title: Dependency grammar and dependency parsing

abstract: Despite a long and venerable tradition in descriptive linguistics, dependency grammar has until recently played a fairly marginal role both in theoretical linguistics and in natural language processing. The increasing interest in dependency-based representations in natural language parsing in recent years appears to be motivated both by the potential usefulness of bilexical relations in disambiguation and by the gains in efficiency that result from the more constrained parsing problem for these representations. In this paper, we will review the state of the art in dependency-based parsing, starting with the theoretical foundations of dependency grammar and moving on to consider both grammar-driven and data-driven methods for dependency parsing. We will limit our attention to systems for dependency parsing in a narrow sense, i.e. systems where the analysis assigned to an input sentence takes the form of a dependency structure. This means that we will not discuss systems that exploit dependency relations for the construction of another type of representation, such as the head-driven parsing models of Collins (1997, 1999). Moreover, we will restrict ourselves to systems for full parsing, which means that we will not deal with systems that produce a partial or underspecified representation of dependency structure, such as Constraint Grammar parsers (Karlsson, 1990; Karlsson et al., 1995).


title: An FPGA-based queue management system for high speed networking devices

abstract: Abstract One of the main bottlenecks when designing a network system is very often its memory subsystem. This is mainly due to the extremely high speed of the state-of-the-art network links and to the fact that in order to support advanced Quality of Service (QoS), a large number of independent queues is desirable. In this paper we describe the architecture and performance of a memory manager, the QMS that is tailored to FPGA technology and can provide up to 6.2 Gbps of aggregate throughput, while handling 32 K independent queues. The presented system supports a complete instruction set and thus we believe it can be used as a hardware component in any suitable networking system. It also supports a large number of different interfaces, and it is designed in a very scalable way. The QMS uses a double data rate DRAM for data storage and a typical SRAM for keeping data structures-pointers, therefore minimizing the system's cost. In order to deal with the problems of refreshing and bank conflicts in the DRAM, several optimization techniques have been employed. In this paper we also present the architecture of a network-processing device that fully utilizes the advanced feature of the QMS. The QMS consists of 8500 Slices in a XILINX FPGA and works at 125 MHz.


title: Slip detection and prediction in human walking using only wearable inertial measurement units (IMUs)

abstract: Slip and fall is one of the major causes for human injuries for elders and professional workers. Real-time detection and prediction of the foot slip is critical for developing effective assistive and rehabilitation devices to prevent falls and train balance disorder patients. This paper presents a novel real-time slip detection and prediction scheme with wearable inertial measurement units (IMUs). The slip-detection algorithm is built on a new dynamic model for bipedal walking with slips. An extended Kalman filter is designed to reliably predict the foot slip displacement using the wearable IMU measurements and kinematic constraints. The proposed slip detection and prediction scheme has been demonstrated by extensive experiments.


title: 60 GHz circularly polarized dielectric resonator antenna fed by printed ridge gap waveguide

abstract: A novel 60 GHz dielectric resonator antenna (DRA) with wide circular polarization bandwidth is presented. The performance of a stair-like rectangular DRA with x-shaped feeding slot, which is fed with a printed ridge gap waveguide (PRGW) is analyzed. The antenna has a 10.9% circularly polarized bandwidth (axial ratio < 3 dB) and 28% matching bandwidth (S11 < −10 dB). The PRGW feeding technology ensures a low loss feeding mechanism with no radiation losses.


title: Edge Preserving and Multi-Scale Contextual Neural Network for Salient Object Detection

abstract: In this paper, we propose a novel edge preserving and multi-scale contextual neural network for salient object detection. The proposed framework is aiming to address two limits of the existing CNN based methods. First, region-based CNN methods lack sufficient context to accurately locate salient object since they deal with each region independently. Second, pixel-based CNN methods suffer from blurry boundaries due to the presence of convolutional and pooling layers. Motivated by these, we first propose an end-to-end edge-preserved neural network based on Fast R-CNN framework (named RegionNet ) to efficiently generate saliency map with sharp object boundaries. Later, to further improve it, multi-scale spatial context is attached to RegionNet to consider the relationship between regions and the global scenes. Furthermore, our method can be generally applied to RGB-D saliency detection by depth refinement. The proposed framework achieves both clear detection boundary and multi-scale contextual robustness simultaneously for the first time, and thus achieves an optimized performance. Experiments on six RGB and two RGB-D benchmark datasets demonstrate that the proposed method achieves state-of-the-art performance.


title: Engineering Human Cooperation -- Does Involuntary Neural Activation Increase Public Goods Contributions?

abstract: In a laboratory experiment, we use a public goods game to examine the hypothesis that human subjects use an involuntary eye-detector mechanism for evaluating the level of privacy. Half of our subjects are “watched” by images of a robot presented on their computer screen. The robot—named Kismet and invented at MIT—is constructed from objects that are obviously not human with the exception of its eyes. In our experiment, Kismet produces a significant difference in behavior that is not consistent with existing economic models of preferences, either self- or other-regarding. Subjects who are “watched” by Kismet contribute 29% more to the public good than do subjects in the same setting without Kismet.


title: Imaging with referenceless distortion correction and flexible regions of interest using single-shot biaxial spatiotemporally encoded MRI

abstract: Abstract Owing to its intrinsic characteristics, spatiotemporally encoded (SPEN) imaging is less sensitive to adverse effects due to field inhomogeneity in comparison with echo planar imaging, a feature highly desired for functional, diffusion, and real-time MRI. However, the quality of images obtained with SPEN MRI is still degraded by geometric distortions when field inhomogeneity exists. In this study, a single-shot biaxial SPEN (bi-SPEN) pulse sequence is implemented, utilizing a 90° and a 180° chirp pulse incorporated with two orthogonal gradients. A referenceless geometric-distortion correction based on the single-shot bi-SPEN sequence is then proposed. The distorted image acquired with the single-shot bi-SPEN sequence is corrected by iterative super-resolved reconstruction involving the field gradients estimated from a field map, which in turn is obtained from its own super-resolved data after a phase-unwrapping procedure without additional scans. In addition, the distortion correction method is applied to improve the quality of the multiple region-of-interest images obtained with single-shot bi-SPEN sequence.


A case study of algorithm-assisted decision making in child maltreatment hotline screening decisions


title: Dense matching of multiple wide-baseline views

abstract: This paper describes a PDE-based method for dense depth extraction from multiple wide-baseline images. Emphasis lies on the usage of only a small amount of images. The integration of these multiple wide-baseline views is guided by the relative confidence that the system has in the matching to different views. This weighting is fine-grained in that it is determined for every pixel at every iteration. Reliable information spreads fast at the expense of less reliable data, both in terms of spatial communications within a view and in terms of information exchange between the views. Changes in intensity between images can be handled in a similar fine grained fashion.


title: Ocular adnexal (orbital) solitary fibrous tumor: nuclear STAT6 expression and literature review

abstract: PURPOSE ::: To report the clinico-pathological features of solitary fibrous tumor occurring in the ocular adnexa (OA) in a single center. To assess the presence of NAB2-STAT6 genes fusion in OA solitary fibrous tumor detected by nuclear overexpression of STAT6. ::: ::: ::: METHODS ::: Retrospective study including orbital and OA solitary fibrous tumors treated between 2006 and 2014 in our center. The clinical, radiological, and histopathological findings were evaluated. STAT6 expression was assessed by immunohistochemistry. ::: ::: ::: RESULTS ::: Five patients were identified and presented with a chronic OA mass. The tumors were radiologically well delimited, highly vascularized and without bone erosion. All the patients underwent complete surgical excision. Pathological examination confirmed solitary fibrous tumor in all cases. All tumors demonstrated a nuclear expression of STAT6. There were no recurrences, with a mean follow-up of 5 years after surgery. Our review demonstrated that proptosis was the most common presentation occurring in 60 % of the cases. In the ocular adnexa, adverse histological criteria were found in 19.7 % of the tumors, and recurrences were observed in 48 % of these cases. Thirty-six percent of patients presented at least one local recurrence, and metastastic spread was found in 2.4 % of the cases. Tumor-related death was described in two cases. ::: ::: ::: CONCLUSION ::: Ocular adnexal SFT are rare and usually present as a chronic orbital mass with proptosis. In the OA, solitary fibrous tumor demonstrates STAT6 nuclear expression, as documented in other locations. Recurrences are unusual and metastasis exceptional. Initial surgical resection should be complete in order to avoid recurrence.


title: Innovation ecosystems and the pace of substitution: Re-examining technology S-curves

abstract: Why do some new technologies emerge and quickly supplant incumbent technologies while others take years or decades to take off? We explore this question by presenting a framework that considers both the focal competing technologies as well as the ecosystems in which they are embedded. Within our framework, each episode of technology transition is characterized by the ecosystem emergence challenge that confronts the new technology and the ecosystem extension opportunity that is available to the old technology. We identify four qualitatively distinct regimes with clear predictions for the pace of substitution. Evidence from 10 episodes of technology transitions in the semiconductor lithography equipment industry from 1972 to 2009 offers strong support for our framework. We discuss the implication of our approach for firm strategy. Copyright © 2015 John Wiley & Sons, Ltd.


