title: Discriminative Unsupervised Alignment of Natural Language Instructions with Corresponding Video Segments

abstract: We address the problem of automatically aligning natural language sentences with corresponding video segments without any direct supervision. Most existing algorithms for integrating language with videos rely on handaligned parallel data, where each natural language sentence is manually aligned with its corresponding image or video segment. Recently, fully unsupervised alignment of text with video has been shown to be feasible using hierarchical generative models. In contrast to the previous generative models, we propose three latent-variable discriminative models for the unsupervised alignment task. The proposed discriminative models are capable of incorporating domain knowledge, by adding diverse and overlapping features. The results show that discriminative models outperform the generative models in terms of alignment accuracy.


title: A novel approach to mechanical design of articulated fingers for robotic hands

abstract: The paper first discusses the reasons why simplified solutions for the mechanical structure of fingers in robotic hands should be considered a worthy design goal. After a brief discussion about the mechanical solutions proposed so far for robotic fingers, a different design approach is proposed. It considers finger structures made of rigid links connected by flexural hinges, with joint actuation obtained by means of flexures that can be guided inside each finger according to different patterns. A simplified model of one of these structures is then presented, together with preliminary results of simulation, in order to evaluate the feasibility of the concept. Examples of technological implementation are finally presented and the perspective and problems of application are briefly discussed.


title: Inet-3 . 0 : Internet Topology Generator

abstract: In this report we present version 3.0 of Inet, an Autonomous System (AS) level Internet topology generator. Our understanding of the Internet topology is quickly evolving, and thus, our understanding of how synthetic topologies should be generated is changing too. We document our analysis of Inet2.2, which highlighted two shortcommings in its topologies. Inet-3.0 improves upon Inet-2.2’s two main weaknesses by creating topologies with more accurate degree distributions and minimum vertex covers as compared to Internet topologies. We also examine numerous other metrics to show that Inet3.0 better approximates the actual Internet AS topology than does Inet-2.2. Inet-3.0’s topologies still do not well represent the Internet in terms of maximum clique size and clustering coefficient. These related problems stress a need for a better understanding of Internet connectivity and will be addressed in future work.


Subgraph Pattern Neural Networks for High-Order Graph Evolution Prediction


title: Design and analysis of a touch mode MEMS capacitive pressure sensor for IUPC

abstract: In this paper, a novel design of diaphragm based pressure sensor has been proposed, which may find its application in Intra Uterine Pressure Catheter (IUPC) system. The novelty of the device lies in the fact that it has got patterned slots over the top layer of the diaphragm similar to a chess-board pattern. This sensor is then compared with a plane diaphragm of same dimensions. In same range of input pressure, results show that the plane diaphragm has capacitive sensitivity of 0.06385 pF/kPa, while in the slotted diaphragm, it is 0.17355 pF/kPa, three times as that of the plane one. However, the static pull-in voltage comes out to be almost 16 volt due to constant air gap of 0.4 μm for both the cases. Due to pull-in voltage in the range of 11–20 volts, it can also be used as an electrostatic actuator in switching networks.


Cell‐free fetal DNA: the new tool in fetal medicine


title: Evaluating event credibility on twitter

abstract: Though Twitter acts as a realtime news source with people acting as sensors and sending event updates from all over the world, rumors spread via Twitter have been noted to cause considerable damage. Given a set of popular Twitter events along with related users and tweets, we study the problem of automatically assessing the credibility of such events. We propose a credibility analysis approach enhanced with event graph-based optimization to solve the problem. First we experiment by performing PageRanklike credibility propagation on a multi-typed network consisting of events, tweets, and users. Further, within each iteration, we enhance the basic trust analysis by updating event credibility scores using regularization on a new graph of events. Our experiments using events extracted from two tweet feed datasets, each with millions of tweets show that our event graph optimization approach outperforms the basic credibility analysis approach. Also, our methods are significantly more accurate (∼86%) than the decision tree classifier approach (∼72%).


title: Coordinated reset neuromodulation for Parkinson's disease: Proof-of-concept study

abstract: Background ::: ::: The discovery of abnormal synchronization of neuronal activity in the basal ganglia in Parkinson's disease (PD) has prompted the development of novel neuromodulation paradigms. Coordinated reset neuromodulation intends to specifically counteract excessive synchronization and to induce cumulative unlearning of pathological synaptic connectivity and neuronal synchrony. ::: ::: ::: ::: Methods ::: ::: In this prospective case series, six PD patients were evaluated before and after coordinated reset neuromodulation according to a standardized protocol that included both electrophysiological recordings and clinical assessments. ::: ::: ::: ::: Results ::: ::: Coordinated reset neuromodulation of the subthalamic nucleus (STN) applied to six PD patients in an externalized setting during three stimulation days induced a significant and cumulative reduction of beta band activity that correlated with a significant improvement of motor function. ::: ::: ::: ::: Conclusions ::: ::: These results highlight the potential effects of coordinated reset neuromodulation of the STN in PD patients and encourage further development of this approach as an alternative to conventional high-frequency deep brain stimulation in PD. © 2014 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.


