title: Maximizing Air Gap and Efficiency of Magnetic Resonant Coupling for Wireless Power Transfer Using Equivalent Circuit and Neumann Formula

abstract: The progress in the field of wireless power transfer in the last few years is remarkable. With recent research, transferring power across large air gaps has been achieved. Both small and large electric equipment have been proposed, e.g., wireless power transfer for small equipment (mobile phones and laptops) and for large equipment (electric vehicles). Furthermore, replacing every cord with wireless power transfer is proposed. The coupled mode theory was proposed in 2006 and proven in 2007. Magnetic and electric resonant couplings allow power to traverse large air gaps with high efficiency. This technology is closely related to electromagnetic induction and has been applied to antennas and resonators used for filters in communication technology. We have studied these phenomena and technologies using equivalent circuits, which is a more familiar format for electrical engineers than the coupled mode theory. In this paper, we analyzed the relationship between maximum efficiency air gap using equivalent circuits and the Neumann formula and proposed equations for the conditions required to achieve maximum efficiency for a given air gap. The results of these equations match well with the results of electromagnetic field analysis and experiments.


ASR-based corrective feedback on pronunciation: does it really work?


title: Shape Context: A New Descriptor for Shape Matching and Object Recognition

abstract: We develop an approach to object recognition based on matching shapes and using a resulting measure of similarity in a nearest neighbor classifier. The key algorithmic problem here is that of finding pointwise correspondences between an image shape and a stored prototype shape. We introduce a new shape descriptor, the shape context, which makes this possible, using a simple and robust algorithm. The shape context at a point captures the distribution over relative positions of other shape points and thus summarizes global shape in a rich, local descriptor. We demonstrate that shape contexts greatly simplify recovery of correspondences between points of two given shapes. Once shapes are aligned, shape contexts are used to define a robust score for measuring shape similarity. We have used this score in a nearest-neighbor classifier for recognition of hand written digits as well as 3D objects, using exactly the same distance function. On the benchmark MNIST dataset of handwritten digits, this yields an error rate of 0.63%, outperforming other published techniques.


title: Fast influence-based coarsening for large networks

abstract: Given a social network, can we quickly 'zoom-out' of the graph? Is there a smaller equivalent representation of the graph that preserves its propagation characteristics? Can we group nodes together based on their influence properties? These are important problems with applications to influence analysis, epidemiology and viral marketing applications. In this paper, we first formulate a novel Graph Coarsening Problem to find a succinct representation of any graph while preserving key characteristics for diffusion processes on that graph. We then provide a fast and effective near-linear-time (in nodes and edges) algorithm COARSENET for the same. Using extensive experiments on multiple real datasets, we demonstrate the quality and scalability of COARSENET, enabling us to reduce the graph by 90% in some cases without much loss of information. Finally we also show how our method can help in diverse applications like influence maximization and detecting patterns of propagation at the level of automatically created groups on real cascade data.


title: Reactive Power Compensation in Electric Arc Furnaces Using Prediction Intervals

abstract: This paper proposes a probabilistic method to model the uncertainty of reactive power compensation by static VAr compensators (SVCs) in electric arc furnaces (EAFs). The time-varying characteristics of EAF accentuate the voltage fluctuations and produce flicker in power lines as well as neighboring loads. In order to solve this issue, quick and accurate response of SVC within a half-cycle ahead is required. This paper proposes a nonparametric approach based on lower upper bound estimation method to construct prediction intervals (PIs) for the reactive power in EAFs. Due to the nonlinear nature of reactive power signals in EAFs, a set of PIs are produced and combined to find an optimal aggregated PI. The proposed prediction method provides a faster-than-real-time monitoring of SVC, which aims at high speed and efficient reactive power compensation. In order to find the most satisfying PIs with high coverage probability and low average width, an optimization algorithm is developed to improve the training process of neural networks. The appropriate performance of the proposed method is examined on the practical data gathered from the Mobarakeh Steel Company, Iran.


title: Design and Analysis of 42-V Coreless Axial-Flux Permanent-Magnet Generators for Automotive Applications

abstract: This paper presents the design and analysis of a novel structure of an axial-flux permanent-magnet machine with iron less stator to generate sinusoidal electromotive force with very low harmonics. The structure is of a special design that is suitable for flux weakening in variable speeds. Flux weakening is done by a mechanical actuator to change the position of windings relative to each other. To confirm the machine performance and the design, analytical and three-dimensional finite-element numerical methods are implemented. The simulations results of the proposed generator coincide very well with the experimental results on a prototype 42-V system realized in the laboratory.


title: EXMOVES: Mid-level Features for Efficient Action Recognition and Video Analysis

abstract: In this paper we present EXMOVES--learned exemplar-based features for efficient recognition and analysis of actions in videos. The entries in our descriptor are produced by evaluating a set of movement classifiers over spatial-temporal volumes of the input video sequences. Each movement classifier is a simple exemplar-SVM trained on low-level features, i.e., an SVM learned using a single annotated positive space-time volume and a large number of unannotated videos. Our representation offers several advantages. First, since our mid-level features are learned from individual video exemplars, they require minimal amount of supervision. Second, we show that simple linear classification models trained on our global video descriptor yield action recognition accuracy approaching the state-of-the-art but at orders of magnitude lower cost, since at test-time no sliding window is necessary and linear models are efficient to train and test. This enables scalable action recognition, i.e., efficient classification of a large number of actions even in massive video databases. Third, we show the generality of our approach by training our mid-level descriptors from different low-level features and testing them on two distinct video analysis tasks: human activity recognition as well as action similarity labeling. Experiments on large-scale benchmarks demonstrate the accuracy and efficiency of our proposed method on both these tasks.


title: USC-HAD: a daily activity dataset for ubiquitous activity recognition using wearable sensors

abstract: Many ubiquitous computing applications involve human activity recognition based on wearable sensors. Although this problem has been studied for a decade, there are a limited number of publicly available datasets to use as standard benchmarks to compare the performance of activity models and recognition algorithms. In this paper, we describe the freely available USC human activity dataset (USC-HAD), consisting of well-defined low-level daily activities intended as a benchmark for algorithm comparison particularly for healthcare scenarios. We briefly review some existing publicly available datasets and compare them with USC-HAD. We describe the wearable sensors used and details of dataset construction. We use high-precision well-calibrated sensing hardware such that the collected data is accurate, reliable, and easy to interpret. The goal is to make the dataset and research based on it repeatable and extendible by others.


title: Quinoa (Chenopodium quinoa Willd.): composition, chemistry, nutritional, and functional properties.

abstract: Quinoa (Chenopodium quinoa Willd.), which is considered a pseudocereal or pseudograin, has been recognized as a complete food due to its protein quality. It has remarkable nutritional properties; not only from its protein content (15%) but also from its great amino acid balance. It is an important source of minerals and vitamins, and has also been found to contain compounds like polyphenols, phytosterols, and flavonoids with possible nutraceutical benefits. It has some functional (technological) properties like solubility, water-holding capacity (WHC), gelation, emulsifying, and foaming that allow diversified uses. Besides, it has been considered an oil crop, with an interesting proportion of omega-6 and a notable vitamin E content. Quinoa starch has physicochemical properties (such as viscosity, freeze stability) which give it functional properties with novel uses. Quinoa has a high nutritional value and has recently been used as a novel functional food because of all these properties; it is a promising alternative cultivar.


title: Adaptive N-best-list handwritten word recognition

abstract: We investigate a novel method for adaptively improving the machine recognition of handwritten words by applying a k-nearest neighbor (k-NN) classifier to the N-best word-hypothesis lists generated by a writer-independent hidden Markov model (HMM). Each new N-best list from the HMM is compared to the N-best lists in the k-NN classifier. A decision module is used to select between the output of the HMM and the matches found by the k-NN classifier. The N-best list chosen by the decision module can be automatically added to the k-NN classifier if it is not already in the k-NN classifier. This dynamic update of the k-NN classifier enables the system to adapt to new data without retraining. On a writer-independent set of 1158 handwritten words, this method reduces the error rate by approximately 30%. This method is fast and memory-efficient, and lends itself to many interesting generalizations.


title: Reliability Issues in Photovoltaic Power Processing Systems

abstract: Power processing systems will be a key factor of future photovoltaic (PV) applications. They will play a central role in transferring, to the load and/or to the grid, the electric power produced by the high-efficiency PV cells of the next generation. In order to come up the expectations related to the use of solar energy for producing electrical energy, such systems must ensure high efficiency, modularity, and, particularly, high reliability. The goal of this paper is to provide an overview of the open problems related to PV power processing systems and to focus the attention of researchers and industries on present and future challenges in this field.


title: Toward an Ontopedia for Historical Hebrew Manuscripts

abstract: Historical handwritten Hebrew manuscripts are one of the most unique and authentic evidences of the Jewish culture and thought that survived through the centuries. In order to enable a systematic research of the knowledge embedded in the manuscripts there is a need for a formal conceptual data model with high level of semantic granularity, an ontology. We propose to build a dynamic web-based framework that will allow scholars to create, enrich and consult an "ontopedia" (ontology-based encyclopedia) of Hebrew manuscripts. The framework is based on an ontology especially designed and implemented for this domain and goals. We view a manuscript as a "living entity" and propose to design a new ontological data model of the narrative for a manuscript, stages/milestones in its biography (creation, copying, acquisition). A sequence of events and places constitutes a timeline of history against which manuscripts, people and their relationships can be placed. A large-scale automated reasoning based on the ontology will also enable us to construct a semantically rich social network of people and manuscripts, and to compare the effect of time and place on the manuscripts’ qualitative characteristics and quantitative distribution.


