title: Limited Discrepancy Beam Search

abstract: Beam search reduces the memory consumption of best-first search at the cost of finding longer paths but its memory consumption can still exceed the given memory capacity quickly. We therefore develop BULB (Beam search Using Limited discrepancy Backtracking), a complete memory-bounded search method that is able to solve more problem instances of large search problems than beam search and does so with a reasonable runtime. At the same time, BULB tends to find shorter paths than beam search because it is able to use larger beam widths without running out of memory. We demonstrate these properties of BULB experimentally for three standard benchmark domains.


Preparing for the Future: Understanding the Seven Capabilities of Cloud Computing


title: A basis for information retrieval in context

abstract: Information retrieval (IR) models based on vector spaces have been investigated for a long time. Nevertheless, they have recently attracted much research interest. In parallel, context has been rediscovered as a crucial issue in information retrieval. This article presents a principled approach to modeling context and its role in ranking information objects using vector spaces. First, the article outlines how a basis of a vector space naturally represents context, both its properties and factors. Second, a ranking function computes the probability of context in the objects represented in a vector space, namely, the probability that a contextual factor has affected the preparation of an object.


title: Solving mazes with memristors: a massively-parallel approach

abstract: Solving mazes is not just a fun pastime: They are prototype models in several areas of science and technology. However, when maze complexity increases, their solution becomes cumbersome and very time consuming. Here, we show that a network of memristors--resistors with memory--can solve such a nontrivial problem quite easily. In particular, maze solving by the network of memristors occurs in a massively parallel fashion since all memristors in the network participate simultaneously in the calculation. The result of the calculation is then recorded into the memristors' states and can be used and/or recovered at a later time. Furthermore, the network of memristors finds all possible solutions in multiple-solution mazes and sorts out the solution paths according to their length. Our results demonstrate not only the application of memristive networks to the field of massively parallel computing, but also an algorithm to solve mazes, which could find applications in different fields.


Literature Survey on Relation Extraction and Relational Learning


title: What Makes a Photograph Memorable?

abstract: When glancing at a magazine, or browsing the Internet, we are continuously exposed to photographs. Despite this overflow of visual information, humans are extremely good at remembering thousands of pictures along with some of their visual details. But not all images are equal in memory. Some stick in our minds while others are quickly forgotten. In this paper, we focus on the problem of predicting how memorable an image will be. We show that memorability is an intrinsic and stable property of an image that is shared across different viewers, and remains stable across delays. We introduce a database for which we have measured the probability that each picture will be recognized after a single view. We analyze a collection of image features, labels, and attributes that contribute to making an image memorable, and we train a predictor based on global image descriptors. We find that predicting image memorability is a task that can be addressed with current computer vision techniques. While making memorable images is a challenging task in visualization, photography, and education, this work is a first attempt to quantify this useful property of images.


title: Learned spatiotemporal sequence recognition and prediction in primary visual cortex

abstract: Here the authors report that repeated presentations of a visual sequence over a course of days causes evoked response potentiation in mouse V1 that is highly specific for stimulus order and timing. After V1 is trained to recognize a sequence, cortical activity regenerates the full sequence even when individual stimulus elements are omitted.


title: Discriminant kernel and regularization parameter learning via semidefinite programming

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


title: On the notion of salience in spoken discourse - prominence cues shaping discourse structure and comprehension

abstract: Understanding spoken discourse is a complex task that implies monitoring and memorizing relations between important discourse units. Discourse comprehension has therefore been described as a process entailing a continuous competition between attention and memory resources for the sake of discourse structure building. The notion of salience has become a key issue in this debate. Salience refers to the fact that some parts of the discourse are more activated or accessible in memory than others. This is particularly relevant to set up an adequate mental representation of the ongoing discourse. In this contribution, I outline the challenges of defining salience and related processes in spoken discourse. By considering theoretical and experimental results, salience is related to prominence perception and production, and the interpretation of syntactic and prosodic prominence cues. As a result, the role of predictions in discourse processing is identified as one of the major issues to be addressed in future research.


title: Contextual Diversity, Not Word Frequency, Determines Word-Naming and Lexical Decision Times

abstract: Word frequency is an important predictor of word-naming and lexical decision times. It is, however, confounded with contextual diversity, the number of contexts in which a word has been seen. In a study using a normative, corpus-based measure of contextual diversity, word-frequency effects were eliminated when effects of contextual diversity were taken into account (but not vice versa) across three naming and three lexical decision data sets; the same pattern of results was obtained regardless of which of three corpora was used to derive the frequency and contextual-diversity values. The results are incompatible with existing models of visual word recognition, which attribute frequency effects directly to frequency, and are particularly problematic for accounts in which frequency effects reflect learning. We argue that the results reflect the importance of likely need in memory processes, and that the continuity between reading and memory suggests using principles from memory research to inform theories of reading.


title: Reconfigurable Multi-Band Microwave Filters

abstract: An original and simple approach to the design of fully reconfigurable multi-band microwave bandpass filters (BPFs) with an arbitrary number of passbands is reported in this paper. It exploits the use of an innovative quasi-BPF configuration made up of different sets of controllable mono-frequency resonators to separately shape each tunable passband. Thus, high-selectivity multi-band bandpass filtering transfer functions exhibiting independent control in terms of center frequency, bandwidth, and transmission zeros can be synthesized. Furthermore, as an unprecedented frequency-agility feature of the proposed reconfigurable multi-band BPF structure when compared to the state-of-the-art, its passbands can be merged together to form broader, and for certain realizations, higher order transmission bands. This allows even more degrees of reconfiguration to be achieved in the devised circuit, which can also operate as ultra-wideband BPF with flexible in-band notches or self-equalized flat-group-delay quasi-elliptic-type BPF. The theoretical foundations of the described reconfigurable multi-band BPF scheme, along with guidelines for its design and a triple-passband filter synthesis example based on the coupled-node formalism, are expounded. In addition, as an experimental proof-of-concept, two microstrip prototypes with high- $Q$ tuning implemented through mechanically variable capacitors are manufactured and tested. They are a wideband dual-band BPF and a quadruple-band BPF with narrow-bandwidth passbands.


title: Sharp Analysis for Nonconvex SGD Escaping from Saddle Points

abstract: In this paper, we give a sharp analysis for Stochastic Gradient Descent (SGD) and prove that SGD is able to efficiently escape from saddle points and find an $(\epsilon, O(\epsilon^{0.5}))$-approximate second-order stationary point in $\tilde{O}(\epsilon^{-3.5})$ stochastic gradient computations for generic nonconvex optimization problems, when the objective function satisfies gradient-Lipschitz, Hessian-Lipschitz, and dispersive noise assumptions. This result subverts the classical belief that SGD requires at least $O(\epsilon^{-4})$ stochastic gradient computations for obtaining an $(\epsilon,O(\epsilon^{0.5}))$-approximate second-order stationary point. Such SGD rate matches, up to a polylogarithmic factor of problem-dependent parameters, the rate of most accelerated nonconvex stochastic optimization algorithms that adopt additional techniques, such as Nesterov's momentum acceleration, negative curvature search, as well as quadratic and cubic regularization tricks. Our novel analysis gives new insights into nonconvex SGD and can be potentially generalized to a broad class of stochastic optimization algorithms.


title: Biological Goal Seeking

abstract: Obstacle avoidance is a critical aspect of control for a mobile robot when navigating towards a goal in an unfamiliar environment. Where traditional methods for obstacle avoidance depend heavily on path planning to reach a specific goal location whilst avoiding obstacles, the method proposed uses an adaption of a potential fields algorithm to successfully avoid obstacles whilst the robot is being guided to a non-specific goal location. Details of a generalised finite state machine based control algorithm that enable the robot to pursue a dynamic goal location, whilst avoiding obstacles and without the need for specific path planning, are presented. We have developed a novel potential fields algorithm for obstacle avoidance for use within Robot Operating Software (ROS) and made it available for download within the open source community. We evaluated the control algorithm in a high-speed predator-prey scenario in which the predator could successfully catch the moving prey whilst avoiding collision with all obstacles within the environment.


title: Writer identification and verification using GMM supervectors

abstract: This paper proposes a new system for offline writer identification and writer verification. The proposed method uses GMM supervectors to encode the feature distribution of individual writers. Each supervector originates from an individual GMM which has been adapted from a background model via a maximum-a-posteriori step followed by mixing the new statistics with the background model. We show that this approach improves the TOP-1 accuracy of the current best ranked methods evaluated at the ICDAR-2013 competition dataset from 95.1% [13] to 97.1%, and from 97.9% [11] to 99.2% at the CVL dataset, respectively. Additionally, we compare the GMM supervector encoding with other encoding schemes, namely Fisher vectors and Vectors of Locally Aggregated Descriptors.


title: Playing with Video Games: Going to a New Addiction?

abstract: The frequent and protracted use of video games with serious personal, family and social consequences is no longer just a pleasant pastime and could lead to mental and physical health problems. Although there is no official recognition of video game addiction on the Internet as a mild mental health disorder, further scientific research is needed.