title: Deep Networks with Stochastic Depth

abstract: Very deep convolutional networks with hundreds of layers have led to significant reductions in error on competitive benchmarks. Although the unmatched expressiveness of the many layers can be highly desirable at test time, training very deep networks comes with its own set of challenges. The gradients can vanish, the forward flow often diminishes, and the training time can be painfully slow. To address these problems, we propose stochastic depth, a training procedure that enables the seemingly contradictory setup to train short networks and use deep networks at test time. We start with very deep networks but during training, for each mini-batch, randomly drop a subset of layers and bypass them with the identity function. This simple approach complements the recent success of residual networks. It reduces training time substantially and improves the test error significantly on almost all data sets that we used for evaluation. With stochastic depth we can increase the depth of residual networks even beyond 1200 layers and still yield meaningful improvements in test error (4.91 % on CIFAR-10).


title: Lifting Object Detection Datasets into 3D

abstract: While data has certainly taken the center stage in computer vision in recent years, it can still be difficult to obtain in certain scenarios. In particular, acquiring ground truth 3D shapes of objects pictured in 2D images remains a challenging feat and this has hampered progress in recognition-based object reconstruction from a single image. Here we propose to bypass previous solutions such as 3D scanning or manual design, that scale poorly, and instead populate object category detection datasets semi-automatically with dense, per-object 3D reconstructions, bootstrapped from:(i) class labels, (ii) ground truth figure-ground segmentations and (iii) a small set of keypoint annotations. Our proposed algorithm first estimates camera viewpoint using rigid structure-from-motion and then reconstructs object shapes by optimizing over visual hull proposals guided by loose within-class shape similarity assumptions. The visual hull sampling process attempts to intersect an object's projection cone with the cones of minimal subsets of other similar objects among those pictured from certain vantage points. We show that our method is able to produce convincing per-object 3D reconstructions and to accurately estimate cameras viewpoints on one of the most challenging existing object-category detection datasets, PASCAL VOC. We hope that our results will re-stimulate interest on joint object recognition and 3D reconstruction from a single image.


title: Ontological Semantics for Data Privacy Compliance: The NEURONA Project

abstract: This paper describes the analysis of the requirements and the knowledge acquisition process for the development of a legal ontology for the representation of data protection knowledge in the framework of the NEURONA project. This modular ontology is used in the NEURONA application to reason about the correctness of the measures of protection applied to these data files by an organization. In this sense the use of legal ontologies could not only provide legal professionals and citizens with better access to legal information, but could also support data protection and privacy compliance in organizations and administrations.


title: Image Inpainting by Patch Propagation Using Patch Sparsity

abstract: This paper introduces a novel examplar-based inpainting algorithm through investigating the sparsity of natural image patches. Two novel concepts of sparsity at the patch level are proposed for modeling the patch priority and patch representation, which are two crucial steps for patch propagation in the examplar-based inpainting approach. First, patch structure sparsity is designed to measure the confidence of a patch located at the image structure (e.g., the edge or corner) by the sparseness of its nonzero similarities to the neighboring patches. The patch with larger structure sparsity will be assigned higher priority for further inpainting. Second, it is assumed that the patch to be filled can be represented by the sparse linear combination of candidate patches under the local patch consistency constraint in a framework of sparse representation. Compared with the traditional examplar-based inpainting approach, structure sparsity enables better discrimination of structure and texture, and the patch sparse representation forces the newly inpainted regions to be sharp and consistent with the surrounding textures. Experiments on synthetic and natural images show the advantages of the proposed approach.


title: Cross-Camera Knowledge Transfer for Multiview People Counting

abstract: We present a novel two-pass framework for counting the number of people in an environment, where multiple cameras provide different views of the subjects. By exploiting the complementary information captured by the cameras, we can transfer knowledge between the cameras to address the difficulties of people counting and improve the performance. The contribution of this paper is threefold. First, normalizing the perspective of visual features and estimating the size of a crowd are highly correlated tasks. Hence, we treat them as a joint learning problem. The derived counting model is scalable and it provides more accurate results than existing approaches. Second, we introduce an algorithm that matches groups of pedestrians in images captured by different cameras. The results provide a common domain for knowledge transfer, so we can work with multiple cameras without worrying about their differences. Third, the proposed counting system is comprised of a pair of collaborative regressors. The first one determines the people count based on features extracted from intracamera visual information, whereas the second calculates the residual by considering the conflicts between intercamera predictions. The two regressors are elegantly coupled and provide an accurate people counting system. The results of experiments in various settings show that, overall, our approach outperforms comparable baseline methods. The significant performance improvement demonstrates the effectiveness of our two-pass regression framework.