title: Minimal criterion coevolution: a new approach to open-ended search

abstract: Recent studies have emphasized the merits of search processes that lack overarching objectives, instead promoting divergence by rewarding behavioral novelty. While this less objective search paradigm is more open-ended and divergent, it still differs significantly from nature's mechanism of divergence. Rather than measuring novelty explicitly nature is guided by a single, fundamental constraint: survive long enough to reproduce. Surprisingly, this simple constraint produces both complexity and diversity in a continual process unparalleled by any algorithm to date. Inspired by the relative simplicity of open-endedness in nature in comparison to recent non-objective algorithms, this paper investigates the extent to which interactions between two coevolving populations, both subject to their own constraint, or minimal criterion, can produce results that are both functional and diverse even without any behavior characterization or novelty archive. To test this new approach, a novel maze navigation domain is introduced wherein evolved agents must learn to navigate mazes whose structures are simultaneously coevolving and increasing in complexity. The result is a broad range of maze topologies and successful agent trajectories in a single run, thereby suggesting the viability of minimal criterion coevolution as a new approach to non-objective search and a step towards genuinely open-ended algorithms.


title: Spatiotemporal social media analytics for abnormal event detection and examination using seasonal-trend decomposition

abstract: Recent advances in technology have enabled social media services to support space-time indexed data, and internet users from all over the world have created a large volume of time-stamped, geo-located data. Such spatiotemporal data has immense value for increasing situational awareness of local events, providing insights for investigations and understanding the extent of incidents, their severity, and consequences, as well as their time-evolving nature. In analyzing social media data, researchers have mainly focused on finding temporal trends according to volume-based importance. Hence, a relatively small volume of relevant messages may easily be obscured by a huge data set indicating normal situations. In this paper, we present a visual analytics approach that provides users with scalable and interactive social media data analysis and visualization including the exploration and examination of abnormal topics and events within various social media data sources, such as Twitter, Flickr and YouTube. In order to find and understand abnormal events, the analyst can first extract major topics from a set of selected messages and rank them probabilistically using Latent Dirichlet Allocation. He can then apply seasonal trend decomposition together with traditional control chart methods to find unusual peaks and outliers within topic time series. Our case studies show that situational awareness can be improved by incorporating the anomaly and trend examination techniques into a highly interactive visual analysis process.


title: Hidden Conditional Random Fields for Gesture Recognition

abstract: We introduce a discriminative hidden-state approach for the recognition of human gestures. Gesture sequences often have a complex underlying structure, and models that can incorporate hidden structures have proven to be advantageous for recognition tasks. Most existing approaches to gesture recognition with hidden states employ a Hidden Markov Model or suitable variant (e.g., a factored or coupled state model) to model gesture streams; a significant limitation of these models is the requirement of conditional independence of observations. In addition, hidden states in a generative model are selected to maximize the likelihood of generating all the examples of a given gesture class, which is not necessarily optimal for discriminating the gesture class against other gestures. Previous discriminative approaches to gesture sequence recognition have shown promising results, but have not incorporated hidden states nor addressed the problem of predicting the label of an entire sequence. In this paper, we derive a discriminative sequence model with a hidden state structure, and demonstrate its utility both in a detection and in a multi-way classification formulation. We evaluate our method on the task of recognizing human arm and head gestures, and compare the performance of our method to both generative hidden state and discriminative fully-observable models.


title: Overcoming beta-agonist tolerance: high dose salbutamol and ipratropium bromide. Two randomised controlled trials

abstract: BackgroundAsthmatics treated with long-acting beta-agonists have a reduced bronchodilator response to moderate doses of inhaled short acting beta-agonists during acute bronchoconstriction. It is not known if the response to higher doses of nebulised beta-agonists or other bronchodilators is impaired. We assessed the effect of long-acting beta-agonist treatment on the response to 5 mg nebulised salbutamol and to ipratropium bromide.MethodsTwo double-blind, placebo-controlled, crossover studies of inhaled formoterol 12 μg twice daily in patients with asthma.High-dose salbutamol: 36 hours after the last dose of 1 week of formoterol or placebo treatment, 11 subjects inhaled methacholine to produce a 20% fall in FEV1. Salbutamol 5 mg was then administered via nebuliser and the FEV1 was monitored for 20 minutes. Ipratropium: 36 hours after the last dose of 1 week of formoterol or placebo treatment, 11 subjects inhaled 4.5% saline to produce a 20% fall in FEV1. Salbutamol 200 μg or ipratropium bromide 40 μg was then inhaled and the FEV1 was monitored for 30 minutes. Four study arms compared the response to each bronchodilator after formoterol and placebo. Analyses compared the area under the bronchodilator response curves, adjusting for changes in pre-challenge FEV1, dose of provocational agent and FEV1 fall during the challenge procedure.ResultsThe response to nebulised salbutamol was 15% lower after formoterol therapy compared to placebo (95% confidence 5 to 25%, p = 0.008). The response to ipratropium was unchanged.ConclusionLong-acting beta-agonist treatment induces tolerance to the bronchodilator effect of beta-agonists, which is not overcome by higher dose nebulised salbutamol. However, the bronchodilator response to ipratropium bromide is unaffected.


title: Analyzing log analysis: an empirical study of user log mining

abstract: We present an in-depth study of over 200K log analysis queries from Splunk, a platform for data analytics. Using these queries, we quantitatively describe log analysis behavior to inform the design of analysis tools. This study includes state machine based descriptions of typical log analysis pipelines, cluster analysis of the most common transformation types, and survey data about Splunk user roles, use cases, and skill sets. We find that log analysis primarily involves filtering, reformatting, and summarizing data and that non-technical users increasingly need data from logs to drive their decision making. We conclude with a number of suggestions for future research.


title: Feature Selection for Neuro-Dynamic Programming

abstract: AbstractNeuro-Dynamic Programming encompasses techniques from both reinforcement learn-ing and approximate dynamic programming. Feature selection refers to the choice of basisthat de nes the function class that is required in the application of these techniques.This chapter reviews two popular approaches to neuro-dynamic programming, TD-learning and Q-learning. The main goal of the chapter is to demonstrate how insightfrom idealized models can be used as a guide for feature selection for these algorithms.Several approaches are surveyed, including uid and di usion models, and the applicationof idealized models arising from mean- eld game approximations. The theory is illustratedwith several examples.Keywords: Optimal Control, Stochastic Control, Approximate Dynamic Programming,Reinforcement Learning2000 AMS Subject Classi cation: 49L20, 93E20, 93E35, 60J10 1 Introduction If you have taken a course that mentioned a Riccati equation, then you have already beenexposed to approximate dynamic programming: No physical system is linear! The modelis assumed to be linear, and the cost function is assumed quadratic, so that a closed-formexpression for the optimal feedback law can be computed.In many cases, a linear approximation is not easily justi ed. In particular, in applicationsfrom operations research or chemical engineering, the state space is usually constrained. Toobtain an e ective feedback law for control, we must nd alternative approaches to approxi-mation.Techniques from approximate dynamic programming and reinforcement learning are de-signed to obtain such approximations [1, 18]. In particular, in TD-learning and relatedapproaches, there is no attempt to approximate the system or cost function. Instead, thesolution to a dynamic programming equation is approximated directly, within a prescribed nite-dimensional function class. A key determinant of the success of these techniques is theselection of this function class, also known as the basis.


title: Artificial life, complex systems and cloud computing: a short review

abstract: Cloud computing is the prevailing mode of designing, creating and deploying complex applications nowadays. Its underlying assumptions include distributed computing, but also new concepts that need to be incorporated in the different fields. In this short paper we will make a review of how the world of cloud computing has intersected the complex systems and artificial life field, and how it has been used as inspiration for new models or implementation of new and powerful algorithms


title: On Sequential Sampling for Global . . .

abstract: Approximation models (also known as metamodels) have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is directly related to the sampling strategies used. Our goal in this paper is to investigate the general applicability of sequential sampling for creating global metamodels. Various sequential sampling approaches are reviewed and new approaches are proposed. The performances of these approaches are investigated against that of the one-stage approach using a set of test problems with a variety of features. The potential usages of sequential sampling strategies are also discussed.Copyright © 2002 by ASME


title: A compact microstrip ultra-wideband (UWB) antenna for VHF/UHF band applications

abstract: A compact ultra-wideband quasi log-periodic dipole antenna (LPDA) printed on dielectric substrate is proposed in this paper. The antenna is realized by cascading straight line LPDA with 29 elements and meander line LPDA with 11 elements. An ultra-wideband of 20-2200 MHz was obtained, and the antenna gain was about 6 dBi in the band of 100-2200 MHz. The simulation results indicate that the designed UWB LPDA has very stable radiation patterns throughout the whole frequency band combined with low profile and ease fabrication, which show great potential for VHF and UHF bands wireless communications.


title: BLDC Motor Drive Based on Bridgeless Landsman PFC Converter With Single Sensor and Reduced Stress on Power Devices

abstract: The Landsman converter based on a power factor correction (PFC) in bridgeless (BL) configuration feeding a brushless dc motor (BLDCM) drive is proposed for low-power household appliances. The conduction losses associated with diodes are reduced by BL configuration and switching losses of solid-state switches of voltage source inverter are reduced by the use of low frequency switching signals in electronic commutation for the BLDCM. The front-end BL PFC based Landsman converter operating in the discontinuous inductor current mode is used for controlling the dc link voltage, and PFC is attained naturally with reduced conduction losses and switch stress. A single voltage sensor is used for controlling the dc bus voltage. A prototype is developed to study performance of the system for wide range speed control and power quality improvement. The experimental performance of BLDCM is presented for its functions at varying voltages of ac mains (90–265 V) to adhere to the limits defined by IEC61000-3-2 standard.