title: Towards Bayesian Deep Learning: A Framework and Some Existing Methods

abstract: While perception tasks such as visual object recognition and text understanding play an important role in human intelligence, the subsequent tasks that involve inference, reasoning and planning require an even higher level of intelligence. The past few years have seen major advances in many perception tasks using deep learning models. For higher-level inference, however, probabilistic graphical models with their Bayesian nature are still more powerful and flexible. To achieve integrated intelligence that involves both perception and inference, it is naturally desirable to tightly integrate deep learning and Bayesian models within a principled probabilistic framework, which we call Bayesian deep learning. In this unified framework, the perception of text or images using deep learning can boost the performance of higher-level inference and in return, the feedback from the inference process is able to enhance the perception of text or images. This paper proposes a general framework for Bayesian deep learning and reviews its recent applications on recommender systems, topic models, and control. In this paper, we also discuss the relationship and differences between Bayesian deep learning and other related topics like Bayesian treatment of neural networks.


title: Towards Linear Time Overlapping Community Detection in Social Networks

abstract: Membership diversity is a characteristic aspect of social networks in which a person may belong to more than one social group. For this reason, discovering overlapping structures is necessary for realistic social analysis. In this paper, we present a fast algorithm, called SLPA, for overlapping community detection in large-scale networks. SLPA spreads labels according to dynamic interaction rules. It can be applied to both unipartite and bipartite networks. It is also able to uncover overlapping nested hierarchy . The time complexity of SLPA scales linearly with the number of edges in the network. Experiments in both synthetic and real-world networks show that SLPA has an excellent performance in identifying both node and community level overlapping structures.


title: Raman spectrum of graphene and graphene layers.

abstract: Graphene is the two-dimensional building block for carbon allotropes of every other dimensionality. We show that its electronic structure is captured in its Raman spectrum that clearly evolves with the number of layers. The D peak second order changes in shape, width, and position for an increasing number of layers, reflecting the change in the electron bands via a double resonant Raman process. The G peak slightly down-shifts. This allows unambiguous, high-throughput, nondestructive identification of graphene layers, which is critically lacking in this emerging research area.


title: LireSolr: A Visual Information Retrieval Server

abstract: In this paper, we present LireSolr, an open source image retrieval server, build on top of the LIRE library and the Apache Solr search server. With LireSolr, visual information retrieval can be run on a server, which allows better distribution of workloads and simplifies applications in several areas including mobile and web. Furthermore, we showcase several example scenarios how LireSolr can be used to point out the broad range of possibilities and applications. The system is easy to install and setup, and the large number of retrieval tools either provided by LIRE or by other Apache Solr is made easily available on the search server. Moreover, our tool demonstrates how predictions from CNNs can easily be used to extend the visual information retrieval functionality.


title: A Low-Multipath Wideband GPS Antenna With Cutoff or Non-Cutoff Corrugated Ground Plane

abstract: We compare the performance of the classic global positioning system (GPS) choke ring ground plane with a new shallower design. Both the choke ring and the new ground plane are here designed to operate uniformly between 1.15 GHz and 1.60 GHz while maintaining the required low-multipath performance in the whole bandwidth. To achieve reception in this wide range of frequencies, the radiating element chosen is a droopy bowtie turnstile. A classic choke ring is composed of deep concentric rings on a flat circular metal ground plane. We have modeled the choke ring as a metal corrugated surface of depth d such that lambda/4lesdleslambda/2 , operating at cutoff: the multipath suppression (i.e., the proper RHCP/LHCP pattern shaping) is obtained by eliminating the surface wave on the ground plane in a certain frequency band. When shallower concentric rings of depth dleslambda/4 are used, the corrugated surface operates at non-cutoff: the required multipath rejection can be achieved by cancellation (destructive interference) rather than suppression of surface waves. The comparisons performed in this study include theory, numerical simulations, and hardware tests. Our results show that both configurations, when properly optimized, are good candidates for reception of modernized GPS, GLONASS, and GALILEO satellite signals from GPS permanent stations.


title: InLoc: An end-to-end robust indoor localization and routing solution using mobile phones and BLE beacons

abstract: This paper discusses ‘InLoc’, an accurate and a robust positioning and tracking system using commercial mobile devices, with an integrated feature of route finding for the user from a source to the desired destination. The system exploits easily available building floor maps in raster form, with an easy conversion to vector model, eliminating the need of specially designed vector maps, thereby making the system scalable for large indoor maps and more implementation friendly. This also enables the vector map to be used in both Particle Filter based IMU tracking, and routing. Additionally, an efficient method for independent fusion of location information from phone IMU sensors and Bluetooth Low Energy (BLE) beacons is demonstrated. The method caters both dynamic and static properties of the system state. Furthermore, the paper proposes a novel approach for estimating the distance from BLE beacons using RSSI (Received Signal Strength Indication) measurement. InLoc can be readily used for any size of building floors for applications like tracking, routing and guiding system, emergency evacuation, meeting planners etc. requiring no separate effort to rebuild the vector map from scratch. The system yields a mean tracking error of less than 0.4m in location, and yields 0.9m as an average positioning error using fusion.


title: An Effective Heuristic Algorithm for the Traveling-Salesman Problem

abstract: This paper discusses a highly effective heuristic procedure for generating optimum and near-optimum solutions for the symmetric traveling-salesman problem. The procedure is based on a general approach to heuristics that is believed to have wide applicability in combinatorial optimization problems. The procedure produces optimum solutions for all problems tested, "classical" problems appearing in the literature, as well as randomly generated test problems, up to 110 cities. Run times grow approximately as n2; in absolute terms, a typical 100-city problem requires less than 25 seconds for one case GE635, and about three minutes to obtain the optimum with above 95 per cent confidence.


title: Real-time intrusion detection in power system operations

abstract: Increasing power system security is crucial to the future of the electric power grid. In this paper, we define a new class of cyberattacks to power systems-malicious modification of network data stored in an accessible database. Grounded in multivariate statistical process control, our approach to intrusion detection ensures data integrity in power system operations. We develop an algorithm that monitors power flow results and detects anomalies in the input values that could have been modified by cyberattacks. Our algorithm uses principal component analysis to separate power flow variability into regular and irregular subspaces. Analysis of the information in the irregular subspace determines whether the power system data has been compromised. We verify the efficacy of the algorithm using both the IEEE 24-bus and 118-bus reliability test systems. The results show that the developed algorithm is a promising enhancement to data security procedures in a control center.


title: Neuronal Reward and Decision Signals: From Theories to Data

abstract: Rewards are crucial objects that induce learning, approach behavior, choices, and emotions. Whereas emotions are difficult to investigate in animals, the learning function is mediated by neuronal reward prediction error signals which implement basic constructs of reinforcement learning theory. These signals are found in dopamine neurons, which emit a global reward signal to striatum and frontal cortex, and in specific neurons in striatum, amygdala, and frontal cortex projecting to select neuronal populations. The approach and choice functions involve subjective value, which is objectively assessed by behavioral choices eliciting internal, subjective reward preferences. Utility is the formal mathematical characterization of subjective value and a prime decision variable in economic choice theory. It is coded as utility prediction error by phasic dopamine responses. Utility can incorporate various influences, including risk, delay, effort, and social interaction. Appropriate for formal decision mechanisms, rewards are coded as object value, action value, difference value, and chosen value by specific neurons. Although all reward, reinforcement, and decision variables are theoretical constructs, their neuronal signals constitute measurable physical implementations and as such confirm the validity of these concepts. The neuronal reward signals provide guidance for behavior while constraining the free will to act.


title: Revising Immersion: A Conceptual Model for the Analysis of Digital Game Involvement

abstract: Game studies literature has recently seen a renewed interest in game experience with the recent publication of a number of edited collections, dissertations and conferences focusing on the subject. This paper aims to contribute to that growing body of literature by presenting a summary of my doctoral research in digital game involvement and immersion. It outlines a segment of a conceptual model that describes and analyzes the moment by moment involvement with digital games on a variety of experiential dimensions corresponding to six broad categories of game features. The paper ends with a proposal to replace the metaphor of immersion with one of incorporation. Incorporation aims to avoid the binary notion of the player’s plunge into the virtual environment characteristic of “immersion” while dispelling the vagueness of application that all too often surrounds the term. AUTHOR KEYWORDS MMOGs, immersion, involvement, incorporation, player experience. This paper proposes an analytical model for describing and analyzing players’ moment by moment involvement in digital games. The game studies literature archive is steadily increasing in volume, but there has so far been a marked shying away from a structured framework analyzing game involvement and immersion. The first step in establishing such a framework is the identification of a clear set of game involvement specific terms. Using qualitative methods conducted in conjunction with the PhD project of which this paper is part, I have aimed to create a more detailed map of the phenomena of game involvement, building a vocabulary that acknowledges its varied and complex nature and in so doing contribute to the existing body of literature on game experience. In the process of this explication the model also affirms the importance of positing specific terms for addressing game involvement over other non-ergodic media objects. The aim is thus to enact a framework which will enable game students, theorists and designers to address issues of game involvement in more nuanced ways than the current conceptualizations allow.


