title: Adaptive Binary Search Trees Jonathan Carlyle Derryberry

text: A ubiquitous problem in the field of algorithms and data structures is that of searching for an element from an ordered universe. The simple yet powerful binary search tree (BST) model provides a rich family of solutions to this problem. Although BSTs require Ω(lg n) time per operation in the worst case, various adaptive BST algorithms are capable of exploiting patterns in the sequence of queries to achieve tighter, input-sensitive, bounds that can be o(lg n) in many cases. This thesis furthers our understanding of what is achievable in the BST model along two directions. First, we make progress in improving instance-specific lower bounds in the BST model. In particular, we introduce a framework for generating lower bounds on the cost that any BST algorithm must pay to execute a query sequence, and we show that this framework generalizes previous lower bounds. This suggests that the optimal lower bound in the framework is a good candidate for being tight to within a constant factor of the optimal BST algorithm for each input. Further, we show that lower bounds in this framework are also valid lower bounds for instances of the partial-sums problem in a restricted model of computation, which suggests that augmented BSTs may be the most efficient way of maintaining sums over ranges of an array when the entries of the array can be updated throughout time. Second, we improve the input-sensitive upper bounds that are known to be achievable in the BST model by introducing two new BST algorithms, skip-splay and cache-splay. These two algorithms are the first BSTs that are known to have running times that have nontrivial competitiveness to Iacono’s Unified Bound, which is a generalization of the dynamic finger and working set bounds. Skip-splay is a simple algorithm that is nearly identical to splaying, and it achieves a running time that is within additive O(lg lg n) per operation of the Unified Bound. Cache-splay is a slightly more complicated splay-based algorithm that is the first BST to achieve the Unified Bound to within a constant factor.


title: Multilevel Color Transfer on Images for Providing an Artistic Sight of the World

text: This paper represents a twofold approach. On the one hand, we introduce a simple method that encompasses an ensemble of image processing algorithms with a multilevel color transfer approach at its core. On the other hand, the method is applied for providing an artistic look to standard images. The approach proposes a multilevel color transfer in a chromatic channel of the CIELAB color space. Once converted from red, green and blue, a specific channel on both images, input and target (reference), is thresholded in a number of levels. Later, the color transfer is performed between regions from corresponding levels using a classical color transfer method. In the application phase, the color palette of a recognized artwork of the Fauve movement is mapped to the input image, emulating the sight of the artist, characterized by the use of vivid colors. Filtering techniques are applied to the outcome, in order to emulate the basic brushstrokes of the artist. Experimental results are shown, visualizing and comparing the input images with the outcomes.


title: Collaborative process planning and manufacturing in product lifecycle management

text: Companies are moving towards quickly providing better customer-centric products and services improve market share and market size with continuously growing revenue. As such, the effective collaboration among customers, developers, suppliers, and manufacturers throughout the entire product lifecycle is becoming much more important for the most advanced competitiveness. To address this need, a framework for product lifecycle collaboration is proposed in this study. The details of these collaboration models throughout the entire product lifecycle are depicted. As one of the key elements in product lifecycle collaboration, technology to support collaborative product manufacturing is proposed, developed and implemented in this study. It is hoped that the developed technology for collaborative product manufacturing will lay a frontier basis for further research and development in product lifecycle management. # 2007 Elsevier B.V. All rights reserved.


title: Kalman Filtering in Wireless Sensor Networks

text: Challenges associated with the scarcity of bandwidth and power in wireless communications have to be addressed. For the state-estimation problems discussed in the paper, observations about a common state are collected by physically distributed terminals. To perform state estimation, wireless sensor networks (WSNs) may share these observations with each other or communicate them to a fusion center for centralized processing. With K vector observations {yk(n)}K k=1 available, the optimal mean squared error (MSE) estimation of the state x(n) for the linear model is accomplished by a Kalman filter.


title: A Novel Four-DOF Parallel Manipulator Mechanism and Its Kinematics

text: A novel 4-UPU parallel manipulator mechanism that can perform three-dimensional translations and rotation about Z axis is presented. The principle that the mechanism can perform the above motions is analyzed based on the screw theory, the mobility of the mechanism is calculated, and the rationality of the chosen input joints is discussed. The forward and inverse position kinematics solutions of the mechanism and corresponding numerical examples are given, the workspace and the singularity of the parallel mechanism are discussed. The mechanism having the advantages of simple symmetric structure and large stiffness can be applied to the developments of NC positioning platforms, parallel machine tools, four-dimensional force sensors and micro-positional parallel manipulators


title: A highly efficient power harvester with wide dynamic input power range for 900 MHz wireless power transfer applications

text: This work demonstrates the design of an adaptive reconfigurable rectifier to address the issue of early breakdown voltage in a conventional rectifier and extends the rectifier's operation for wide dynamic input power range. A depletion-mode field-effect transistor has been introduced to operate as a switch and compensate at low and high input power levels for rectifier. This design accomplishes 40% of RF-DC power conversion efficiency over a wide dynamic input power range from -10 dBm to 27 dBm, while exhibiting 78% of peak power efficiency at 22 dBm. The power harvester is designed to operate in the 900 MHz ISM band and suitable for Wireless Power Transfer applications.


title: FCNN: Fourier Convolutional Neural Networks

text: The Fourier domain is used in computer vision and machine learning as image analysis tasks in the Fourier domain are analogous to spatial domain methods but are achieved using different operations. Convolutional Neural Networks (CNNs) use machine learning to achieve state-of-the-art results with respect to many computer vision tasks. One of the main limiting aspects of CNNs is the computational cost of updating a large number of convolution parameters. Further, in the spatial domain, larger images take exponentially longer than smaller image to train on CNNs due to the operations involved in convolution methods. Consequently, CNNs are often not a viable solution for large image computer vision tasks. In this paper a Fourier Convolution Neural Network (FCNN) is proposed whereby training is conducted entirely within the Fourier domain. The advantage offered is that there is a significant speed up in training time without loss of effectiveness. Using the proposed approach larger images can therefore be processed within viable computation time. The FCNN is fully described and evaluated. The evaluation was conducted using the benchmark Cifar10 and MNIST datasets, and a bespoke fundus retina image dataset. The results demonstrate that convolution in the Fourier domain gives a significant speed up without adversely affecting accuracy. For simplicity the proposed FCNN concept is presented in the context of a basic CNN architecture, however, the FCNN concept has the potential to improve the speed of any neural network system involving convolution.


title: Information Gain-based Exploration Using Rao-Blackwellized Particle Filters

text: This paper presents an integrated approach to exploration, mapping, and localization. Our algorithm uses a highly efficient Rao-Blackwellized particle filter to represent the posterior about maps and poses. It applies a decision-theoretic framework which simultaneously considers the uncertainty in the map and in the pose of the vehicle to evaluate potential actions. Thereby, it trades off the cost of executing an action with the expected information gain and takes into account possible sensor measurements gathered along the path taken by the robot. We furthermore describe how to utilize the properties of the Rao-Blackwellization to efficiently compute the expected information gain. We present experimental results obtained in the real world and in simulation to demonstrate the effectiveness of our approach.


title: A Hybrid Approach to Vietnamese Word Segmentation Using Part of Speech Tags

text: Word segmentation is one of the most important tasks in NLP. This task, within Vietnamese language and its own features, faces some challenges, especially in words boundary determination. To tackle the task of Vietnamese word segmentation, in this paper, we propose the WS4VN system that uses a new approach based on Maximum matching algorithm combining with stochastic models using part-of-speech information. The approach can resolve word ambiguity and choose the best segmentation for each input sentence. Our system gives a promising result with an F-measure of 97%, higher than the results of existing publicly available Vietnamese word segmentation systems.


title: 20 years of the SMART protein domain annotation resource

text: SMART (Simple Modular Architecture Research Tool) is a web resource (http://smart.embl.de) for the identification and annotation of protein domains and the analysis of protein domain architectures. SMART version 8 contains manually curated models for more than 1300 protein domains, with approximately 100 new models added since our last update article (1). The underlying protein databases were synchronized with UniProt (2), Ensembl (3) and STRING (4), doubling the total number of annotated domains and other protein features to more than 200 million. In its 20th year, the SMART analysis results pages have been streamlined again and its information sources have been updated. SMART's vector based display engine has been extended to all protein schematics in SMART and rewritten to use the latest web technologies. The internal full text search engine has been redesigned and updated, resulting in greatly increased search speed.


title: 2-hop Blockchain : Combining Proof-of-Work and Proof-of-Stake Securely

text: Cryptocurrencies like Bitcoin have proven to be a phenomenal success. Bitcoin-like systems use proofof-work mechanism which is therefore considered as 1-hop blockchain, and their security holds if the majority of the computing power is under the control of honest players. However, this assumption has been seriously challenged recently and Bitcoin-like systems will fail when this assumption is broken. We propose the rst provably secure 2-hop blockchain by combining proof-of-work (rst hop) and proof-of-stake (second hop) mechanisms. On top of Bitcoin’s brilliant ideas of utilizing the power of the honest miners, via their computing resources, to secure the blockchain, we further leverage the power of the honest users/stakeholders, via their coins/stake, to achieve this goal. e security of our blockchain holds if the honest players control majority of the collective resources (which consists of both computing power and stake). at said, even if the adversary controls more than 50% computing power, the honest players still have the chance to defend the blockchain via honest stake. ∗An early version with title “Securing Bitcoin-like Blockchains against a Malicious Majority of Computing Power” appeared in ePrint Archive in July 2016. e current version shares the same motivation. But the construction idea and modeling approach have been completely revised. †Virginia Commonwealth University. Email: duong3@vcu.edu. ‡Shanghai Jiao Tong University. Most work done while visiting Cryptography Lab at Virginia Commonwealth University. Email: fanlei@sjtu.edu.cn. §Virginia Commonwealth University. Email: hszhou@vcu.edu.