title: Increased human intestinal barrier permeability plasma biomarkers zonulin and FABP2 correlated with plasma LPS and altered gut microbiome in anxiety or depression

abstract: We read with interest the recent work by Uhde et al ,1 which demonstrated that physically asymptomatic non-coeliac gluten/wheat sensitivity involves compromised intestinal epithelium barrier dysfunction in conjunction with systemic immune activation events. We also read with interest the recent work by Marchesi et al ,2 which comprehensively reviewed the role of microbiota in physical disorders of the gut and extra-gut organs. ::: ::: But common to these Gut papers was the lack of accounting for anxiety and depression, comorbidities often experienced in gastroenterology clinics. Patients who are otherwise physically asymptomatic often do not explicitly divulge these mental disorders, or the disorders are unintentionally overlooked, yet they report ‘diminished quality of life’. ::: ::: In response to this gap, we explored roles of dysbiosis and gut barrier integrity in individuals physically asymptomatic for gastrointestinal distress, yet nonetheless experiencing mental distress. We hypothesised that anxiety and depressive disorders are linked to human gut dysbiosis with microbiota that secrete lipopolysaccharide (LPS) endotoxin into plasma, which in conjunction with compromised gut barrier integrity has systemic manifestations including the brain. We further hypothesised that this correlates with altered intestinal epithelium paracellular integrity molecules discharged …


title: Mobile data offloading: how much can WiFi deliver?

abstract: This paper presents a quantitative study on the performance of 3G mobile data offloading through WiFi networks. We recruited about 100 iPhone users from metropolitan areas and collected statistics on their WiFi connectivity during about a two and half week period in February 2010. Our trace-driven simulation using the acquired traces indicates that WiFi already offloads about 65% of the total mobile data traffic and saves 55% of battery power without using any delayed transmission. If data transfers can be delayed with some deadline until users enter a WiFi zone, substantial gains can be achieved only when the deadline is fairly larger than tens of minutes. With 100 second delays, the achievable gain is less than only 2--3%. But with 1 hour or longer deadline, traffic and energy saving gains increase beyond 29% and 20%, respectively. These results are in stark contrast to the substantial gain (20 to 33%) reported by the existing work even for 100 second delayed transmission using traces taken from transit buses or war-driving. The major performance difference comes from traces: while bus and war-driving traces contain much shorter connection and inter-connection times, our traces reflects the daily mobility patterns of average users more accurately.


title: On Dual-Rail Control Logic for Enhanced Circuit Robustness

abstract: Ultra low-power design and energy harvesting applications require digital systems to operate under extremely low voltages approaching the point of balance between dynamic and static power consumption which is attained in the sub-threshold operation mode. Delay variations are extremely large in this mode, which calls for the use of asynchronous circuits that are speed-independent or quasi-delay-insensitive. However, even these classes of asynchronous logic become vulnerable because certain timing assumptions commonly accepted under normal operating conditions are no longer valid. In particular, the delay of inverters, often used as the so-called input 'bubbles', can no longer be neglected and they have to be either removed or properly acknowledged to ensure speed-independence. This paper presents an automated approach to synthesis of robust controllers for sub-threshold digital systems based on dual-rail implementation of control logic which eliminates inverters completely. This and other important properties are analysed and compared to the standard single-rail solutions. Dual-rail controllers are shown not to have significant overheads in terms of area and power consumption and are even faster in some cases due to the elimination of inverters from critical paths. The presented automated synthesis techniques are very efficient and can be applied to very large controllers as demonstrated in benchmarks.


title: Deep Graph Clustering in Social Network

abstract: In this paper, we present deep attributes residue graph algorithm (DARG), a novel model for learning deep representations of graph. The algorithm can discover clusters by taking into consideration node relevance. DARG does so by first learns attributes relevance and cluster deep representations of vertices appearing in a graph, unlike existing work, integrates content interactions of the nodes into the graph learning process. First, the relevance of contents between each node pair within the network is abstracted. Then we turn the problem of computing the first k eigenvectors in spectral clustering into a computing deep representations task. This model just need learns content information to represent vertices appearing in a graph and without the need for considering topological information. Such content information is much easier to obtain than topological links in the real world. We conduct an experiment on SNAP Facebook dataset, empirical results demonstrate that proposed approach significantly outperforms other state-of-the-art methods in such task.


title: Perspectives and limitations of touchless fingerprints

abstract: In recent years, fingerprinting technology has developed at a rapid pace. Fingerprinting is still the most prevalent method of verifying an individual's identity because every person, has unique fingerprints. However, fingerprinting technology does have its limitations. The perception that fingerprint, as biometric trait, does not change with time is not correct. Aging and medical conditions of an individual do impact the overall shape of a fingerprint. Current study reviews the state of the art in fingerprinting technology emphasizing limitations of fingerprints produced by contact methods versus contactless techniques, describing drawbacks of the hardware as well as the limitations of software.


Chapter 7 Shading Through Multi-Pass Rendering


title: Securing communication in 6LoWPAN with compressed IPsec

abstract: Real-world deployments of wireless sensor networks (WSNs) require secure communication. It is important that a receiver is able to verify that sensor data was generated by trusted nodes. It may also be necessary to encrypt sensor data in transit. Recently, WSNs and traditional IP networks are more tightly integrated using IPv6 and 6LoWPAN. Available IPv6 protocol stacks can use IPsec to secure data exchange. Thus, it is desirable to extend 6LoWPAN such that IPsec communication with IPv6 nodes is possible. It is beneficial to use IPsec because the existing end-points on the Internet do not need to be modified to communicate securely with the WSN. Moreover, using IPsec, true end-to-end security is implemented and the need for a trustworthy gateway is removed. In this paper we provide End-to-End (E2E) secure communication between IP enabled sensor networks and the traditional Internet. This is the first compressed lightweight design, implementation, and evaluation of 6LoWPAN extension for IPsec. Our extension supports both IPsec's Authentication Header (AH) and Encapsulation Security Payload (ESP). Thus, communication endpoints are able to authenticate, encrypt and check the integrity of messages using standardized and established IPv6 mechanisms.


Alos PALSAR polarimetric data for land use / land cover classification in Amazon


title: Utilizing concept correlations for effective imbalanced data classification

abstract: Data imbalance is a challenging and common problem in data mining and machine learning areas, and has attracted significant research efforts. A data set is considered imbalanced when the data instances (samples) are not close to uniformly distributed across different classes/categories, which is very common in real-world data sets. It is likely to result in biased classification results. In this paper, a two-phase classification framework is proposed to make the classification of imbalanced data more accurate and stable. The proposed framework is based on the correlations generated between concepts. The general idea is to identify negative data instances which have certain positive correlations with data instances in the target concept to facilitate the classification task. The experimental results show that our framework is effective in imbalanced data classification and is robust to feature descriptors by comparing it with four existing approaches using four different kinds of feature representations.


title: Efficient mitigation strategies for epidemics in rural regions

abstract: Containing an epidemic at its origin is the most desirable mitigation. Epidemics have often originated in rural areas, with rural communities among the first affected. Disease dynamics in rural regions have received limited attention, and results of general studies cannot be directly applied since population densities and human mobility factors are very different in rural regions from those in cities. We create a network model of a rural community in Kansas, USA, by collecting data on the contact patterns and computing rates of contact among a sampled population. We model the impact of different mitigation strategies detecting closely connected groups of people and frequently visited locations. Within those groups and locations, we compare the effectiveness of random and targeted vaccinations using a Susceptible-Exposed-Infected-Recovered compartmental model on the contact network. Our simulations show that the targeted vaccinations of only 10% of the sampled population reduced the size of the epidemic by 34.5%. Additionally, if 10% of the population visiting one of the most popular locations is randomly vaccinated, the epidemic size is reduced by 19%. Our results suggest a new implementation of a highly effective strategy for targeted vaccinations through the use of popular locations in rural communities.


I Aesthetic Surgery of the Face 6 Anatomy of the aging face


title: Optimal principal component analysis in distributed and streaming models

abstract: This paper studies the Principal Component Analysis (PCA) problem in the distributed and streaming models of computation. Given a matrix A ∈ Rm×n, a rank parameter k Our contributions are summarized as follows: 1. In the arbitrary partition distributed model of Kannan et al. (COLT 2014), each of s machines holds a matrix Ai and A=ΣAi. Each machine should output U. Kannan et al. achieve O(skm/e)+poly(sk/e) words (of O(log(nm)) bits) communication. We obtain the improved bound of O(skm)+poly(sk/e) words, and show an optimal (up to low order terms) Ω(skm) lower bound. This resolves an open question in the literature. A poly(e-1) dependence is known to be required, but we separate this dependence from m. 2. In a more specific distributed model where each server receives a subset of columns of A, we bypass the above lower bound when A is φ-sparse in each column. Here we obtain an O(skφ/e)+poly(sk/e) word protocol. Our communication is independent of the matrix dimensions, and achieves the guarantee that each server, in addition to outputting U, outputs a subset of O(k/e) columns of A containing a U in its span (that is, for the first time, we solve distributed column subset selection). Additionally, we show a matching Ω(skφ/e) lower bound for distributed column subset selection. Achieving our communication bound when A is sparse in general but not sparse in each column, is impossible. 3. In the streaming model of computation, in which the columns of the matrix A arrive one at a time, an algorithm of Liberty (KDD, 2013) with an improved analysis by Ghashami and Phillips (SODA, 2014) achieves O(km/e) "real numbers" space complexity. We improve this result, since our one-pass streaming PCA algorithm achieves an O(km/e)+poly(k/e) word space upper bound. This almost matches a known Ω(km/e) bit lower bound of Woodruff (NIPS, 2014). We show that with two passes over the columns of A one can achieve an O(km)+poly(k/e) word space upper bound; another lower bound of Woodruff (NIPS, 2014) shows that this is optimal for any constant number of passes (up to the poly(k/e) term and the distinction between words versus bits). 4. Finally, in turnstile streams, in which we receive entries of A one at a time in an arbitrary order, we describe an algorithm with O((m+n)ke-1) words of space. This improves the O((m+n)e-2)ke-2) upper bound of Clarkson and Woodruff (STOC 2009), and matches their Ω((m+n)ke-1) word lower bound. Notably, our results do not depend on the condition number or any singular value gaps of A.