title: A Low Phase Noise Quadrature Injection Locked Frequency Synthesizer for MM-Wave Applications

abstract: This paper proposes a 60 GHz quadrature PLL frequency synthesizer for the IEEE802.15.3c with wide tuning range and low phase noise. The synthesizer is constructed using a 20 GHz PLL that is coupled with a Quadrature Injection Locked Oscillator (QILO) as a frequency tripler to generate the 60 GHz signal. The 20 GHz PLL generates a signal with a phase noise that is lower than -105 dBc/Hz using tail feedback to improve the phase noise while having a 17% tuning range. The proposed 60 GHz QILO uses a combination of parallel and tail injection to enhance the locking range by improving the QILO injection efficiency at the moment of injection and has a 12% tuning range. Both the 20 GHz PLL and the QILO were fabricated as separate chips using a 65 nm CMOS process and measurement results show a phase noise that is less than -95 dBc/Hz@1 MHz at 60 GHz while consuming 80 mW from a 1.2 V supply. To the author's knowledge this phase noise is about 20 dB better than recently reported QPLLs and about 10 dB compared to differential PLLs operating at a similar frequency and at a similar offset.


title: Building A Large Ontology For Machine Translation

abstract: This paper describes efforts underway to construct a large-scale ontology to support semantic processing in the PAN-GLOSS knowledge-base machine translation system. Because we are aiming at broad semantic coverage, we are focusing on automatic and semi-automatic methods of knowledge acquisition. Here we report on algorithms for merging complementary online resources, in particular the LDOCE and WordNet dictionaries. We discuss empirical results, and how these results have been incorporated into the PANGLOSS ontology.


title: New Paths in Music Recommender Systems Research

abstract: The particularities of musical data and its multiple modalities make original contributions possible in many core RecSys topics such as content-based and hybrid recommendation, user modeling, interfaces, and context-aware and mobile recommendations. But more urgently, the current revolution in the music industry represents major opportunities and challenges for recommendation systems in general. Recommendation systems are now central to music streaming platforms, which are rapidly increasing in listenership and becoming the top source of revenue for the music industry. It is increasingly more common for a music listener to simply access music than to purchase and own it in a personal collection. In this scenario, recommendation calls no longer for a one-shot recommendation for the purpose of a track or album purchase, but for a recommendation of a listening experience, comprising a very wide range of challenges, such as sequential recommendation, or conversational and contextual recommendations. Recommendation technologies now impact all actors in the rich and complex music industry ecosystem (listeners, labels, music makers and producers, concert halls, advertisers, etc.). To highlight these developments, we focus on three use cases: automatic playlist generation, context-aware music recommendation, and recommendation in the creative process of music making.


title: Pharmacological treatment of Parkinson disease: a review.

abstract: IMPORTANCE ::: Parkinson disease is the second most common neurodegenerative disease worldwide. Although no available therapies alter the underlying neurodegenerative process, symptomatic therapies can improve patient quality of life. ::: ::: ::: OBJECTIVE ::: To provide an evidence-based review of the initial pharmacological management of the classic motor symptoms of Parkinson disease; describe management of medication-related motor complications (such as motor fluctuations and dyskinesia), and other medication adverse effects (nausea, psychosis, and impulse control disorders and related behaviors); and discuss the management of selected nonmotor symptoms of Parkinson disease, including rapid eye movement sleep behavior disorder, cognitive impairment, depression, orthostatic hypotension, and sialorrhea. ::: ::: ::: EVIDENCE REVIEW ::: References were identified using searches of PubMed between January 1985 and February 2014 for English-language human studies and the full database of the Cochrane Library. The classification of studies by quality (classes I-IV) was assessed using the levels of evidence guidelines from the American Academy of Neurology and the highest-quality data for each topic. ::: ::: ::: RESULTS ::: Although levodopa is the most effective medication available for treating the motor symptoms of Parkinson disease, in certain instances (eg, mild symptoms, tremor as the only or most prominent symptom, aged <60 years) other medications (eg, monoamine oxidase type B inhibitors [MAOBIs], amantadine, anticholinergics, β-blockers, or dopamine agonists) may be initiated first to avoid levodopa-related motor complications. Motor fluctuations may be managed by modifying the levodopa dosing regimen or by adding several other medications, such as MAOBIs, catechol-O-methyltransferase inhibitors, or dopamine agonists. Impulse control disorders are typically managed by reducing or withdrawing dopaminergic medication, particularly dopamine agonists. Evidence-based management of some nonmotor symptoms is limited by a paucity of high-quality positive studies. ::: ::: ::: CONCLUSIONS AND RELEVANCE ::: Strong evidence supports using levodopa and dopamine agonists for motor symptoms at all stages of Parkinson disease. Dopamine agonists and drugs that block dopamine metabolism are effective for motor fluctuations and clozapine is effective for hallucinations. Cholinesterase inhibitors may improve symptoms of dementia and antidepressants and pramipexole may improve depression. Evidence supporting other therapies for motor and nonmotor features is less well established.


title: Supervised Text-based Geolocation Using Language Models on an Adaptive Grid

abstract: The geographical properties of words have recently begun to be exploited for geolocating documents based solely on their text, often in the context of social media and online content. One common approach for geolocating texts is rooted in information retrieval. Given training documents labeled with latitude/longitude coordinates, a grid is overlaid on the Earth and pseudo-documents constructed by concatenating the documents within a given grid cell; then a location for a test document is chosen based on the most similar pseudo-document. Uniform grids are normally used, but they are sensitive to the dispersion of documents over the earth. We define an alternative grid construction using k-d trees that more robustly adapts to data, especially with larger training sets. We also provide a better way of choosing the locations for pseudo-documents. We evaluate these strategies on existing Wikipedia and Twitter corpora, as well as a new, larger Twitter corpus. The adaptive grid achieves competitive results with a uniform grid on small training sets and outperforms it on the large Twitter corpus. The two grid constructions can also be combined to produce consistently strong results across all training sets.


title: Super-resolution image synthesis using the physical pixel arrangementofalight field camera

abstract: We propose a method for super-resolution image synthesis that accurately handles the physical pixel arrangement of a light field (plenoptic) camera. We use a Lytro camera to obtain 4D light field data (a set of multi-viewpoint images) through a micro-lens array. The light field data are multiplexed on a single image sensor, and thus, the data is first de-multiplexed into a set of multi-viewpoint (sub-aperture) images. However, the de-multiplexing process usually involves interpolation of the original data such as demosaicing for a color filter array and pixel resampling for the non-square micro-lens arrangement. During this interpolation, some information is added or lost to/from the original data. In contrast, our method can preserve the originally captured data as they are, and directly use them for the super-resolution image synthesis, where the super-resolved image and the corresponding depth map are alternatively refined. We experimentally demonstrate that our method can achieve higher image quality than that with a standard Light Field Toolbox.


Overview of Amazon Web Services-AWS Whitepaper


Critical arterial stenosis.


title: Performance Analysis of the CS-DCSK/BPSK Communication System

abstract: As a spread spectrum communication system, code-shifted differential chaos shift keying (CS-DCSK) is required to provide reasonable bit error performance in a coexisting scenario where the conventional communication system is an interferer. Meantime, the bit error performance of the conventional communication system should not deteriorate dramatically in the presence of a CS-DCSK signal, which is as an interferer. This paper studies the performances of the CS-DCSK/BPSK coexisting system. By comparisons on the bit error rate (BER) of two coexisting systems, i.e, CS-DCSK/BPSK and DCSK/BPSK systems, in different channel environments at the same interference signal ratio, it reveals that CS-DCSK/BPSK has better performances than DCSK/BPSK. The BER expression of the CS-DCSK sub-system is derived in the presence of BPSK interference signals when two sub-systems are synchronized. Also derived is the BER expression of the BPSK sub-system in the presence of CS-DCSK signals when two sub-systems are asynchronized and synchronized, respectively. For the synchronization case, the BER expression of the BPSK sub-system is independent of the strengths of CS-DCSK signals. As a new interferer, CS-DCSK has less effect on the conventional communication system. It is shown that numerical simulations are in good agreement with the analytic results.


title: Fast forward selection to speed up sparse gaussian process regression

abstract: We present a method for the sparse greedy approximation of Bayesian Gaussian process regression, featuring a novel heuristic for very fast forward selection. Our method is essentially as fast as an equivalent one which selects the "support" patterns at random, yet it can outperform random selection on hard curve fitting tasks. More importantly, it leads to a sufficiently stable approximation of the log marginal likelihood of the training data, which can be optimised to adjust a large number of hyperparameters automatically. We demonstrate the model selection capabilities of the algorithm in a range of experiments. In line with the development of our method, we present a simple view on sparse approximations for GP models and their underlying assumptions and show relations to other methods.


