestepping scalability problems with device characterization, compilation and configuration. Such systems can contain large numbers of defective components, but we will not need to discover where they are—in fact, we will not care where they are. The system can adapt and ‘rewire’ itself around them. 2. Boolean logic is hard with crummy, dynamical devices 2.1. Reconfiguration to the rescue? Several researchers have proposed using a combination of device characterization, defect avoidance, and configuration to handle initial static defects (Stan et al 2003, DeHon 2003, Snider et al 2004, Strukov and Likharev 2005, Snider and Williams 2007). The strategy is a three-pass algorithm: (1) Characterization. Analyze every nanowire and nanodevice in the system and compile a list of resources which are defective (stuck open or stuck closed, shorted, broken, out-of-spec, etc). Such analysis algorithms were used in the Teramac system (Culbertson et al 1997). (2) Defect avoidance. Give the list of defects from pass 1 to a compiler which maps a desired circuit onto the defective fabric, routing around defective components (Culbertson et al 1997, Snider et al 2004, Strukov and Likharev 2005, Snider and Williams 2007). (3) Configuration. Give the mapping determined in pass 2 to a controller that electrically configures each of the mapped components. Since every chip will have a unique set of defects, the above process must be applied to each and every chip. This presents some interesting challenges for manufacturing, since the time required to perform the above steps will contribute to production cost. Characterization (pass 1) is problematic due to device variability—the functional state of a device (or wire) is not necessarily discrete (working versus nonworking) but can lie on a continuum. And characterizing, say, 1012 nanodevices (DeHon et al 2005) in a reasonable amount of time is not likely to be trivial, especially given the bottleneck of pins on the chip. It is not clear if existing characterization algorithms could be applied to systems like this, or how well they would scale. Compilation (pass 2) also presents considerable risk. Compiling circuits onto configurable chips (such as FPGAs) today is a time-consuming process, due to the NP-hard placement and routing problems that lie at the compiler’s core. Even circuits comprising only a few tens of thousands of gates can require several minutes to compile, depending on the degree of optimization needed—and that’s assuming a defect-free target, where a single compilation can be used to manufacture thousands of parts. One proposal for minimizing this problem requires an initial ‘ideal’ compilation onto a hypothetical defect-free fabric, laying out components a little more sparsely than optimal. This would be done only once for each (circuit type, chip type) combination, so one could afford to spend enormous amounts of compute time on this to arrive at this ideal configuration. The configuration of an individual chip on the production line would then be viewed as a ‘perturbation’ of the ideal configuration, with resource allocation shifted as necessary to avoid defects. One might even combine the characterization pass with this pass for further speed improvements. This strategy might be workable. But it is not clear how well this would scale, or how robust this would be in the presence of defect clustering. Configuration (pass 3) is the most straightforward, with the most significant risk being configuration time restrictions due to the pin bottleneck and power dissipation. Note that the above approach of characterize, compile, configure does not handle device death. What happens when a nanodevice stops working and the chip starts computing nonsense? If the nanodevices are reconfigurable, the system can be stopped and reconfigured to work around the newly formed defects. But that assumes additional circuitry to detect the malfunction (e.g. self-checking circuits (Wakerly 1978)), and a companion host processor capable of implementing the three passes in order to reconfigure the chip. Such a processor would have to be fast, reliable (which probably means it would not be implemented with nanodevices), and likely would require a significant amount of memory. Implementing such a coprocessor would seem to negate the benefits that one was presumably getting from the nanoscale circuit in the first place. 2.2. Coding theory to the rescue? Coding theory has been used for decades to robustly transmit information over noisy channels by adding a small amount of redundant information. Can coding theory do the same for logic circuits by adding a small number of redundant gates or components in order to achieve reliable operation? von Neumann (1956), looking forward to nanoscale computation, was perhaps the first to address this question. His approach used a code that replicated each logic gate, and combined the replicated gate outputs in a clever way so that the entire system achieved a desired level of reliability. Although his primary concern was correcting errors induced by transient faults, the approach could also compensate for initially defective devices or ‘device deaths’ as long as the failing devices were randomly distributed and the number did not exceed a threshold (the trade-off being that device deaths would reduce the system’s tolerance of transient faults). The overhead for his scheme could be enormous, though. Replication factors for Boolean logic went to infinity as fault rates approached about 1%. This bound has been improved by later researchers, with