title: Dead-time effect analysis of dual active bridge DC-DC converter with dual-phase-shift control

abstract: This paper gives a detailed analysis about the causes of the voltage distortion caused by dead time in dual active bridge (DAB) DC-DC converter. The zero-crossing point of the equivalent leakage inductor's current is the definitive factor for various voltage distortion phenomena, which is determined by dead time and phase-shift ratios. Based on the switching characterization of DAB in dual-phase-shift scheme, a comprehensive summary of dead-time effect is acquired, including voltage polarity reversal, voltage sag and duty-cycle abnormity. At the same time, the causes of these phenomena are analyzed in detail. Considering all kinds of voltage distortion phenomena will affect the transmission power and other issues, a special operation mode defined as recessive-dead-time mode is obtained. In recessive-dead-time mode, the dead-time effect can be avoided by reasonable parameter-setting, thus, the voltage distortion will not occur. Finally, experimental results verify the theoretical analysis.


title: Unsupervised similarity-based word sense disambiguation using context vectors and sentential word importance

abstract: The process of identifying the actual meanings of words in a given text fragment has a long history in the field of computational linguistics. Due to its importance in understanding the semantics of natural language, it is considered one of the most challenging problems facing this field. In this article we propose a new unsupervised similarity-based word sense disambiguation (WSD) algorithm that operates by computing the semantic similarity between glosses of the target word and a context vector. The sense of the target word is determined as that for which the similarity between gloss and context vector is greatest. Thus, whereas conventional unsupervised WSD methods are based on measuring pairwise similarity between words, our approach is based on measuring semantic similarity between sentences. This enables it to utilize a higher degree of semantic information, and is more consistent with the way that human beings disambiguate; that is, by considering the greater context in which the word appears. We also show how performance can be further improved by incorporating a preliminary step in which the relative importance of words within the original text fragment is estimated, thereby providing an ordering that can be used to determine the sequence in which words should be disambiguated. We provide empirical results that show that our method performs favorably against the state-of-the-art unsupervised word sense disambiguation methods, as evaluated on several benchmark datasets through different models of evaluation.


title: Reweighted Data for Robust Probabilistic Models

abstract: Probabilistic models analyze data by relying on a set of assumptions. When a model performs poorly, we challenge its assumptions. This approach has led to myriad hand-crafted robust models; they offer protection against small deviations from their assumptions. We propose a simple way to systematically mitigate mismatch of a large class of probabilistic models. The idea is to raise the likelihood of each observation to a weight. Inferring these weights allows a model to identify observations that match its assumptions; down-weighting others enables robust inference and improved predictive accuracy. We study four different forms of model mismatch, ranging from missing latent groups to structure misspecification. A Poisson factorization analysis of the Movielens dataset shows the benefits of reweighting in a real data scenario.


title: Content Aware Neural Style Transfer

abstract: This paper presents a content-aware style transfer algorithm for paintings and photos of similar content using pre-trained neural network, obtaining better results than the previous work. In addition, the numerical experiments show that the style pattern and the content information is not completely separated by neural network.


title: A synchronous buck-boost converter on a Silicon-On-Sapphire 0.5µm process

abstract: This paper presents the design of a synchronous non-inverting buck-boost DC-DC converter on a Silicon-on-Sapphire (SOS) 0.5µm process. The converter uses voltage-mode feedback and PWM control to regulate the power delivered to a range of output voltages and loads. The circuit has been simulated using Cadence and its performance has been measured. The converter has an output voltage range of 1.2–4V and can deliver up to 750 mA. It is up to 92% efficient with a maximum ripple voltage of 80mV and uses significantly less die area than similar converters on standard CMOS processes.


title: A highly-efficient wait-free universal construction

abstract: We present a new simple wait-free universal construction, called Sim, that uses just a FetchA in practice though, we experimentally show that P-SIM outperforms several state-of-the-art lock-based and lock-free techniques, and this given that it is wait-free, i.e., that it satisfies a stronger progress condition than all the algorithms it outperforms. We have used P-SIM to get highly-efficient wait-free implementations of stacks and queues. Our experiments show that our implementations outperform the currently state-of-the-art shared stack and queue implementations which ensure only weaker progress properties than wait-freedom.


title: Test for Success: Next Generation Aircraft Identification System RF Simulation

abstract: Advancements in Identification Friend or Foe (IFF) systems are generating a variety of signals operating with a common purpose and often over common radio frequency (RF) spectrum. This paper will describe the top level characteristics of each of the following IFF/Surveillance systems and the plans and challenges of modeling these systems in ViaSat's RF stimulator systems. (1.) Mark XIIA, Mode S (2.) Mark XIIA, Mode 5 (3.) Automatic Dependent Surveillance -Broadcast (ADS-B) (4.) Department of Defense (DOD) Blue Force Tracking (BFT) Mode S is deployed and provides a directed interrogation and transpond response from airborne platforms. Mode 5 is planned for deployment and provides an anti-spoof encrypted transpond response. ADS-B is also planned for deployment in the commercial National Airspace System (NAS) and will use two separate systems for providing situational awareness transpond responses: Universal Access Transceiver (UAT) and Mode S 1090 Extended Squitter (ES). DOD BFT will use an advanced satellite network to receive platform state-vector transmissions and re-broadcast a theater level image of IFF data via digital video broadcast, satellite, second generation (DVB-S2). Each system presents unique challenges in modeling, but each are readily accommodated by ViaSat's Communication, Navigation, and Identification (CNI) RF function stimulator architecture. ViaSat will use our programmable arbitrary waveform generators to provide the RF waveforms. The scenario generation processors can then provide the geographic and RF propagation models to allow high density signal environments. These characteristics enable developmental testing for system improvements as well as verification testing for compliance with system level requirements. Use of an RF stimulator to generate a real-world RF environment is invaluable in testing systems to reduce risk and improve the probability of successfully deploying these next generation IFF systems.


title: A framework for relating syntactic and semantic model differences

abstract: Model differencing is an important activity in model-based development processes. Differences need to be detected, analyzed, and understood to evolve systems and explore alternatives. Two distinct approaches have been studied in the literature: syntactic differencing, which compares the concrete or abstract syntax of models, and semantic differencing, which compares models in terms of their meaning. Syntactic differencing identifies change operations that transform the syntactical representation of one model to the syntactical representation of the other. However, it does not explain their impact on the meaning of the model. Semantic model differencing is independent of syntactic changes and presents differences as elements in the semantics of one model but not the other. However, it does not reveal the syntactic changes causing these semantic differences. We define Diffuse, a language-independent, abstract framework, which relates syntactic change operations and semantic difference witnesses. We formalize fundamental relations of necessary, exhibiting, and sufficient sets of change operations and analyze their properties. We further demonstrate concrete instances of the Diffuse framework for three different popular modeling languages, namely class diagrams, activity diagrams, and feature models. The Diffuse framework provides a novel foundation for combining syntactic and semantic differencing.


title: Spatial Co-Registration of Ultra-High Resolution Visible, Multispectral and Thermal Images Acquired with a Micro-UAV over Antarctic Moss Beds

abstract: In recent times, the use of Unmanned Aerial Vehicles (UAVs) as tools for environmental remote sensing has become more commonplace. Compared to traditional airborne remote sensing, UAVs can provide finer spatial resolution data (up to 1 cm/pixel) and higher temporal resolution data. For the purposes of vegetation monitoring, the use of multiple sensors such as near infrared and thermal infrared cameras are of benefit. Collecting data with multiple sensors, however, requires an accurate spatial co-registration of the various UAV image datasets. In this study, we used an Oktokopter UAV to investigate the physiological state of Antarctic moss ecosystems using three sensors: (i) a visible camera (1 cm/pixel), (ii) a 6 band multispectral camera (3 cm/pixel), and (iii) a thermal infrared camera (10 cm/pixel). Imagery from each sensor was geo-referenced and mosaicked with a combination of commercially available software and our own algorithms based on the Scale Invariant Feature Transform (SIFT). The validation of the mosaic’s spatial co-registration revealed a mean root mean squared error (RMSE) of 1.78 pixels. A thematic map of moss health, derived from the multispectral mosaic using a Modified Triangular Vegetation Index (MTVI2), and an indicative map of moss surface temperature were then combined to demonstrate sufficient accuracy of our co-registration methodology for UAV-based monitoring of Antarctic moss beds.


title: Machine learning for large-scale wearable sensor data in Parkinson's disease: Concepts, promises, pitfalls, and futures

abstract: For the treatment and monitoring of Parkinson's disease (PD) to be scientific, a key requirement is that measurement of disease stages and severity is quantitative, reliable, and repeatable. The last 50 years in PD research have been dominated by qualitative, subjective ratings obtained by human interpretation of the presentation of disease signs and symptoms at clinical visits. More recently, “wearable,” sensor-based, quantitative, objective, and easy-to-use systems for quantifying PD signs for large numbers of participants over extended durations have been developed. This technology has the potential to significantly improve both clinical diagnosis and management in PD and the conduct of clinical studies. However, the large-scale, high-dimensional character of the data captured by these wearable sensors requires sophisticated signal processing and machine-learning algorithms to transform it into scientifically and clinically meaningful information. Such algorithms that “learn” from data have shown remarkable success in making accurate predictions for complex problems in which human skill has been required to date, but they are challenging to evaluate and apply without a basic understanding of the underlying logic on which they are based. This article contains a nontechnical tutorial review of relevant machine-learning algorithms, also describing their limitations and how these can be overcome. It discusses implications of this technology and a practical road map for realizing the full potential of this technology in PD research and practice.