title: An Approximation Algorithm for the Noah's Ark Problem with Random Feature Loss

text: The phylogenetic diversity (PD) of a set of species is a measure of their evolutionary distinctness based on a phylogenetic tree. PD is increasingly being adopted as an index of biodiversity in ecological conservation projects. The Noah's Ark Problem (NAP) is an NP-Hard optimization problem that abstracts a fundamental conservation challenge in asking to maximize the expected PD of a set of taxa given a fixed budget, where each taxon is associated with a cost of conservation and a probability of extinction. Only simplified instances of the problem, where one or more parameters are fixed as constants, have as of yet been addressed in the literature. Furthermore, it has been argued that PD is not an appropriate metric for models that allow information to be lost along paths in the tree. We therefore generalize the NAP to incorporate a proposed model of feature loss according to an exponential distribution and term this problem NAP with Loss (NAPL). In this paper, we present a pseudopolynomial time approximation scheme for NAPL.


title: Digital anthropometry for human body measurement on android platform

text: Anthropometry is the science of measuring the size and proportions of the human body. Based on the current researches, callipers and tape measures are the instruments used in measuring body length of an individual. Anthropometry is mostly conducted by the tailors as well as interior designers. This study aims to develop Digital Anthropometry for Human Body Measurement Using OpenCV on Android Platform which automatically measures human body using android phone. These body parts to be measured are represented by a marker; shoulder width, sleeve length, body length, back-waist length, back-neck-to-cuff, cross-back, outseam and inseam. For the accuracy, the distance requirement of the device is 30 inches away from the subject for upper body and 62 inches for the lower body. The required color shirt of the subject is a white and black pants or shorts, the required background color is white and bright surrounding is required.


title: Skin lesions on yellowfin tuna Thunnus albacares from Gulf of Mexico outer continental shelf: Morphological, molecular, and histological diagnosis of infection by a capsalid monogenoid.

text: We characterize lesion-associated capsaline infections on yellowfin tuna, Thunnus albacares, in the Gulf of Mexico by comparing our specimens with published descriptions and museum specimens ascribed to Capsala biparasiticum and its synonyms: vouchers of C. biparasiticum from parasitic copepods; the holotype of Capsala neothunni; and vouchers of Capsala abidjani. Those from parasitic copepods differed by having a small, rounded body, large anterior attachment organs, closely spaced dorsomarginal body sclerites, small testes, and a short and wide testicular field. No morphometric feature in the holotype of C. neothunni ranged outside of that reported for the newly-collected specimens, indicating conspecificity of our specimens. The specimens of C. abidjani differed by having a large anterior attachment organ, few and dendritic testes, and a short, wide testicular field. Large subunit ribosomal DNA (28S) sequences grouped our specimens and Capsala sp. as sister taxa and indicated a phylogenetic affinity of Nasicola klawei. The haptoral attachment site comprised a crater-like depression surrounded by a blackish-colored halo of extensively rugose skin, with abundant pockmarked-like, irregularly-shaped oblong or semi-circular epidermal pits surrounding these attachment sites. Histology confirmed extensive folding of epidermis and underlying stratum laxum, likely epidermal hyperplasia, foci of weak cell-to-cell adhesions among apical malpighian cells as well as that between stratum germinativum and stratum laxum, myriad goblet cells in epidermis, rodlet cells in apical layer of epidermis, and lymphocytic infiltrates and melanin in dermis. The present study comprises (i) the first published report of this parasite from yellowfin tuna captured in the Gulf of Mexico-NW Atlantic Ocean Basin, (ii) confirmation of its infection on the skin (rather than on a parasitic copepod), (iii) the first molecular data for this capsaline, and (iv) the first observations of histopathological changes associated with a capsalid infection on a wild-caught epipelagic fish.


title: Capturing Handwritten Ink Strokes with a Fast Video Camera

text: We present a system for capturing ink strokes written with ordinary pen and paper using a fast camera with a frame rate comparable to a stylus digitizer. From the video frames, ink strokes are extracted and used as input to an online handwriting recognition engine. A key component in our system is a pen up/down detection model for detecting the contact of the pen-tip with the paper in the video frames. The proposed model consists of feature representation with convolutional neural networks and classification with a recurrent neural network. We also use a high speed tracker with kernelized correlation filters to track the pen-tip. For training and evaluation, we collected labeled video data of users writing English and Japanese phrases from public datasets, and we report on character accuracy scores for different frame rates in the two languages.


title: Accelerating computer vision algorithms using OpenCL framework on the mobile GPU - A case study

text: Recently, general-purpose computing on graphics processing units (GPGPU) has been enabled on mobile devices thanks to the emerging heterogeneous programming models such as OpenCL. The capability of GPGPU on mobile devices opens a new era for mobile computing and can enable many computationally demanding computer vision algorithms on mobile devices. As a case study, this paper proposes to accelerate an exemplar-based inpainting algorithm for object removal on a mobile GPU using OpenCL. We discuss the methodology of exploring the parallelism in the algorithm as well as several optimization techniques. Experimental results demonstrate that our optimization strategies for mobile GPUs have significantly reduced the processing time and make computationally intensive computer vision algorithms feasible for a mobile device. To the best of the authors' knowledge, this work is the first published implementation of general-purpose computing using OpenCL on mobile GPUs.


title: Business ecosystem analysis framework

text: The concept of business ecosystems has gained popularity from researchers and practitioners since first introduced in Moore's article in Harvard Business Review in 1993 “Predators and prey: a new ecology of competition”. Inspired by biology, the concept of business ecosystems provides a metaphor to comprehend the inter-wined nature of industries [1]. The reason behind the popularity of this concept and its differentiation from other business theories like Osterwalder's Business Model Canvas [2] and Porter's Value Chain Approach [3] is its comprehensiveness and inclusion of several other components surrounding any business. In this paper, the necessary methods, components and tools for creating, analyzing and managing a business ecosystem successfully will be introduced. The aim of the paper is to propose a Business Ecosystem Analysis Framework that covers all the steps needed to analyze, manage and ensure the feasibility and sustainability of any ecosystem. This framework can be used to locate strengths and weaknesses of an ecosystem, identify its main actors and define how their maturity, interconnectedness and continuity can be improved. The results of the research can be applied to any industry sector.


title: A compact cavity filter with novel TM mode dielectric resonator structure

text: A novel transverse magnetic (TM) mode dielectric resonator and its filter realization are proposed for mobile communication system miniaturization applications. Single dielectric resonator and an eight-pole filter with three-transmission-zeros are modeled and designed for desired specification by using efficient optimization technique. About 30% horizontal dimensions are saved comparing with the common coaxial filter. Excellent filter responses are obtained and measured.


title: Exploiting Morphological Regularities in Distributional Word Representations

text: We present an unsupervised, language agnostic approach for exploiting morphological regularities present in high dimensional vector spaces. We propose a novel method for generating embeddings of words from their morphological variants using morphological transformation operators. We evaluate this approach on MSR word analogy test set (Mikolov et al., 2013d) with an accuracy of 85% which is 12% higher than the previous best known system.


title: Evolution for automatic assessment of the difficulty of sokoban boards

text: Many games have a collection of boards with the difficulty of an instance of the game determined by the starting configuration of the board. Correctly rating the difficulty of the boards is somewhat haphazard and required either a remarkable level of understanding of the game or a good deal of play-testing. In this study we explore evolutionary algorithms as a tool to automatically grade the difficulty of boards for a version of the game sokoban. Mean time-to-solution by an evolutionary algorithm and number of failures to solve a board are used as a surrogate for the difficulty of a board. Initial testing with a simple string-based representation, giving a sequence of moves for the sokoban agent, provided very little signal; it usually failed. Two other representations, based on a reactive linear genetic programming structure called an ISAc list, generated useful hardness-classification information for both hardness surrogates. These two representations differ in that one uses a randomly initialized population of ISAc lists while the other initializes populations with competent agents pre-trained on random collections of sokoban boards. The study encompasses four hardness surrogates: probability-of-failure and mean time-to-solution for each of these two representations. All four are found to generate similar information about board hardness, but probability-of-failure with pre-evolved agents is found to be faster to compute and to have a clearer meaning than the other three board-hardness surrogates.


title: Survey of microphthalmia in Japan

text: To report the current status of patients with microphthalmia based on a cross-sectional survey of patient hospital visits. A questionnaire was sent to the departments of ophthalmology in 1,151 major Japanese hospitals to survey the following: the number of patients with microphthalmia who visited the outpatient clinics between January 2008 and December 2009; gender; age; family history; associated ocular anomalies; complications and systemic diseases; surgical treatment; vision and management. A retrospective quantitative registry of 1,254 microphthalmic eyes (851 patients) from 454 hospitals (39.4%) was compiled. Of the patients for whom data were available, 50% ranged in age from 0 to 9 years. The major ocular findings were nanophthalmos, coloboma, and vitreoretinal malformations. Ocular complications frequently developed, including cataracts, glaucoma, and retinal detachment. Surgery was performed in 21.4% of all cases, and systemic diseases were present in 31% of all cases. The vision associated with microphthalmia exceeded 0.1 in about 30% of the eyes. Glasses and low vision aids were used by 21.6% of patients. Patients with microphthalmia often have ocular and systemic anomalies. Early assessment and preservation of vision and long-term complication management are needed.