negative: Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. Electrophysiological and neuroimaging studies have shown that attention to visual motion can increase the responsiveness of the motion-selective cortical area V5 and the posterior parietal cortex (PP). Increased or decreased activation in a cortical area is often attributed to attentional modulation of the cortical projections to that area. This leads to the notion that attention is associated with changes in connectivity. We have addressed attentional modulation of effective connectivity using functional magnetic resonance imaging (fMRI). Three subjects were scanned under identical stimulus conditions (visual motion) while varying only the attentional component of the task. Haemodynamic responses defined an occipito-parieto-frontal network, including the, primary visual cortex (V1), V5 and PR A structural equation model of the interactions among these dorsal visual pathway areas revealed increased connectivity between V5 and PP related to attention. On the basis of our analysis and the neuroanatomical pattern of projections from the prefrontal cortex to PP we attributed the source of modulatory influences, on the posterior visual pathway, to the prefrontal cortex (PFC). To test this hypothesis we included the PFC in our model as a 'modulator' of the pathway between V5 and PP, using interaction terms in the structural equation model. This analysis revealed a significant modulatory effect of prefrontal regions on V5 afferents to posterior parietal cortex.

negative: ActionVLAD: Learning Spatio-Temporal Aggregation for Action Classification In this work, we introduce a new video representation for action classification that aggregates local convolutional features across the entire spatio-temporal extent of the video. We do so by integrating state-of-the-art two-stream networks [42] with learnable spatio-temporal feature aggregation [6]. The resulting architecture is end-to-end trainable for whole-video classification. We investigate different strategies for pooling across space and time and combining signals from the different streams. We find that: (i) it is important to pool jointly across space and time, but (ii) appearance and motion streams are best aggregated into their own separate representations. Finally, we show that our representation outperforms the two-stream base architecture by a large margin (13% relative) as well as outperforms other baselines with comparable base architectures on HMDB51, UCF101, and Charades video classification benchmarks.

negative: A practical model for subsurface light transport This paper introduces a simple model for subsurface light transport in translucent materials. The model enables efficient simulation of effects that BRDF models cannot capture, such as color bleeding within materials and diffusion of light across shadow boundaries. The technique is efficient even for anisotropic, highly scattering media that are expensive to simulate using existing methods. The model combines an exact solution for single scattering with a dipole point source diffusion approximation for multiple scattering. We also have designed a new, rapid image-based measurement technique for determining the optical properties of translucent materials. We validate the model by comparing predicted and measured values and show how the technique can be used to recover the optical properties of a variety of materials, including milk, marble, and skin. Finally, we describe sampling techniques that allow the model to be used within a conventional ray tracer.

negative:
Effects of lumbar stabilization exercise on functional disability and lumbar
lordosis angle in patients with chronic low back pain [Purpose] This study examined the effects of lumbar stabilization exercises on the functional disability and lumbar lordosis angles in patients with chronic low back pain. [Subjects] The subjects were 30 patients with chronic low back pain divided into a lumbar stabilization exercise group (n = 15) and a conservative treatment group (n = 15). [Methods] The lumbar stabilization exercise and conservative treatment groups performed an exercise program and conservative physical treatment, respectively. Both programs were performed 3 times a week for 6 weeks. The degree of functional disability was assessed by the Oswestry disability index, and lumbar lordosis angles were measured by plain radiography. [Results] The Oswestry disability index decreased significantly in the both groups; however, it was significantly lower in the lumbar stabilization exercise group. The lumbar lordosis angle increased significantly in the lumbar stabilization exercise group after treatment and was also significantly greater than that in the conservative treatment group. [Conclusion] Lumbar stabilization exercise is more effective than conservative treatment for improving functional disability and lumbar lordosis angles.

negative: Tetris game design based on the FPGA Tetris game is a classic game of logic control. This article gives a programming design of Tetris game based on the FPGA using VHDL. Game players can move and rotate blocks with the PS/2 interface keyboard, and the game video is showed in a VGA monitor. The game realized the function of the movement and rotation of blocks, randomly generating next blocks, eliminating rows, getting scores and speeding up. It also contained the normal mode corresponding to 7 types of blocks and the expert mode corresponding to 11 types of blocks. The successful transplant of Tetris game provides a template for the development of other visual control systems in the FPGA.