Investing in health systems for universal health coverage in Africa


title: Energy Efficient Joint Resource Allocation and Power Control for D2D Communications

abstract: In this paper, joint resource allocation and power control for energy-efficient device-to-device (D2D) communications underlaying cellular networks are investigated. The resource and power are optimized for maximization of the energy efficiency (EE) of D2D communications. Exploiting the properties of fractional programming, we transform the original nonconvex optimization problem in fractional form into an equivalent optimization problem in subtractive form. Then, an efficient iterative resource allocation and power control scheme is proposed. In each iteration, part of the constraints of the EE optimization problem are removed by exploiting the penalty function approach. We further propose a novel two-layer approach, which allows finding the optimum at each iteration by decoupling the EE optimization problem of joint resource allocation and power control into two separate steps. In the first layer, the optimal power values are obtained by solving a series of maximization problems through root finding, with or without considering the loss of cellular users' rates. In the second layer, the formulated optimization problem belongs to a classical resource-allocation problem with single allocation format, which admits a network flow formulation so that it can be solved to optimality. Simulation results demonstrate the remarkable improvements in terms of EE by using the proposed iterative resource allocation and power control scheme.


title: A hierarchical maze navigation algorithm with Reinforcement Learning and mapping

abstract: Goal-finding in an unknown maze is a challenging problem for a Reinforcement Learning agent, because the corresponding state space can be large if not intractable, and the agent does not usually have a model of the environment. Hierarchical Reinforcement Learning has been shown in the past to improve tractability and learning time of complex problems, as well as facilitate learning a coherent transition model for the environment. Nonetheless, considerable time is still needed to learn the transition model, so that initially the agent can perform poorly by getting trapped into dead ends and colliding with obstacles. This paper proposes a strategy for maze exploration that, by means of sequential tasking and off-line training on an abstract environment, provides the agent with a minimal level of performance from the very beginning of exploration. In particular, this approach allows to prevent collisions with obstacles, thus enforcing a safety restraint on the agent.


title: From eye movements to actions: how batsmen hit the ball

abstract: In cricket, a batsman watches a fast bowler's ball come toward him at a high and unpredictable speed, bouncing off ground of uncertain hardness. Although he views the trajectory for little more than half a second, he can accurately judge where and when the ball will reach him. Batsmen's eye movements monitor the moment when the ball is released, make a predictive saccade to the place where they expect it to hit the ground, wait for it to bounce, and follow its trajectory for 100−200 ms after the bounce. We show how information provided by these fixations may allow precise prediction of the ball's timing and placement. Comparing players with different skill levels, we found that a short latency for the first saccade distinguished good from poor batsmen, and that a cricket player's eye movement strategy contributes to his skill in the game.


title: Efficient Secure Outsourcing of Genome-Wide Association Studies

abstract: A genome-wide association study aimed at finding genetic variations associated with a particular disease is a common approach used in genetic epidemiology. We present a new efficient secure outsourcing computation of GWAS using homomorphic encryption based on ring-LWE. Our method works by virtue of the fact that integer vectors can be packed into a single cipher text and a scalar product of integer vectors can be evaluated using a single homomorphic multiplication. We demonstrate by experimentation that secure outsourcing computation of a ?2 test for independence with 5,000 samples can be processed in one second including communication time, which is 250 times faster than an existing FHE solution.


HUMAN WAYFINDING IN UNFAMILIAR BUILDINGS: A SIMULATION WITH A COGNIZING AGENT


title: FOSTERING INFORMATION SECURITY CULTURE IN SMALL AND MEDIUM SIZE ENTERPRISES: AN INTERPRETIVE STUDY IN AUSTRALIA

abstract: By having an effective organisational information security culture where employees intuitively protect corporate information assets, small and medium size enterprises (SMEs) could improve information security. However, previous research has largely overlooked the development of such a culture for SMEs, and the national context in which SMEs operate. The paper explores this topic and provides key findings from an interpretive Australian study based on a literature review, two focus groups and three case studies. A holistic framework is provided for fostering an information security culture in SMEs in a national setting. The paper discusses key managerial challenges for SMEs attempting to develop such a culture. The main findings suggest that Australian SME owners do not provide sufficient support for information security due to insufficient awareness of its importance and may also be affected by national attitudes to risk. The paper concludes that Australian SME owners may benefit from adopting a risk-based approach to information security and should be educated about the potential strategic role of information technology and information security. The paper also identifies the value and difficulty of promoting a behavioural and learning approach to information security to complement traditional technological and managerial approaches. Implications for theory and practice are discussed.