title: Pose Estimation from Line Correspondences using Direct Linear Transformation

text: Ansar, the method by Ansar and Daniilidis (2003), implementation from Xu et al. (2016). Mirzaei, the method by Mirzaei and Roumeliotis (2011). RPnL, the method by Zhang et al. (2013). ASPnL, the method by Xu et al. (2016). LPnL Bar LS, the method by Xu et al. (2016). LPnL Bar ENull, the method by Xu et al. (2016). DLT-Lines, the method by Hartley and Zisserman (2004, p. 180), our implementation. DLT-Plücker-Lines, the method by Přibyl et al. (2015), our implementation. DLT-Combined-Lines, the proposed method.


title: New Maximum Power Point Tracker Using Sliding-Mode Observer for Estimation of Solar Array Current in the Grid-Connected Photovoltaic System

text: A new maximum power point tracker (MPPT) for a grid-connected photovoltaic system without solar array current sensor is proposed. The solar array current information is obtained from the sliding-mode observer and fed into the MPPT to generate the reference voltage. The parameter values such as capacitances can be changed up to 50% from their nominal values, and the linear observer cannot estimate the correct state values under the parameter variations and noisy environments. The structure of a sliding-mode observer is simple, but it shows the robust tracking property against modeling uncertainties and parameter variations. In this paper, the sliding-mode observer for the solar array current has been proposed to compensate for the parameter variations. The mathematical modeling and the experimental results verify the validity of the proposed method


title: THE CRIMINAL PSYCHOPATH: HISTORY, NEUROSCIENCE, TREATMENT, AND ECONOMICS.

text: The manuscript surveys the history of psychopathic personality, from its origins in psychiatric folklore to its modern assessment in the forensic arena. Individuals with psychopathic personality, or psychopaths, have a disproportionate impact on the criminal justice system. Psychopaths are twenty to twenty-five times more likely than non-psychopaths to be in prison, four to eight times more likely to violently recidivate compared to non-psychopaths, and are resistant to most forms of treatment. This article presents the most current clinical efforts and neuroscience research in the field of psychopathy. Given psychopathy's enormous impact on society in general and on the criminal justice system in particular, there are significant benefits to increasing awareness of the condition. This review also highlights a recent, compelling and cost-effective treatment program that has shown a significant reduction in violent recidivism in youth on a putative trajectory to psychopathic personality.


title: A Precoding-Based Multicarrier Non-Orthogonal Multiple Access Scheme for 5G Cellular Networks

text: Non-orthogonal multiple access (NOMA) has become one of the desirable schemes for the 5G cellular network standard due to its better cell coverage capability, higher data rate, and massive connectivity. Orthogonal frequency division multiplexing (OFDM) can be combined with NOMA to get the higher spectral efficiency. The main drawback of OFDM-based NOMA (OFDM-NOMA) scheme is high peak-to-average power ratio (PAPR). Therefore, this paper presents a new discrete-cosine transform matrix precoding-based uplink OFDM-NOMA scheme for PAPR reduction. Additionally, the proposed precoding-based uplink multicarrier NOMA scheme takes advantage of information spreading over the entire signal spectrum; thus, bit-error rate is also reduced. Simulation results show that the proposed precoding-based NOMA scheme outperforms as compared with the non-precoding-based NOMA schemes available in the literature.


title: Moral Responsibility and Determinism : The Cognitive Science of Folk Intuitions

text: The dispute between compatibilists and incompatibilists must be one of the most persistent and heated deadlocks in Western philosophy. Incompatibilists maintain that people are not fully morally responsible if determinism is true, i.e., if every event is an inevitable consequence of the prior conditions and the natural laws. By contrast, compatibilists maintain that even if determinism is true our moral responsibility is not undermined in the slightest, for determinism and moral responsibility are perfectly consistent.1 The debate between these two positions has invoked many different resources, including quantum mechanics, social psychology, and basic metaphysics. But recent discussions have relied heavily on arguments that draw on people’s intuitions about particular cases. Some philosophers have claimed that people have incompatibilist intuitions (e.g., Kane 1999, 218; Strawson 1986, 30; Vargas 2006); others have challenged this claim and suggested that people’s intuitions actually fit with compatibilism (Nahmias et al. 2005). But although philosophers have constructed increasingly sophisticated arguments about the implications of people’s intuitions, there has been remarkably little discussion about why people have the intuitions they do. That is to say, relatively little has been said about the specific psychological processes that generate or sustain people’s intuitions. And yet, it seems clear that questions about the sources of people’s intuitions could have a major impact on debates


title: Persian-English Machine Translation: An Overview of the Shiraz Project

text: This report describes the Shiraz project MT prototype for a Persian to English machine translation system using typed feature structures and unification. An overview of the linguistic properties of Persian is presented and the morphological and syntactic grammars developed within the Shiraz project are discussed. The underlying model for the system is a layered chart, capable of representing heterogeneous types of hypotheses in an integrated way. .. 2 ...... 2 ..... 4 ... 5 ..... 5 ... .. 9 ... 9 .... 9 .. 11 ... 11 ... 11 ... 11 13 ... 13 ..... 15 ..... 15 ... .... ... 16 ... 17 .... 17 ... 18 ... 18 ... 18 .... 19 ... 19 ... 19 .... 19 .... 20


title: False data injection attacks against state estimation in electric power grids

text:
A power grid is a complex system connecting electric power generators to consumers through power transmission and distribution networks across a large geographical area. System monitoring is necessary to ensure the reliable operation of power grids, and state estimation is used in system monitoring to best estimate the power grid state through analysis of meter measurements and power system models. Various techniques have been developed to detect and identify bad measurements, including interacting bad measurements introduced by arbitrary, nonrandom causes. At first glance, it seems that these techniques can also defeat malicious measurements injected by attackers.
 In this article, we expose an unknown vulnerability of existing bad measurement detection algorithms by presenting and analyzing a new class of attacks, called false data injection attacks, against state estimation in electric power grids. Under the assumption that the attacker can access the current power system configuration information and manipulate the measurements of meters at physically protected locations such as substations, such attacks can introduce arbitrary errors into certain state variables without being detected by existing algorithms. Moreover, we look at two scenarios, where the attacker is either constrained to specific meters or limited in the resources required to compromise meters. We show that the attacker can systematically and efficiently construct attack vectors in both scenarios to change the results of state estimation in arbitrary ways. We also extend these attacks to generalized false data injection attacks, which can further increase the impact by exploiting measurement errors typically tolerated in state estimation. We demonstrate the success of these attacks through simulation using IEEE test systems, and also discuss the practicality of these attacks and the real-world constraints that limit their effectiveness.


title: Real versus imagined locomotion: A [<ce:sup loc=post>18</ce:sup>F]-FDG PET-fMRI comparison

text: The cortical, cerebellar and brainstem BOLD-signal changes have been identified with fMRI in humans during mental imagery of walking. In this study the whole brain activation and deactivation pattern during real locomotion was investigated by [(18)F]-FDG-PET and compared to BOLD-signal changes during imagined locomotion in the same subjects using fMRI. Sixteen healthy subjects were scanned at locomotion and rest with [(18)F]-FDG-PET. In the locomotion paradigm subjects walked at constant velocity for 10 min. Then [(18)F]-FDG was injected intravenously while subjects continued walking for another 10 min. For comparison fMRI was performed in the same subjects during imagined walking. During real and imagined locomotion a basic locomotion network including activations in the frontal cortex, cerebellum, pontomesencephalic tegmentum, parahippocampal, fusiform and occipital gyri, and deactivations in the multisensory vestibular cortices (esp. superior temporal gyrus, inferior parietal lobule) was shown. As a difference, the primary motor and somatosensory cortices were activated during real locomotion as distinct to the supplementary motor cortex and basal ganglia during imagined locomotion. Activations of the brainstem locomotor centers were more prominent in imagined locomotion. In conclusion, basic activation and deactivation patterns of real locomotion correspond to that of imagined locomotion. The differences may be due to distinct patterns of locomotion tested. Contrary to constant velocity real locomotion (10 min) in [(18)F]-FDG-PET, mental imagery of locomotion over repeated 20-s periods includes gait initiation and velocity changes. Real steady-state locomotion seems to use a direct pathway via the primary motor cortex, whereas imagined modulatory locomotion an indirect pathway via a supplementary motor cortex and basal ganglia loop.


title: ToneTrack: Leveraging Frequency-Agile Radios for Time-Based Indoor Wireless Localization

text: Indoor localization of mobile devices and tags has received much attention recently, with encouraging fine-grained localization results available with enough line-of-sight coverage and hardware infrastructure. Some of the most promising techniques analyze the time-of-arrival of incoming signals, but the limited bandwidth available to most wireless transmissions fundamentally constrains their resolution. Frequency-agile wireless networks utilize bandwidths of varying sizes and locations in a wireless band to efficiently share the wireless medium between users. ToneTrack is an indoor location system that achieves sub-meter accuracy with minimal hardware and antennas, by leveraging frequency-agile wireless networks to increase the effective bandwidth. Our novel signal combination algorithm combines time-of-arrival data from different transmissions as a mobile device hops across different channels, approaching time resolutions previously not possible with a single narrowband channel. ToneTrack's novel channel combination and spectrum identification algorithms together with the triangle inequality scheme yield superior results even in non-line-of-sight scenarios with one to two walls separating client and APs and also in the case where the direct path from mobile client to an AP is completely blocked. We implement ToneTrack on the WARP hardware radio platform and use six of them served as APs to localize Wi-Fi clients in an indoor testbed over one floor of an office building. Experimental results show that ToneTrack can achieve a median 90 cm accuracy when 20 MHz bandwidth APs overhear three packets from adjacent channels.