title: A Novel Multiband Planar Antenna for GSM/UMTS/LTE/Zigbee/RFID Mobile Devices

abstract: A new multiband planar antenna with a compact size is designed and developed for mobile devices. The proposed antenna consists of a two-strip monopole and a meandered strip antenna which occupy a compact area of only 15 mm × 42 mm. This planar antenna has a bandwidth of ~ 42% at the 900 MHz band and ~ 53% at the 1900-MHz band. The wide bandwidth at the low frequency is attributed to the mutual coupling of an S-shaped strip and an inverted-F strip which are separately printed on the two sides of a thin substrate, forming a two-strip monopole configuration. The bandwidth at the high frequency is enhanced by inserting a meandered strip which improves the impedance matching for the high-frequency band. The experimental results verify the simulations. The featured broad bandwidths over two frequency bands and the miniaturized size of the proposed antenna make it very promising for applications in wireless communication and wireless sensing devices.


title: Robust Systems for Preposition Error Correction Using Wikipedia Revisions

abstract: We show that existing methods for training preposition error correction systems, whether using well-edited text or error-annotated corpora, do not generalize across very different test sets. We present a new, large errorannotated corpus and use it to train systems that generalize across three different test sets, each from a different domain and with different error characteristics. This new corpus is automatically extracted from Wikipedia revisions and contains over one million instances of preposition corrections.


title: A Comparative Study of Bandwidth Choice in Kernel Density Estimation for Naive Bayesian Classification

abstract: Kernel density estimation (KDE) is an important method in nonparametric learning. While KDE has been studied extensively in the context of accuracy of distribution estimation, it has not been studied extensively in the context of classification. This paper studies nine bandwidth selection schemes for kernel density estimation in Naive Bayesian classification context, using 52 machine learning benchmark datasets. The contributions of this paper are threefold. First, it shows that some commonly used and very sophisticated bandwidth selection schemes do not give good performance in Naive Bayes. Surprisingly, some very simple bandwidth selection schemes give statistically significantly better performance. Second, it shows that kernel density estimation can achieve statistically significantly better classification performance than a commonly used discretization method in Naive Bayes, but only when appropriate bandwidth selection schemes are applied. Third, this study gives bandwidth distribution patterns for the investigated bandwidth selection schemes.


title: Web-scale k-means clustering

abstract: We present two modifications to the popular k-means clustering algorithm to address the extreme requirements for latency, scalability, and sparsity encountered in user-facing web applications. First, we propose the use of mini-batch optimization for k-means clustering. This reduces computation cost by orders of magnitude compared to the classic batch algorithm while yielding significantly better solutions than online stochastic gradient descent. Second, we achieve sparsity with projected gradient descent, and give a fast e-accurate projection onto the L1-ball. Source code is freely available: http://code.google.com/p/sofia-ml


Optimality Principles for Conceptual Blending: A First


Comprehensive Part-Of-Speech Tag Set and SVM Based POS Tagger for Sinhala


title: BALSA: Bluetooth Low Energy Application Layer Security Add-on

abstract: Bluetooth Low Energy (BLE) is ideally suited to exchange information between mobile devices and Internet-of-Things (IoT) sensors. It is supported by most recent consumer mobile devices and can be integrated into sensors enabling them to exchange information in an energy-efficient manner. However, when BLE is used to access or modify sensitive sensor parameters, exchanged messages need to be suitably protected, which may not be possible with the security mechanisms defined in the BLE specification. Consequently we contribute BALSA, a set of cryptographic protocols, a BLE service and a suggested usage architecture aiming to provide a suitable level of security. In this paper we define and analyze these components and describe our proof-of-concept, which demonstrates the feasibility and benefits of BALSA.


title: Autonomic Management of Cloud Service Centers with Availability Guarantees

abstract: Modern cloud infrastructures live in an open world, characterized by continuous changes in the environment and in the requirements they have to meet. Continuous changes occur autonomously and unpredictably, and they are out of control of the cloud provider. Therefore, advanced solutions have to be developed able to dynamically adapt the cloud infrastructure, while providing continuous service and performance guarantees. A number of autonomic computing solutions have been developed such that resources are dynamically allocated among running applications on the basis of short-term demand estimates. However, only performance and energy trade-off have been considered so far with a lower emphasis on the infrastructure dependability/availability which has been demonstrated to be the weakest link in the chain for early cloud providers. The aim of this paper is to fill this literature gap devising resource allocation policies for cloud virtualized environments able to identify performance and energy trade-offs, providing a priori availability guarantees for cloud end-users.


title: Safe-Level-SMOTE: Safe-Level-Synthetic Minority Over-Sampling TEchnique for Handling the Class Imbalanced Problem

abstract: The class imbalanced problem occurs in various disciplines when one of target classes has a tiny number of instances comparing to other classes. A typical classifier normally ignores or neglects to detect a minority class due to the small number of class instances. SMOTE is one of over-sampling techniques that remedies this situation. It generates minority instances within the overlapping regions. However, SMOTE randomly synthesizes the minority instances along a line joining a minority instance and its selected nearest neighbours, ignoring nearby majority instances. Our technique called Safe-Level-SMOTE carefully samples minority instances along the same line with different weight degree, called safe level. The safe level computes by using nearest neighbour minority instances. By synthesizing the minority instances more around larger safe level, we achieve a better accuracy performance than SMOTE and Borderline-SMOTE.


title: A 6-Port Two-Dimensional $3\times 3$ Series-Fed Planar Array Antenna for Dual-Polarized X-Band Airborne Synthetic Aperture Radar Applications

abstract: A six-port $3\times 3$ series-fed planar antenna array design for dual polarized X-band airborne synthetic aperture radar (SAR) applications is presented in this paper. The antenna array patches interconnected with series feeding one to another as the structure to realize directional radiation and vertical/horizontal polarization. The patch elements are excited through $50~\Omega $ microstrip line feeding in series with quarter wave transformer for good impedance characteristics. A six-port feeding allows selecting the direction of the traveling waves, and consequently the sense of linear (vertical and horizontal) polarization. A prototype of the antenna is fabricated and validated the proposed method. The dimensions of the fabricated prototype $3\times 3$ array antenna are $3.256\lambda \text {g} \times 3.256\lambda _{\mathrm {g}}\times 0.0645\lambda _{\mathrm {g}}$ ( $\lambda _{\mathrm {g}}$ is guided wavelength at center frequency 9.65 GHz). The measured S11 < −10 dB reflection bandwidths are 1.4% at each port. Furthermore, an interport and intraport isolation S21<−25 dB was also achieved across the operation band. The measured peak gains are higher than 11.5 dBi with 90% efficiency for all individual ports/polarizations V1, V2, V3 H1, H2, and H3). In order to satisfy the different performance requirement of series-fed array antenna, such as realized gain, radiation efficiency, side lobe level, half power beam width and front to back ratio are also examined. Measured results of an antenna prototype successfully validate the concept. The proposed antenna is found suitable for dual polarized airborne SAR applications.


title: Multiband smart fractal antenna design for converged 5G wireless networks

abstract: Forthcoming Fifth Generation wireless network will be converged version of all the available wireless and wired networks including cognitive radio network, Wi-Fi, cellular, Wi-Max, WSN, IoT, Li-Fi, satellite communication and optical fiber network. Presently in the era of 4G, numbers of researchers have designed different antennas which can integrate maximum four services. This research work proposes design and implementation of Multiband Fractal smart antenna for at least seven converged wireless network services. The antenna works for several services such as GSM (0.89–0.96GHz), DCS (1.71–1.88GHz), WLAN/WSN (2.44–2.45GHz), LTE (1.7–1.9GHz), Wi-Fi/Wi-Max (2.68–6.45GHz), HIPER LAN2 (5.15–5.25GHz) and Ku Band (8–12GHz) application. The antenna planed to be designed and optimized by making using of commercially available software-High Frequency Structure Simulator (HFSS).


title: The Body Keeps the Score: Memory and the Evolving Psychobiology of Posttraumatic Stress

abstract: Ever since people's responses to overwhelming experiences have been systematically explored, researchers have noted that a trauma is stored in somatic memory and expressed as changes in the biological stress response. Intense emotions at the time of the trauma initiate the long-term conditional responses to reminders of the event, which are associated both with chronic alterations in the physiological stress response and with the amnesias and hypermnesias characteristic of posttraumatic stress disorder (PTSD). Continued physiological hyperarousal and altered stress hormone secretion affect the ongoing evaluation of sensory stimuli as well. Although memory is ordinarily an active and constructive process, in PTSD failure of declarative memory may lead to organization of the trauma on a somatosensory level (as visual images or physical sensations) that is relatively impervious to change. The inability of people with PTSD to integrate traumatic experiences and their tendency, instead, to continuously relive ...