title: Taxonomy, technology and applications of smart objects

abstract: Deployment of embedded technologies is increasingly being examined in industrial supply chains as a means for improving efficiency through greater control over purchase orders, inventory and product related information. Central to this development has been the advent of technologies such as bar codes, Radio Frequency Identification (RFID) systems, and wireless sensors which when attached to a product, form part of the product's embedded systems infrastructure. The increasing integration of these technologies dramatically contributes to the evolving notion of a "smart product", a product which is capable of incorporating itself into both physical and information environments. The future of this revolution in objects equipped with smart embedded technologies is one in which objects can not only identify themselves, but can also sense and store their condition, communicate with other objects and distributed infrastructures, and take decisions related to managing their life cycle. The object can essentially "plug" itself into a compatible systems infrastructure owned by different partners in a supply chain. However, as in any development process that will involve more than one end user, the establishment of a common foundation and understanding is essential for interoperability, efficient communication among involved parties and for developing novel applications. In this paper, we contribute to creating that common ground by providing a characterization to aid the specification and construction of "smart objects" and their underlying technologies. Furthermore, our work provides an extensive set of examples and potential applications of different categories of smart objects.


title: Parallel CRC Computation in FPGAs

abstract: This paper presents how to compute n-bit CRC checksums on FPGAs in parallel. For this task, a specialized logic minimization strategy is outlined. It achieves significantly better results than standard logic optimizers. For n≥96, CRC designs with an n-bit I/O interface are poorly routable. However, for smaller I/O interfaces even a 128-bit CRC can be implemented.


title: Bayesian Input Variable Selection Using Cross-Validation Predictive Densities and Reversible Jump MCMC

abstract: We consider the problem of input variable selection of a Bayesian model. With suitable priors it is possible to have a large number of input variables in Bayesian models, as less relevant inputs can have a smaller effect in the model. To make the model more explainable and easier to analyse, or to reduce the cost of making measurements or the cost of computation, it may be useful to select a smaller set of input variables. Our goal is to find a model with the smallest number of input variables having statistically or practically the same expected utility as the full model. A good estimate for the expected utility, with any desired utility, can be computed using cross-validation predictive densities (Vehtari and Lampinen, 2001). In the case of input selection, there are 2 K input combinations and computing the cross-validation predictive densities for each model easily becomes computationally prohibitive. We propose to use the reversible jump Markov chain Monte Carlo (RJMCMC) method to find out potentially useful input combinations, for which the final model choice and assessment is done using the cross-validation predictive densities. The RJMCMC visits the models according to their posterior probabilities. As models with negligible probability are probably not visited in finite time, the computational savings can be considerable compared to going through all possible models. The posterior probabilities of the models, given by the RJMCMC, are proportional to the product of the prior probabilities of the models and the prior predictive likelihoods of the models. The prior predictive likelihood measures the goodness of the model if no training data were used, and thus can be used to estimate the lower limit of the expected predictive likelihood. These estimates indicate potentially useful models, for which better estimates of the expected utilities can then be computed using the cross-validation predictive densities. If, due to the size of the problem, the RJMCMC has problems of obtaining enough samples in reasonable time to estimate the probabilities of the models well, we propose to use the marginal posterior probabilities of the inputs to select potentially useful inputs. We also discuss why the posterior probabilities of the models (or inputs) given by the RJMCMC should not be used directly for input selection. As illustrative examples we use MLP neural networks and Gaussian processes (GP) in two real world problems. The first problem was a concrete quality prediction problem, where the primary goal was to make models more explainable. Depending on the target variable, six to nine input variables were chosen from a set of 27‐28 input variables. The predictive capability remained statistically the same as the predictive capability of the full model. Additional benefit was the reduced cost of making future measurements. The second problem was a forest scene classification problem, where the primary goal was to decrease computational cost. In the planned application it turned out to be sufficient to use just 10 out of 84 input variables, without classification rate dropping too much. In this case, the input selection was guided by the marginal posterior probabilities of the inputs.


title: LOW-BIT-RATE SPEECH CODING

abstract: Low-bit-rate speech coding, at rates below 4 kb/s, is needed for both communication and voice storage applications. At such low rates, full encoding of the speech waveform is not possible; therefore, low-rate coders rely instead on parametric models to represent only the most perceptually relevant aspects of speech. While there are a number of different approaches for this modeling, all can be related to the basic linear model of speech production, where an excitation signal drives a vocal-tract filter.