title: The synthesis and rendering of eroded fractal terrains

text: In standard fractal terrain models based on fractional Brownian motion the statistical character of the surface is, by design, the same everywhere. A new approach to the synthesis of fractal terrain height fields is presented which, in contrast to previous techniques, features locally independent control of the frequencies composing the surface, and thus local control of fractal dimension and other statistical characteristics. The new technique, termed noise synthesis, is intermediate in difficulty of implementation, between simple stochastic subdivision and Fourier filtering or generalized stochastic subdivision, and does not suffer the drawbacks of creases or periodicity. Varying the local crossover scale of fractal character or the fractal dimension with altitude or other functions yields more realistic first approximations to eroded landscapes. A simple physical erosion model is then suggested which simulates hydraulic and thermal erosion processes to create gloabl stream/valley networks and talus slopes. Finally, an efficient ray tracing algorithm for general height fields, of which most fractal terrains are a subset, is presented.


title: Design of Two-stage High Gain Operational Amplifier Using Current Buffer Compensation for Low Power Applications

text: i ii ACKNOWLEDGEMENT I wish to express my sincere appreciation to my thesis advisor, Mr. B. K. Hemant for his timely, informative feedback and support during this effort. It has been a great pleasure to learn from him during the process of expanding and refining my research. He has been a generous mentor. I also like to express my gratefulness to Dr. his perpetual encouragement, generous help and inspiring guidance. for their support and guidance. I am also very grateful for all the help I received from my classmates.


title: Simplifying and improving mobile based data collection

text: Social programs often require collection of demographic and program related information. Pen-paper surveys have been the most favorable way of collecting such information. However, with proliferation of smartphones, low cost mobile connectivity with good coverage and availability of several data collection applications that can even work around the connectivity concerns, pen-paper surveys are now being replaced by mobile based data collection. In this work, we discuss the enhancement of Open Data Kit (ODK), an existing mobile based data collection application, with features such as pre-filling and validation SMS. The additional features were motivated from the real world requirements of reducing the data collection effort and minimizing the discrepancies in records for a project requiring information dissemination to beneficiaries of National Rural Employment Guarantee Scheme (NREGS), a government of India scheme. Data collection exercise, using our extended tool, is currently ongoing in two districts across two different states in India. Preliminary results from one deployment indicate that even with 8% of pre-filled fields in a form, there was a 16.1% decrease in the errors. Additionally, time per survey reduced with the pre-filling option. Preliminary results from another deployment indicate that 35% of the pre-filled forms had mismatched or changed information when compared with the data available in government records.


title: DakNet: rethinking connectivity in developing nations

text: DakNet provides extraordinarily low-cost digital communication, letting remote villages leapfrog past the expense of traditional connectivity solutions and begin development of a full-coverage broadband wireless infrastructure. What is the basis for a progressive, market-driven migration from e-governance to universal broadband connectivity that local users will pay for? DakNet, an ad hoc network that uses wireless technology to provide asynchronous digital connectivity, is evidence that the marriage of wireless and asynchronous service may indeed be the beginning of a road to universal broadband connectivity. DakNet has been successfully deployed in remote parts of both India and Cambodia at a cost two orders of magnitude less than that of traditional landline solutions.


title: Open data kit: tools to build information services for developing regions

text: This paper presents Open Data Kit (ODK), an extensible, open-source suite of tools designed to build information services for developing regions. ODK currently provides four tools to this end: Collect, Aggregate, Voice, and Build. Collect is a mobile platform that renders application logic and supports the manipulation of data. Aggregate provides a "click-to-deploy" server that supports data storage and transfer in the "cloud" or on local servers. Voice renders application logic using phone prompts that users respond to with keypad presses. Finally, Build is a application designer that generates the logic used by the tools. Designed to be used together or independently, ODK core tools build on existing open standards and are supported by an open-source community that has contributed additional tools. We describe four deployments that demonstrate how the decisions made in the system architecture of ODK enable services that can both push and pull information in developing regions.


title: MyExperience: a system for in situ tracing and capturing of user feedback on mobile phones

text: This paper presents MyExperience, a system for capturing both objective and subjective in situ data on mobile computing activities. MyExperience combines the following two techniques: 1) passive logging of device usage, user context, and environmental sensor readings, and 2) active context-triggered user experience sampling to collect in situ, subjective user feedback. MyExperience currently runs on mobile phones and supports logging of more than 140 event types, including: 1) device usage such as communication, application usage, and media capture, 2) user context such as calendar appointments, and 3) environmental sensing such as Bluetooth and GPS. In addition, user experience sampling can be targeted to moments of interest by triggering off sensor readings. We present several case studies of field deployments on people's personal phones to demonstrate how MyExperience can be used effectively to understand how people use and experience mobile technology.


title: Study of Brightness Preservation Histogram Equalization Techniques

text: Image captured under various environment conditions with low contrast results in visual deterioration. To enhance the contrast of images a number of techniques have been proposed over the years. Histogram equalization is one of those methods which could be used for this purpose. For the contrast enhancement purpose histogram equalization (HE) attempts reduction in the number of gray levels. Contrast enhancement by histogram equalization method involve mapping of gray levels on the basis of changeover function that is derived from the probability distribution of gray levels embedded in input image. For this, the neighbouring gray levels in image having low probability density values are combined into single gray level value. Whereas for neighbouring gray levels having high probability density value the gap between them is increased, HE often makes over-enhancement of images with frequent gray level region resulting in loss of contrast for less frequent area of the images. Thus, the HE technique is not able to preserve image brightness. So wherever the preservation of image brightness is required this method is not preferred. This paper review various histogram equalization techniques used in preserving image brightness as well as the contrast enhancement is presented. The key approach for this aim is to use image segmentation. The criteria used are decomposition of images and the presentation of the basic difference between these techniques. For comparative study of performance of these techniques, absolute mean brightness error (AMBE) is used to determine degree of brightness preservation while peak signal to noise ratio (PSNR) have been used to fine\ degree of contrast enhancement.


title: Recurrent neural network based recommendation for time heterogeneous feedback

text: In recommender systems, several kinds of user feedback with time stamps are collected and used for recommendation, which is called the time heterogeneous feedback recommendation problem. The existing recommendation methods can handle only one kind of feedback or ignore the time stamps of the feedback. To solve the time heterogeneous feedback recommendation problem, in this paper, we propose a recurrent neural network model to predict the probability that the user will access an item given the time heterogeneous feedback of this user. To our best knowledge, it is the first time to solve the time heterogeneous feedback recommendation problem by deep neural network model. The proposed model is learned by back propagation algorithm and back propagation through time algorithm. The comparison results on four real-life datasets indicate that the proposed method outperforms the compared state-ofthe-art approaches. © 2016 Elsevier B.V. All rights reserved.


title: GaitViewer: Semantic Gait Data Analysis and Visualization Tool

text: Clinical gait analysis studies human locomotion by characterizing movement patterns with heterogeneous acquired gait data (e.g., spatio-temporal parameters, geometry of motion, measures of force). Lack of semantic integration of these heterogeneous data slows down collaborative studies among the laboratories that have different acquisition systems. In this work we propose a semantic integration methodology for gait data, and present GaitViewer a prototype web application for semantic analysis and visualization of gait data. The proposed semantic integration methodology separates heterogeneous and mixed numerical and meta information in gait data. Ontology concepts represent the separated meta information, while numerical information is stored in a NoSQL database. Parallel coordinates visual analytics technique are used as an interface to the analytics tools proposed by the NoSQL database. We tailor GaitViewer for two common use-cases in clinical gait analysis: correlation of measured signals for different subjects, and follow-up analysis of the same subject. Finally, we discuss the potential of a large-scale adoption of frameworks such as GaitViewer for the next generation diagnosis systems for movement disorders.


title: The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions

text: One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. Extracting the information required to answer them demands a variety of image operations from detection and counting, to segmentation and reconstruction. To train a method to perform even one of these operations accurately from {image, question, answer} tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best. Our method thus learns how to exploit a set of external off-the-shelf algorithms to achieve its goal, an approach that has something in common with the Neural Turing Machine [10]. The core of our proposed method is a new co-attention model. In addition, the proposed approach generates human-readable reasons for its decision, and can still be trained end-to-end without ground truth reasons being given. We demonstrate the effectiveness on two publicly available datasets, Visual Genome and VQA, and show that it produces the state-of-the-art results in both cases.


title: BJOLP : The Big Joint Optimal Landmarks Planner

text: BJOLP, The Big Joint Optimal Landmarks Planner uses landmarks to derive an admissible heuristic, which is then used to guide a search for a cost-optimal plan. In this paper we review landmarks and describe how they can be used to derive an admissible heuristic. We conclude with presenting the BJOLP


title: On Minimal Sets to Destroy the k-Core in Random Networks

text: We study the problem of finding the smallest set of nodes in a network whose removal results in an empty k-core; where the k-core is the sub-network obtained after the iterative removal of all nodes of degree smaller than k. This problem is also known in the literature as finding the minimal contagious set. The main contribution of our work is an analysis of the performance of the recently introduced corehd algorithm [Scientific Reports, 6, 37954 (2016)] on random networks taken from the configuration model via a set of deterministic differential equations. Our analyses provides upper bounds on the size of the minimal contagious set that improve over previously known bounds. Our second contribution is a new heuristic called the weak-neighbor algorithm that outperforms all currently known local methods in the regimes considered.