title: Assessment of Micro-Enterprise Development through IT Adoption

abstract: While prior work has focused on the process through which IT may be adopted by micro-enterprises, this research takes it one step further by assessing the outcomes from IT adoption in micro-enterprises that have undergone technology, training and trust building interventions. It analyzes these using a systematic evaluation model that ties in the key components of development. The contribution of this paper is in providing insights into how researchers and practitioners can better stimulate micro-enterprise growth and economic development through IT adoption.


title: The stress-reducing effect of music listening varies depending on the social context

abstract: Abstract Objective Given that music listening often occurs in a social context, and given that social support can be associated with a stress-reducing effect, it was tested whether the mere presence of others while listening to music enhances the stress-reducing effect of listening to music. Methods A total of 53 participants responded to questions on stress, presence of others, and music listening five times per day (30 min after awakening, 1100 h, 1400 h, 1800 h, 2100 h) for seven consecutive days. After each assessment, participants were asked to collect a saliva sample for the later analysis of salivary cortisol (as a marker for the hypothalamic-pituitary-adrenal axis) and salivary alpha-amylase (as a marker for the autonomic nervous system). Results Hierarchical linear modeling revealed that music listening per se was not associated with a stress-reducing effect. However, listening to music in the presence of others led to decreased subjective stress levels, attenuated secretion of salivary cortisol, and higher activity of salivary alpha-amylase. When listening to music alone, music that was listened to for the reason of relaxation predicted lower subjective stress. Conclusion The stress-reducing effect of music listening in daily life varies depending on the presence of others. Music listening in the presence of others enhanced the stress-reducing effect of music listening independently of reasons for music listening. Solitary music listening was stress-reducing when relaxation was stated as the reason for music listening. Thus, in daily life, music listening can be used for stress reduction purposes, with the greatest success when it occurs in the presence of others or when it is deliberately listened to for the reason of relaxation.


title: Secure and efficient access to outsourced data

abstract: Providing secure and efficient access to large scale outsourced data is an important component of cloud computing. In this paper, we propose a mechanism to solve this problem in owner-write-users-read applications. We propose to encrypt every data block with a different key so that flexible cryptography-based access control can be achieved. Through the adoption of key derivation methods, the owner needs to maintain only a few secrets. Analysis shows that the key derivation procedure using hash functions will introduce very limited computation overhead. We propose to use over-encryption and/or lazy revocation to prevent revoked users from getting access to updated data blocks. We design mechanisms to handle both updates to outsourced data and changes in user access rights. We investigate the overhead and safety of the proposed approach, and study mechanisms to improve data access efficiency.


title: The index of autonomous functioning: Development of a scale of human autonomy

abstract: A growing interest in the functional importance of dispositional autonomy led to the development and validation of the Index of Autonomous Functioning (IAF) across seven studies. The IAF provides a measure of trait autonomy based on three theoretically derived subscales assessing authorship/self-congruence, interest-taking, and low susceptibility to control. Results showed consistency within and across subscales, and appropriate placement within a nomological network of constructs. Diary studies demonstrated IAF relations with higher well-being, greater daily satisfaction of basic psychological needs, and more autonomous engagement in daily activities. Using an experimental approach, the IAF was shown to predict more positive interactions among dyads. The studies provided a systematic development and validation of a measure of autonomy that is brief and reliable.


title: MCS-51 microcontroller based industrial automation and control system using CAN protocol

abstract: When we consider an industry of large area the monitoring, controlling of each section involved in the industry is a big task. It involves a large amount of man power and time consumption. To overcome these above factors we developed this technology to make single person for monitoring and controlling the entire network. This can be achieved based on multiprocessor communication by the combination of a wired technology i.e. controller area network (CAN) bus network, and the MCS-51 microcontroller which are the main objectives of our paper. The MCS-51 and the CAN protocol are cost effective, and the CAN protocol is used in various applications like industries, automobiles and home. The software part is done in Keil IDE and Jet Flasher. Hardware is implemented and software porting is done.


title: A retail store SKU promotions optimization model for category multi-period profit maximization

abstract: Consumer promotions are an important element of competitive dynamics in retail markets and make a significant difference in the retailer's profits. But no study has so far included all the elements that are required to meet retail business objectives. We extend the existing literatures by considering all the basic requirements for a promotional Decision Support System (DSS): reliance on operational (store-level) data only, the ability to predict sales as a function of prices and the inclusion of other promotional variables affecting the category. The new model delivers an optimizing promotional schedule at Stock-Keeping-Unit (SKU) level which maximizes multi-period category level profit under the constraints of business rules typically applied in practice. We first develop a high dimensional distributed lag demand model which integrates both cross-SKU competitive promotion information and cross-period promotional influences. We estimate the model by proposing a two stage sign constrained regularization approach to ensure realistic promotional parameters. Based on the demand model, we then build a nonlinear integer programming model to maximize the retailer's category profits over a planning horizon under constraints that model important business rules. The output of the model provides optimized prices, display and feature advertising planning together with sales and profit forecasts. Empirical tests over a number of stores and categories using supermarket data suggest that our model generates accurate sales forecasts and increases category profits by approximately 17% and that including cross-item and cross-period effects is also valuable.


title: Measuring the VC-Dimension of a Learning Machine

abstract: A method for measuring the capacity of learning machines is described. The method is based on fitting a theoretically derived function to empirical measurements of the maximal difference between the error rates on two separate data sets of varying sizes. Experimental measurements of the capacity of various types of linear classifiers are presented.


title: Cooks or cobblers?: crowd creativity through combination

abstract: A sketch combination system is introduced and tested: a crowd of 1047 participated in an iterative process of design, evaluation and combination. Specifically, participants in a crowdsourcing marketplace sketched chairs for children. One crowd created a first generation of chairs, and then successive crowds created new generations by combining the chairs made by previous crowds. Other participants evaluated the chairs. The crowd judged the chairs from the third generation more creative than those from the first generation. An analysis of the design evolution shows that participants inherited and modified presented features, and also added new features. These findings suggest that crowd based design processes may be effective, and point the way toward computer-human interactions that might further encourage crowd creativity.


title: Velocity and position trajectory tracking through sliding mode control of two-wheeled self-balancing mobile robot

abstract: Two-wheeled self balancing mobile robots have many challenging system properties when considering velocity and position tracking. The under-actuated, nonlinear, unstable, non-minimum phase dynamics cause the system to react violently and erratically to controller inputs. In order to properly handle such dynamics, an under-actuated sliding mode control scheme was implemented and tested using a variety of control parameters, desired trajectories, and disturbances. The controller was successfully able to track velocity setpoints while maintaining system stability.


title: New objective functions for social collaborative filtering

abstract: This paper examines the problem of social collaborative filtering (CF) to recommend items of interest to users in a social network setting. Unlike standard CF algorithms using relatively simple user and item features, recommendation in social networks poses the more complex problem of learning user preferences from a rich and complex set of user profile and interaction information. Many existing social CF methods have extended traditional CF matrix factorization, but have overlooked important aspects germane to the social setting. We propose a unified framework for social CF matrix factorization by introducing novel objective functions for training. Our new objective functions have three key features that address main drawbacks of existing approaches: (a) we fully exploit feature-based user similarity, (b) we permit direct learning of user-to-user information diffusion, and (c) we leverage co-preference (dis)agreement between two users to learn restricted areas of common interest. We evaluate these new social CF objectives, comparing them to each other and to a variety of (social) CF baselines, and analyze user behavior on live user trials in a custom-developed Facebook App involving data collected over five months from over 100 App users and their 37,000+ friends.


title: Statistics and Causal Inference: A Review

abstract: This paper aims at assisting empirical researchers benefit from recent advances in causal inference. The paper stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, and the conditional nature of causal claims inferred from nonexperimental studies. These emphases are illustrated through a brief survey of recent results, including the control of confounding, the assessment of causal effects, the interpretation of counterfactuals, and a symbiosis between counterfactual and graphical methods of analysis.


title: Interactive Assessment Tools for Computational Thinking in High School STEM Classrooms

abstract: This paper presents a pair of online, interactive assessments designed to measures students’ computational thinking skills. The assessments are part of a larger project to bring computational thinking into high school STEM classrooms. Each assessment includes interactive tools that highlight the power of computation in the practice of scientific and mathematical inquiry. The computational tools used in our assessments enable students to analyze data with dynamic visualizations and explore concepts with computational models.


title: MHz-frequency operation of flyback converter with monolithic self-synchronized rectifier (SSR)

abstract: Synchronous rectification offers an effective solution to improve efficiency of low-output-voltage flyback converters. Conventional synchronous flyback topologies using discrete synchronous rectifier face challenges in precise timing control on the secondary side. In this paper, MHz-frequency operation of a flyback DC-DC converter using a new monolithic self-synchronized rectifier (SSR) is investigated and demonstrated. The SSR IC, acting solely on its own drain-source voltage instead of external control signals, considerably simplifies converter design, improves system efficiency, and enables MHz operating frequency. Analysis, modeling, and experimental results are presented.


title: Loss analysis of half-bridge LLC resonant converter

abstract: In this paper, a novel approximation method for conduction loss model of half-bridge LLC resonant converter operating below the resonant frequency of resonant inductor and capacitor. In the conventional conduction loss model, only the fundamental component of switching frequency is considered. However, the disregarded harmonics of switching frequency cause considerable deviation. In the novel method, harmonics of switching frequency are included, and it is more precise than conventional model as switching frequency is far from resonant frequency. This method estimates conduction losses according to actual current waveforms instead of calculating amplitudes of harmonics. Also, switching loss and other loss models are derived and described. Novel loss model and conventional loss model are compared with simulation results.