negative: Nonparametric Bayesian Models for Joint Analysis of Imagery and Text Nonparametric Bayesian Models for Joint Analysis of Imagery and Text

negative: The merits of sharing a ride The culture of sharing instead of ownership is sharply increasing in individuals behaviors. Particularly in transportation, concepts of sharing a ride in either carpooling or ridesharing have been recently adopted. An efficient optimization approach to match passengers in real-time is the core of any ridesharing system. In this paper, we model ridesharing as an online matching problem on general graphs such that passengers do not drive private cars and use shared taxis. We propose an optimization algorithm to solve it. The outlined algorithm calculates the optimal waiting time when a passenger arrives. This leads to a matching with minimal overall overheads while maximizing the number of partnerships. To evaluate the behavior of our algorithm, we used NYC taxi real-life data set. Results represent a substantial reduction in overall overheads.

negative: A Survey of Procedural Noise Functions Procedural noise functions are widely used in Computer Graphics, from off-line rendering in movie production to interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one of its main attractions. This survey is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry, and the fact that many recent research developments justify the need for an up-to-date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modeling and how they are applied to surfaces. We then introduce analysis tools and apply them to evaluate and compare the major approaches to noise generation. We finally identify several directions for future work.

negative: Toward Automated Dynamic Malware Analysis Using CWSandbox Malware is notoriously difficult to combat because it appears and spreads so quickly. In this article, we describe the design and implementation of CWSandbox, a malware analysis tool that fulfills our three design criteria of automation, effectiveness, and correctness for the Win32 family of operating systems

negative: Cancer genes and the pathways they control The revolution in cancer research can be summed up in a single sentence: cancer is, in essence, a genetic disease. In the last decade, many important genes responsible for the genesis of various cancers have been discovered, their mutations precisely identified, and the pathways through which they act characterized. The purposes of this review are to highlight examples of progress in these areas, indicate where knowledge is scarce and point out fertile grounds for future investigation.

negative: Pseudo-analysis approach to nonlinear partial differential equations An overview of methods of pseudo-analysis in applications on important classes of nonlinear partial differential equations, occurring in different fields, is given. Hamilton-Jacobi equations, specially important in the control theory, are for important models usually with non-linear Hamiltonian H which is also not smooth, e.g., the absolute value, min or max operations, where it can not apply the classical mathematical analysis. Using the pseudo-analysis with generalized pseudo-convolution it is possible to obtain solutions which can be interpreted in the mentioned classical way. Another important classes of nonlinear equations, where there are applied the pseudo-analysis, are the Burgers type equations and Black and Shole equation in option pricing. Very recent applications of pseudo-analysis are obtained on equations which model fluid mechanics (Navier-Stokes equation) and image processing (Perona and Malik equation).

negative:
A deep learning method for classifying mammographic breast density categories. PURPOSE
Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow.

METHODS
In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier.

RESULTS
The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset.

CONCLUSIONS
Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening.

negative: Gradient Flow in Recurrent Nets : the Difficulty of Learning Long-Term Dependencies

negative: SOCIAL ENTREPRENEURSHIP LEADERSHIP THAT FACILITATES SOCIETAL TRANSFORMATION — AN EXPLORATORY STUDY 5 This study provides a comparative analysis of seven cases of social entrepre-neurship that have been widely recognized as successful. The paper suggests factors associated with successful social entrepreneurship, particularly with social entrepreneurship that leads to significant changes in the social, political and economic contexts for poor and marginalized groups. It generates hypotheses about core innovations, leadership, organization, and scaling up in successful social entrepreneurship. The paper concludes with a discussion of the implications for the practice of social entrepreneurship, for further research, and for the continued development of support technologies and institutions that will encourage future social entrepreneurship.

negative:
Plantar fasciopathy: revisiting the risk factors. BACKGROUND
Plantar fasciopathy is the most common cause of acquired sub-calcaneal heel pain in adults. To-date, research of this condition has mainly focused on management rather than causal mechanisms. The aetiology of plantar fasciopathy is likely to be multifactorial, as both intrinsic and extrinsic risk factors have been reported. The purpose of this review is to critically reevaluate risk factors for plantar fasciopathy.

METHODS
A detailed literature review was undertaken using English language medical databases.

RESULTS
No clear consensus exists as to the relative strength of the risk factors reported.