title: KVLight: A Lightweight Key-Value Store for Distributed Access in Cloud

abstract: Key-value stores (KVS) are finding use in Big Data applications as the store offers a flexible data model, scalability in number of distributed nodes, and high availability. In a cloud environment, a distributed KVS is often deployed over the local file system of the nodes in a cluster of virtual machines (VMs). Parallel file system (PFS) offers an alternate approach to disk storage, however a distributed key value store running over a parallel file system can experience overheads due to its unawareness of the PFS. Additionally, distributed KVS requires persistent running services which is not cost effective under the pay-as-you-go model of cloud computing because resources have to be held even under periods of no workload. We propose KVLight, a lightweight KVS that runs over PFS. It is lightweight in the sense that it shifts the responsibility of reliable data storage to the PFS and focuses on performance. Specifically, KVLight is built on an embedded KVS for high performance but uses novel data structures to support concurrent writes, giving capability that embedded KVSs are not currently designed for. Furthermore, it allows on-demand access without running persistent services in front of the file system. Empirical results show that KVLight outperforms Cassandra and Voldemort, two state-of-the-art KVSs, under both synthetic and realistic workloads.


title: The Feedback-related Negativity Codes Components of Abstract Inference during Reward-based Decision-making

abstract: Behavioral control is influenced not only by learning from the choices made and the rewards obtained but also by "what might have happened," that is, inference about unchosen options and their fictive outcomes. Substantial progress has been made in understanding the neural signatures of direct learning from choices that are actually made and their associated rewards via reward prediction errors RPEs. However, electrophysiological correlates of abstract inference in decision-making are less clear. One seminal theory suggests that the so-called feedback-related negativity FRN, an ERP peaking 200-300 msec after a feedback stimulus at frontocentral sites of the scalp, codes RPEs. Hitherto, the FRN has been predominantly related to a so-called "model-free" RPE: The difference between the observed outcome and what had been expected. Here, by means of computational modeling of choice behavior, we show that individuals employ abstract, "double-update" inference on the task structure by concurrently tracking values of chosen stimuli associated with observed outcomes and unchosen stimuli linked to fictive outcomes. In a parametric analysis, model-free RPEs as well as their modification because of abstract inference were regressed against single-trial FRN amplitudes. We demonstrate that components related to abstract inference uniquely explain variance in the FRN beyond model-free RPEs. These findings advance our understanding of the FRN and its role in behavioral adaptation. This might further the investigation of disturbed abstract inference, as proposed, for example, for psychiatric disorders, and its underlying neural correlates.


title: MAC-Layer Concurrent Beamforming Protocol for Indoor Millimeter-Wave Networks

abstract: In this paper, we study concurrent beamforming issue for achieving high capacity in indoor millimeter-wave (mmWave) networks. The general concurrent beamforming issue is first formulated as an optimization problem to maximize the sum rates of concurrent transmissions, considering the mutual interference. To reduce the complexity of beamforming and the total setup time, concurrent beamforming is decomposed into multiple single-link beamforming, and an iterative searching algorithm is proposed to quickly achieve the suboptimal transmission/reception beam sets. A codebook-based beamforming protocol at medium access control (MAC) layer is then introduced in a distributive manner to determine the beam sets. Both analytical and simulation results demonstrate that the proposed protocol can drastically reduce total setup time, increase system throughput, and improve energy efficiency.


title: A Minimal Intervention Principle for Coordinated Movement

abstract: Behavioral goals are achieved reliably and repeatedly with movements rarely reproducible in their detail. Here we offer an explanation: we show that not only are variability and goal achievement compatible, but indeed that allowing variability in redundant dimensions is the optimal control strategy in the face of uncertainty. The optimal feedback control laws for typical motor tasks obey a "minimal intervention" principle: deviations from the average trajectory are only corrected when they interfere with the task goals. The resulting behavior exhibits task-constrained variability, as well as synergetic coupling among actuators—which is another unexplained empirical phenomenon.


title: A Meaning-based Statistical English Math Word Problem Solver

abstract: We introduce MeSys, a meaning-based approach to solving English math word problems (MWPs) via understanding and reasoning in this paper. It first analyzes the text, transforms both body and question parts into their corresponding logic forms, and then performs inference on them. The associated context of each quantity is represented with proposed role-tags (e.g., nsubj, verb, etc.), which provides the flexibility for annotating an extracted math quantity with its associated context information (i.e., the physical meaning of this quantity). Statistical models are proposed to select the operator and operands. A noisy dataset is designed to assess if a solver solves MWPs mainly via understanding or pattern matching. Experimental results show that our approach outperforms existing systems on both benchmark datasets and the noisy dataset, which demonstrates that the proposed approach more understands the meaning of each quantity in the text.


title: Multi-Temporal Analysis of Forestry and Coastal Environments Using UASs

abstract: Due to strong improvements and developments achieved in the last decade, it is clear that applied research using remote sensing technology such as unmanned aerial vehicles (UAVs) can provide a flexible, efficient, non-destructive, and non-invasive means of acquiring geoscientific data, especially aerial imagery. Simultaneously, there has been an exponential increase in the development of sensors and instruments that can be installed in UAV platforms. By combining the aforementioned factors, unmanned aerial system (UAS) setups composed of UAVs, sensors, and ground control stations, have been increasingly used for remote sensing applications, with growing potential and abilities. This paper’s overall goal is to identify advantages and challenges related to the use of UAVs for aerial imagery acquisition in forestry and coastal environments for preservation/prevention contexts. Moreover, the importance of monitoring these environments over time will be demonstrated. To achieve these goals, two case studies using UASs were conducted. The first focuses on phytosanitary problem detection and monitoring of chestnut tree health (Padrela region, Valpacos, Portugal). The acquired high-resolution imagery allowed for the identification of tree canopy cover decline by means of multi-temporal analysis. The second case study enabled the rigorous and non-evasive registry process of topographic changes that occurred in the sandspit of Cabedelo (Douro estuary, Porto, Portugal) in different time periods. The obtained results allow us to conclude that the UAS constitutes a low-cost, rigorous, and fairly autonomous form of remote sensing technology, capable of covering large geographical areas and acquiring high precision data to aid decision support systems in forestry preservation and coastal monitoring applications. Its swift evolution makes it a potential big player in remote sensing technologies today and in the near future.


title: Adaptation of manipulation skills in physical contact with the environment to reference force profiles

abstract: We propose a new methodology for learning and adaption of manipulation skills that involve physical contact with the environment. Pure position control is unsuitable for such tasks because even small errors in the desired trajectory can cause significant deviations from the desired forces and torques. The proposed algorithm takes a reference Cartesian trajectory and force/torque profile as input and adapts the movement so that the resulting forces and torques match the reference profiles. The learning algorithm is based on dynamic movement primitives and quaternion representation of orientation, which provide a mathematical machinery for efficient and stable adaptation. Experimentally we show that the robot's performance can be significantly improved within a few iteration steps, compensating for vision and other errors that might arise during the execution of the task. We also show that our methodology is suitable both for robots with admittance and for robots with impedance control.


title: Posterior probability support vector Machines for unbalanced data

abstract: This paper proposes a complete framework of posterior probability support vector machines (PPSVMs) for weighted training samples using modified concepts of risks, linear separability, margin, and optimal hyperplane. Within this framework, a new optimization problem for unbalanced classification problems is formulated and a new concept of support vectors established. Furthermore, a soft PPSVM with an interpretable parameter /spl nu/ is obtained which is similar to the /spl nu/-SVM developed by Scho/spl uml/lkopf et al., and an empirical method for determining the posterior probability is proposed as a new approach to determine /spl nu/. The main advantage of an PPSVM classifier lies in that fact that it is closer to the Bayes optimal without knowing the distributions. To validate the proposed method, two synthetic classification examples are used to illustrate the logical correctness of PPSVMs and their relationship to regular SVMs and Bayesian methods. Several other classification experiments are conducted to demonstrate that the performance of PPSVMs is better than regular SVMs in some cases. Compared with fuzzy support vector machines (FSVMs), the proposed PPSVM is a natural and an analytical extension of regular SVMs based on the statistical learning theory.


title: EMD-L1: An Efficient and Robust Algorithm for Comparing Histogram-Based Descriptors

abstract: We propose a fast algorithm, EMD-Li, for computing the Earth Mover's Distance (EMD) between a pair of histograms. Compared to the original formulation, EMD-L 1 has a largely simplified structure. The number of unknown variables in EMD-L 1 is O(N) that is significantly less than O(N 2 ) of the original EMD for a histogram with N bins. In addition, the number of constraints is reduced by half and the objective function is also simplified. We prove that the EMD-L 1 is formally equivalent to the original EMD with L 1 ground distance without approximation. Exploiting the L 1 metric structure, an efficient tree-based algorithm is designed to solve the EMD-L 1 computation. An empirical study demonstrates that. the new algorithm has the time complexity of O(N 2 ), which is much faster than previously reported algorithms with super-cubic complexities. The proposed algorithm thus allows the EMD to be applied for comparing histogram-based features, which is practically impossible with previous algorithms. We conducted experiments for shape recognition and interest point matching. EMD-Li is applied to compare shape contexts on the widely tested MPEG7 shape dataset and SIFT image descriptors on a set of images with large deformation, illumination change and heavy noise. The results show that our EMD-L 1 -based solutions outperform previously reported state-of-the-art features and distance measures in solving the two tasks.