title: User Modeling in Large Social Networks

abstract: This proposal aims to harness the power of data, social, and network sciences to model user behavior in social networks. Specifically, we focus on individual users and investigate the interplay between their behavior and subsequently emergent social phenomena. Work in this proposal unveils the significant social strategies that are used by people to satisfy their social needs. We apply computational methods to address user modeling problems, including demographic inference, link recommendation, and social impact prediction. The proposed research work can be translated into applications in large social systems, such as mobile communication, online social media, and academic collaboration.


title: Analogical reasoning in working memory: Resources shared among relational integration, interference resolution, and maintenance

abstract: We report a series of experiments using a pictorial analogy task designed to manipulate relational integration, interference resolution, and active maintenance simultaneously. The difficulty of the problems was varied in terms of the number of relations to be integrated, the need for interference resolution, and the duration of maintenance required to correctly solve the analogy. The participants showed decreases in performance when integrating multiple relations, as compared with a single relation, and when interference resolution was required in solving the analogy. When the participants were required to integrate multiple relations while simultaneously engaged in interference resolution, performance was worse, as compared with problems that incorporated either of these features alone. Maintenance of information across delays in the range of 1–4.5 sec led to greater decrements in visual memory, as compared with analogical reasoning. Misleading information caused interference when it had been necessarily attended to and maintained in working memory and, hence, had to be actively suppressed. However, sources of conflict within information that had not been attended to or encoded into working memory did not interfere with the ongoing controlled information processing required for relational integration. The findings provide evidence that relational integration and interference resolution depend on shared cognitive resources in working memory during analogical reasoning.


title: Dynamics modeling of a Two-wheeled vehicle using Jourdain's principle

abstract: This paper describes a modeling technique for developing the motion equation of a motorcycles vehicle. Based on the Jourdain's principle approach, the derived model presents a nice analytical formulation with recursive Jacobian matrices computation. The synthesized model takes in consideration the main forces and moments affecting the behavior of the motorcycle and allowing the simulation of 11DOF.


title: Wavelength locking via teleportation using distant quantum entanglement and Bloch–Siegert oscillation

abstract: Abstract Recently, we have shown theoretically [M.S. Shahriar, P. Pradhan, J. Morzinski, Phys. Rev. A 69 (2004) 032308] as well as experimentally [G. Cardoso, P. Pradhan, J. Morzinski, M.S. Shahriar, Phys. Rev. A 71 (2005) 063408] how the phase of an electromagnetic field can be determined by measuring the population of either of the two states of a two-level atomic system excited by this field, via the so-called Bloch–Siegert oscillation resulting from the interference between the co- and counter-rotating excitations. Here, we show how a degenerate entanglement, created without transmitting any timing signal, can be used to teleport this phase information. This phase-teleportation process may be applied to achieve wavelength locking of remote oscillators.


Are Latent Sentence Vectors Cross-Linguistically Invariant ?


title: Automatic Assessment of Speech Intelligibility for Individuals With Aphasia

abstract: Traditional in-person therapy may be difficult to access for individuals with aphasia due to the shortage of speech-language pathologists and high treatment cost. Computerized exercises offer a promising low-cost and constantly accessible supplement to in-person therapy. Unfortunately, the lack of feedback for verbal expression in existing programs hinders the applicability and effectiveness of this form of treatment. A prerequisite for producing meaningful feedback is speech intelligibility assessment. In this work, we investigate the feasibility of an automated system to assess three aspects of aphasic speech intelligibility: clarity, fluidity, and prosody. We introduce our aphasic speech corpus, which contains speech-based interaction between individuals with aphasia and a tablet-based application designed for therapeutic purposes. We present our method for eliciting reliable ground-truth labels for speech intelligibility based on the perceptual judgment of nonexpert human evaluators. We describe and analyze our feature set engineered for capturing pronunciation, rhythm, and intonation. We investigate the classification performance of our system under two conditions, one using human-labeled transcripts to drive feature extraction, and another using transcripts generated automatically. We show that some aspects of aphasic speech intelligibility can be estimated at human-level performance. Our results demonstrate the potential for the computerized treatment of aphasia and lay the groundwork for bridging the gap between human and automatic intelligibility assessment.


title: Verb Physics: Relative Physical Knowledge of Actions and Objects

abstract: Learning commonsense knowledge from natural language text is nontrivial due to reporting bias: people rarely state the obvious, e.g., "My house is bigger than me." However, while rarely stated explicitly, this trivial everyday knowledge does influence the way people talk about the world, which provides indirect clues to reason about the world. For example, a statement like, "Tyler entered his house" implies that his house is bigger than Tyler. ::: In this paper, we present an approach to infer relative physical knowledge of actions and objects along five dimensions (e.g., size, weight, and strength) from unstructured natural language text. We frame knowledge acquisition as joint inference over two closely related problems: learning (1) relative physical knowledge of object pairs and (2) physical implications of actions when applied to those object pairs. Empirical results demonstrate that it is possible to extract knowledge of actions and objects from language and that joint inference over different types of knowledge improves performance.


title: Brain-Computer Interfaces: Beyond Medical Applications

abstract: Brain-computer interaction has already moved from assistive care to applications such as gaming. Improvements in usability, hardware, signal processing, and system integration should yield applications in other nonmedical areas.


title: A study of the documentation essential to software maintenance

abstract: Software engineering has been striving for years to improve the practice of software development and maintenance. Documentation has long been prominent on the list of recommended practices to improve development and help maintenance. Recently however, agile methods started to shake this view, arguing that the goal of the game is to produce software and that documentation is only useful as long as it helps to reach this goal.On the other hand, in the re-engineering field, people wish they could re-document useful legacy software so that they may continue maintain them or migrate them to new platform.In these two case, a crucial question arises: "How much documentation is enough?" In this article, we present the results of a survey of software maintainers to try to establish what documentation artifacts are the most useful to them.


title: Linewidth determination in local oxidation nanolithography of silicon surfaces

abstract: We measure the linewidth of structures fabricated by local oxidation lithography on silicon surfaces. Two different structures, isolated and arrays of parallel lines have been generated. The oxide structures have been fabricated in the proximity of sexithiophene islands whose size is comparable to the oxide motives. The comparison between local oxides and sexithiophene islands reveals that atomic force microscopy (AFM) images faithfully reproduce the size and shape of local silicon oxides. The oxide lines have a trapezoidal shape with a flat section at the top. AFM images of the oxide structures show rather small slopes ∼0.05–0.15 which imply angles with the horizontal between 3° and 8°. The shallow angles imply a minimum feature size of 14 nm at the base for an oxide thickness of 1 nm. Linewidths of 7 nm and 20 nm at the top and base, respectively, have been fabricated. We have also demonstrated the ability to pack structures with a periodicity of 13 nm.


title: Ray tracing animated scenes using coherent grid traversal

abstract: We present a new approach to interactive ray tracing of moderate-sized animated scenes based on traversing frustum-bounded packets of coherent rays through uniform grids. By incrementally computing the overlap of the frustum with a slice of grid cells, we accelerate grid traversal by more than a factor of 10, and achieve ray tracing performance competitive with the fastest known packet-based kd-tree ray tracers. The ability to efficiently rebuild the grid on every frame enables this performance even for fully dynamic scenes that typically challenge interactive ray tracing systems.


title: Using altmetrics for assessing research impact in the humanities

abstract: The prospects of altmetrics are especially encouraging for research fields in the humanities that currently are difficult to study using established bibliometric methods. Yet, little is known about the altmetric impact of research fields in the humanities. Consequently, this paper analyses the altmetric coverage and impact of humanities-oriented articles and books published by Swedish universities during 2012. Some of the most common altmetric sources are examined using a sample of 310 journal articles and 54 books. Mendeley has the highest coverage of journal articles (61 %) followed by Twitter (21 %) while very few of the publications are mentioned in blogs or on Facebook. Books, on the other hand, are quite often tweeted while both Mendeley's and the novel data source Library Thing's coverage is low. Many of the problems of applying bibliometrics to the humanities are also relevant for altmetric approaches; the importance of non-journal publications, the reliance on print as well the limited coverage of non-English language publications. However, the continuing development and diversification of methods suggests that altmetrics could evolve into a valuable tool for assessing research in the humanities.


title: PLC-PIDTuner: Application for PID tuning with SIMATIC S7 PLC controllers

abstract: There is presented PLC-PIDTuner application developed for tuning PID controller in S7 Simatic PLCs. It allows plant identification, calculation of controller parameters and verification of the control system using different quality indexes. It provides data collection from the real process and computer simulation of designed control system. Such solution allows to customize systems properties without carrying out costly experiments. Proposed application allows user to easily develop different algorithms for each stage of PID control system design. Finally, there is presented example of using developed PLC-PIDTuner platform in simulated process described by third order system with delay.


title: Factorized Adversarial Networks for Unsupervised Domain Adaptation

abstract: In this paper, we propose Factorized Adversarial Networks (FAN) to solve unsupervised domain adaptation problems for image classification tasks. Our networks map the data distribution into a latent feature space, which is factorized into a domain-specific subspace that contains domain-specific characteristics and a task-specific subspace that retains category information, for both source and target domains, respectively. Unsupervised domain adaptation is achieved by adversarial training to minimize the discrepancy between the distributions of two task-specific subspaces from source and target domains. We demonstrate that the proposed approach outperforms state-of-the-art methods on multiple benchmark datasets used in the literature for unsupervised domain adaptation. Furthermore, we collect two real-world tagging datasets that are much larger than existing benchmark datasets, and get significant improvement upon baselines, proving the practical value of our approach.