CONCLUSIONS
To-date numerous studies have examined various intrinsic and extrinsic risk factors implicated in the aetiology of plantar fasciopathy. How these factors interact may provide useful data to establish an individuals' risk profile for plantar fasciopathy and their potential for response to treatment. Further research is indicated to rank the relative significance of these risk factors.

negative: Why do Larks Perform Better at School than Owls ? The Mediating Effect of Conscientiousness Article History: Received 26.06.2016 Received in revised form 25.10.2016 Accepted 26.10.2016 Available online 25.11.2016 Circadian preference refers to individuals’ preference for morning or evening activities. Its two dimensions (i.e., morningness and eveningness) are related to a number of academic outcomes. While morningness shows positive relations with academic achievement, eveningness shows negative relations. Further, morningness and eveningness show the same correlational pattern with conscientiousness (i.e., positive relations for morningness, negative relations for eveningness), which – in turn – predicts academic achievement. Therefore, the main aim of the present study was to investigate if the relation between circadian preference and academic achievement was mediated by conscientiousness. The sample comprised 422 students attending the 11th grade at a grammar school in Germany. Circadian preference (morningness and eveningness) and conscientiousness were assessed by self-report questionnaires; academic achievement was operationalized by school grades. Using confirmatory analyses and structural equation modelling, the results supported the assumption that conscientiousness mediates the relation between circadian preference and academic achievement. Implications for research into circadian preference and for education are discussed. © 2016 IOJES. All rights reserved

negative: A Multiband Antenna for Satellite Communications on the Move The design of a multiband reflector antenna for an on-the-move satellite communications terminal is presented. This antenna was designed to operate with numerous modern and future military communications satellites, which in turn requires the antenna to be capable of operation at multiple frequencies and polarizations while maintaining high aperture efficiency. Several feed antenna concepts were developed to accomplish this task, and are discussed in detail. Multiple working prototypes based on this design have been realized, with excellent performance. Measured data of individual antenna components and the complete assembly is also included

negative: Porokeratosis with follicular involvement: report of three cases and review of literatures. Porokeratosis is characterized clinically by annular plaques with a distinct peripheral keratotic ridge and histologically by the cornoid lamella. Porokeratosis with follicular involvement is rarely reported. To provide the basis of that follicular porokeratosis is a clinical variant or not. Biopsy was taken from three patients who were diagnosed porokeratosis. Routine stain was made and reviewed the literatures about well-documented cases of porokeratosis with follicular involvement. Porokeratosis with follicular involvement may have some clinical features: asymptomatic, erythematous, brownish or skin-color, less than 1 cm in the areas excluding palm and plantar, which commonly involved on middle-age. But there have still not enough proof as an independent clinical variant.

negative: Interleaved active clamp flyback inverter using a synchronous rectifier for a photovoltaic AC module system An interleaved active clamp flyback inverter using a synchronous rectifier for a photovoltaic AC module system is proposed. In a conventional single flyback inverter for the photovoltaic AC module system, this inverter has drawbacks of a conduction losses of each switch, transformer copper and high ripple current of capacitor. To overcome these problems, two flyback converter modules with interleaved pulse-width modulation (PWM) are connected in parallel at the input and output sides reducing the ripple current on the input and output capacitors and decrease the current stress on the transformer windings. Thus, the transformer copper losses and the conduction losses on the switches are reduced. Also, reliability and life of capacitor are increased due to reducing the ripple current of input and output capacitors. In order to verify these, theoretical analysis and simulation are performed.

negative: Generative Local Metric Learning for Nearest Neighbor Classification We consider the problem of learning a local metric in order to enhance the performance of nearest neighbor classification. Conventional metric learning methods attempt to separate data distributions in a purely discriminative manner; here we show how to take advantage of information from parametric generative models. We focus on the bias in the information-theoretic error arising from finite sampling effects, and find an appropriate local metric that maximally reduces the bias based upon knowledge from generative models. As a byproduct, the asymptotic theoretical analysis in this work relates metric learning to dimensionality reduction from a novel perspective, which was not understood from previous discriminative approaches. Empirical experiments show that this learned local metric enhances the discriminative nearest neighbor performance on various datasets using simple class conditional generative models such as a Gaussian.