title: Herbal Drugs from Sudan: Traditional Uses and Phytoconstituents

abstract: Sudan folklore medicine is characterized by a unique combination of Islamic, Arabic, and African cultures. In poor communities, traditional medicine has remained as the most reasonable source of treatment of several diseases and microbial infections. Although the traditional medicine is accepted in Sudan, to date there is no updated review available, which focuses on most effective and frequently used Sudanese medicinal plants. Thus, this review aims to summarize the published information on the ethnobotanical uses of medicinal plants from Sudan, preparation methods, phytochemistry, and ethnopharmacology. The collected data demonstrate that Sudanese medicinal plants have been reported to possess a wide range of traditional medicinal uses including different microbial infections, gastrointestinal disorders, malaria, diabetes, rheumatic pain, respiratory system disorders, jaundice, urinary system inflammations, wounds, cancer, and different microbial infections. In most cases, the pharmacological studies were in agreement with traditional uses. Moreover, several bioactive compounds such as flavonoids, saponins, alkaloids, steroids, terpenes, tannins, fatty acids, and essential oils have been identified as active constituents. Although this review demonstrates the importance of ethnomedicine medicines in the treatment of several diseases in Sudan, further researches to validate the therapeutic uses and safety of these plants through phytochemical screening, different biological activity assays, and toxicological studies are still needed.


title: Conceptual domain of the matrix in fragmented landscapes

abstract: In extensively modified landscapes, how the matrix is managed determines many conservation outcomes. Recent publications revise popular conceptions of a homogeneous and static matrix, yet we still lack an adequate conceptual model of the matrix. Here, we identify three core effects that influence patch-dependent species, through impacts associated with movement and dispersal, resource availability, and the abiotic environment. These core effects are modified by five ‘dimensions’: spatial and temporal variation in matrix quality; spatial scale; temporal scale of matrix variation; and adaptation. The conceptual domain of the matrix, defined as three core effects and their interaction with these five dimensions, provides a much-needed framework to underpin management of fragmented landscapes and highlights new research priorities.


title: Workload management for dynamic mobile device clusters in edge femtoclouds

abstract: Edge computing offers an alternative to centralized, in-the-cloud compute services. Among the potential advantages of edge computing are lower latency that improves responsiveness, reduced wide-area network congestion, and possibly greater privacy by keeping data more local. In our previous work on Femtoclouds, we proposed taking advantage of clusters of devices that tend to be co-located in places such as public transit, classrooms or coffee shops. These clusters can perform computations for jobs generated from within or outside of the cluster. In this paper, we address the full requirements of workload management in Femtoclouds. These functions enable a Femtocloud to provide a service to job initiators that is similar to that provided by a centralized cloud service. We develop a system architecture that relies on the cloud to efficiently control and manage a Femtocloud. Within this architecture, we develop adaptive workload management mechanisms and algorithms to manage resources and effectively mask churn. We implement a prototype of our Femtocloud system on Android devices and utilize it to evaluate the overall system performance. We use simulation to isolate and study the impact of our workload management mechanisms and test the system at scale. Our prototype and simulation results demonstrate the efficiency of the Femtocloud workload management mechanisms especially in situations with potentially high churn. For instance, our mechanisms can reduce the average job completion time by up to 26% compared to similar mechanisms used in traditional cloud computing systems when used in situations that suggest high churn.


title: Finite volume methods on spheres and spherical centroidal Voronoi meshes

abstract: We study in this paper a finite volume approximation of linear convection-diffusion equations defined on a sphere using the spherical Voronoi meshes, in particular the spherical centroidal Voronoi meshes. The high quality of spherical centroidal Voronoi meshes is illustrated through both theoretical analysis and computational experiments. In particular, we show that the L2 error of the approximate solution is of quadratic order when the underlying mesh is given by a spherical centroidal Voronoi mesh. We also demonstrate numerically the high accuracy and the superconvergence of the approximate solutions.


title: An Implementation of DES and AES, Secure against Some Attacks

abstract: Since Power Analysis on smart cards was introduced by Paul Kocher [7], many countermeasures have been proposed to protect implementations of cryptographic algorithms. In this paper we propose a new protection principle: the transformed masking method. We apply this method to protect two of the most popular block ciphers: DES and the AES Rijndael. To this end we introduce some transformed S-boxes for DES and a new masking method and its applications to the non-linear part of Rijndael.


title: Optic Disc Detection From Normalized Digital Fundus Images by Means of a Vessels' Direction Matched Filter

abstract: Optic disc (OD) detection is a main step while de- veloping automated screening systems for diabetic retinopathy. We present in this paper a method to automatically detect the position of the OD in digital retinal fundus images. The method starts by normalizing luminosity and contrast through out the image using illumination equalization and adaptive histogram equalization methods respectively. The OD detection algorithm is based on matching the expected directional pattern of the retinal blood vessels. Hence, a simple matched filter is proposed to roughly match the direction of the vessels at the OD vicinity. The retinal vessels are segmented using a simple and standard 2-D Gaussian matched filter. Consequently, a vessels direction map of the segmented retinal vessels is obtained using the same segmentation algorithm. The segmented vessels are then thinned, and filtered using local intensity, to represent finally the OD-center candidates. The difference between the proposed matched filter resized into four different sizes, and the vessels' directions at the surrounding area of each of the OD-center candidates is measured. The minimum difference provides an estimate of the OD-center coordinates. The proposed method was evaluated using a subset of the STARE project's dataset, containing 81 fundus images of both normal and diseased retinas, and initially used by literature OD detection methods. The OD-center was detected correctly in 80 out of the 81 images (98.77%). In addition, the OD-center was detected correctly in all of the 40 images (100%) using the publicly available DRIVE dataset.


title: A Novel Series-Fed Taper Antenna Array Design

abstract: To improve the performance of microstrip antenna array, a matching-in-step (MIS) configuration of microstrip series-fed taper array is designed. Compared to the traditional one, the novel antenna array has a better VSWR characteristic. The design procedure of the MIS antenna array is discussed in detail, and some valuable results are acquired. Numerical results are obtained and a very good agreement is observed between experimental and simulated results of such arrays.


title: Predicting online game loyalty based on need gratification and experiential motives

abstract: Purpose – This paper aims to explore the factors affecting consumers' loyalty toward online games based on the uses and gratifications theory and the flow theory.Design/methodology/approach – The research employed two approaches to collect data: personal interview and online survey. Each data collection approach consists of two phases to overcome method bias. This study adopted structural equation modeling to analyze the data.Findings – The results focusing on popular massively multiplayer online role‐playing games reveal that players' sense of control, perceived entertainment, and challenge affect their loyalty toward an online game. Conversely, sociality and interactivity produce negligible effects on loyalty.Practical implications – First, game designers may strengthen gamers' sense of control and challenge by adding more status information, gaming options, or through the designed system of goals and achievements. Second, the entertaining nature of online gaming suggests greater demand for content desi...


title: Dynamic Model of Basic Oxygen Steelmaking Process Based on Multi-zone Reaction Kinetics: Model Derivation and Validation

abstract: A multi-zone kinetic model coupled with a dynamic slag generation model was developed for the simulation of hot metal and slag composition during the basic oxygen furnace (BOF) operation. The three reaction zones (i) jet impact zone, (ii) slag–bulk metal zone, (iii) slag–metal–gas emulsion zone were considered for the calculation of overall refining kinetics. In the rate equations, the transient rate parameters were mathematically described as a function of process variables. A micro and macroscopic rate calculation methodology (micro-kinetics and macro-kinetics) were developed to estimate the total refining contributed by the recirculating metal droplets through the slag–metal emulsion zone. The micro-kinetics involves developing the rate equation for individual droplets in the emulsion. The mathematical models for the size distribution of initial droplets, kinetics of simultaneous refining of elements, the residence time in the emulsion, and dynamic interfacial area change were established in the micro-kinetic model. In the macro-kinetics calculation, a droplet generation model was employed and the total amount of refining by emulsion was calculated by summing the refining from the entire population of returning droplets. A dynamic FetO generation model based on oxygen mass balance was developed and coupled with the multi-zone kinetic model. The effect of post-combustion on the evolution of slag and metal composition was investigated. The model was applied to a 200-ton top blowing converter and the simulated value of metal and slag was found to be in good agreement with the measured data. The post-combustion ratio was found to be an important factor in controlling FetO content in the slag and the kinetics of Mn and P in a BOF process.


title: iFeel: a system that compares and combines sentiment analysis methods

abstract: Sentiment analysis methods are used to detect polarity in thoughts and opinions of users in online social media. As businesses and companies are interested in knowing how social media users perceive their brands, sentiment analysis can help better evaluate their product and advertisement campaigns. In this paper, we present iFeel, a Web application that allows one to detect sentiments in any form of text including unstructured social media data. iFeel is free and gives access to seven existing sentiment analysis methods: SentiWordNet, Emoticons, PANAS-t, SASA, Happiness Index, SenticNet, and SentiStrength. With iFeel, users can also combine these methods and create a new Combined-Method that achieves high coverage and F-measure. iFeel provides a single platform to compare the strengths and weaknesses of various sentiment analysis methods with a user friendly interface such as file uploading, graphical visualizing, and weight tuning.


title: Using linux device drivers for static verification tools benchmarking

abstract: The Linux Driver Verification system is designed for static analysis of the source code of Linux kernel space device drivers. In this paper, we describe the architecture of the verification system, including the integration of third-party tools for static verification of C programs. We consider characteristics of the Linux drivers source code that are important from the viewpoint of verification algorithms and give examples of comparative analysis of different verification tools, as well as different versions and configurations of a given tool.