title: Random k-Labelsets for Multilabel Classification

text: A simple yet effective multilabel learning method, called label powerset (LP), considers each distinct combination of labels that exist in the training set as a different class value of a single-label classification task. The computational efficiency and predictive performance of LP is challenged by application domains with large number of labels and training examples. In these cases, the number of classes may become very large and at the same time many classes are associated with very few training examples. To deal with these problems, this paper proposes breaking the initial set of labels into a number of small random subsets, called labelsets and employing LP to train a corresponding classifier. The labelsets can be either disjoint or overlapping depending on which of two strategies is used to construct them. The proposed method is called RAkEL (RAndom k labELsets), where k is a parameter that specifies the size of the subsets. Empirical evidence indicates that RAkEL manages to improve substantially over LP, especially in domains with large number of labels and exhibits competitive performance against other high-performing multilabel learning methods.


title: Financial Frictions and the Persistence of History : A Quantitative Exploration

text: The transitional dynamics of the neoclassical growth model is at odds with the growth experiences of many developing economies. For example, consider the post-communist transition of Eastern Europe: Following liberalization, output grew slowly (and even fell), while interest rates and investment rates remained low, contrary to the predictions of the standard growth theory. We incorporate financial frictions and resource misallocation into an otherwise-standard growth model, and account for the observed growth dynamics. We discipline the model by calibrating its stationary equilibrium to the US data on standard macroeconomic aggregates, wealth distribution, and firms’ internal v. external financing. Our model economy converges slowly to the steady state, with the interest rate and the investment rate starting low and rising over time. We also obtain a new result on heterogeneity and macroeconomic dynamics: Even though strict approximate aggregation—that relies only on unconditional first moments—does not hold in our economy, the effect of heterogeneity on aggregate dynamics is almost completely captured by conditional first moments. Department of Economics, Northwestern University, 2001 Sheridan Road, Evanston, IL 60208, USA (e-mail: f-buera@northwestern.edu). Department of Economics, University of Wisconsin, 1180 Observatory Drive, Madison, WI 53706, USA (email: yshin@ssc.wisc.edu). The transitional dynamics of the neoclassical growth model is characterized by swift convergence to the steady state, staggeringly high real interest rates in the early stages of development, and decreasing investment-to-output ratio over time. However, most economies’ growth experiences are at odds with these neoclassical predictions. One such example is the “miracle” economies of East Asia. As Figure 1 illustrates, growth and investment rates were relatively low initially and peaked in the latter stages. More dramatic evidence against the neoclassical growth model can be found in the post-communist transition of the Eastern European economies. The liberalization of these economies was followed by disappointingly low—even negative—growth rates and slowly increasing investment rates (Figure 2).1 Even after going through an exhaustive list of modified neoclassical growth models, King and Rebelo (1993) conclude that neoclassical transitional dynamics can only play a minor role in explaining observed growth experiences. This is a fundamental challenge for the neoclassical growth theory. Output Per Capita Relative to the US + + + + + + + + + + + JPN KOR SGP bc bc bc bc bc bc bc bc bc bc TWN rs rs rs rs rs rs rs rs rs rs rs THA 195


title: Neural interactions between motor cortical hemispheres during bimanual and unimanual arm movements.

text: Cortico-cortical connections through the corpus callosum are a major candidate for mediating bimanual coordination. However, aside from the deficits observed after lesioning this connection, little positive evidence indicates its function in bimanual tasks. In order to address this issue, we simultaneously recorded neuronal activity at multiple sites within the arm area of motor cortex in both hemispheres of awake primates performing different bimanual and unimanual movements. By employing an adapted form of the joint peri-stimulus time histogram technique, we discovered rapid movement-related correlation changes between the local field potentials (LFPs) of the two hemispheres that escaped detection by time-averaged cross-correlation methods. The frequency and amplitude of dynamic modifications in correlations between the hemispheres were similar to those within the same hemisphere. As in previous EEG studies, we found that, on average, correlation decreased during movements. However, a subset of recording site pairs did show transiently increased correlations around movement onset (57% of all pairs and conditions in monkey G, 39% in monkey P). In interhemispheric pairs, these increases were consistently related to the mode of coupling between the two arms. Both the correlations between the movements themselves and the interhemispheric LFP correlation increases were strongest during bimanual symmetric movements, and weaker during bimanual asymmetric and unimanual movements. Increased correlations occurred mainly around movement onset, whilst decreases in correlation dominated during movement execution. The task-specific way in which interhemispheric correlations are modulated is compatible with the notion that interactions between the hemispheres contribute to behavioural coupling between the arms.


title: Modeling and performance evaluation of IEEE 802.22 physical layer

text: IEEE 802.22, also called Wireless Regional Area Network (WRAN), is the newest wireless standard being developed for remote and rural areas. In this paper an overview of the standard, and more specifically its PHY layer is introduced. In order to evaluate the performance of the system, we model the PHY layer in MATLAB/SIMULINK and extract the Bit Error Rate (BER) of the system for different code rates and modulation schemes with noisy channel.


title: Using deep networks for fraud detection in the credit card transactions

text: Deep learning is a very noteworthy technic that is take into consideration in the several fields. One of the most attractive subjects that need more attention in the prediction accuracy is fraud detection. As the deep network can gradually learn the concepts of any complicated problem, using this technic in this realm is very beneficial. To do so, we propose a deep autoencoder to extract best features from the information of the credit card transactions and then append a softmax network to determine the class labels. Regarding the effect of features in such data employing an overcomplete autoencoder can map data to a high dimensional space and using the sparse models leads to be in a discriminative space that is useful for classification aims. The benefit of this method is the generality virtues that we can use such networks in several realms e.g. national intelligence, cyber security, marketing, medical informatics and so on. Another advantage is the ability to facing big datasets. As the learning phase is offline we can use it for a huge amount of data and generalize that is earned. Results can reveal the advantages of proposed method comparing to the state of the arts.


title: Robust control and H∞-optimization - Tutorial paper

text: -The paper presents a tutorial exposition of ~=-optimal regulation theory, emphasizing the relevance of the mixed sensitivity problem for robust control system design.


title: Rogue-Access-Point Detection: Challenges, Solutions, and Future Directions

text: Rogue devices are an increasingly dangerous reality in the insider threat problem domain. Industry, government, and academia need to be aware of this problem and promote state-of-the-art detection methods.


title: Using Summarization to Discover Argument Facets in Online Idealogical Dialog

text: More and more of the information available on the web is dialogic, and a significant portion of it takes place in online forum conversations about current social and political topics. We aim to develop tools to summarize what these conversations are about. What are the CENTRAL PROPOSITIONS associated with different stances on an issue; what are the abstract objects under discussion that are central to a speaker’s argument? How can we recognize that two CENTRAL PROPOSITIONS realize the same FACET of the argument? We hypothesize that the CENTRAL PROPOSITIONS are exactly those arguments that people find most salient, and use human summarization as a probe for discovering them. We describe our corpus of human summaries of opinionated dialogs, then show how we can identify similar repeated arguments, and group them into FACETS across many discussions of a topic. We define a new task, ARGUMENT FACET SIMILARITY (AFS), and show that we can predict AFS with a .54 correlation score, versus an ngram system baseline of .39 and a semantic textual similarity system baseline of .45.


Ensemble Methods in Machine Learning


title: QELAR: A Machine-Learning-Based Adaptive Routing Protocol for Energy-Efficient and Lifetime-Extended Underwater Sensor Networks

text: Underwater sensor network (UWSN) has emerged in recent years as a promising networking technique for various aquatic applications. Due to specific characteristics of UWSNs, such as high latency, low bandwidth, and high energy consumption, it is challenging to build networking protocols for UWSNs. In this paper, we focus on addressing the routing issue in UWSNs. We propose an adaptive, energy-efficient, and lifetime-aware routing protocol based on reinforcement learning, QELAR. Our protocol assumes generic MAC protocols and aims at prolonging the lifetime of networks by making residual energy of sensor nodes more evenly distributed. The residual energy of each node as well as the energy distribution among a group of nodes is factored in throughout the routing process to calculate the reward function, which aids in selecting the adequate forwarders for packets. We have performed extensive simulations of the proposed protocol on the Aqua-sim platform and compared with one existing routing protocol (VBF) in terms of packet delivery rate, energy efficiency, latency, and lifetime. The results show that QELAR yields 20 percent longer lifetime on average than VBF.


title: Mechanical design of a novel Hand Exoskeleton for accurate force displaying

text: This paper deals with the mechanical design of a novel haptic Hand Exoskeleton (HE) that allows exerting controlled forces on the fingertip of the index and thumb of the operator. The proposed device includes several design solutions for optimizing the accuracy and mechanical performances. Remote Centers of Motion mechanisms have been adopted for delocalizing the encumbrance of linkages of the structure away from the operator's fingers. An improved stiffness of the transmission and reduced requirements for the actuators have been achieved thanks to a novel Patent Pending principle for integrating speed reduction ratio with the transmission system.


title: End-fire Quasi-Yagi antennas with pattern diversity on LTCC technology for 5G mobile communications

text: We have designed two end-fire antennas on LTCC with horizontal and vertical polarizations respectively. The antennas operate at 38GHz, a potential frequency for 5G applications. The horizontally-polarized antenna provide a broadband performance about 27% and 6dB end-fire gain and the vertically-polarized one provide a 12.5% bandwidth and 5dB gain. Both antenna are integrated under a compact substrate. Excellent isolation is achieved between the nearby elements making these antennas suitable for corner elements in 5G mobile system.