negative: Efficient Authentication and Signing of Multicast Streams over Lossy Channels Multicast stream authentication and signing is an important and challenging problem. Applications include the continuous authentication of radio and TV Internet broadcasts, and authenticated data distribution by satellite. The main challenges are fourfold. First, authenticity must be guaranteed even when only the sender of the data is trusted. Second, the scheme needs to scale to potentially millions of receivers. Third, streamed media distribution can have high packet loss. Finally, the system needs to be efficient to support fast packet rates. We propose two efficient schemes, TESLA and EMSS, for secure lossy multicast streams. TESLA, short for Timed Efficient Stream Loss-tolerant Authentication, offers sender authentication, strong loss robustness, high scalability, and minimal overhead, at the cost of loose initial time synchronization and slightly delayed authentication. EMSS, short for Efficient Multi-chained Stream Signature, provides nonrepudiation of origin, high loss resistance, and low overhead, at the cost of slightly delayed verification. This work began in Summer 1999 when Adrian Perrig and Dawn Song were visiting the IBM T. J. Watson research lab. Initial research on stream authentication was done during Summer 1999 by Ran Canetti, Adrian Perrig, and Dawn Song at IBM. Additional improvements were suggested by J. D. Tygar in Fall 1999 at UC Berkeley. Implementation was done in Fall 1999 by Adrian Perrig at UC Berkeley. The work on stream signatures was done by J. D. Tygar, Adrian Perrig, and Dawn Song at UC Berkeley. Additional work was performed by Ran Canetti in Spring 2000. Ran Canetti is at IBM T. J. Watson Research Center, and Adrian Perrig, Dawn Song, and J. D. Tygar are at the Computer Science Division, UC Berkeley. This research was suported in part by the Defense Advanced Research Projects Agency under DARPA contract N6601-99-28913 (under supervision of the Space and Naval Warfare Systems Center San Diego), by the National Science foundation under grant FD99-79852, and by the United States Postal Service under grant USPS 1025 90-98-C-3513. Views and conclusions contained in this document are those of the authors and do not necessarily represent the official opinion or policies, either expressed or implied of the US government or any of its agencies, DARPA, NSF, USPS, or IBM.

negative: Task-Oriented Query Reformulation with Reinforcement Learning Search engines play an important role in our everyday lives by assisting us in finding the information we need. When we input a complex query, however, results are often far from satisfactory. In this work, we introduce a query reformulation system based on a neural network that rewrites a query to maximize the number of relevant documents returned. We train this neural network with reinforcement learning. The actions correspond to selecting terms to build a reformulated query, and the reward is the document recall. We evaluate our approach on three datasets against strong baselines and show a relative improvement of 5-20% in terms of recall. Furthermore, we present a simple method to estimate a conservative upperbound performance of a model in a particular environment and verify that there is still large room for improvements.

negative: Aggressive driving with model predictive path integral control In this paper we present a model predictive control algorithm designed for optimizing non-linear systems subject to complex cost criteria. The algorithm is based on a stochastic optimal control framework using a fundamental relationship between the information theoretic notions of free energy and relative entropy. The optimal controls in this setting take the form of a path integral, which we approximate using an efficient importance sampling scheme. We experimentally verify the algorithm by implementing it on a Graphics Processing Unit (GPU) and apply it to the problem of controlling a fifth-scale Auto-Rally vehicle in an aggressive driving task.

negative: Modelling consumer credit risk via survival analysis Credit risk models are used by financial companies to evaluate in advance the insolvency risk caused by credits that enter into default. Many models for credit risk have been developed over the past few decades. In this paper, we focus on those models that can be formulated in terms of the probability of default by using survival analysis techniques. With this objective three different mechanisms are proposed based on the key idea of writing the default probability in terms of the conditional distribution function of the time to default. The first method is based on a Cox’s regression model, the second approach uses generalized linear models under censoring and the third one is based on nonparametric kernel estimation, using the product-limit conditional distribution function estimator by Beran. The resulting nonparametric estimator of the default probability is proved to be consistent and asymptotically normal. An empirical study, based on modified real data, illustrates the three methods. MSC: 62P20, 91B30, 62G05, 62N01

negative: Discriminant kernel and regularization parameter learning via semidefinite programming Regularized Kernel Discriminant Analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. The performance of RKDA depends on the selection of kernels. In this paper, we consider the problem of learning an optimal kernel over a convex set of kernels. We show that the kernel learning problem can be formulated as a semidefinite program (SDP) in the binary-class case. We further extend the SDP formulation to the multi-class case. It is based on a key result established in this paper, that is, the multi-class kernel learning problem can be decomposed into a set of binary-class kernel learning problems. In addition, we propose an approximation scheme to reduce the computational complexity of the multi-class SDP formulation. The performance of RKDA also depends on the value of the regularization parameter. We show that this value can be learned automatically in the framework. Experimental results on benchmark data sets demonstrate the efficacy of the proposed SDP formulations.