title: A multi-standard analog front-end circuit for 13.56MHz RFID reader

abstract: A multi-standard analog front-end architecture SoC (System on Chip) for 13.56MHz radio frequency is proposed. It consists of a transmitter and a receiver. The transmitter adjusts its ASK modulation index with a digitally controlled mode select register, thus supporting RFID standards of ISO/IEC 14443 Type A/B. In the receiver, an envelope detector, a variable gain amplifier and a shaping circuit are introduced. The envelope detector based on operational amplifier can even detector signals with a magnitude of only 100mV. Different gain and compare voltage can be achieved through the variable gain amplifier and shaping circuit which controlled by registers. The proposed analog front-end is implemented in SMIC 0.18um CMOS process at 1.8V single supply. The simulation results indicate the analog front-end satisfies the ISO/IEC 14443 Type A/B standards. The proposed circuit is suitable for RFID reader and NFC transceiver.


title: Experiential avoidance and well-being: a daily diary analysis.

abstract: Experiential avoidance (EA) is a regulatory strategy characterised by efforts to control or avoid unpleasant thoughts, feelings and bodily sensations. Most studies of EA have used trait measures without considering the effects of EA on psychological functioning in naturalistic settings. To address this gap, we used daily diary methodology to examine the influence of EA of anxiety on everyday well-being. For two weeks, 89 participants provided daily reports of EA, positive and negative affect, enjoyment of daily events and meaning in life (MIL). Daily EA predicted higher negative affect, lower positive affect, less enjoyment of daily events (exercising, eating food and listening to music) and less MIL. The effect of EA on positive affect was not accounted for by the amount of negative affect experienced. Our daily measure of EA was a stronger predictor of daily well-being than a traditional trait measure (The Acceptance and Action Questionnaire). Taken together, results offer insights into the adverse effe...


title: A Survey of Deep Learning Methods for Relation Extraction

abstract: Relation Extraction is an important sub-task of Information Extraction which has the potential of employing deep learning (DL) models with the creation of large datasets using distant supervision. In this review, we compare the contributions and pitfalls of the various DL models that have been used for the task, to help guide the path ahead.


title: Deep Voice 2: Multi-Speaker Neural Text-to-Speech

abstract: We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.


title: Curriculum Q-Learning for Visual Vocabulary Acquisition

abstract: The structure of curriculum plays a vital role in our learning process, both as children and adults. Presenting material in ascending order of difficulty that also exploits prior knowledge can have a significant impact on the rate of learning. However, the notion of difficulty and prior knowledge differs from person to person. Motivated by the need for a personalised curriculum, we present a novel method of curriculum learning for vocabulary words in the form of visual prompts. We employ a reinforcement learning model grounded in pedagogical theories that emulates the actions of a tutor. We simulate three students with different levels of vocabulary knowledge in order to evaluate the how well our model adapts to the environment. The results of the simulation reveal that through interaction, the model is able to identify the areas of weakness, as well as push students to the edge of their ZPD. We hypothesise that these methods can also be effective in training agents to learn language representations in a simulated environment where it has previously been shown that order of words and prior knowledge play an important role in the efficacy of language learning.


title: Mining academic data to improve college student retention: an open source perspective

abstract: In this paper we report ongoing research on the Open Academic Analytics Initiative (OAAI), a project aimed at increasing college student retention by performing early detection of academic risk using data mining methods. The paper describes the goals and objectives of the OAAI, and lays out a methodological framework to develop models that can be used to perform inferential queries on student performance using open source course management system data and student academic records. Preliminary results on initial model development using several data mining algorithms for classification are presented.


title: Ship Surveillance With TerraSAR-X

abstract: Ship detection is an important application of global monitoring of environment and security. In order to overcome the limitations by other systems, surveillance with satellite synthetic aperture radar (SAR) is used because of its possibility to provide ship detection at high resolution over wide swaths and in all weather conditions. A new X-band radar onboard the TerraSAR-X (TS-X) satellite gives access to spatial resolution as fine as 1 m. In this paper, first results on the combined use of TS-X ship detection, automatic identification system (AIS), and satellite AIS (SatAIS) is presented. The AIS system is an effective terrestrial method for tracking vessels in real time typically up to 40 km off the coast. SatAIS, as a space-based system, allows almost global coverage for monitoring of ships since not all ships operate their AIS and smaller ships are not equipped with AIS. The system is considered to be of cooperative nature. In this paper, the quality of TS-X images with respect to ship detection is evaluated, and a first assessment of its performance for ship detection is given. The velocity of a moving ship is estimated using complex TS-X data. As test cases, images were acquired over the North Sea, Baltic Sea, Atlantic Ocean, and Pacific Ocean in Stripmap mode with a resolution of 3 m at a coverage of 30 km 100 km. Simultaneous information on ship positions was available from TS-X and terrestrial as well as SatAIS. First results on the simultaneous superposition of SatAIS and high-resolution radar images are presented.


title: De novo Families: Lesbian Motherhood

abstract: ABSTRACTThe aim of this recent Australian study was to explore the experiences of lesbian women choosing motherhood. Participants included 30 self-identifying lesbian mothers. Data were collected via interviews, data collection sheets, and online journaling. The interview and journaling data were analyzed using constant comparative analysis and subsequently illuminated three main themes: becoming mothers, constructing motherhood, and legitimizing our family. These papers described the journey to motherhood and specifically explored the various methods of conception and other decisions that participants made when planning their family, the challenge of heteronormativity in health care, and legitimizing the role of the non-birth mother in de novo families. This article aims to present the participants’ experiences of motherhood that was a constant topic found woven throughout all three of the identified themes. Participants identified that they had a unique opportunity to create their own mothering roles in...


title: Symbiotic model for a working cyberschool: the tentative findings of a two-year case study

abstract: The debate surrounding the implications of use of computers and online media in our schools is presently an ongoing conversation in scholarly and educational circles. As a contribution to this conversation, the paper discusses the tentative findings of a two-year case study at Kamiak Cyberschool, a technology based public high school for credit deficient juniors and seniors in Mukilteo School District, Mukilteo, Washington. The background information is first given and the research methodology is detailed next. Parameters of data used for the paper are briefly noted. The findings will be organized around responses to four questions: (1) has the Cyberschool engaged its students? (2) To what extent has the program been successful and unsuccessful? (3) To what extent has the use of online media contributed to the success of students? (4) What are the female students telling us in this technology based academic environment? The symbiotic model of this working cyberschool as constructed through the perspective of its students is then portrayed. This model points to a set of interconnected factors that the students see as essential to their improved learning at this Cyberschool.


title: Providing robust and ubiquitous security support for mobile ad-hoc networks

abstract: Providing security support for mobile ad-hoc networks is challenging for several reasons: (a) wireless networks are susceptible to attacks ranging from passive eavesdropping to active interfering, occasional break-ins by adversaries may be inevitable in a large time window; (b) mobile users demand "anywhere, anytime" services; (c) a scalable solution is needed for a large-scale mobile network. In this paper, we describe a solution that supports ubiquitous security services for mobile hosts, scales to network size, and is robust against break-ins. In our design, we distribute the certification authority functions through a threshold secret sharing mechanism, in which each entity holds a secret share and multiple entities in a local neighborhood jointly provide complete services. We employ localized certification schemes to enable ubiquitous services. We also update the secret shares to further enhance robustness against break-ins. Both simulations and implementation confirm the effectiveness of our design.


title: Neural Codes for Image Retrieval

abstract: It has been shown that the activations invoked by an image within the top layers of a large convolutional neural network provide a high-level descriptor of the visual content of the image. In this paper, we investigate the use of such descriptors (neural codes) within the image retrieval application. In the experiments with several standard retrieval benchmarks, we establish that neural codes perform competitively even when the convolutional neural network has been trained for an unrelated classification task (e.g. Image-Net). We also evaluate the improvement in the retrieval performance of neural codes, when the network is retrained on a dataset of images that are similar to images encountered at test time.


title: Models for open innovation in the pharmaceutical industry.

abstract: The nature of the pharmaceutical industry is such that the main driver for its growth is innovation. In view of the vast challenges that the industry has been facing for several years and, in particular, how to manage stagnating research and development (R&D) productivity, pharmaceutical companies have opened their R&D organizations to external innovation. Here, we identify and characterize four new types of open innovator, which we call ‘knowledge creator’, ‘knowledge integrator’, ‘knowledge translator’ and ‘knowledge leverager’, and which describe current open R&D models.


title: Flock: Hybrid Crowd-Machine Learning Classifiers

abstract: We present hybrid crowd-machine learning classifiers: classification models that start with a written description of a learning goal, use the crowd to suggest predictive features and label data, and then weigh these features using machine learning to produce models that are accurate and use human-understandable features. These hybrid classifiers enable fast prototyping of machine learning models that can improve on both algorithm performance and human judgment, and accomplish tasks where automated feature extraction is not yet feasible. Flock, an interactive machine learning platform, instantiates this approach. To generate informative features, Flock asks the crowd to compare paired examples, an approach inspired by analogical encoding. The crowd's efforts can be focused on specific subsets of the input space where machine-extracted features are not predictive, or instead used to partition the input space and improve algorithm performance in subregions of the space. An evaluation on six prediction tasks, ranging from detecting deception to differentiating impressionist artists, demonstrated that aggregating crowd features improves upon both asking the crowd for a direct prediction and off-the-shelf machine learning features by over 10%. Further, hybrid systems that use both crowd-nominated and machine-extracted features can outperform those that use either in isolation.