title: Algorithmic Bias in Autonomous Systems

text: Algorithms play a key role in the functioning of autonomous systems, and so concerns have periodically been raised about the possibility of algorithmic bias. However, debates in this area have been hampered by different meanings and uses of the term, “bias.” It is sometimes used as a purely descriptive term, sometimes as a pejorative term, and such variations can promote confusion and hamper discussions about when and how to respond to algorithmic bias. In this paper, we first provide a taxonomy of different types and sources of algorithmic bias, with a focus on their different impacts on the proper functioning of autonomous systems. We then use this taxonomy to distinguish between algorithmic biases that are neutral or unobjectionable, and those that are problematic in some way and require a response. In some cases, there are technological or algorithmic adjustments that developers can use to compensate for problematic bias. In other cases, however, responses require adjustments by the agent, whether human or autonomous system, who uses the results of the algorithm. There is no “one size fits all” solution to algorithmic bias.


title: A power line communication network infrastructure for the smart home

text: Low voltage electrical wiring in homes has largely been dismissed as too noisy and unpredictable to support high speed communication signals. However, recent advances in communication and modulation methodologies as well as in adaptive digital signal processing and error detection and correction have spawned novel media access control (MAC) and physical layer (PHY) protocols, capable of supporting power line communication networks at speeds comparable to wired local area networks (LANs). In this paper we motivate the use of power line LAN’s as a basic infrastructure for building integrated ”smart homes,” wherein information appliances (IA)—ranging from simple control or monitoring devices to multimedia entertainment systems—are seamlessly interconnected by the very wires which provide them electricity. By simulation and actual measurements using ”reference design” prototype commercial powerline products, we show that the HomePlugMAC and PHY layers can guarantee QoS for real-time communications, supporting delay sensitive data streams for ”Smart Home” applications.


title: Learning Deep Boltzmann Machines using Adaptive MCMC

text: When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with many local minima separated by high energy barriers. The commonly used Gibbs sampler tends to get trapped in one local mode, which often results in unstable learning dynamics and leads to poor parameter estimates. In this paper, we concentrate on learning DBM’s using adaptive MCMC algorithms. We first show a close connection between Fast PCD and adaptive MCMC. We then develop a Coupled Adaptive Simulated Tempering algorithm that can be used to better explore a highly multimodal energy landscape. Finally, we demonstrate that the proposed algorithm considerably improves parameter estimates, particularly when learning large-scale DBM’s.


title: Classification using discriminative restricted Boltzmann machines

text: Recently, many applications for Restricted Boltzmann Machines (RBMs) have been developed for a large variety of learning problems. However, RBMs are usually used as feature extractors for another learning algorithm or to provide a good initialization for deep feed-forward neural network classifiers, and are not considered as a standalone solution to classification problems. In this paper, we argue that RBMs provide a self-contained framework for deriving competitive non-linear classifiers. We present an evaluation of different learning algorithms for RBMs which aim at introducing a discriminative component to RBM training and improve their performance as classifiers. This approach is simple in that RBMs are used directly to build a classifier, rather than as a stepping stone. Finally, we demonstrate how discriminative RBMs can also be successfully employed in a semi-supervised setting.


title: Deep Belief Networks Are Compact Universal Approximators

text: Deep belief networks (DBN) are generative models with many layers of hidden causal variables, recently introduced by Hinton, Osindero, and Teh (2006), along with a greedy layer-wise unsupervised learning algorithm. Building on Le Roux and Bengio (2008) and Sutskever and Hinton (2008), we show that deep but narrow generative networks do not require more parameters than shallow ones to achieve universal approximation. Exploiting the proof technique, we prove that deep but narrow feedforward neural networks with sigmoidal units can represent any Boolean expression.


title: Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000

text:
BACKGROUND
Information about the distribution of causes of and time trends for child mortality should be periodically updated. We report the latest estimates of causes of child mortality in 2010 with time trends since 2000.

METHODS
Updated total numbers of deaths in children aged 0-27 days and 1-59 months were applied to the corresponding country-specific distribution of deaths by cause. We did the following to derive the number of deaths in children aged 1-59 months: we used vital registration data for countries with an adequate vital registration system; we applied a multinomial logistic regression model to vital registration data for low-mortality countries without adequate vital registration; we used a similar multinomial logistic regression with verbal autopsy data for high-mortality countries; for India and China, we developed national models. We aggregated country results to generate regional and global estimates.

FINDINGS
Of 7·6 million deaths in children younger than 5 years in 2010, 64·0% (4·879 million) were attributable to infectious causes and 40·3% (3·072 million) occurred in neonates. Preterm birth complications (14·1%; 1·078 million, uncertainty range [UR] 0·916-1·325), intrapartum-related complications (9·4%; 0·717 million, 0·610-0·876), and sepsis or meningitis (5·2%; 0·393 million, 0·252-0·552) were the leading causes of neonatal death. In older children, pneumonia (14·1%; 1·071 million, 0·977-1·176), diarrhoea (9·9%; 0·751 million, 0·538-1·031), and malaria (7·4%; 0·564 million, 0·432-0·709) claimed the most lives. Despite tremendous efforts to identify relevant data, the causes of only 2·7% (0·205 million) of deaths in children younger than 5 years were medically certified in 2010. Between 2000 and 2010, the global burden of deaths in children younger than 5 years decreased by 2 million, of which pneumonia, measles, and diarrhoea contributed the most to the overall reduction (0·451 million [0·339-0·547], 0·363 million [0·283-0·419], and 0·359 million [0·215-0·476], respectively). However, only tetanus, measles, AIDS, and malaria (in Africa) decreased at an annual rate sufficient to attain the Millennium Development Goal 4.

INTERPRETATION
Child survival strategies should direct resources toward the leading causes of child mortality, with attention focusing on infectious and neonatal causes. More rapid decreases from 2010-15 will need accelerated reduction for the most common causes of death, notably pneumonia and preterm birth complications. Continued efforts to gather high-quality data and enhance estimation methods are essential for the improvement of future estimates.

FUNDING
The Bill & Melinda Gates Foundation.


title: Theory and Applications of Robust Optimization

text: In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus will be on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying the most prominent theoretical results of RO over the past decade, we will also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we will highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.


title: Building a motor simulation de novo: Observation of dance by dancers

text: Research on action simulation identifies brain areas that are active while imagining or performing simple overlearned actions. Are areas engaged during imagined movement sensitive to the amount of actual physical practice? In the present study, participants were expert dancers who learned and rehearsed novel, complex whole-body dance sequences 5 h a week across 5 weeks. Brain activity was recorded weekly by fMRI as dancers observed and imagined performing different movement sequences. Half these sequences were rehearsed and half were unpracticed control movements. After each trial, participants rated how well they could perform the movement. We hypothesized that activity in premotor areas would increase as participants observed and simulated movements that they had learnt outside the scanner. Dancers' ratings of their ability to perform rehearsed sequences, but not the control sequences, increased with training. When dancers observed and simulated another dancer's movements, brain regions classically associated with both action simulation and action observation were active, including inferior parietal lobule, cingulate and supplementary motor areas, ventral premotor cortex, superior temporal sulcus and primary motor cortex. Critically, inferior parietal lobule and ventral premotor activity was modulated as a function of dancers' ratings of their own ability to perform the observed movements and their motor experience. These data demonstrate that a complex motor resonance can be built de novo over 5 weeks of rehearsal. Furthermore, activity in premotor and parietal areas during action simulation is enhanced by the ability to execute a learned action irrespective of stimulus familiarity or semantic label.


title: A Novel Miniature YIG Tuned Oscillator Achieves Octave Tuning Bandwidth with Ultra Low Phase Noise in X and Ku Bands

text: Traditional YIG tuned oscillators use negative resistance type circuits and large magnetic structure to produce excellent phase noise with octave plus tuning ranges, but at the expense of size and weight. This paper describes a revolutionary approach to YIG tuned oscillator design which makes use of a unique combination of a miniaturized magnetic structure, and a GaAs HBT ring oscillator circuit topology, working through a YIG tuned filter to produce octave tuning ranges in X and Ku bands with phase noise less than -125 dBC per Hz at 100 KHz. Frequency pulling is less than 10 KHz into a 2:1 VSWR load over all phases. Simulations and measured data are in excellent agreement


title: Perceptual category mapping between English and Korean obstruents in non-CV positions: Prosodic location effects in second language identification skills

text: This study examines the degree to which mapping patterns between native language (L1) and second language (L2) categories for one prosodic context will generalize to other prosodic contexts, and how position-specific neutralization in the L1 influences the category mappings. Forty L1-Korean learners of English listened to English nonsense words consisting of /p b t d f v h ð/ and /ɑ/, with the consonants appearing in pre-stressed intervocalic, poststressed intervocalic, or coda context, and were asked to identify the consonant with both Korean and English labeling and to give gradient evaluations of the goodness of each label to the stimuli. Results show that the mapping patterns differ extensively from those found previously with the same subjects for consonants in initial, onset context. The mapping patterns for the intervocalic context also differed by position with respect to stress location. Coda consonants elicited poor goodness-of-fit and noisier mapping patterns for all segments, suggesting that an L1 coda neutralization process put all L2-English sounds in codas as “new” sounds under the Speech Learning Model (SLM) framework (Flege, 1995). Taken together, the results indicate that consonant learning needs to be evaluated in terms of position-by-position variants, rather than just being a general property of the overall


title: Viraliency: Pooling Local Virality

text: In our overly-connected world, the automatic recognition of virality – the quality of an image or video to be rapidly and widely spread in social networks – is of crucial importance, and has recently awaken the interest of the computer vision community. Concurrently, recent progress in deep learning architectures showed that global pooling strategies allow the extraction of activation maps, which highlight the parts of the image most likely to contain instances of a certain class. We extend this concept by introducing a pooling layer that learns the size of the support area to be averaged: the learned top-N average (LENA) pooling. We hypothesize that the latent concepts (feature maps) describing virality may require such a rich pooling strategy. We assess the effectiveness of the LENA layer by appending it on top of a convolutional siamese architecture and evaluate its performance on the task of predicting and localizing virality. We report experiments on two publicly available datasets annotated for virality and show that our method outperforms state-of-the-art approaches.