title: Continuous Cuffless Blood Pressure Estimation Using Pulse Transit Time and Photoplethysmogram Intensity Ratio

abstract: Pulse transit time (PTT) has attracted much interest for cuffless blood pressure (BP) measurement. However, its limited accuracy is one of the main problems preventing its widespread acceptance. Arterial BP oscillates mainly at high frequency (HF) because of respiratory activity, and at low frequency (LF) because of vasomotor tone. Prior studies suggested that PTT can track BP variation in HF range, but was inadequate to follow the LF variation, which is probably the main reason for its unsatisfactory accuracy. This paper presents a new indicator, the photoplethysmogram intensity ratio (PIR), which can be affected by changes in the arterial diameter, and, thus, trace the LF variation of BP. Spectral analysis of BP, PTT, PIR, and respiratory signal confirmed that PTT was related to BP in HF at the respiratory frequency, while PIR was associated with BP in LF range. We, therefore, develop a novel BP estimation algorithm by using both PTT and PIR. The proposed algorithm was validated on 27 healthy subjects with continuous Finapres BP as reference. The results showed that the mean ± standard deviation (SD) for the estimated systolic, diastolic, and mean BP with the proposed method against reference were $-0.37 \pm 5.21$ , $-0.08 \pm 4.06$ , $-0.18 \pm 4.13$ mmHg, and mean absolute difference (MAD) were 4.09, 3.18, 3.18 mmHg, respectively. Furthermore, the proposed method outperformed the two most cited PTT algorithms for about 2 mmHg in SD and MAD. These results demonstrated that the proposed BP model using PIR and PTT can estimate continuous BP with improved accuracy.


title: Retrieval-Based Learning: Positive Effects of Retrieval Practice in Elementary School Children

abstract: A wealth of research has demonstrated that practicing retrieval is a powerful way to enhance learning. However, nearly all prior research has examined retrieval practice with college students. Little is known about retrieval practice in children, and even less is known about possible individual differences in retrieval practice. In three experiments, 88 children (mean age 10 years) studied a list of words and either restudied the items or practiced retrieving them. They then took a final free recall test (Experiments 1 and 2) or recognition test (Experiment 3). In all experiments, children showed robust retrieval practice effects. Although a range of individual differences in reading comprehension and processing speed were observed among these children, the benefits of retrieval practice were independent of these factors. The results contribute to the growing body of research supporting the mnemonic benefits of retrieval practice and provide preliminary evidence that practicing retrieval may be an effective learning strategy for children with varying levels of reading comprehension and processing speed.


title: Generic ontology of datatypes

abstract: We present OntoDT, a generic ontology for the representation of scientific knowledge about datatypes. OntoDT defines basic entities, such as datatype, properties of datatypes, specifications, characterizing operations, and a datatype taxonomy. We demonstrate the utility of OntoDT on several use cases. OntoDT was used within an Ontology of core data mining entities for constructing taxonomies of datasets, data mining tasks, generalizations and data mining algorithms. Furthermore, we show how OntoDT can be used to annotate and query dataset repositories. We also show how OntoDT can improve the representation of datatypes in the BioXSD exchange format for basic bio-informatics types of data. The generic nature of OntoDT enables it to support a wide range of other applications, especially in combination with other domain specific ontologies: the construction of data mining workflows, annotation of software and algorithms, semantic annotation of scientific articles, etc. OntoDT is open source and is available at http://www.ontodt.com.


title: Chemical Scalp Burns Secondary to the Hair Highlighting Process: Clinical and Histopathologic Features

abstract: Hair highlighting procedures are very common throughout the world. While rarely reported, potential adverse events to such procedures include allergic and irritant contact dermatitis, thermal burns, and chemical burns. Herein, we report two cases of female adolescents who underwent a hair highlighting procedure at local salons and sustained a chemical burn to the scalp. The burn etiology, clinical and histologic features, the expected sequelae, and a review of the literature are described. Language: en


title: ViP-CNN: Visual Phrase Guided Convolutional Neural Network

abstract: As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers attention because of its descriptive power and clear structure. It detects the objects and captures their pair-wise interactions with a subject-predicate-object triplet, e.g. person-ride-horse. In this paper, each visual relationship is considered as a phrase with three components. We formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase guided Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Phrase-guided Message Passing Structure (PMPS) to establish the connection among relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the state-of-art method both in speed and accuracy. We further pretrain ViP-CNN on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.


title: A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis

abstract: Abstract Bearing fault diagnosis is a significant part of rotating machine health monitoring. In the era of big data, various data-driven methods, which are mainly based on deep learning (DL), have been applied in the field of fault diagnosis. As a common deep learning method, convolutional neural networks play an important role in the field of image classification. To take advantage of existing research results of convolutional neural networks that were originally designed for images, short-time Fourier transform is applied to convert signals to two-dimensional graphs. However, the traditional convolutional neural network with max-pooling layers may disregard the positional relationship between features. Inspired by dynamic routing capsule net, a novel capsule network with an inception block and a regression branch is proposed. First, during the preprocessing stage, the one-dimensional signals are transformed into a time-frequency graph. Second, the graph data are fed into the network, and two convolution layers are applied to extract higher information; following that, an inception block is applied to the output feature maps to improve the nonlinearity of the capsule. Then, after dynamic routing, the lengths of the capsules are used to classify the fault category. There are two other branches: one uses the longest capsule to regress the damage size of the capsule, and the other reconstructs the input graph. To verify the generalization ability of the proposed model, comparisons among the proposed model, conventional methods and state-of-the-art convolutional neural network models are carried out.