title: Environmental arguments and the survival of small-scale fishing in Iceland

abstract: This thesis is offered as a contribution to studies of social and cultural change in the Icelandic fisheries and fishing communities. Such changes may be seen as a result of the interplay of internal dynamics with both national and global forces and processes, not least with regard to the impacts of fisheries governance. These changes occur also in an international context of new environmental ideologies and perceptions of marine mammals, with consequences for social dynamics of local resource-use. Here it is argued that the conflicts over the harvesting or conservation of cetaceans can productively be understood from a cultural perspective. The thesis discusses the elevation of whales as symbols of particular value, and the metaphorical and cognitive aspects of, in particular, anthropomorphism, the projection of human motives and values onto animal behaviour, as a significant and effective part of conservation rhetoric and ideology.Specifically, the thesis deals with issues concerning whaling and whale watching along with issues and debates concerning these alternative forms of exploiting marine mammals. It also discusses central questions regarding fisheries governance and rights to fishing with reference to social and economic viability in Icelandic fishing communities. The unifying themes of this thesis are: how marine-mammal issues and controversies and social impacts of fisheries governance form part of globalization processes; how environmental and economic paradigms influence change, particularly in terms of marine-mammal conservation campaigns and market liberalist resource policy; and how these external ideological forces call for responses at local and national levels. The adaptive actions of the human agents and communities involved are described as creative, cumulative and complex. The thesis also highlights the central transformative role of the new regime of private property rights introduced into Icelandic fisheries governance in the 1980s.


title: GreenDroid: Automated Diagnosis of Energy Inefficiency for Smartphone Applications

abstract: Smartphone applications’ energy efficiency is vital, but many Android applications suffer from serious energy inefficiency problems. Locating these problems is labor-intensive and automated diagnosis is highly desirable. However, a key challenge is the lack of a decidable criterion that facilitates automated judgment of such energy problems. Our work aims to address this challenge. We conducted an in-depth study of 173 open-source and 229 commercial Android applications, and observed two common causes of energy problems: missing deactivation of sensors or wake locks, and cost-ineffective use of sensory data. With these findings, we propose an automated approach to diagnosing energy problems in Android applications. Our approach explores an application’s state space by systematically executing the application using Java PathFinder (JPF). It monitors sensor and wake lock operations to detect missing deactivation of sensors and wake locks. It also tracks the transformation and usage of sensory data and judges whether they are effectively utilized by the application using our state-sensitive data utilization metric. In this way, our approach can generate detailed reports with actionable information to assist developers in validating detected energy problems. We built our approach as a tool, GreenDroid, on top of JPF. Technically, we addressed the challenges of generating user interaction events and scheduling event handlers in extending JPF for analyzing Android applications. We evaluated GreenDroid using 14 real-world popular Android applications. GreenDroid completed energy efficiency diagnosis for these applications in a few minutes. It successfully located real energy problems in these applications, and additionally found new unreported energy problems that were later confirmed by developers.


title: Design guidelines for wireless sensor networks: communication, clustering and aggregation

abstract: Abstract When sensor nodes are organized in clusters, they could use either single hop or multi-hop mode of communication to send their data to their respective cluster heads. We present a systematic cost-based analysis of both the modes, and provide results that could serve as guidelines to decide which mode should be used for given settings. We determine closed form expressions for the required number of cluster heads and the required battery energy of nodes for both the modes. We also propose a hybrid communication mode which is a combination of single hop and multi-hop modes, and which is more cost-effective than either of the two modes. Our problem formulation also allows for the application to be taken into account in the overall design problem through a data aggregation model.


title: Model-based recognition in robot vision

abstract: This paper presents a comparative study and survey of model-based object-recognition algorithms for robot vision. The goal of these algorithms is to recognize the identity, position, and orientation of randomly oriented industrial parts. In one form this is commonly referred to as the "bin-picking" problem, in which the parts to be recognized are presented in a jumbled bin. The paper is organized according to 2-D, 2½-D, and 3-D object representations, which are used as the basis for the recognition algorithms. Three central issues common to each category, namely, feature extraction, modeling, and matching, are examined in detail. An evaluation and comparison of existing industrial part-recognition systems and algorithms is given, providing insights for progress toward future robot vision systems.


title: Video Surveillance Based on Cloud Storage

abstract: Traditional video surveillance systems require infrastructures including expensive servers with capabilities to process images and store video recordings. These surveillance systems produce and need to store a huge amount of data and to execute on them specific image analysis in real-time in order to detect safety events. We propose a video surveillance system based on Cloud Computing that collects multimedia streams generated by surveillance cameras, optimizes their transmissions according to network condition and stores them in a cloud storage system in an efficient and secure way.


Surface Measurement and Analysis of Drillstring Vibrations While Drilling


title: Novel Multi-Stage Stochastic DG Investment Planning with Recourse

abstract: This paper presents a novel multi-stage stochastic distributed generation investment planning model for making investment decisions under uncertainty. The problem, formulated from a coordinated system planning viewpoint, simultaneously minimizes the net present value of costs rated to losses, emission, operation, and maintenance, as well as the cost of unserved energy. The formulation is anchored on a two-period planning horizon, each having multiple stages. The first period is a short-term horizon in which robust decisions are pursued in the face of uncertainty; whereas, the second one spans over a medium to long-term horizon involving exploratory and/or flexible investment decisions. The operational variability and uncertainty introduced by intermittent generation sources, electricity demand, emission prices, demand growth, and others are accounted for via probabilistic and stochastic methods, respectively. Metrics such as cost of ignoring uncertainty and value of perfect information are used to clearly demonstrate the benefits of the proposed stochastic model. A real-life distribution network system is used as a case study and the results show the effectiveness of the proposed model.


title: Grouping Synonymous Sentences from a Parallel Corpus

abstract: Abstract Recently, natural language processing researches have focused on data or processing techniques for paraphrasing. Unfortunately, however, we have little data for paraphrasing. There are some research reports on collecting synonymous expressions with parallel corpus, though no suitable corpus for collecting a set of paraphrases is yet available. Therefore, we obtain a few variations of expression in paraphrase sets when we tried to apply this method with a parallel corpus. In this paper, we propose a grouping method based on the basic idea of grouping synonymous sentences related to the translation recursively, and decompose incorrect groups using the DMdecomposition algorithm. The incorrect groups include expressions that cannot be paraphrased because some words or expressions have different meanings in different situations. We discuss our method and experimental results with respect to BTEC, which is a multilingual parallel corpus.


title: Better Punctuation Prediction with Dynamic Conditional Random Fields

abstract: This paper focuses on the task of inserting punctuation symbols into transcribed conversational speech texts, without relying on prosodic cues. We investigate limitations associated with previous methods, and propose a novel approach based on dynamic conditional random fields. Different from previous work, our proposed approach is designed to jointly perform both sentence boundary and sentence type prediction, and punctuation prediction on speech utterances. ::: ::: We performed evaluations on a transcribed conversational speech domain consisting of both English and Chinese texts. Empirical results show that our method outperforms an approach based on linear-chain conditional random fields and other previous approaches.


title: The evolution of CycL, the Cyc representation language

abstract: CycL is the language in which the Cyc Knowledge Base is being encoded. This paper reviews some of the methodological considerations and the technical decisions (resulting from these considerations) that are both different from conventional thinking and that have significantly influenced the design of the language.


title: A Content-Boosted Collaborative Filtering Algorithm for Personalized Training in Interpretation of Radiological Imaging

abstract: Devising a method that can select cases based on the performance levels of trainees and the characteristics of cases is essential for developing a personalized training program in radiology education. In this paper, we propose a novel hybrid prediction algorithm called content-boosted collaborative filtering (CBCF) to predict the difficulty level of each case for each trainee. The CBCF utilizes a content-based filtering (CBF) method to enhance existing trainee-case ratings data and then provides final predictions through a collaborative filtering (CF) algorithm. The CBCF algorithm incorporates the advantages of both CBF and CF, while not inheriting the disadvantages of either. The CBCF method is compared with the pure CBF and pure CF approaches using three datasets. The experimental data are then evaluated in terms of the MAE metric. Our experimental results show that the CBCF outperforms the pure CBF and CF methods by 13.33 and 12.17 %, respectively, in terms of prediction precision. This also suggests that the CBCF can be used in the development of personalized training systems in radiology education.


title: Cloud Computing: A Qualitative Study and Conceptual Model

abstract: “Cloud computing” is the new buzz term in the IT industry. Many practitioners have already adopted the technology or are in the process of adopting it. Yet, there is no methodological research investigating the adoption process. This paper reviews articles and interviews published mostly in practitioner journals to develop a theoretical framework that help us to better understand the cloud computing phenomena. As such, the framework provides a stepping stone for future cloud computing studies.