title: Convolutional Deep Neural Networks for Document-Based Question Answering

abstract: Document-based Question Answering aims to compute the similarity or relevance between two texts: question and answer. It is a typical and core task and considered as a touchstone of natural language understanding. In this article, we present a convolutional neural network based architecture to learn feature representations of each question-answer pair and compute its match score. By taking the interaction and attention between question and answer into consideration, as well as word overlap indices, the empirical study on Chinese Open-Domain Question Answering (DBQA) Task (document-based) demonstrates the efficacy of the proposed model, which achieves the best result on NLPCC-ICCPOL 2016 Shared Task on DBQA.


title: Robust Extreme Learning Machine With its Application to Indoor Positioning

abstract: The increasing demands of location-based services have spurred the rapid development of indoor positioning system and indoor localization system interchangeably (IPSs). However, the performance of IPSs suffers from noisy measurements. In this paper, two kinds of robust extreme learning machines (RELMs), corresponding to the close-to-mean constraint, and the small-residual constraint, have been proposed to address the issue of noisy measurements in IPSs. Based on whether the feature mapping in extreme learning machine is explicit, we respectively provide random-hidden-nodes and kernelized formulations of RELMs by second order cone programming. Furthermore, the computation of the covariance in feature space is discussed. Simulations and real-world indoor localization experiments are extensively carried out and the results demonstrate that the proposed algorithms can not only improve the accuracy and repeatability, but also reduce the deviation and worst case error of IPSs compared with other baseline algorithms.


title: Characterization of noise and vibration sources in interior permanent-magnet brushless DC motors

abstract: This work characterizes electromagnetic excitation forces in interior permanent-magnet (IPM) brushless direct current (BLDC) motors and investigates their effects on noise and vibration. First, the electromagnetic excitations are classified into three sources: 1) so-called cogging torque, for which we propose an efficient technique of computation that takes into account saturation effects as a function of rotor position; 2) ripples of mutual and reluctance torque, for which we develop an equation to characterize the combination of space harmonics of inductances and flux linkages related to permanent magnets and time harmonics of current; and 3) fluctuation of attractive forces in the radial direction between the stator and rotor, for which we analyze contributions of electric currents as well as permanent magnets by the finite-element method. Then, the paper reports on an experimental investigation of influences of structural dynamic characteristics such as natural frequencies and mode shapes, as well as electromagnetic excitation forces, on noise and vibration in an IPM motor used in washing machines.


title: ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation

abstract: The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in practical mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point operations and have long run-times that hinder their usability. In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation. ENet is up to 18x faster, requires 75x less FLOPs, has 79x less parameters, and provides similar or better accuracy to existing models. ::: We have tested it on CamVid, Cityscapes and SUN datasets and report on comparisons with existing state-of-the-art methods, and the trade-offs between accuracy and processing time of a network. We present performance measurements of the proposed architecture on embedded systems and suggest possible software improvements that could make ENet even faster.


title: 1 Fuzzy Computing Applications for Anti-Money Laundering and Distributed Storage System Load Monitoring

abstract: Fuzzy computing (FC) has made a great impact in capturing human domain knowledge and modeling non-linear mapping of input-output space. In this paper, we describe the design and implementation of FC systems for detection of money laundering behaviors in financial transactions and monitoring of distributed storage system load. Our objective is to demonstrate the power of FC for real-world applications which are characterized by imprecise, uncertain data, and incomplete domain knowledge. For both applications, we designed fuzzy rules based on experts’ domain knowledge, depending on money laundering scenarios in transactions or the “health” of a distributed storage system. In addition, we developped a generic fuzzy inference engine and contributed to the open source community.


title: Lessons from Stuxnet

abstract: The article mentions that the malware such as Stuxnet can affect critical physical infrastructures that are controlled by software, which implies that threats might extend to real lives. Stuxnet differs from past malware in several ways. First, most malware tries to infect as many computers as possible, whereas Stuxnet appears to target industrial control systems and delivers its pay load under very specific conditions. Second, Stuxnet is larger and more complex than other malware. It cotains exploits for four vulnerabilities.


title: Learning a Multi-class Discriminative Dictionary with Nonredundancy Constraints for Visual Classification

abstract: Recent studies have demonstrated advantages of sparse representation in providing an appealing paradigm for visual classification tasks. However, how to effectively learn a compact dictionary of superior reconstruction and discrimination power is still a challenging problem. In this paper, we concurrently exploit both the intra-class and the inter-class visual correlations to learn a multi-class discriminative dictionary. The intra-nonredundancy constraint prevents zero entities from appearing in the class-specific bases, thereby making the learned dictionary more stable. The inter-nonredundancy constraint effectively separates the common visual patterns from all the class-specific bases, yielding a more compact dictionary. Combining nonredundancy constraints with the reconstruction error and the classification error to form a unified objective function, our method can learn a superior dictionary and an optimal linear classifier simultaneously. Extensive experimental results demonstrate that the proposed algorithm achieves notable improvement over the state-of-the-art methods in image classification and visual tracking tasks.


The Usable Privacy Policy Project : Combining Crowdsourcing , Machine Learning and Natural Language Processing to Semi-Automatically Answer Those Privacy Questions Users Care About


title: NREL-Exo: A 4-DoFs wearable hip exoskeleton for walking and balance assistance in locomotion

abstract: In this paper, we presented a high-power, self-balancing, passively and software-controlled active compliant, and wearable hip exoskeleton to provide walking and balance assistance. The device features powered hip abduction/adduction (HAA) and hip flexion/extension (HFE) modules to provide assistance in both sagittal and frontal planes. Each module's actuation unit employs a Series Elastic Actuator (SEA) to achieve passive compliance. The hip exoskeleton can work in two basic operation modes: human-in-charge and robot-in-charge. Both modes are integrated into the low-level controller based on the admittance control, making transitions smooth and stable. A new balance controller based on the “extrapolated center of mass” (XCoM) concept is presented for real-time control hip abduction/adduction to keep the center of mass (CoM) within the support polygon. The exoskeleton controller is designed to encourage participation in walking instead of overriding users' intrinsic behavior to achieve effective assistance and training. Our preliminary experiments on a healthy subject using the hip exoskeleton demonstrated the potential effectiveness of the device and controller in assisting locomotion.


title: A 2.5-GHz low-power, high dynamic range, self-tuned Q-enhanced LC filter in SOI

abstract: Q-enhanced LC filter technology offers a promising approach to remove the off-chip preselect filter still required in current transceivers. However, reported designs fail to meet stringent system specifications such as dynamic range and noise figure for existing wireless standards. The complexity and inaccuracy of frequency and Q tuning have also prevented acceptance in industry applications. This paper presents a low-power and high-performance design targeted at Bluetooth in a silicon-on-isolator (SOI) CMOS process. Drawing 5 mA from a 3-V supply, it achieves 23-dB voltage gain (14-dB power gain), approximately 6-dB noise figure, and a 153-dB/spl middot/Hz 1-dB compression point dynamic range, when operating with a 70-MHz bandwidth at 2.5 GHz. A simplified approach to frequency and Q tuning is also demonstrated, making Q-enhanced LC filtering feasible in industry application for the first time.


title: ECU Software Updating in Future Vehicle Networks

abstract: Electronic control units (ECUs) of in-vehicle systems are being designed to handle an increasing number of functions. However, malfunctions may occur when an in-vehicle system is upgraded. Moreover, because they are visible to hackers, connected vehicles are becoming the object of cyberattacks. Therefore, the necessity of updating ECU software is increasing. Car owners cannot use their cars during the update process. Therefore, reducing updating time is important. In this paper, we propose a method that reduces updating time by dividing the update data. During simulations, we found that the proposed method can slightly reduce updating time.


title: Coronary CT angiography-derived fractional flow reserve correlated with invasive fractional flow reserve measurements – initial experience with a novel physician-driven algorithm

abstract: Objectives ::: The present study aimed to determine the feasibility of a novel fractional flow reserve (FFR) algorithm based on coronary CT angiography (cCTA) that permits point-of-care assessment, without data transfer to core laboratories, for the evaluation of potentially ischemia-causing stenoses.


High Performance Post-Quantum Key Exchange on FPGAs


title: Dyna-C: A topology for a bi-directional solid-state transformer

abstract: This paper presents a compact bi-directional solidstate transformer (SST) based on a current-source topology, featuring high-frequency galvanic isolation and only two minimal power conversion stages. The topology, referenced as Dynamic Current or Dyna-C, can be configured for multiterminal DC and/or multi-phase AC applications. The Dyna-C is robust, can be stacked for MV applications, and be paralleled for high current and high power applications. The paper will present some of the possible configurations of Dyna-C, and will discuss challenges associated with the control and operation. One core innovation presented in this paper is the management of transformer leakage energy when transitioning from one bridge to another while maintaining low device stresses and losses. Simulation and experimental results are used to validate operation of the topology.


title: A Cross-Lingual Joint Aspect/Sentiment Model for Sentiment Analysis

abstract: Sentiment analysis in various languages has been a research hotspot with many applications. However, sentiment resources (e.g., labeled corpora, sentiment lexicons) of different languages are unbalanced in terms of quality and quantity, which arouses interests in cross-lingual sentiment analysis aiming at using the resources in a source language to improve sentiment analysis in a target language. Nevertheless, many existing cross-lingual related works rely on a certain machine translation system to directly adapt the labeled data from the source language to the target language, which usually suffers from inaccurate results generated by the machine translation system. On the other hand, most sentiment analysis studies focus on document-level sentiment classification that cannot solve the aspect dependency problem of sentiment words. For instance, in the reviews on a cell phone, long is positive for the lifespan of its battery, but negative for the response time of its operating system. To solve these problems, this paper develops a novel Cross-Lingual Joint Aspect/Sentiment (CLJAS) model to carry out aspect-specific sentiment analysis in a target language using the knowledge learned from a source language. Specifically, the CLJAS model jointly detects aspects and sentiments of two languages simultaneously by incorporating sentiments into a cross-lingual topic model framework. Extensive experiments on different domains and different languages demonstrate that the proposed model can significantly improve the accuracy of sentiment classification in the target language.