negative: KeLP: a Kernel-based Learning Platform for Natural Language Processing Kernel-based learning algorithms have been shown to achieve state-of-the-art results in many Natural Language Processing (NLP) tasks. We present KELP, a Java framework that supports the implementation of both kernel-based learning algorithms and kernel functions over generic data representation, e.g. vectorial data or discrete structures. The framework has been designed to decouple kernel functions and learning algorithms: once a new kernel function has been implemented it can be adopted in all the available kernelmachine algorithms. The platform includes different Online and Batch Learning algorithms for Classification, Regression and Clustering, as well as several Kernel functions, ranging from vector-based to structural kernels. This paper will show the main aspects of the framework by applying it to different NLP tasks.

negative: Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition Domain adaptation aims to correct the mismatch in statistical properties between the source domain on which a classifier is trained and the target domain to which the classifier is to be applied. In this paper, we address the challenging scenario of unsupervised domain adaptation, where the target domain does not provide any annotated data to assist in adapting the classifier. Our strategy is to learn robust features which are resilient to the mismatch across domains and then use them to construct classifiers that will perform well on the target domain. To this end, we propose novel kernel learning approaches to infer such features for adaptation. Concretely, we explore two closely related directions. In the first direction, we propose unsupervised learning of a geodesic flow kernel (GFK). The GFK summarizes the inner products in an infinite sequence of feature subspaces that smoothly interpolates between the source and target domains. In the second direction, we propose supervised learning of a kernel that discriminatively combines multiple base GFKs. Those base kernels model the source and the target domains at fine-grained granularities. In particular, each base kernel pivots on a different set of landmarks—the most useful data instances that reveal the similarity between the source and the target domains, thus bridging them to achieve adaptation. Our approaches are computationally convenient, automatically infer important hyper-parameters, and are capable of learning features and classifiers discriminatively without demanding labeled data from the target domain. In extensive empirical studies on standard benchmark recognition datasets, our appraches yield state-of-the-art results compared to a variety of competing methods.

negative: Deep Kernelized Autoencoders In this paper we introduce the deep kernelized autoencoder, a neural network model that allows an explicit approximation of (i) the mapping from an input space to an arbitrary, user-specified kernel space and (ii) the back-projection from such a kernel space to input space. The proposed method is based on traditional autoencoders and is trained through a new unsupervised loss function. During training, we optimize both the reconstruction accuracy of input samples and the alignment between a kernel matrix given as prior and the inner products of the hidden representations computed by the autoencoder. Kernel alignment provides control over the hidden representation learned by the autoencoder. Experiments have been performed to evaluate both reconstruction and kernel alignment performance. Additionally, we applied our method to emulate kPCA on a denoising task obtaining promising results.

negative: Less is More: Nyström Computational Regularization We study Nyström type subsampling approaches to large scale kernel methods, and prove learning bounds in the statistical learning setting, where random sampling and high probability estimates are considered. In particular, we prove that these approaches can achieve optimal learning bounds, provided the subsampling level is suitably chosen. These results suggest a simple incremental variant of Nyström Kernel Regularized Least Squares, where the subsampling level implements a form of computational regularization, in the sense that it controls at the same time regularization and computations. Extensive experimental analysis shows that the considered approach achieves state of the art performances on benchmark large scale datasets.

negative:
BLIS: A Framework for Rapidly Instantiating BLAS Functionality The BLAS-like Library Instantiation Software (BLIS) framework is a new infrastructure for rapidly instantiating Basic Linear Algebra Subprograms (BLAS) functionality. Its fundamental innovation is that virtually all computation within level-2 (matrix-vector) and level-3 (matrix-matrix) BLAS operations can be expressed and optimized in terms of very simple kernels. While others have had similar insights, BLIS reduces the necessary kernels to what we believe is the simplest set that still supports the high performance that the computational science community demands. Higher-level framework code is generalized and implemented in ISO C99 so that it can be reused and/or reparameterized for different operations (and different architectures) with little to no modification. Inserting high-performance kernels into the framework facilitates the immediate optimization of any BLAS-like operations which are cast in terms of these kernels, and thus the framework acts as a productivity multiplier. Users of BLAS-dependent applications are given a choice of using the traditional Fortran-77 BLAS interface, a generalized C interface, or any other higher level interface that builds upon this latter API. Preliminary performance of level-2 and level-3 operations is observed to be competitive with two mature open source libraries (OpenBLAS and ATLAS) as well as an established commercial product (Intel MKL).
 Replicated Computational Results Certified: This article was subjected to an optional supplementary review process in which the computational results leading to the main conclusions of the paper were independently replicated. A separate report with the results of this process is published in this issue (see: http://dx.doi.org/10.1145/2738033). The certification process is described in Michael Heroux's editorial: http://dx.doi.org/10.1145/2743015.