title: Automated Detection and Analysis for Android Ransomware

abstract: Along with the rapid growth of new science andtechnology, the functions of smartphones become more and morepowerful. Nevertheless, everything has two aspects. Smartphonesbring so much convenience for people and also bring the securityrisks at the same time. Malicious application has become a bigthreat to the mobile security. Thus, an efficiency security analysisand detection method is important and necessary. Due toattacking of malicious application, user could not use smartphonenormally and personal information could be stolen. What isworse, attacking proliferation will impact the healthy growth ofthe mobile Internet industry. To limit the growing speed ofmalicious application, the first thing we need to know whatmalicious application is and how to deal with. Detecting andanalyzing their behaviors helps us deeply understand theattacking principle such that we can take effectivecountermeasures against malicious application. This articledescribes the basic Android component and manifest, the reasonthat Android is prevalent and why attacking came in. This paperanalyzed and penetrated malicious ransomware which threatsmobile security now with our developed automated analysisapproach for such mobile malware detection.


title: A survey on signals and systems in ambulatory blood pressure monitoring using pulse transit time.

abstract: Blood pressure monitoring based on pulse transit or arrival time has been the focus of much research in order to design ambulatory blood pressure monitors. The accuracy of these monitors is limited by several challenges, such as acquisition and processing of physiological signals as well as changes in vascular tone and the pre-ejection period. In this work, a literature survey covering recent developments is presented in order to identify gaps in the literature. The findings of the literature are classified according to three aspects. These are the calibration of pulse transit/arrival times to blood pressure, acquisition and processing of physiological signals and finally, the design of fully integrated blood pressure measurement systems. Alternative technologies as well as locations for the measurement of the pulse wave signal should be investigated in order to improve the accuracy during calibration. Furthermore, the integration and validation of monitoring systems needs to be improved in current ambulatory blood pressure monitors.


title: Conditional Computation in Neural Networks for faster models

abstract: Deep learning has become the state-of-art tool in many applications, but the evaluation and training of deep models can be time-consuming and computationally expensive. The conditional computation approach has been proposed to tackle this problem (Bengio et al., 2013; Davis & Arel, 2013). It operates by selectively activating only parts of the network at a time. In this paper, we use reinforcement learning as a tool to optimize conditional computation policies. More specifically, we cast the problem of learning activation-dependent policies for dropping out blocks of units as a reinforcement learning problem. We propose a learning scheme motivated by computation speed, capturing the idea of wanting to have parsimonious activations while maintaining prediction accuracy. We apply a policy gradient algorithm for learning policies that optimize this loss function and propose a regularization mechanism that encourages diversification of the dropout policy. We present encouraging empirical results showing that this approach improves the speed of computation without impacting the quality of the approximation.


title: Automated News Suggestions for Populating Wikipedia Entity Pages

abstract: Wikipedia entity pages are a valuable source of information for direct consumption and for knowledge-base construction, update and maintenance. Facts in these entity pages are typically supported by references. Recent studies show that as much as 20% of the references are from online news sources. However, many entity pages are incomplete even if relevant information is already available in existing news articles. Even for the already present references, there is often a delay between the news article publication time and the reference time. In this work, we therefore look at Wikipedia through the lens of news and propose a novel news-article suggestion task to improve news coverage in Wikipedia, and reduce the lag of newsworthy references. Our work finds direct application, as a precursor, to Wikipedia page generation and knowledge-base acceleration tasks that rely on relevant and high quality input sources. We propose a two-stage supervised approach for suggesting news articles to entity pages for a given state of Wikipedia. First, we suggest news articles to Wikipedia entities (article-entity placement) relying on a rich set of features which take into account the salience and relative authority of entities, and the novelty of news articles to entity pages. Second, we determine the exact section in the entity page for the input article (article-section placement) guided by class-based section templates. We perform an extensive evaluation of our approach based on ground-truth data that is extracted from external references in Wikipedia. We achieve a high precision value of up to 93% in the article-entity suggestion stage and upto 84% for the article-section placement. Finally, we compare our approach against competitive baselines and show significant improvements.


title: Many-body Landau-Zener dynamics in coupled 1D Bose liquids

abstract: The Landau-Zener model of a quantum mechanical two-level system driven with a linearly time dependent detuning has served over decades as a textbook paradigm of quantum dynamics. In their seminal work [L. D. Landau, Physik. Z. Sowjet. 2, 46 (1932); C. Zener, Proc. Royal Soc. London 137, 696 (1932)], Landau and Zener derived a non-perturbative prediction for the transition probability between two states, which often serves as a reference point for the analysis of more complex systems. A particularly intriguing question is whether that framework can be extended to describe many-body quantum dynamics. Here we report an experimental and theoretical study of a system of ultracold atoms, offering a direct many-body generalization of the Landau-Zener problem. In a system of pairwise tunnel-coupled 1D Bose liquids we show how tuning the correlations of the 1D gases, the tunnel coupling between the tubes and the inter-tube interactions strongly modify the original Landau-Zener picture. The results are explained using a mean-field description of the inter-tube condensate wave-function, coupled to the low-energy phonons of the 1D Bose liquid.


title: Stack Overflow Considered Harmful? The Impact of Copy&Paste on Android Application Security

abstract: Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and oftentimes ready-to-use code snippets. Anecdotes report that software developers copy and paste code snippets from those information sources for convenience reasons. Such behavior results in a constant flow of community-provided code snippets into production software. To date, the impact of this behaviour on code security is unknown. We answer this highly important question by quantifying the proliferation of security-related code snippets from Stack Overflow in Android applications available on Google Play. Access to the rich source of information available on Stack Overflow including ready-to-use code snippets provides huge benefits for software developers. However, when it comes to code security there are some caveats to bear in mind: Due to the complex nature of code security, it is very difficult to provide ready-to-use and secure solutions for every problem. Hence, integrating a security-related code snippet from Stack Overflow into production software requires caution and expertise. Unsurprisingly, we observed insecure code snippets being copied into Android applications millions of users install from Google Play every day. To quantitatively evaluate the extent of this observation, we scanned Stack Overflow for code snippets and evaluated their security score using a stochastic gradient descent classifier. In order to identify code reuse in Android applications, we applied state-of-the-art static analysis. Our results are alarming: 15.4% of the 1.3 million Android applications we analyzed, contained security-related code snippets from Stack Overflow. Out of these 97.9% contain at least one insecure code snippet.


title: Boundary-guided Feature Aggregation Network for Salient Object Detection

abstract: Fully convolutional networks (FCN) has significantly improved the performance of many pixel-labeling tasks, such as semantic segmentation and depth estimation. However, it still remains nontrivial to thoroughly utilize the multilevel convolutional feature maps and boundary information for salient object detection. In this letter, we propose a novel FCN framework to integrate multilevel convolutional features recurrently with the guidance of object boundary information. First, a deep convolutional network is used to extract multilevel feature maps and separately aggregate them into multiple resolutions, which can be used to generate coarse saliency maps. Meanwhile, another boundary information extraction branch is proposed to generate boundary features. Finally, an attention-based feature fusion module is designed to fuse boundary information into salient regions to achieve accurate boundary inference and semantic enhancement. The final saliency maps are the combination of the predicted boundary maps and integrated saliency maps, which are more closer to the ground truths. Experiments and analysis on four large-scale benchmarks verify that our framework achieves new state-of-the-art results.


title: Improved Gradient-Based Optimization Over Discrete Distributions

abstract: In many applications we seek to maximize an expectation with respect to a distribution over discrete variables. Estimating gradients of such objectives with respect to the distribution parameters is a challenging problem. We analyze existing solutions including finite-difference (FD) estimators and continuous relaxation (CR) estimators in terms of bias and variance. We show that the commonly used Gumbel-Softmax estimator is biased and propose a simple method to reduce it. We also derive a simpler piece-wise linear continuous relaxation that also possesses reduced bias. We demonstrate empirically that reduced bias leads to a better performance in variational inference and on binary optimization tasks.


title: Semi-supervised Learning Meets Factorization: Learning to Recommend with Chain Graph Model

abstract: Recently latent factor model (LFM) has been drawing much attention in recommender systems due to its good performance and scalability. However, existing LFMs predict missing values in a user-item rating matrix only based on the known ones, and thus the sparsity of the rating matrix always limits their performance. Meanwhile, semi-supervised learning (SSL) provides an effective way to alleviate the label (i.e., rating) sparsity problem by performing label propagation, which is mainly based on the smoothness insight on affinity graphs. However, graph-based SSL suffers serious scalability and graph unreliable problems when directly being applied to do recommendation. In this paper, we propose a novel probabilistic chain graph model (CGM) to marry SSL with LFM. The proposed CGM is a combination of Bayesian network and Markov random field. The Bayesian network is used to model the rating generation and regression procedures, and the Markov random field is used to model the confidence-aware smoothness constraint between the generated ratings. Experimental results show that our proposed CGM significantly outperforms the state-of-the-art approaches in terms of four evaluation metrics, and with a larger performance margin when data sparsity increases.


title: S-band internally harmonic matched GaN FET with 330W output power and 62% PAE

abstract: In this paper, an S-band internally harmonic matched GaN FET is presented, which is designed so that up to third harmonic impedance is tuned to high efficiency condition. Harmonic load pull measurements were done for a small transistor cell at first. It was found that power added efficiency (PAE) of 78% together with 6W output power can be obtained by tuning impedances up to 3rd harmonic for both input and output sides. Then matching circuit was designed for large gate periphery multi-cell transistor. To make the circuit size small, harmonic matching was done in a hermetically sealed package. With total gate width of 64mm, 330W output power and 62% PAE was successfully obtained.