title: IT to support knowledge sharing in communities, towards a social capital analysis

abstract: Ignoring the informal, non-canonical nature of knowledge sharing, including people's motivation, ability and opportunity to share knowledge, is one of the key causes of resistance to use knowledge-sharing tools. In order to improve knowledge sharing supported by information technology (IT), tools need to be embedded in the social networks of which it is part. This has implications for our knowledge on the design requirements of such socially embedded IT. The paper reviews tools that are designed for the purpose to foster social capital. We will then discuss what is needed for an IS design theory related to knowledge communities and how such a theory could incorporate social capital theory.


title: Automated social hierarchy detection through email network analysis

abstract: This paper provides a novel algorithm for automatically extracting social hierarchy data from electronic communication behavior. The algorithm is based on data mining user behaviors to automatically analyze and catalog patterns of communications between entities in a email collection to extract social standing. The advantage to such automatic methods is that they extract relevancy between hierarchy levels and are dynamic over time. We illustrate the algorithms over real world data using the Enron corporation's email archive. The results show great promise when compared to the corporations work chart and judicial proceeding analyzing the major players.


title: Improving distant supervision using inference learning

abstract: Distant supervision is a widely applied approach to automatic training of relation extraction systems and has the advantage that it can generate large amounts of labelled data with minimal effort. However, this data may contain errors and consequently systems trained using distant supervision tend not to perform as well as those based on manually labelled data. This work proposes a novel method for detecting potential false negative training examples using a knowledge inference method. Results show that our approach improves the performance of relation extraction systems trained using distantly supervised data.


title: A Novel 2.4GHz CMOS Up-Conversion Current-Mode Mixer

abstract: In this paper, a low-power up-conversion current-mode mixer, designed in the chartered 0.18-μm RFCMOS technology, is proposed to realize the transmitter front-end in the frequency band of 2.4 GHz. The proposed mixer can convert a 10 MHz intermediate frequency (IF) signal to a 2.4 GHz RF signal, with a local oscillator power of 2 dBm at 2.39 GHz. A comparison with conventional voltage-mode up-conversion mixer shows that this mixer has advantages of low voltage, low power consumption and high performance. Simulation results demonstrate that at 2.4 GHz, the circuit provides 6.5 dB of conversion gain and the input-referred third-order intercept point (IIP3) of 15.3 dBm, while drawing only 5.7 mA from a 1.2V supply voltage. The chip area is only 0.7 mm × 0.8 mm.


title: Medical telerobotic systems: current status and future trends

abstract: Teleoperated medical robotic systems allow procedures such as surgeries, treatments, and diagnoses to be conducted across short or long distances while utilizing wired and/or wireless communication networks. This study presents a systematic review of the relevant literature between the years 2004 and 2015, focusing on medical teleoperated robotic systems which have witnessed tremendous growth over the examined period. A thorough insight of telerobotics systems discussing design concepts, enabling technologies (namely robotic manipulation, telecommunications, and vision systems), and potential applications in clinical practice is provided, while existing limitations and future trends are also highlighted. A representative paradigm of the short-distance case is the da Vinci Surgical System which is described in order to highlight relevant issues. The long-distance telerobotics concept is exemplified through a case study on diagnostic ultrasound scanning. Moreover, the present review provides a classification into short- and long-distance telerobotic systems, depending on the distance from which they are operated. Telerobotic systems are further categorized with respect to their application field. For the reviewed systems are also examined their engineering characteristics and the employed robotics technology. The current status of the field, its significance, the potential, as well as the challenges that lie ahead are thoroughly discussed.


title: Variational Depth Superresolution Using Example-Based Edge Representations

abstract: In this paper we propose a novel method for depth image superresolution which combines recent advances in example based upsampling with variational superresolution based on a known blur kernel. Most traditional depth superresolution approaches try to use additional high resolution intensity images as guidance for superresolution. In our method we learn a dictionary of edge priors from an external database of high and low resolution examples. In a novel variational sparse coding approach this dictionary is used to infer strong edge priors. Additionally to the traditional sparse coding constraints the difference in the overlap of neighboring edge patches is minimized in our optimization. These edge priors are used in a novel variational superresolution as anisotropic guidance of the higher order regularization. Both the sparse coding and the variational superresolution of the depth are solved based on a primal-dual formulation. In an exhaustive numerical and visual evaluation we show that our method clearly outperforms existing approaches on multiple real and synthetic datasets.


title: Edge-adaptive transforms for efficient depth map coding

abstract: In this work a new set of edge-adaptive transforms (EATs) is presented as an alternative to the standard DCTs used in image and video coding applications. These transforms avoid filtering across edges in each image block, thus, they avoid creating large high frequency coefficients. These transforms are then combined with the DCT in H.264/AVC and a transform mode selection algorithm is used to choose between DCT and EAT in an RD-optimized manner. These transforms are applied to coding depth maps used for view synthesis in a multi-view video coding system, and provides up to 29% bit rate reduction for a fixed quality in the synthesized views.


title: Hybrid Intrusion Detection with Weighted Signature Generation over Anomalous Internet Episodes

abstract: This paper reports the design principles and evaluation results of a new experimental hybrid intrusion detection system (HIDS). This hybrid system combines the advantages of low false-positive rate of signature-based intrusion detection system (IDS) and the ability of anomaly detection system (ADS) to detect novel unknown attacks. By mining anomalous traffic episodes from Internet connections, we build an ADS that detects anomalies beyond the capabilities of signature-based SNORT or Bro systems. A weighted signature generation scheme is developed to integrate ADS with SNORT by extracting signatures from anomalies detected. HIDS extracts signatures from the output of ADS and adds them into the SNORT signature database for fast and accurate intrusion detection. By testing our HIDS scheme over real-life Internet trace data mixed with 10 days of Massachusetts Institute of Technology/Lincoln Laboratory (MIT/LL) attack data set, our experimental results show a 60 percent detection rate of the HIDS, compared with 30 percent and 22 percent in using the SNORT and Bro systems, respectively. This sharp increase in detection rate is obtained with less than 3 percent false alarms. The signatures generated by ADS upgrade the SNORT performance by 33 percent. The HIDS approach proves the vitality of detecting intrusions and anomalies, simultaneously, by automated data mining and signature generation over Internet connection episodes


title: RSGAN: face swapping and editing using face and hair representation in latent spaces

abstract: In this paper, we present an integrated system for automatically generating and editing face images through face swapping, attribute-based editing, and random face parts synthesis. The proposed system is based on a deep neural network that variationally learns the face and hair regions with large-scale face image datasets. Different from conventional variational methods, the proposed network represents the latent spaces individually for faces and hairs. We refer to the proposed network as region-separative generative adversarial network (RSGAN). The proposed network independently handles face and hair appearances in the latent spaces, and then, face swapping is achieved by replacing the latent-space representations of the faces, and reconstruct the entire face image with them. This approach in the latent space robustly performs face swapping even for images which the previous methods result in failure due to inappropriate fitting or the 3D morphable models. In addition, the proposed system can further edit face-swapped images with the same network by manipulating visual attributes or by composing them with randomly generated face or hair parts.


title: Oxytosis/Ferroptosis—(Re-) Emerging Roles for Oxidative Stress-Dependent Non-apoptotic Cell Death in Diseases of the Central Nervous System

abstract: Although neuronal cell death is the hallmark of many neurological diseases, the processes underlying this death are still poorly defined. However, there is a general consensus that nerve cell death predominantly proceeds by regulated processes. Almost 30 years ago, a cell death pathway eventually named oxytosis was described in neuronal cells that involved glutathione depletion, reactive oxygen species production, lipoxygenase activation and calcium influx. More recently, a cell death pathway that involved many of the same steps was described in tumor cells and termed ferroptosis due to a dependence on iron. Since then there has been a great deal of discussion in the literature about whether these are two distinct pathways or cell type- and insult-dependent variations on the same pathway. In this review, we compare and contrast in detail the commonalities and distinctions between the two pathways concluding that the molecular pathways involved in the regulation of ferroptosis and oxytosis are highly similar if not identical. Thus, we suggest that oxytosis and ferroptosis should be regarded as two names for the same cell death pathway. In addition, we describe the potential physiological relevance of oxytosis/ferroptosis in multiple neurological diseases.


title: Applications of perceptual hash algorithm in agriculture images

abstract: According to the characteristics of agriculture image, new image recognition scheme and image authentication scheme were designed, which based on perceptual hash algorithm. Some tomato leaf diseases pictures were used to achieve image perceptual hash feature extraction. And the experimental results show that the same diseases images have closer perceptual hash characteristics.