title: Indirect Image Registration with Large Diffeomorphic Deformations

text: The paper adapts the large deformation diffeomorphic metric mapping framework for image registration to the indirect setting where a template is registered against a target that is given through indirect noisy observations. The registration uses diffeomorphisms that transform the template through a (group) action. These diffeomorphisms are generated by solving a flow equation that is defined by a velocity field with certain regularity. The theoretical analysis includes a proof that indirect image registration has solutions (existence) that are stable and that converge as the data error tends so zero, so it becomes a well-defined regularization method. The paper concludes with examples of indirect image registration in 2D tomography with very sparse and/or highly noisy data.


title: Shape-based recognition of wiry objects

text: We present an approach to the recognition of complex-shaped objects in cluttered environments based on edge information. We first use example images of a target object in typical environments to train a classifier cascade that determines whether edge pixels in an image belong to an instance of the desired object or the clutter. Presented with a novel image, we use the cascade to discard clutter edge pixels and group the object edge pixels into overall detections of the object. The features used for the edge pixel classification are localized, sparse edge density operations. Experiments validate the effectiveness of the technique for recognition of a set of complex objects in a variety of cluttered indoor scenes under arbitrary out-of-image-plane rotation. Furthermore, our experiments suggest that the technique is robust to variations between training and testing environments and is efficient at runtime.


title: Consistent Linear Tracker With Converted Range, Bearing, and Range Rate Measurements

text: Active sonar and radar systems often include a measurement of range rate in addition to the position-based measurements of range and bearing. Due to the nonlinearity of the range rate measurement with respect to a target state in the Cartesian coordinate system, this measurement is not always fully exploited. The state of the art methods to utilize range rate include the extended Kalman filter and the unscented Kalman filter with sequential processing of the position based measurements and the range rate measurement. Common to these approaches is that the measurement prediction function remains nonlinear. The goal of this work is to develop a measurement conversion from range, bearing, and range rate to Cartesian position and velocity that is unbiased and consistent, with appropriate elimination of estimation bias. The converted measurement is then used with a linear Kalman filter. Performance of this new method is compared to state of the art techniques and shown to match or exceed that of existing techniques over a wide range of scenarios.


title: A term-based methodology for query reformulation understanding

text: Key to any research involving session search is the understanding of how a user’s queries evolve throughout the session. When a user creates a query reformulation, he or she is consciously retaining terms from their original query, removing others and adding new terms. By measuring the similarity between queries we can make inferences on the user’s information need and how successful their new query is likely to be. By identifying the origins of added terms we can infer the user’s motivations and gain an understanding of their interactions. In this paper we present a novel term-based methodology for understanding and interpreting query reformulation actions. We use TREC Session Track data to demonstrate how our technique is able to learn from query logs and we make use of click data to test user interaction behavior when reformulating queries. We identify and evaluate a range of term-based query reformulation strategies and show that our methods provide valuable insight into understanding query reformulation in session search.


title: Object-Aware Identification of Microservices

text: Microservices is an architectural style inspired by service-oriented computing that structures an application as a collection of cohesive and loosely coupled components, which implement business capabilities. One of today’s problems in designing microservice architectures is to decompose a system into cohesive, loosely coupled, and fine-grained microservices. Identification of microservices is usually performed intuitively, based on the experience of the system designers, however, if the functionalities of a system are highly interconnected, it is a challenging task to decompose the system into appropriate microservices. To tackle this challenge, we present a microservice identification method that decomposes a system using clustering technique. To this end, we model a system as a set of business processes and take two aspects of structural dependency and data object dependency of functionalities into account. Furthermore, we conduct a study to evaluate the effect of process characteristics on the accuracy of identification approaches.


title: On the Reception and Detection of Pseudo-profound Bullshit

text: Although bullshit is common in everyday life and has attracted attention from philosophers, its reception (critical or ingenuous) has not, to our knowledge, been subject to empirical investigation. Here we focus on pseudo-profound bullshit, which consists of seemingly impressive assertions that are presented as true and meaningful but are actually vacuous. We presented participants with bullshit statements consisting of buzzwords randomly organized into statements with syntactic structure but no discernible meaning (e.g., “Wholeness quiets infinite phenomena”). Across multiple studies, the propensity to judge bullshit statements as profound was associated with a variety of conceptually relevant variables (e.g., intuitive cognitive style, supernatural belief). Parallel associations were less evident among profundity judgments for more conventionally profound (e.g., “A wet person does not fear the rain”) or mundane (e.g., “Newborn babies require constant attention”) statements. These results support the idea that some people are more receptive to this type of bullshit and that detecting it is not merely a matter of indiscriminate skepticism but rather a discernment of deceptive vagueness in otherwise impressive sounding claims. Our results also suggest that a bias toward accepting statements as true may be an important component of pseudo-profound bullshit receptivity.


title: Model Shrinking for Embedded Keyword Spotting

text: In this paper we present two approaches to improve computational efficiency of a keyword spotting system running on a resource constrained device. This embedded keyword spotting system detects a pre-specified keyword in real time at low cost of CPU and memory. Our system is a two stage cascade. The first stage extracts keyword hypotheses from input audio streams. After the first stage is triggered, hand-crafted features are extracted from the keyword hypothesis and fed to a support vector machine (SVM) classifier on the second stage. This paper focuses on improving the computational efficiency of the second stage SVM classifier. More specifically, select a subset of feature dimensions and merge the SVM classifier to a smaller size, while maintaining the keyword spotting performance. Experimental results indicate that we can remove more than 36% of the non-discriminative SVM features, and reduce the number of support vectors by more than 60% without significant performance degradation. This results in more than 15% relative reduction in CPU utilization.


title: Predictive Analytics On Public Data - The Case Of Stock Markets

text: This work examines the predictive power of public data by aggregating information from multiple online sources. Our sources include microblogging sites like Twitter, online message boards like Yahoo! Finance, and traditional news articles. The subject of prediction are daily stock price movements from Standard & Poor’s 500 index (S&P 500) during a period from June 2011 to November 2011. To forecast price movements we filter messages by stocks, apply state-of-the-art sentiment analysis to message texts, and aggregate message sentiments to generate trading signals for daily buy and sell decisions. We evaluate prediction quality through a simple trading model considering real-world limitations like transaction costs or broker commission fees. Considering 833 virtual trades, our model outperformed the S&P 500 and achieved a positive return on investment of up to ~0.49% per trade or ~0.24% when adjusted by market, depending on supposed trading costs.


title: Soft pneumatic actuator with adjustable stiffness layers for Multi-DoF Actuation

text: The soft pneumatic actuators (SPAs) are a solution toward the highly customizable and light actuators with the versatility of actuation modes, and an inherent compliance. Such flexibility allows SPAs to be considered as alternative actuators for wearable rehabilitative devices and search and rescue robots. The actuator material and air-chamber design dictate the actuator's mechanical performance. Therefore, each actuator design with a single pressure source produces a highly customized motion but only a single degree of freedom (DoF). We present a novel design and fabrication method for a SPA with different modes of actuation using integrated adjustable stiffness layers (ASLs). Unlike the most SPA designs where one independent chamber is needed for each mode of actuation, here we have a single chamber that drives three different modes of actuation by activating different combinations of ASLs. Adapting customized micro heaters and thermistors for modulating the temperature and stiffness of ASLs, we considerably broaden the work space of the SPA actuator. Here, a thorough characterization of the materials and the modeling of the actuator are presented. We propose a design methodology for developing application specific actuators with multi-DoFs that are light and compact.


title: Deep learning traffic sign detection, recognition and augmentation

text: Driving is a complex, continuous, and multitask process that involves driver's cognition, perception, and motor movements. The way road traffic signs and vehicle information is displayed impacts strongly driver's attention with increased mental workload leading to safety concerns. Drivers must keep their eyes on the road, but can always use some assistance in maintaining their awareness and directing their attention to potential emerging hazards. Research in perceptual and human factors assessment is needed for relevant and correct display of this information for maximal road traffic safety as well as optimal driver comfort. In-vehicle contextual Augmented Reality (AR) has the potential to provide novel visual feedbacks to drivers for an enhanced driving experience. In this paper, we present a new real-time approach for fast and accurate framework for traffic sign recognition, based on Cascade Deep learning and AR, which superimposes augmented virtual objects onto a real scene under all types of driving situations, including unfavorable weather conditions. Experiments results show that, by combining the Haar Cascade and deep convolutional neural networks show that the joint learning greatly enhances the capability of detection and still retains its realtime performance.