title: Detection of Covert Channel Encoding in Network Packet Delays

abstract: Covert channels are mechanisms for communicating information in ways that are difficult to detect. Data exfiltration can be an indication that a computer has been compromised by an attacker even when other intrusion detection schemes have failed to detect a successful attack. Covert timing channels use packet interarrival times, not header or payload embedded information, to encode covert messages. This paper investigates the channel capacity of Internet-based timing channels and proposes a methodology for detecting covert timing channels based on how close a source comes to achieving that channel capacity. A statistical approach is then used for the special case of binary codes.


title: 3D Pictorial Structures Revisited: Multiple Human Pose Estimation

abstract: We address the problem of 3D pose estimation of multiple humans from multiple views. The transition from single to multiple human pose estimation and from the 2D to 3D space is challenging due to a much larger state space, occlusions and across-view ambiguities when not knowing the identity of the humans in advance. To address these problems, we first create a reduced state space by triangulation of corresponding pairs of body parts obtained by part detectors for each camera view. In order to resolve ambiguities of wrong and mixed parts of multiple humans after triangulation and also those coming from false positive detections, we introduce a 3D pictorial structures (3DPS) model. Our model builds on multi-view unary potentials, while a prior model is integrated into pairwise and ternary potential functions. To balance the potentials’ influence, the model parameters are learnt using a Structured SVM (SSVM). The model is generic and applicable to both single and multiple human pose estimation. To evaluate our model on single and multiple human pose estimation, we rely on four different datasets. We first analyse the contribution of the potentials and then compare our results with related work where we demonstrate superior performance.


title: Computer simulation of three-dimensional plaque formation and progression in the carotid artery

abstract: Atherosclerosis is becoming the number one cause of death worldwide. In this study, three-dimensional computer model of plaque formation and development for human carotid artery is developed. The three-dimensional blood flow is described by the Navier-Stokes equation, together with the continuity equation. Mass transfer within the blood lumen and through the arterial wall is coupled with the blood flow and is modeled by a convection-diffusion equation. The low-density lipoproteins transports in lumen of the vessel and through the vessel tissue are coupled by Kedem-Katchalsky equations. The inflammatory process is modeled using three additional reaction-diffusion partial differential equations. Fluid-structure interaction is used to estimate effective wall stress analysis. Plaque growth functions for volume progression are correlated with shear stress and effective wall stress distribution. We choose two specific patients from MRI study with significant plaque progression. Plaque volume progression using three time points for baseline, 3- and 12-month follow up is fitted. Our results for plaque localization correspond to low shear stress zone and we fitted parameters from our model using nonlinear least-square method. Determination of plaque location and composition, and computer simulation of progression in time for a specific patient shows a potential benefit for the prediction of disease progression. The proof of validity of three-dimensional computer modeling in the evaluation of atherosclerotic plaque burden may shift the clinical information of MRI from morphological assessment toward a functional tool. Understanding and prediction of the evolution of atherosclerotic plaques either into vulnerable or stable plaques are major tasks for the medical community.


title: Revealing the problems with 802.11 medium access control protocol in multi-hop wireless ad hoc networks

abstract: The IEEE 802.11 medium access control (MAC) protocol is a standard for wireless LANs, it is also widely used in almost all test beds and simulations for the research in wireless mobile multi-hop ad hoc networks. However, this protocol was not designed for multi-hop networks. Although it can support some ad hoc network architecture, it is not intended to support the wireless mobile ad hoc network, in which multi-hop connectivity is one of the most prominent features. In this paper, we focus on the following question: can IEEE 802.11 MAC protocol function well in multihop networks? By presenting several serious problems encountered in transmission control protocol (TCP) connections in an IEEE 802.11 based multi-hop network, we show that the current TCP protocol does not work well above the current 802.11 MAC layer. The relevant problems include the TCP instability problem found in this kind of network, the severe unfairness problem, and the incompatibility problem. We illustrate that all these problems are rooted in the MAC layer. Furthermore, by revealing the in-depth cause of these problems, we conclude that the current version of this wireless LAN protocol does not function well in multi-hop ad hoc networks. We thus doubt whether the current WaveLAN based system is workable as a mobile multi-hop ad hoc test bed. All the results shown in this paper are based on NS2 simulations, and are compatible with the results from the OPNET simulations.


title: Do GAN Loss Functions Really Matter

abstract: In this paper, we address the recent controversy between Lipschitz regularization and the choice of loss function for the training of Generative Adversarial Networks (GANs). One side argues that the success of the GAN training should be attributed to the choice of loss function [16, 2, 5], while the other suggests that the Lipschitz regularization is the key to good results [17, 3, 18, 19]. We provide a theoretical and experimental analysis of how Lipschitz regularization interacts with the loss function to derive the following insights: (i) We show that popular GANs (NS-GAN [4], LS-GAN [16], WGAN [2]) perform equally well when the discriminator is regularized with a small Lipschitz constant, but the performance in terms of quality and diversity gets worse for larger Lipschitz constants, except for WGAN. (ii) We show that all loss functions degenerate to linear ones for small Lipschitz constants to explain why the performance of these GANs is similar. For higher Lipschitz constants, we observe that only WGAN performs well while NS-GAN and LS-GAN break down. For lower Lipschitz constants, NS-GAN and LS-GAN perform similarly to WGAN only because they degenerate to the WGAN loss. In order to further illustrate this issue, we demonstrate that even "ridiculous" loss functions such as sin and exp have similar performance to NS-GAN, LS-GAN, and WGAN, when small Lipschitz constants are used.


title: Bayesian Deep Learning-based Confidence-aware Solar Irradiance Forecasting System

abstract: For stable and successful use of grid-connected PV (photovoltaic) plants, it is quite necessary to know the expected power from PV plants in advance. However, forecasting PV output power accurately is difficult in practical cases where uncertainties are unavoidable. In this paper, we propose a confidence-aware forecasting system that produces a point forecast together with its confidence information. Our system classifies forecast outputs into confident forecasts and non-confident forecasts using the confidence information. Then, the confident forecast is used directly and the non-confident forecast is replaced by its lower bound, which is desirable for conservative scheduling of existing power plants. Through the experiments, we show that MAPE (maximum absolute percentage error) of the confident forecasts and the non-confident forecasts are 9.8% and 21.5%, respectively. We also show that the lower bound is lower than actual value in over 95% of the non-confident forecasts. The results show that our approach is good to classify forecasts into confident forecasts and non-confident forecasts and to produce effective lower bounds.


title: Multi-scale 3D Convolution Network for Video Based Person Re-Identification

abstract: This paper proposes a two-stream convolution network to extract spatial and temporal cues for video based person ReIdentification (ReID). A temporal stream in this network is constructed by inserting several Multi-scale 3D (M3D) convolution layers into a 2D CNN network. The resulting M3D convolution network introduces a fraction of parameters into the 2D CNN, but gains the ability of multi-scale temporal feature learning. With this compact architecture, M3D convolution network is also more efficient and easier to optimize than existing 3D convolution networks. The temporal stream further involves Residual Attention Layers (RAL) to refine the temporal features. By jointly learning spatial-temporal attention masks in a residual manner, RAL identifies the discriminative spatial regions and temporal cues. The other stream in our network is implemented with a 2D CNN for spatial feature extraction. The spatial and temporal features from two streams are finally fused for the video based person ReID. Evaluations on three widely used benchmarks datasets, i.e.,MARS, PRID2011, and iLIDS-VID demonstrate the substantial advantages of our method over existing 3D convolution networks and state-of-art methods.


title: Joint Monocular 3D Vehicle Detection and Tracking

abstract: Vehicle 3D extents and trajectories are critical cues for predicting the future location of vehicles and planning future agent ego-motion based on those predictions. In this paper, we propose a novel online framework for 3D vehicle detection and tracking from monocular videos. The framework can not only associate detections of vehicles in motion over time, but also estimate their complete 3D bounding box information from a sequence of 2D images captured on a moving platform. Our method leverages 3D box depth-ordering matching for robust instance association and utilizes 3D trajectory prediction for re-identification of occluded vehicles. We also design a motion learning module based on an LSTM for more accurate long-term motion extrapolation. Our experiments on simulation, KITTI, and Argoverse datasets show that our 3D tracking pipeline offers robust data association and tracking. On Argoverse, our image-based method is significantly better for tracking 3D vehicles within 30 meters than the LiDAR-centric baseline methods.


title: A novel hybridization of artificial neural networks and ARIMA models for time series forecasting

abstract: Improving forecasting especially time series forecasting accuracy is an important yet often difficult task facing decision makers in many areas. Both theoretical and empirical findings have indicated that integration of different models can be an effective way of improving upon their predictive performance, especially when the models in combination are quite different. Artificial neural networks (ANNs) are flexible computing frameworks and universal approximators that can be applied to a wide range of forecasting problems with a high degree of accuracy. However, using ANNs to model linear problems have yielded mixed results, and hence; it is not wise to apply ANNs blindly to any type of data. Autoregressive integrated moving average (ARIMA) models are one of the most popular linear models in time series forecasting, which have been widely applied in order to construct more accurate hybrid models during the past decade. Although, hybrid techniques, which decompose a time series into its linear and nonlinear components, have recently been shown to be successful for single models, these models have some disadvantages. In this paper, a novel hybridization of artificial neural networks and ARIMA model is proposed in order to overcome mentioned limitation of ANNs and yield more general and more accurate forecasting model than traditional hybrid ARIMA-ANNs models. In our proposed model, the unique advantages of ARIMA models in linear modeling are used in order to identify and magnify the existing linear structure in data, and then a neural network is used in order to determine a model to capture the underlying data generating process and predict, using preprocessed data. Empirical results with three well-known real data sets indicate that the proposed model can be an effective way to improve forecasting accuracy achieved by traditional hybrid models and also either of the components models used separately.