title: N-rich porous carbons with a high graphitization degree and multiscale pore network for boosting high-rate supercapacitor with ultrafast charging

abstract: Abstract An increasing energy storage property with high rate capability during fast charging at the high current density has been a long-standing challenge for supercapacitors. In this work, a novel nitrogen-enriched graphitized hierarchical porous carbon (NGHPC) was synthesized by a convenient one-step carbonization process from melamine(M)-resorcinol(R)-formaldehyde(F) co-polymeric microspheres using nano γ-Fe2O3 as template, graphitized catalyst precursor and activation promoting agent coupled with ZnCl2 activation. The nano-sized γ-Fe2O3 was reduced to Fe metal and other Fe-containing species in the carbonization process, and Fe served as catalyst to generate perfectly graphitized carbon skeleton, and other nanoparticles acted as the template of macropores. Meanwhile, CO2 which generated from the oxidation reactions of CO and γ-Fe2O3 reacted with carbon to bring the in situ CO2 physical activation, resulting in the promotive tailoring in microporosity. Interestingly, the morphology of the obtaining NGHPC materials could be controlled from spherical aspect to the structure of sphere coated by thin sheet by varying the M/R mass ratio. As supercapacitor electrodes materials, the optimal material achieved an outstanding specific capacitance of 417.5 F g−1 at 0.5 A g−1 in 6 M KOH electrolyte. More importantly, a superb rate capability was also exhibited, and a capacitance of 296.4 F g−1 was still retained at an ultrahigh current density of 100 A g−1. Besides, the assembled symmetric supercapacitor with the optimal material displayed a high integrated energy density of 33.9 Wh kg−1 at a power density of 809.9 W kg−1 within a voltage range of 0–1.8 V in 0.5 M Na2SO4 natural electrolyte and an approximate 84.8% capacitance retention after consecutive 10,000 cycles at 5 A g−1.


title: Lightning-Induced Magnetic Field Distribution on Cable-Stayed Bridge Based on Modified Equivalent Circuit Method

abstract: In order to protect sensitive devices on the cable-stayed bridge against the effects of lightning electromagnetic pulse, detailed information about the lightning currents and the radiated electromagnetic fields are necessary. For this purpose, a modified equivalent electric circuit method is presented for evaluating the electromagnetic environment on a cable-stayed bridge during direct lightning strikes. The stayed cables of the bridge are modeled as cascade polylines to transform the internal metallic structure of the bridge into an equivalent electrical circuit network. Taking a bridge for example, the current distribution and the lightning-induced magnetic field distribution are calculated based on the proposed method. The guidelines for the protection of sensitive electrical and electronic equipment against lightning are provided.


title: A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

abstract: Fuzzy systems have been used widely thanks to their ability to successfully solve a wide range of problems in different application fields. However, their replication and application require a high level of knowledge and experience. Furthermore, few researchers publish the software and/or source code associated with their proposals, which is a major obstacle to scientific progress in other disciplines and in industry. In recent years, most fuzzy system software has been developed in order to facilitate the use of fuzzy systems. Some software is commercially distributed, but most software is available as free and open-source software, reducing such obstacles and providing many advantages: quicker detection of errors, innovative applications, faster adoption of fuzzy systems, etc. In this paper, we present an overview of freely available and open-source fuzzy systems software in order to provide a well-established framework that helps researchers to find existing proposals easily and to develop well-founded future work. To accomplish this, we propose a two-level taxonomy, and we describe the main contributions related to each field. Moreover, we provide a snapshot of the status of the publications in this field according to the ISI Web of Knowledge. Finally, some considerations regarding recent trends and potential research directions are presented.


Outline for a Logical Theory of Adaptive Systems


title: Multi-transformer flyback converter for supplying isolated IGBT and MOSFET drivers

abstract: A multi transformer flyback converter topology for supplying transistor drivers is presented. The topology presents some advantages over typical multi output single transformer, as reduction of effective leakage inductance, equal magnetic coupling between primary and secondary circuits and better isolation between outputs. Simulation study carried out in the LTSpice IV program and preliminary experimental results indicate high performance of the six isolated output symmetrical positive and negative dc power supply.


title: On-board real-time state-of-charge estimators of hybrid electric vehicles Ni-MH battery

abstract: In this research is developed and implemented in MATLAB simulation environment two real-time estimators, such as an Unscented Kalman Filter (UKF) and a sliding mode observer (SMO). The intent is to use these estimators to estimate the state-of-charge (SOC) of a generic nickel — metal hydride (Ni-MH) battery integrated in the battery management system (BMS) structure of hybrid electric vehicles (HEVs). The novelty of this paper is that the proposed estimators can be tailored to estimate the battery SOC of different chemistries, and also could be extended to detect and estimate the severity of battery faults. The preliminary results obtained in this research are encouraging and reveal the effectiveness of the real-time implementation of the proposed estimators.


title: Does Restraining End Effect Matter in EMD-Based Modeling Framework for Time Series Prediction? Some Experimental Evidences

abstract: Following the"decomposition-and-ensemble"principle, the empirical mode decomposition (EMD)-based modeling framework has been widely used as a promising alternative for nonlinear and nonstationary time series modeling and prediction. The end effect, which occurs during the sifting process of EMD and is apt to distort the decomposed sub-series and hurt the modeling process followed, however, has been ignored in previous studies. Addressing the end effect issue, this study proposes to incorporate end condition methods into EMD-based decomposition and ensemble modeling framework for one- and multi-step ahead time series prediction. Four well-established end condition methods, Mirror method, Coughlin's method, Slope-based method, and Rato's method, are selected, and support vector regression (SVR) is employed as the modeling technique. For the purpose of justification and comparison, well-known NN3 competition data sets are used and four well-established prediction models are selected as benchmarks. The experimental results demonstrated that significant improvement can be achieved by the proposed EMD-based SVR models with end condition methods. The EMD-SBM-SVR model and EMD-Rato-SVR model, in particular, achieved the best prediction performances in terms of goodness of forecast measures and equality of accuracy of competing forecasts test.


title: Solar shirt: design of an environmental awareness wearable

abstract: The Solar Shirt is a wearable computing design concept and demo in the area of sustainable and ecological design. The Solar Shirt showcases a concept, which detects the level of noise pollution in the wearer's environment and illustrates it with a garment-integrated display. In addition, the design concept utilizes printed electronic solar cells as part of the garment design, illustrating a design vision towards zero energy wearable computing. The Solar Shirt uses reindeer leather as its main material, giving a soft and luxurious feeling to the garment. The material selections and the style of the garment derive their inspiration from Arctic Design, reflecting the purity of nature and the simplicity and silence of a snowy world.


title: Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP]

abstract: The purpose of this article is to introduce the readers to the emerging technologies enabled by deep learning and to review the research work conducted in this area that is of direct relevance to signal processing. We also point out, in our view, the future research directions that may attract interests of and require efforts from more signal processing researchers and practitioners in this emerging area for advancing signal and information processing technology and applications.


title: The dangers of replication and a solution

abstract: Update anywhere-anytime-anyway transactional replication has unstable behavior as the workload scales up: a ten-fold increase in nodes and traffic gives a thousand fold increase in deadlocks or reconciliations. Master copy replication (primary copy) schemes reduce this problem. A simple analytic model demonstrates these results. A new two-tier replication algorithm is proposed that allows mobile (disconnected) applications to propose tentative update transactions that are later applied to a master copy. Commutative update transactions avoid the instability of other replication schemes.


title: The role of brand logos in firm performance

abstract: This research demonstrates that the positive effects of brand logos on customer brand commitment and firm performance derive not from enabling brand identification, as is currently understood, but primarily from facilitating customer self-identity/expressiveness, representing a brand's functional benefits, and offering aesthetic appeal. This study examines whether brand names or visual symbols as logos are more effective at creating these benefits and whether or not the impact of the three aforementioned brand logo benefits on customer brand commitment and firm performance is contingent on the extent to which a firm leverages its brand (i.e., employs brand extensions to different product categories).


title: A New Image Steganography Based on 2k Correction and Edge-Detection

abstract: In this paper, we propose a new image steganography scheme which is a kind of spatial domain technique. In order to hide secret data in cover-image, we use the just noticeable difference (JND) technique and method of contrast sensitivity function (CSF). This is an edge-detection which uses part information of each pixel-value. In order to have better imperceptibility, we propose a mathematical method which is the 2k correction. Proposed scheme can embed more data than previous schemes, and shows better imperceptibility. To prove this scheme, we performed several experiments, and compared the experimental results with the related previous works.


A Game Storyboard Design for Avoiding Phishing Attacks


title: Forward Thinking: Building Deep Random Forests

abstract: The success of deep neural networks has inspired many to wonder whether other learners could benefit from deep, layered architectures. We present a general framework called forward thinking for deep learning that generalizes the architectural flexibility and sophistication of deep neural networks while also allowing for (i) different types of learning functions in the network, other than neurons, and (ii) the ability to adaptively deepen the network as needed to improve results. This is done by training one layer at a time, and once a layer is trained, the input data are mapped forward through the layer to create a new learning problem. The process is then repeated, transforming the data through multiple layers, one at a time, rendering a new dataset, which is expected to be better behaved, and on which a final output layer can achieve good performance. In the case where the neurons of deep neural nets are replaced with decision trees, we call the result a Forward Thinking Deep Random Forest (FTDRF). We demonstrate a proof of concept by applying FTDRF on the MNIST dataset. We also provide a general mathematical formulation that allows for other types of deep learning problems to be considered.


title: Multiplier design based on ancient Indian Vedic Mathematics

abstract: Vedic mathematics is the name given to the ancient Indian system of mathematics that was rediscovered in the early twentieth century from ancient Indian sculptures (Vedas). It mainly deals with Vedic mathematical formulae and their application to various branches of mathematics. The algorithms based on conventional mathematics can be simplified and even optimized by the use of Vedic Sutras. These methods and ideas can be directly applied to trigonometry, plain and spherical geometry, conics, calculus (both differential and integral), and applied mathematics of various kinds. In this paper new multiplier and square architecture is proposed based on algorithm of ancient Indian Vedic Mathematics, for low power and high speed applications. It is based on generating all partial products and their sums in one step. The design implementation on ALTERA Cyclone -II FPGA shows that the proposed Vedic multiplier and square are faster than array multiplier and Booth multiplier.