Intrusion Detection System Techniques and Tools : A Survey


title: Collaborative augmented reality

abstract: Blending reality and virtuality, these interfaces let users see each other, along with virtual objects, allowing communication behaviors much more like face-to-face than like screen-based collaboration.


title: 3D‐printed shepp‐logan phantom as a real‐world benchmark for MRI

abstract: Purpose: As prescribed and reliable geometrical entities, phantoms have served as indispensable validation tools in a variety of MR-related topics. Though a number of phantoms have been conceived, certain applications may warrant highly customized geometries. The purpose of this study was to demonstrate the expediency of rapid prototyping for generating a flexible class of MR-compatible phantom designs. Methods: An incarnation of the three-dimensional SheppLogan numerical phantom, amended for use in magnetic resonance spectroscopic imaging, was actualized using rapid prototyping. Each of the comprising compartments was filled with a solution containing prepared concentrations of common H-1 brain metabolites. Analytical Fourier expressions for the phantom class were established in order to generate a set of simulated measurements, which were then contrasted with acquired data. Results: Experimental results for both structural and spectroscopic imaging substantiate the suitability of rapid prototyping for MR phantom applications. The analytically simulated measurements show excellent agreement with the measured data, but also highlight the various consequences effectuated when certain aspects of the acquisition model are disregarded or misrepresented. Conclusion: Rapid prototyping offers a novel and versatile framework for MR phantom-based validation studies. Furthermore, the growing accessibility and open-source compatibility may provide an important link between the often disparate numerical and haptic testing. (C) 2015 Wiley Periodicals, Inc.


title: Wideband mm-wave phase shifters based on constant-impedance tunable transmission lines

abstract: This paper presents an integrated inverted stripline-like tunable transmission line structure where the propagation velocity can be modified as the characteristic impedance remains constant. As one application of this structure, a mm-wave phase shifter for massive hybrid MIMO applications is implemented in a 45 nm CMOS SOI process. Measurement results at 45 GHz of this phase shifter demonstrate a 79° phase shift tuning range, worst-case insertion loss of 3.3 dB, and effective area of 0.072 mm2. Compared to an on-chip reference phase shifter implemented based on a previously-reported tunable transmission line structure, this work achieves 35% less area occupied and 1.0 dB less insertion loss, while maintaining approximately the same phase shift tuning range.


10-401 Project Final Report : Algorhythms


title: Asirra: a CAPTCHA that exploits interest-aligned manual image categorization

abstract: We present Asirra (Figure 1), a CAPTCHA that asks users to identify cats out of a set of 12 photographs of both cats and dogs. Asirra is easy for users; user studies indicate it can be solved by humans 99.6% of the time in under 30 seconds. Barring a major advance in machine vision, we expect computers will have no better than a 1/54,000 chance of solving it. Asirra’s image database is provided by a novel, mutually beneficial partnership with Petfinder.com. In exchange for the use of their three million images, we display an “adopt me” link beneath each one, promoting Petfinder’s primary mission of finding homes for homeless animals. We describe the design of Asirra, discuss threats to its security, and report early deployment experiences. We also describe two novel algorithms for amplifying the skill gap between humans and computers that can be used on many existing CAPTCHAs.


title: A bridgeless PFC converter for on-board battery charger

abstract: This paper presents a bridgeless SEPIC topology with Power Factor Correction (PFC) to charge electric vehicle battery. The converter offers a few advantages such as reduce total component count and reduction of conduction loss during the conversion AC to DC, hence higher overall system efficiency. The PFC converter operated under continuous conduction mode (CCM) with average current mode control strategy. The model is built and simulated in MATLAB. From the simulation study, it is found that the proposed topology and the proposed control strategy provide a promising result.


title: Multi-input bidirectional DC-DC converter combining DC-link and magnetic-coupling for fuel cell systems

abstract: This paper presents a new multi-input bidirectional dc-dc converter which connects a fuel cell, storage and load by a combination of a dc-link and magnetic-coupling. A boost-dual-half-bridge and a bidirectional direct-connected switching cell are used. The topology is simple and only needs six power switches. The load and the sources are galvanically isolated. Furthermore, the proposed converter draws/injects smooth current from the fuel cell and the supercapacitor. The system is suitable for medium-power applications where simple topology, autonomous operation, compact packaging, and low cost are required. Different control schemes to manage power flow are proposed and compared. Simulation and experimental results that verify the effectiveness of the topology and its control scheme are presented. Moreover, it is shown that the idea of combining the dc-link and magnetic-coupling can be further developed to a general topology for multi-port bidirectional dc-dc converters.