title: Grit in the path to e-learning success

abstract: E-learning systems are emerging in many settings of our society. Schools, universities, and several other organizations use these systems. E-learning systems allow learning anytime and anywhere. This medium may seem to be the answer to all learning barriers, but the effect of non-cognitive skills on the success of e-learning systems is yet to be explored. Latest studies demonstrate that students with high grit levels, stamina in pursuing long term objectives, achieve better results in learning. In our study we propose a theoretical model studying grit as a determinant of e-learning systems success. The study was conducted on a sample of university students' sample, and a theoretical model was validated using structural equation modeling (SEM). Results indicate that grit has positive effects on satisfaction and in students' individual performance. The results help to improve e-learning strategies and to understand e-learning success, explained by students skills and systems characteristics. Display Omitted Study about Grit impact on the use and satisfaction of e-learning systems.Study supports Grit as a second-order construct of perseverance and consistency.Non-cognitive student's characteristic determines information systems' success.E-learning success determined by Grit, information, system, and service quality.


title: Assessment of Gender Variance in Children

abstract: This article focuses on the assessment of children who display gender-atypical behavior and, perhaps, identity. The aim is to provide an overview of assessment techniques that can be used clinically with children who show this behavioral pattern and that complement the routine use of DSM-IV criteria for the diagnosis of gender identity disorder in clinical practice. It will also provide an overview of some common approaches to the assessment of other types of behavioral and socioemotional issues that may require clinical attention in these youngsters.


title: Robust LSTM-Autoencoders for Face De-Occlusion in the Wild

abstract: Face recognition techniques have been developed significantly in recent years. However, recognizing faces with partial occlusion is still challenging for existing face recognizers, which is heavily desired in real-world applications concerning surveillance and security. Although much research effort has been devoted to developing face de-occlusion methods, most of them can only work well under constrained conditions, such as all of faces are from a pre-defined closed set of subjects. In this paper, we propose a robust LSTM-Autoencoders (RLA) model to effectively restore partially occluded faces even in the wild. The RLA model consists of two LSTM components, which aims at occlusion-robust face encoding and recurrent occlusion removal respectively. The first one, named multi-scale spatial LSTM encoder, reads facial patches of various scales sequentially to output a latent representation, and occlusion-robustness is achieved owing to the fact that the influence of occlusion is only upon some of the patches. Receiving the representation learned by the encoder, the LSTM decoder with a dual channel architecture reconstructs the overall face and detects occlusion simultaneously, and by feat of LSTM, the decoder breaks down the task of face de-occlusion into restoring the occluded part step by step. Moreover, to minimize identify information loss and guarantee face recognition accuracy over recovered faces, we introduce an identity-preserving adversarial training scheme to further improve RLA. Extensive experiments on both synthetic and real data sets of faces with occlusion clearly demonstrate the effectiveness of our proposed RLA in removing different types of facial occlusion at various locations. The proposed method also provides significantly larger performance gain than other de-occlusion methods in promoting recognition performance over partially-occluded faces.


title: Hyperspherical Prototype Networks

abstract: This paper introduces hyperspherical prototype networks, which unify classification and regression with prototypes on hyperspherical output spaces. For classification, a common approach is to define prototypes as the mean output vector over training examples per class. Here, we propose to use hyperspheres as output spaces, with class prototypes defined a priori with large margin separation. We position prototypes through data-independent optimization, with an extension to incorporate priors from class semantics. By doing so, we do not require any prototype updating, we can handle any training size, and the output dimensionality is no longer constrained to the number of classes. Furthermore, we generalize to regression, by optimizing outputs as an interpolation between two prototypes on the hypersphere. Since both tasks are now defined by the same loss function, they can be jointly trained for multi-task problems. Experimentally, we show the benefit of hyperspherical prototype networks for classification, regression, and their combination over other prototype methods, softmax cross-entropy, and mean squared error approaches.


title: The Real-World Reading and Writing U.S. Children Need:

abstract: The 2001 Progress in International Reading Literacy (PIRLS) study--a study of the reading achievement of mostly 10-year-old students in 35 nations--found that U.S. students had the largest gap between literary reading achievement and informational reading achievement of any nation studied (Mullis et al. 2003), with the gap favoring literary reading. In the 2006 study, this gap narrowed somewhat, but average scores of U.S. students were statistically unchanged (Mullis et al. 2007). What causes this? In this "information age," why are U.S. students relatively poor at reading to acquire and use real-world information (informational texts) when compared to reading to experience imagined worlds (literary texts)? Some might say that informational reading is inherently more difficult than literary reading, but many nations had comparable literary and informational reading achievement, and some even had stronger informational achievement, so that explanation seems insufficient. Others might say that it's the nature of the particular reading assessment used in the PIRLS study, but studies using other assessments have also shown that U.S. students struggle with comprehension of informational text as compared to narrative or literary reading. A more plausible explanation is that U.S. students have little experience with informational text in the early years of schooling. In fact, they get very little. In a study I conducted in U.S. 1st-grade classrooms (Duke 2000), I found that only 9.8% of the books and other materials in classroom libraries and only 2.6% of the materials on classroom walls and other surfaces were informational text. More important, important, only 3.6 minutes of the classroom day, on average, were devoted to informational text--only 1.9 minutes in classrooms in low-socioeconomic-status settings. Studies that have looked at this issue in other ways--for example, by examining basal reading programs--have also found a relative lack of informational text (e.g., Moss and Newton 2002). When I tell others about this finding, a common response is that the focus of primary grade education should be on developing decoding and fluency, not comprehension and certainly not comprehension of informational text. However, we found that 1st graders whose teachers provided greater experience with informational text had decoding and spelling skills comparable to those of other students (Duke et al. 2009). Another response is that informational text is simply too hard, perhaps even developmentally inappropriate, for young children. This stance has long been noted, and countered, in research (Duke and Kays 1998; Newkirk 1987; Pappas 1993). Young children can be successful with informational text--in fact, some studies in this area (Maduram 2000; Tower 2002) have included three-and four-year-old children! There are some indications that attention to informational text in early schooling is increasing. For example, in 2008, Moss found more informational text in basal reading programs than in the past. This is encouraging-informational reading ability is arguably more important now than it ever has been. Reflecting this, the 2009 National Assessment of Educational Progress (NAEP) Framework calls for 50% informational passages by 4th grade, 55% by 8th grade, and 70% by 12th grade (National Assessment Governing Board 2007). In sum, there is little question about whether we should involve informational text in early schooling; the question now is how. THE USUAL SUSPECTS So far, most instructional practices shown to help develop young children's comprehension of informational text come as no surprise. For example, just as teaching young children the structure of stories has long been shown to improve their comprehension of stories, so, too, does teaching young children the structures of informational text (specifically, compare-contrast and cause-effect) improve their comprehension of these texts (Williams 2005, with 2nd graders). …


title: Predictive and statistical analyses for academic advisory support

abstract: The ability to recognize weakness of students and solving any problem may confront them in timely fashion is always a target of all educational institutions. This study was designed to explore how can predictive and statistical analysis support the academic work of adviser mainly in analysis progress of students . The sample consisted of a total of 249 undergraduate students: 46 % of them were Female and 54% Male. A one-way analysis of variance and t-test were conducted to analysis if there was different behavior in registering courses. Predictive data mining is used for support adviser in decision making. Several classification techniques with 10-fold Cross validation were applied. Among of them, C 4.5 constitutes the best agreement among the finding results.


title: Interpretable and Globally Optimal Prediction for Textual Grounding using Image Concepts

abstract: Textual grounding is an important but challenging task for human-computer interaction, robotics and knowledge mining. Existing algorithms generally formulate the task as selection from a set of bounding box proposals obtained from deep net based systems. In this work, we demonstrate that we can cast the problem of textual grounding into a unified framework that permits efficient search over all possible bounding boxes. Hence, the method is able to consider significantly more proposals and doesn't rely on a successful first stage hypothesizing bounding box proposals. Beyond, we demonstrate that the trained parameters of our model can be used as word-embeddings which capture spatial-image relationships and provide interpretability. Lastly, at the time of submission, our approach outperformed the current state-of-the-art methods on the Flickr 30k Entities and the ReferItGame dataset by 3.08% and 7.77% respectively.


title: Broadband Capacitively Coupled Stacked Patch Antenna for GNSS Applications

abstract: A broadband stacked circular patch antenna is presented in this letter. The broadband characteristic is achieved by employing capacitively coupled feed structure. The antenna is fed by a wideband four-output-ports feed network with equal magnitude and consistent 90° phase shift. The final antenna provides very good circularly polarized radiation for Global Navigation Satellite System applications including GPS, GLONASS, Galileo, and Compass.