title: A Design and Assessment of a Direction Finding Proximity Fuze Sensor

text: This paper presents the implementation and assessment of a direction finding proximity fuze sensor for anti-aircrafts or anti-air missiles. A higher rejection of clutter signals is achieved by employing a binary phase shift keying modulation using Legendre sequence. The direction finding is implemented by comparing received powers from six receiving antennas equally spaced by an angle of 60° around a cylindrical surface. In addition, target detection algorithms have been developed for a robust detection of the target, taking the wide variation of target related parameters into considerations. The performances of the developed fuze sensor are experimentally verified by constructing the fuze-specific encounter simulation test apparatus, which collects and analyzes reflected signals from a standard target. The developed fuze sensor can successfully detect the -10 dBsm target over a 10 m range as well as the direction with an out-of-range rejection of about 40 dB. Furthermore, the developed fuze sensor can clearly detect the target with mesh clutter environment. To assess realistic operation, the fuze sensor is tested using 155 mm gun firing test setup. Through the gun firing test, the successful fuzing range and direction finding performances are demonstrated.


title: Cognitive Context Models and Discourse

text: 1. Mental models Since the early 1980s, the notion of mental model has been quite successful in cognitive psychology in general, and in the theory of text processing in particu-Such models have been conceptualized as representations in episodic memory of situations, acts or events spoken or thought about, observed or participated in by human actors, that is of experiences (Ehrlich, Tardieu & Cavazza 1993). In the theory of text processing, such (situation or event) models played a crucial role in establishing the necessary referential basis for the processing of anaphora and other phenomena of coherence (Albrecht & O Brien 1993). They further explained, among many other things, why text recall does not seem to be based on semantic representations of texts, but rather on the mental model construed or updated of the event the text is about (Bower & Morrow 1990). Conversely, mental models also play a role in the much neglected theory of discourse production, viz., as the mental point of departure of all text and talk, from which relevant information may be selected for the strategic construction of their global and local semantic structures. Many experiments have confirmed these hypotheses, and have shown at text comprehension and recall essentially involve a strategic manipulation of models, for instance by matching text information with structures of the The notion of mental space is sometimes also used in formal linguistics as a construct that has similar functions as our notion of a mental model (Faucormier 1985).


title: XBOS: An Extensible Building Operating System

text: We present XBOS, an eXtensible Building Operating System for integrated management of previously isolated building subsystems. The key contribution of XBOS is the development of the Building Profile, a canonical, executable description of a building and its subsystems that changes with a building and evolves the control processes and applications accordingly. We discuss the design and implementation of XBOS in the context of 6 dimensions of an effective BAS – hardware presentation layer, canonical metadata, control process management, building evolution management, security, scalable UX and API – and evaluate against several recent building and sensor management systems. Lastly, we examine the evolution of a real, 10 month deployment of XBOS in a 7000 sq ft office building.


title: Survey on Collaborative AR for Multi-user in Urban Simulation

text: This paper describes an augmented reality (AR) environment that allows multiple participants or multi-user to interact with 2D and 3D data. AR simply can provide a collaborative interactive AR environment for urban simulation, where users can interact naturally and intuitively. In addition, the collaborative AR makes multi-user in urban simulation to share simultaneously a real world and virtual world. The fusion between real and virtual world, existed in AR environment by see-through HMDs, achieves higher interactivity as a key features of collaborative AR. In real-time, precise registration between both worlds and multiuser are crucial for the collaborations. Collaborative AR allow multi-user to simultaneously share a real world surrounding them and a virtual world. Common problems in AR environment will be discussed and major issues in collaborative AR will be explained details in this paper. The features of collaboration in AR environment are will be identified and the requirements of collaborative AR will be defined. This paper will give an overview on collaborative AR environment for multi-user in urban studies and planning. The work will also cover numerous systems of collaborative AR environments for multi-user.


title: Training Deep and Recurrent Networks with Hessian-Free Optimization

text: Hessian-Free optimization (HF) is an approach for unconstrained minimization of real-valued smooth objective functions. Like standard Newton’s method, it uses local quadratic approximations to generate update proposals. It belongs to the broad class of approximate Newton methods that are practical for problems of very high dimensionality, such as the training objectives of large neural networks. Different algorithms that use many of the same key principles have appeared in the literatures of various communities under different names such as Newton-CG, CG-Steihaug, Newton-Lanczos, and Truncated Newton [27, 28], but applications to machine learning and especially neural networks, have been limited or non-existent until recently. With the work of Martens [22] and later Martens and Sutskever [23] it has been demonstrated that such an approach, if carefully designed and implemented, can work very well for optimizing nonconvex functions such as the training objective for deep neural networks and recurrent neural networks (RNNs), given sensible random initializations. This was significant because gradient descent methods have been observed to be very slow and sometimes completely ineffective [16, 4, 17] on these problems, unless special non-random initializations schemes like layer-wise pre-training [16, 15, 3] are used. HF, which is a general optimization-based approach, can be used in conjunction with or as an alternative to existing pre-training methods and is more widely applicable, since it relies on fewer assumptions about the specific structure of the network. In this report we will first describe the basic HF approach, and then examine well-known general purpose performance-improving techniques as well as others that are specific to HF (versus other Truncated-Newton type approaches) or to neural networks. We will also provide practical tips for creating efficient and correct implementations, and discuss the pitfalls which may arise when designing and using an HF-based approach in a particular application.


title: Neuronlike adaptive elements that can solve difficult learning control problems

text: It is shown how a system consisting of two neuronlike adaptive elements can solve a difficult learning control problem. The task is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. It is argued that the learning problems faced by adaptive elements that are components of adaptive networks are at least as difficult as this version of the pole-balancing problem. The learning system consists of a single associative search element (ASE) and a single adaptive critic element (ACE). In the course of learning to balance the pole, the ASE constructs associations between input and output by searching under the influence of reinforcement feedback, and the ACE constructs a more informative evaluation function than reinforcement feedback alone can provide. The differences between this approach and other attempts to solve problems using neurolike elements are discussed, as is the relation of this work to classical and instrumental conditioning in animal learning studies and its possible implications for research in the neurosciences.


title: Convergent Temporal-Difference Learning with Arbitrary Smooth Function Approximation

text: We introduce the first temporal-difference learning algorithms that converge with smooth value function approximators, such as neural networks. Conventional temporal-difference (TD) methods, such as TD(λ), Q-learning and Sarsa have been used successfully with function approximation in many applications. However, it is well known that off-policy sampling, as well as nonlinear function approximation, can cause these algorithms to become unstable (i.e., the parameters of the approximator may diverge). Sutton et al. (2009a, 2009b) solved the problem of off-policy learning with linear TD algorithms by introducing a new objective function, related to the Bellman error, and algorithms that perform stochastic gradient-descent on this function. These methods can be viewed as natural generalizations to previous TD methods, as they converge to the same limit points when used with linear function approximation methods. We generalize this work to nonlinear function approximation. We present a Bellman error objective function and two gradient-descent TD algorithms that optimize it. We prove the asymptotic almost-sure convergence of both algorithms, for any finite Markov decision process and any smooth value function approximator, to a locally optimal solution. The algorithms are incremental and the computational complexity per time step scales linearly with the number of parameters of the approximator. Empirical results obtained in the game of Go demonstrate the algorithms’ effectiveness.


title: A 0.5V supply, 49nW band-gap reference and crystal oscillator in 40nm CMOS

text: This paper presents the co-design of 0.5V operational band-gap reference (BGR) and 32kHz crystal oscillator (XO) in 40nm CMOS process. The proposed BJT-based BGR offers 10× resistor area reduction compared to the conventional BGR. The proposed XO provides trans-conductance enhancement without the need for large coupling capacitor and uses a feedback loop to regulate its bias current, oscillation amplitude across PVT. The total power consumption is less than 49nW from −40°C to 120°C. Without any trimming, the BGR achieves temperature coefficients of 8ppm/°C and XO has temperature coefficient of 0.25ppm/°C. The BGR offers 5× lower area, 2× lower power consumption at 120°C compared to prior work on resistor-based, non-duty cycled BGR. The XO improves power consumption by at least 2× compared to prior work at 120°C, 0.5V supply.


title: Multiobjective Optimization of Ultraflat Magnetic Components With PCB-Integrated Core

text: In future applications, e.g., in ultra-flat OLED lamp drivers or flat screen power supplies, ultra-flat ac/dc and dc/dc converter systems are highly demanded. Therefore, the design and implementation of a printed circuit board (PCB)-integrated flyback transformer for a 1-mm-thin single-phase power factor correction rectifier is under investigation. In this paper, first an overview on several integration methods is given. It is shown that the PCB integration of magnetic cores allows us to achieve the required thickness of 1 mm and a high energy density. In a next step, the design and the realization of ultra-flat magnetic components with PCB-integrated cores are discussed in detail. The presented multi-objective design procedure determines the inductor and/or transformer setup optimal with respect to minimal losses and/or minimal footprint area; for this purpose, all required electrical, magnetic, and geometrical parameters of the magnetic component are considered in the design process. Furthermore, all specific implications entailed by the PCB-integrated core, e.g., the core setup, anisotropic core losses, the interleaving of windings, or an accurate reluctance model are treated. Finally, experimental results are used to verify the design procedure.


title: The Logic of Plausible Reasoning: A Core Theory

text: 1. o formal representation of plausible inference patterns; such OS deductions, inductions, ond analogies, thot ore frequently employed in answering every doy questions: 2. a set of parameters, such OS conditional likelihood, typicality, and similarity, that affect the certainty of people’s onswers to such questions; ond 3. a system relating the different plorisible inference patterns and the different certainty parameters.