title: Monitoring temporal properties of continuous signals

abstract: In this paper we introduce a variant of temporal logic tailored for specifying desired properties of continuous signals. The logic is based on a bounded subset of the real-time logic mitl, augmented with a static mapping from continuous domains into propositions. From formulae in this logic we create automatically property monitors that can check whether a given signal of bounded length and finite variability satisfies the property. A prototype implementation of this procedure was used to check properties of simulation traces generated by Matlab/Simulink.


title: Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy

abstract: Measurements of forest structure are important for wildlife habitat management. An optimal strategy for mapping forest structure would include detailed measurements of the vertical dimension, which are traditionally provided by field sampling, together with the broad spatial coverage afforded by remote sensing. While no single sensor is capable of delivering this at the present time, it should be possible to combine information from multiple sensors to achieve a reasonable approximation. In this study, we compare estimates of forest structural metrics derived from remote sensing to measurements obtained in the field (large tree maximum canopy height, mean canopy height, standard deviation canopy height, and biomass). We then statistically combine structural information from LiDAR, RaDAR, and passive optical sensors in an attempt to improve accuracy of our estimates. The results of this study indicate that LiDAR is the best single sensor for estimating canopy height and biomass. The addition of ETM+ metrics significantly improved LiDAR estimates of large tree structure, while Quickbird and InSAR/SAR improved estimates either marginally or not at all. The combination of all sensors was more accurate than LiDAR alone, but only marginally better than the combination of LiDAR and ETM+. Structure metrics from LiDAR and RaDAR are essentially redundant, as are ETM+ and Quickbird.


title: Potential and Pitfalls of Domain-Specific Information Extraction at Web Scale

abstract: In many domains, a plethora of textual information is available on the web as news reports, blog posts, community portals, etc. Information extraction (IE) is the default technique to turn unstructured text into structured fact databases, but systematically applying IE techniques to web input requires highly complex systems, starting from focused crawlers over quality assurance methods to cope with the HTML input to long pipelines of natural language processing and IE algorithms. Although a number of tools for each of these steps exists, their seamless, flexible, and scalable combination into a web scale end-to-end text analytics system still is a true challenge. In this paper, we report our experiences from building such a system for comparing the "web view" on health related topics with that derived from a controlled scientific corpus, i.e., Medline. The system combines a focused crawler, applying shallow text analysis and classification to maintain focus, with a sophisticated text analytic engine inside the Big Data processing system Stratosphere. We describe a practical approach to seed generation which led us crawl a corpus of ~1 TB web pages highly enriched for the biomedical domain. Pages were run through a complex pipeline of best-of-breed tools for a multitude of necessary tasks, such as HTML repair, boilerplate detection, sentence detection, linguistic annotation, parsing, and eventually named entity recognition for several types of entities. Results are compared with those from running the same pipeline (without the web-related tasks) on a corpus of 24 million scientific abstracts and a third corpus made of ~250K scientific full texts. We evaluate scalability, quality, and robustness of the employed methods and tools. The focus of this paper is to provide a large, real-life use case to inspire future research into robust, easy-to-use, and scalable methods for domain-specific IE at web scale.


title: Design of Power-Rail ESD Clamp With Dynamic Timing-Voltage Detection Against False Trigger During Fast Power-ON Events

abstract: The RC -based power-rail electrostatic discharge (ESD) clamp with nMOS of large size has been widely utilized to enhance the ESD robustness of CMOS integrated circuits. However, such circuit design that only detects the rising time of ESD pulse may be accidentally triggered in some conditions, such as fast power-ON, hot-plug, and envelope tracking applications. In this paper, a new power-rail ESD clamp circuit with transient and voltage detection function has been proposed and implemented in a 0.18- $\mu \text{m}$ 1.8-V CMOS technology. The measurement results from the silicon chip have demonstrated that the new proposed power-rail ESD clamp circuit with adjustable minimum starting voltage ( ${V}_{\text {starting}}$ ) can achieve good ESD robustness and avoid triggering under fast power-ON condition. In addition, the proposed circuit has a low standby leakage current of 270 nA at 125 °C under normal power-ON condition.


title: A Note on Learning Algorithms for Quadratic Assignment with Graph Neural Networks

abstract: Inverse problems correspond to a certain type of optimization problems formulated over appropriate input distributions. Recently, there has been a growing interest in understanding the computational hardness of these optimization problems, not only in the worst case, but in an average-complexity sense under this same input distribution. ::: In this revised note, we are interested in studying another aspect of hardness, related to the ability to learn how to solve a problem by simply observing a collection of previously solved instances. These 'planted solutions' are used to supervise the training of an appropriate predictive model that parametrizes a broad class of algorithms, with the hope that the resulting model will provide good accuracy-complexity tradeoffs in the average sense. ::: We illustrate this setup on the Quadratic Assignment Problem, a fundamental problem in Network Science. We observe that data-driven models based on Graph Neural Networks offer intriguingly good performance, even in regimes where standard relaxation based techniques appear to suffer.