title: Surface versus edge-based determinants of visual recognition

abstract: Two roles hypothesized for surface characteristics, such as color, brightness, and texture, in object recognition are that such information can (a) define the gradients needed for a 212-D sketch so that a 3-D representation can be derived (e.g., Marr & Nishihara, 1978) and (b) provide additional distinctive features for accessing memory. In a series of five experiments, subjects either named or verified (against a target name) brief (50–100 ms) presentations of slides of common objects. Each object was shown in two versions: professionally photographed in full color or as a simplified line drawing showing only the object's major components (which typically corresponded to its parts). Although one or the other type of picture would be slightly favored in a particular condition of exposure (duration or masking), overall mean reaction times and error rates were virtually identical for the two types of stimuli. These results support a view that edge-based representations mediate real-time object recognition in contrast to surface gradient or multiple cue representations. A previously unexplored distinction of color diagnosticity allowed us to determine whether color (and brightness) was employed as an additional feature in accessing memory for those objects or conditions where there might have been an advantage for the color slides. For some objects, e.g., banana, fork, fish, and camera, color is diagnostic as to the object's classification. For other objects, e.g., chair, pen, mitten, and bicycle pump, color is not diagnostic, as such objects can be of any color. If color was employed in accessing memory, color-diagnostic objects should have shown a relative advantage when presented as color slides compared to the line drawing versions of the same objects. Also, this advantage would be magnified when subjects could anticipate the color of an object in the verification task, particularly on NO trials when the foil was of a different color. Neither an overall advantage for color-diagnostic objects when presented in color nor a magnification of a relative advantage on the NO trials in the verification task was obtained. Although differences in surface characteristics such as color, brightness, and texture can be instrumental in defining edges and can provide cues for visual search, they play only a secondary role in the real-time recognition of an intact object when its edges can be readily extracted.


title: Predicting latent attributes of Twitter user by employing lexical features

abstract: The rapid growth of social media, especially Twitter in Indonesia, has produced a large amount of user generated texts in the form of tweets. Since Twitter only provides the name and location of its users, we develop a classification system that predicts latent attributes of Twitter user based on his tweets. Latent attribute is an attribute that is not stated directly. Our system predicts age and job attributes of Twitter users that use Indonesian language. Classification model is developed by employing lexical features and three learning algorithms (Naive Bayes, SVM, and Random Forest). Based on the experimental results, it can be concluded that the SVM method produces the best accuracy for balanced data.


title: Domain Adaptation for Visual Applications: A Comprehensive Survey

abstract: The aim of this paper is to give an overview of domain adaptation and transfer learning with a specific view on visual applications. After a general motivation, we first position domain adaptation in the larger transfer learning problem. Second, we try to address and analyze briefly the state-of-the-art methods for different types of scenarios, first describing the historical shallow methods, addressing both the homogeneous and the heterogeneous domain adaptation methods. Third, we discuss the effect of the success of deep convolutional architectures which led to new type of domain adaptation methods that integrate the adaptation within the deep architecture. Fourth, we overview the methods that go beyond image categorization, such as object detection or image segmentation, video analyses or learning visual attributes. Finally, we conclude the paper with a section where we relate domain adaptation to other machine learning solutions.


title: Using multiple regression analysis to develop energy consumption indicators for commercial buildings in the U.S.

abstract: Abstract Multiple regression analysis plays an important role in evaluating the energy consumption of buildings. These models are commonly used to assess the energy performance of commercial buildings and to predict any potential for energy consumption reduction. In this study, the building simulation software DOE-2 was used to predict energy consumption. A total of 17 key building design variables were identified related to building envelope, building orientation, and occupant schedule. Since, building energy consumption depends on many operational and design parameters; large numbers of simulations are needed to generate data for the multiple regression models. To tackle this problem, a randomized approach was adopted to reduce the required number of simulations examining the whole design space. Monte Carlo simulation technique was used to generate thirty thousand combinations of design parameters, covering the full range for each climate region. In order to implement the Monte Carlo simulation, an in-house computer program was developed to interface with DOE-2 energy simulation software. Stepwise regression was used to reduce the number of parameters and only include the most effective parameters. R statistical analysis program was also used to develop the set of linear regression equations. Parametric study and sensitivity analysis between levels of most effective parameters were performed. The developing models can be used to estimate the energy consumption of office buildings in early stages of design.


Mediation and moderation in ratings of hostile jokes by men and women


title: Liquid piston gas compression

abstract: A liquid piston concept is proposed to improve the efficiency of gas compression and expansion. Because a liquid can conform to an irregular chamber volume, the surface area to volume ratio in the gas chamber can be maximized using a liquid piston. This creates near-isothermal operation, which minimizes energy lost to heat generation. A liquid piston eliminates gas leakage and replaces sliding seal friction with viscous friction. The liquid can also be used as a medium to carry heat into and out of the compression chamber. A simulation is presented of the heat transfer and frictional forces for a reciprocating piston and a liquid piston. In the application of an air compressor, with a pressure ratio of 9.5:1 and a cycle frequency of 20Â Hz, the liquid piston decreased the energy consumption by 19% over the reciprocating piston. The liquid piston and the reciprocating piston exhibited a total efficiency of 83% and 70% respectively. The liquid piston demonstrated significant improvements in the total compression efficiency in comparison to a conventional reciprocating piston. This gain in efficiency was accomplished through increasing the heat transfer during the gas compression by increasing the surface area to volume ratio in the compression chamber.


title: Facial Expression Recognition via a Boosted Deep Belief Network

abstract: A training process for facial expression recognition is usually performed sequentially in three individual stages: feature learning, feature selection, and classifier construction. Extensive empirical studies are needed to search for an optimal combination of feature representation, feature set, and classifier to achieve good recognition performance. This paper presents a novel Boosted Deep Belief Network (BDBN) for performing the three training stages iteratively in a unified loopy framework. Through the proposed BDBN framework, a set of features, which is effective to characterize expression-related facial appearance/shape changes, can be learned and selected to form a boosted strong classifier in a statistical way. As learning continues, the strong classifier is improved iteratively and more importantly, the discriminative capabilities of selected features are strengthened as well according to their relative importance to the strong classifier via a joint fine-tune process in the BDBN framework. Extensive experiments on two public databases showed that the BDBN framework yielded dramatic improvements in facial expression analysis.


title: Video corpus annotation using active learning

abstract: Concept indexing in multimedia libraries is very useful for users searching and browsing but it is a very challenging research problem as well. Beyond the systems' implementations issues, semantic indexing is strongly dependent upon the size and quality of the training examples. In this paper, we describe the collaborative annotation system used to annotate the High Level Features (HLF) in the development set of TRECVID 2007. This system is web-based and takes advantage of Active Learning approach. We show that Active Learning allows simultaneously getting the most useful information from the partial annotation and significantly reducing the annotation effort per participant relatively to previous collaborative annotations.


title: Heroism: A Conceptual Analysis and Differentiation Between Heroic Action and Altruism

abstract: Heroism represents the ideal of citizens transforming civic virtue into the highest form of civic action, accepting either physical peril or social sacrifice. While implicit theories of heroism abo...


title: Variational Autoencoders for Semi-supervised Text Classification

abstract: Although semi-supervised variational autoencoder (SemiVAE) works in image classification task, it fails in text classification task if using vanilla LSTM as its decoder. From a perspective of reinforcement learning, it is verified that the decoder's capability to distinguish between different categorical labels is essential. Therefore, Semi-supervised Sequential Variational Autoencoder (SSVAE) is proposed, which increases the capability by feeding label into its decoder RNN at each time-step. Two specific decoder structures are investigated and both of them are verified to be effective. Besides, in order to reduce the computational complexity in training, a novel optimization method is proposed, which estimates the gradient of the unlabeled objective function by sampling, along with two variance reduction techniques. Experimental results on Large Movie Review Dataset (IMDB) and AG's News corpus show that the proposed approach significantly improves the classification accuracy compared with pure-supervised classifiers, and achieves competitive performance against previous advanced methods. State-of-the-art results can be obtained by integrating other pretraining-based methods.


title: Hallucinating Very Low-Resolution Unaligned and Noisy Face Images by Transformative Discriminative Autoencoders

abstract: Most of the conventional face hallucination methods assume the input image is sufficiently large and aligned, and all require the input image to be noise-free. Their performance degrades drastically if the input image is tiny, unaligned, and contaminated by noise. In this paper, we introduce a novel transformative discriminative autoencoder to 8X super-resolve unaligned noisy and tiny (16X16) low-resolution face images. In contrast to encoder-decoder based autoencoders, our method uses decoder-encoder-decoder networks. We first employ a transformative discriminative decoder network to upsample and denoise simultaneously. Then we use a transformative encoder network to project the intermediate HR faces to aligned and noise-free LR faces. Finally, we use the second decoder to generate hallucinated HR images. Our extensive evaluations on a very large face dataset show that our method achieves superior hallucination results and outperforms the state-of-the-art by a large margin of 1.82dB PSNR.


title: Two-class 3D-CNN classifiers combination for video copy detection

abstract: 3D-CNN is the latest CNN model used for video classification. However, the required amount of computation and training data for training 3D-CNN, especially for complex classification tasks with large video data, hinders the wide application of 3D-CNN. In this paper, inspired by the exclusion method in human’s judgment, a parallel 3D-CNN architecture is proposed to decompose the multi-class classification task using one 3D-CNN into the combination of multiple two-class classification tasks. 3D-CNN is used as a two-class classifier for each of the two-class classification tasks, and the difficulty and the data requirement on training such a 3D-CNN is reduced greatly comparing with the 3D-CNN for multi-class classification. In addition, the combination of two-class classifiers provides the ability of recognizing unknown class to the proposed 3D-CNN model. The feasibility of this proposed 3D-CNN model is verified via its application on video copy detection on the CC_WEB_VIDEO dataset. The experimental results show the potentiality of the proposed parallel two-class 3D-CNN model in video classification.


title: MarkPad: Augmenting Touchpads for Command Selection

abstract: We present MarkPad, a novel interaction technique taking advantage of the touchpad. MarkPad allows creating a large number of size-dependent gestural shortcuts that can be spatially organized as desired by the user. It relies on the idea of using visual or tactile marks on the touchpad or a combination of them. Gestures start from a mark on the border and end on another mark anywhere. MarkPad does not conflict with standard interactions and provides a novice mode that acts as a rehearsal of the expert mode. A first study showed that an accuracy of 95% could be achieved for a dense configuration of tactile and/or visual marks allowing many gestures. Performance was 5% lower in a second study where the marks were only on the borders. A last study showed that borders are rarely used, even when the users are unaware of the technique. Finally, we present a working prototype and briefly report on how it was used by two users for a few months.


title: Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints

abstract: Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals. We study the generalization performance for such constrained optimization problems, in terms of how well the constraints are satisfied at evaluation time, given that they are satisfied at training time. To improve generalization performance, we frame the problem as a two-player game where one player optimizes the model parameters on a training dataset, and the other player enforces the constraints on an independent validation dataset. We build on recent work in two-player constrained optimization to show that if one uses this two-dataset approach, then constraint generalization can be significantly improved. As we illustrate experimentally, this approach works not only in theory, but also in practice.


title: Learning to Navigate in Cities Without a Map

abstract: Navigating through unstructured environments is a basic capability of intelligent creatures, and thus is of fundamental interest in the study and development of artificial intelligence. Long-range navigation is a complex cognitive task that relies on developing an internal representation of space, grounded by recognisable landmarks and robust visual processing, that can simultaneously support continuous self-localisation ("I am here") and a representation of the goal ("I am going there"). Building upon recent research that applies deep reinforcement learning to maze navigation problems, we present an end-to-end deep reinforcement learning approach that can be applied on a city scale. Recognising that successful navigation relies on integration of general policies with locale-specific knowledge, we propose a dual pathway architecture that allows locale-specific features to be encapsulated, while still enabling transfer to multiple cities. We present an interactive navigation environment that uses Google StreetView for its photographic content and worldwide coverage, and demonstrate that our learning method allows agents to learn to navigate multiple cities and to traverse to target destinations that may be kilometres away. The project webpage http://streetlearn.cc contains a video summarising our research and showing the trained agent in diverse city environments and on the transfer task, the form to request the StreetLearn dataset and links to further resources. The StreetLearn environment code is available at https://github.com/deepmind/streetlearn


title: Experimental Robot Musicians

abstract: This work discusses the development of robot musicians that perform on real instruments through the usage of mechanical devices such as servomotors and solenoids. Construction of the hardware and programming of the software are discussed, as well as the research innovations linking music, robotics and computer science.


title: Internal plate fixation of fractures: short history and recent developments

abstract: Metal plates for internal fixation of fractures have been used for more than 100 years. Although initial shortcomings such as corrosion and insufficient strength have been overcome, more recent designs have not solved all problems. Further research is needed to develop a plate that accelerates fracture healing while not interfering with bone physiology.The introduction of rigid plates had by far the greatest impact on plate fixation of fractures. However, it led to cortical porosis, delayed bridging, and refractures after plate removal. These unwarranted effects were said to be caused by bone–plate contact interfering with cortical perfusion. Consequently, further plate modifications aimed to reduce this contact area to minimize necrosis and subsequent porosis.The advocates of limited-contact plates have not published measurements of the contact area or proof of the temporary nature of the porosis. Moreover, clinical studies of newer plate types have failed to show a superior outcome. Histomor-phometric measurements of the cortex showed no difference in the extent of necrosis under plates having different contact areas. Necrosis was predominant in the periosteal cortical half, whereas porosis occurred mostly in the endosteal cortical half. No positive correlation was found between either.The scientific evidence to date strongly suggests that bone loss is caused by stress shielding and not interference with cortical perfusion secondary to bone–plate contact. Consequently, an axially compressible plate (ACP) incorporating polylactide (PLA) inserts press-fit around screw holes was designed. The bioresorbable inserts should allow for (1) increased micromotion in the axial plane to promote healing during the union phase and (2) gradual degradation over time to decrease stress shielding during the remodeling phase.Results of ongoing experimental results are encouraging. Only plates allowing dynamic compression in the axial plane can lead to a revolution in fracture fixation.


title: Maximum likelihood segmentation of ultrasound images with Rayleigh distribution

abstract: This study presents a geometric model and a computational algorithm for segmentation of ultrasound images. A partial differential equation (PDE)-based flow is designed in order to achieve a maximum likelihood segmentation of the target in the scene. The flow is derived as the steepest descent of an energy functional taking into account the density probability distribution of the gray levels of the image as well as smoothness constraints. To model gray level behavior of ultrasound images, the classic Rayleigh probability distribution is considered. The steady state of the flow presents a maximum likelihood segmentation of the target. A finite difference approximation of the flow is derived, and numerical experiments are provided. Results are presented on ultrasound medical images as fetal echography and echocardiography.


title: Unsupervised refinement of color and stroke features for text binarization

abstract: Color and strokes are the salient features of text regions in an image. In this work, we use both these features as cues, and introduce a novel energy function to formulate the text binarization problem. The minimum of this energy function corresponds to the optimal binarization. We minimize the energy function with an iterative graph cut-based algorithm. Our model is robust to variations in foreground and background as we learn Gaussian mixture models for color and strokes in each iteration of the graph cut. We show results on word images from the challenging ICDAR 2003/2011, born-digital image and street view text datasets, as well as full scene images containing text from ICDAR 2013 datasets, and compare our performance with state-of-the-art methods. Our approach shows significant improvements in performance under a variety of performance measures commonly used to assess text binarization schemes. In addition, our method adapts to diverse document images, like text in videos, handwritten text images.


title: The PeerRank Method for Peer Assessment

abstract: We propose the PeerRank method for peer assessment. This constructs a grade for an agent based on the grades proposed by the agents evaluating the agent. Since the grade of an agent is a measure of their ability to grade correctly, the PeerRank method weights grades by the grades of the grading agent. The PeerRank method also provides an incentive for agents to grade correctly. As the grades of an agent depend on the grades of the grading agents, and as these grades themselves depend on the grades of other agents, we define the PeerRank method by a fixed point equation similar to the PageRank method for ranking web-pages. We identify some formal properties of the PeerRank method (for example, it satisfies axioms of unanimity, no dummy, no discrimination and symmetry), discuss some examples, compare with related work and evaluate the performance on some synthetic data. Our results show considerable promise, reducing the error in grade predictions by a factor of 2 or more in many cases over the natural baseline of averaging peer grades.


title: Biometrics: a tool for information security

abstract: Establishing identity is becoming critical in our vastly interconnected society. Questions such as "Is she really who she claims to be?," "Is this person authorized to use this facility?," or "Is he in the watchlist posted by the government?" are routinely being posed in a variety of scenarios ranging from issuing a driver's license to gaining entry into a country. The need for reliable user authentication techniques has increased in the wake of heightened concerns about security and rapid advancements in networking, communication, and mobility. Biometrics, described as the science of recognizing an individual based on his or her physical or behavioral traits, is beginning to gain acceptance as a legitimate method for determining an individual's identity. Biometric systems have now been deployed in various commercial, civilian, and forensic applications as a means of establishing identity. In this paper, we provide an overview of biometrics and discuss some of the salient research issues that need to be addressed for making biometric technology an effective tool for providing information security. The primary contribution of this overview includes: 1) examining applications where biometric scan solve issues pertaining to information security; 2) enumerating the fundamental challenges encountered by biometric systems in real-world applications; and 3) discussing solutions to address the problems of scalability and security in large-scale authentication systems.


title: Bayesian Robust Tensor Factorization for Incomplete Multiway Data

abstract: We propose a generative model for robust tensor factorization in the presence of both missing data and outliers. The objective is to explicitly infer the underlying low-CANDECOMP/PARAFAC (CP)-rank tensor capturing the global information and a sparse tensor capturing the local information (also considered as outliers), thus providing the robust predictive distribution over missing entries. The low-CP-rank tensor is modeled by multilinear interactions between multiple latent factors on which the column sparsity is enforced by a hierarchical prior, while the sparse tensor is modeled by a hierarchical view of Student-t distribution that associates an individual hyperparameter with each element independently. For model learning, we develop an efficient variational inference under a fully Bayesian treatment, which can effectively prevent the overfitting problem and scales linearly with data size. In contrast to existing related works, our method can perform model selection automatically and implicitly without the need of tuning parameters. More specifically, it can discover the groundtruth of CP rank and automatically adapt the sparsity inducing priors to various types of outliers. In addition, the tradeoff between the low-rank approximation and the sparse representation can be optimized in the sense of maximum model evidence. The extensive experiments and comparisons with many state-of-the-art algorithms on both synthetic and real-world data sets demonstrate the superiorities of our method from several perspectives.


title: Stock price prediction using financial news articles

abstract: Stock price prediction is one of the most important issues to be investigated in academic and financial researches. Data mining techniques are frequently involved in the studies aimed to achieve this problem. In this paper we investigate predicting stock prices using financial news articles. A prediction model, finding and analyzing correlation between contents of news articles and stock prices and then making predictions for future prices, was developed. We retrieve financial news articles published in last year, and we get stock prices for same period. All articles are labeled positive or negative according to their effects on stock price. So we use price changes to label the articles. While analyzing textual data, we use word couples consisting of a noun and a verb as features instead of using single words. Afterwards, support vector machines classifier is trained with labeled train articles. Finally, classes of test articles are predicted with using the model resulted from train phase. We achieve serious success rates that prove predictive power of our system.


title: Personalized hybrid recommendation for group of users

abstract: Novel group hybrid method combining collaborative and content-based recommendation.Proposed method improves the quality of recommended items ordering.Proposed method increases the recommendation precision for very Top-N results.Applicable for single user as well as group recommendation. Nowadays, the increasing demand for group recommendations can be observed. In this paper we address the problem of recommendation performance for groups of users (group recommendation). We focus on the performance of very Top-N recommendations, which are important when recommending the long lasting items (only a few such items are consumed per session, e.g. movie). To improve existing group recommenders we propose a mixed hybrid recommender for groups combining content-based and collaborative strategies. The principle of proposed group recommender is to generate content and collaborative recommendations for each user, apply an aggregation strategy to solve the group conflict preferences for the content and collaborative sets separately, and finally reorder the collaborative candidates based on the content-based ones. It is based on an idea that candidates recommended by both recommendation strategies at the same time are presumably more appropriate for the group than the candidates recommended by individual strategies. The evaluation is performed by several experiments in the multimedia domain (as typical representative for group recommendations). Both, online and offline experiments were performed in order to compare real users' satisfaction to the standard group recommenders and also, to compare performance of proposed approach to the state-of-the-art recommenders based on the MovieLens dataset. Finally, we experimented with the proposed hybrid recommender to generate the recommendation for a group of size one (i.e. single user recommendation). Obtained results, support our hypothesis that proposed mixed hybrid approach improves the precision of the recommendation for groups of users and for the single-user recommendation respectively on very Top-N recommended items.


title: Circulating currents and CM EMI reduction for interleaved three-phase VSC

abstract: This paper presents a method to reduce the circulating current and common-mode electromagnetic interference (CM EMI) in interleaved three-phase voltage-source converters (VSC). Use of coupled three-phase inductors is shown to generate large CM circulating currents among the parallel modules. To reduce this CM circulating current, a CM chokes is placed at the dc input of each module. A new method to reduce system CM filtering requirements is also presented. The proposed method uses CM capacitors connected between the ac and dc terminals to provide attenuation for conducted CM current emission at both the dc and the ac terminals. The method is particularly attractive for applications where both input and output EMI has to be controlled, and can be applied to individual VSC as well as multiple VSCs operating in parallel.


title: Determination of tissue folate composition by affinity chromatography followed by high-pressure ion pair liquid chromatography.

abstract: Abstract A recent report from this laboratory described the use of affinity chromatography for the isolation of pure folates from tissue extracts (J. Selhub, B. Darcy-Vrillon, and D. Fell (1988) Anal. Biochem. 168, 247–251). The present study was undertaken to develop chromatographic procedures for quantitative analysis of the individual folates in the affinity-purified mixture. Methods were devised whereby mixtures containing pteroylglutamates (PteGlu 1–7 ) were batch reduced to the dihydro, H 2 PteGlu 1–7 , and tetrahydro, H 4 PteGlu 1–7 , forms. The 5-methylH 4 PteGlu 1–7 and the 10-formylH 4 PteGlu 1–7 series were prepared from H 4 PteGlu 1–7 . These compounds were used to calibrate a liquid chromatographic system for the resolution of folate mixtures. This system included reverse-phase ion pair chromatography and a diode array detector. A mixture containing oxidized and reduced PteGlu 1–7 , a total of 35 derivatives, was separated into seven clusters arranged in an order of increasing number of glutamate residues. Each cluster was represented by two or more peaks which were due to folates that differed in the pteridine ring structure but had the same number of glutamate residues. In clusters containing mono and diglutamyl derivatives the 10-formyltetrahydro-, the tetrahydro-, and the dihydrofolate forms appeared as separate peaks while those representing folic acid and 5-methyltetrahydrofolate derivatives eluted in coinciding peaks. This hierarchy was maintained in the following clusters except for increasing tendency of the former three forms of folates to elute in the same peak. The number of glutamate residues of any eluting folate can be determined on the basis of retention time in relation to those of the clusters. The pteridine ring structure of that same folate can be determined on the basis of its elution position within that cluster and spectral characteristics determined by the diode array detection system. If that position is common for more than one derivative then identification is based on differential spectral properties. Using uv absorption signals at 280 nm to determine indiscriminate folate activity, absorption signals at 350 nm are used to identify folic acid and dihydrofolate derivatives and signals at 258 nm are used to identify 10-formyltetrahydrofolate derivatives. These principles were incorporated into mathematic expressions which were used for quantitative resolution of simulated mixtures containing oxidized and reduced PteGlu 5 and for the analysis of folate composition in rat liver, human milk, and cows milk.


title: Controller for swing-up and balance of single inverted pendulum using SDRE-based solution

abstract: In this paper, we propose a design of nonlinear controller for the underactuated systems by taking inverted pendulum as an example. The design of the controller is based on one of the recently proposed methods by Cloutier et al. for designing nonlinear controller, state dependent Riccati equation (SDRE) method. The effectiveness of this controller is verified by conducting a real time experiment for the swing-up and balance of single inverted pendulum on a linear track. One of the important aspects of using SDRE controller is that only one controller is required for both swing-up and balance as compared to other strategies where switching between controllers is performed. The results demonstrate the effectiveness of the SDRE control over the LQR control for the same control parameters in the linear range. The procedure for solving Riccati equation in real time is also highlighted.


title: Rule based Autonomous Citation Mining with TIERL

abstract: Citations management is an important task in managing digital libraries. Citations provide valuable information e.g., used in evaluating an author's influences or scholarly quality (the impact factor of research journals). But although a reliable and effective autonomous citation management is essential, manual citation management can be extremely costly. Automatic citation mining on the other hand is a non-trivial task mainly due to non-conforming citation styles, spelling errors and the difficulty of reliably extracting text from PDF documents. In this paper we propose a novel rule-based autonomous citation mining technique, to address this important task. We define a set of common heuristics that together allow to improve the state of the art in automatic citation mining. Moreover, by first disambiguating citations based on venues, our technique significantly enhances the correct discovery of citations. Our experiments show that the proposed approach is indeed able to overcome limitations of current leading citation indexes such as ISI Web of Knowl- edge, Citeseer and Google Scholar. Categories and Subject Descriptors H.3.7 (Digital Libraries); System issues: H.3.3 (Information Search and Retrieval); Retrieval models: I.1.2 (Algorithms); Analysis of algorithms: I.2.8 (Problem Solving, Control Methods, and Search);


title: Design and control of a two-wheel self-balancing robot using the arduino microcontroller board

abstract: This paper reports the design, construction and control of a two-wheel self-balancing robot. The system architecture comprises a pair of DC motor and an Arduino microcontroller board; a single-axis gyroscope and a 2-axis accelerometer are employed for attitude determination. In addition, a complementary filter is implemented to compensate for gyro drifts. Electrical and kinematic parameters are determined experimentally; PID and LQR-based PI-PD control designs, respectively, are performed on the linearized equations of motion. Experimental results show that self-balancing can be achieved with PI-PD control in the vicinity of the upright position.


title: A Survey on the Preprocessing Techniques of Mammogram for the Detection of Breast Cancer

abstract: The aim of this paper is to review existing approaches of preprocessing in mammographic images. The objective of preprocessing is to improve the quality of the image and make it ready for further processing by removing the irrelevant noise and unwanted parts in the background of the mammogram. There are different of methods of preprocessing a mammogram image. Their advantages and disadvantages are discussed.