title: Dose-response meta-analysis of differences in means

abstract: Meta-analytical methods are frequently used to combine dose-response findings expressed in terms of relative risks. However, no methodology has been established when results are summarized in terms of differences in means of quantitative outcomes. We proposed a two-stage approach. A flexible dose-response model is estimated within each study (first stage) taking into account the covariance of the data points (mean differences, standardized mean differences). Parameters describing the study-specific curves are then combined using a multivariate random-effects model (second stage) to address heterogeneity across studies. The method is fairly general and can accommodate a variety of parametric functions. Compared to traditional non-linear models (e.g. Emax, logistic), spline models do not assume any pre-specified dose-response curve. Spline models allow inclusion of studies with a small number of dose levels, and almost any shape, even non monotonic ones, can be estimated using only two parameters. We illustrated the method using dose-response data arising from five clinical trials on an antipsychotic drug, aripiprazole, and improvement in symptoms in shizoaffective patients. Using the Positive and Negative Syndrome Scale (PANSS), pooled results indicated a non-linear association with the maximum change in mean PANSS score equal to 10.40 (95 % confidence interval 7.48, 13.30) observed for 19.32 mg/day of aripiprazole. No substantial change in PANSS score was observed above this value. An estimated dose of 10.43 mg/day was found to produce 80 % of the maximum predicted response. The described approach should be adopted to combine correlated differences in means of quantitative outcomes arising from multiple studies. Sensitivity analysis can be a useful tool to assess the robustness of the overall dose-response curve to different modelling strategies. A user-friendly R package has been developed to facilitate applications by practitioners.


title: 3D Scene Flow Estimation with a Piecewise Rigid Scene Model

abstract: 3D scene flow estimation aims to jointly recover dense geometry and 3D motion from stereoscopic image sequences, thus generalizes classical disparity and 2D optical flow estimation. To realize its conceptual benefits and overcome limitations of many existing methods, we propose to represent the dynamic scene as a collection of rigidly moving planes, into which the input images are segmented. Geometry and 3D motion are then jointly recovered alongside an over-segmentation of the scene. This piecewise rigid scene model is significantly more parsimonious than conventional pixel-based representations, yet retains the ability to represent real-world scenes with independent object motion. It, furthermore, enables us to define suitable scene priors, perform occlusion reasoning, and leverage discrete optimization schemes toward stable and accurate results. Assuming the rigid motion to persist approximately over time additionally enables us to incorporate multiple frames into the inference. To that end, each view holds its own representation, which is encouraged to be consistent across all other viewpoints and frames in a temporal window. We show that such a view-consistent multi-frame scheme significantly improves accuracy, especially in the presence of occlusions, and increases robustness against adverse imaging conditions. Our method currently achieves leading performance on the KITTI benchmark, for both flow and stereo.


title: Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised Learning

abstract: Recently, graph convolutional network (GCN) has been widely used for semi-supervised classification and deep feature representation on graph-structured data. However, existing GCN generally fails to consider the local invariance constraint in learning and representation process. That is, if two data points Xi and Xj are close in the intrinsic geometry of the data distribution, then their labels/representations should also be close to each other. This is known as local invariance assumption which plays an essential role in the development of various kinds of traditional algorithms, such as dimensionality reduction and semi-supervised learning, in machine learning area. To overcome this limitation, we introduce a graph Laplacian GCN (gLGCN) approach for graph data representation and semi-supervised classification. The proposed gLGCN model is capable of encoding both graph structure and node features together while maintains the local invariance constraint naturally for robust data representation and semi-supervised classification. Experiments show the benefit of the benefits the proposed gLGCN network.


title: Impacts of Channel Reconstruction on Invertebrate Assemblages in a Restored River

abstract: Ecosystem restoration often aims to recreate the physical habitat needed to support a particular life-stage of a focal species. For example, river channel reconstruction, a common restoration practice along the Pacific coast, is typically used to enhance spawning habitat for adult Chinook salmon, a species experiencing large population declines. These restoration efforts rarely consider, however, that altering spawning habitat could have indirect effects on other life-stages, such as juveniles, which might occur if, e.g. reconstruction alters the benthic food web. To determine how channel reconstruction impacts benthic macroinvertebrates, juvenile Chinook’s primary prey, we conducted two studies at a restoration site in the Merced River, California. We asked (1) has gravel enhancement altered invertebrate assemblages in the restored reach compared with an unrestored reach? and, if so, (2) can shifts in the invertebrate community be explained by increased substrate mobility and by reduced heterogeneity that results from restoration? We show that invertebrate abundance and biomass were lower in the restored reach and that these changes were accompanied by a shift from dominance by filter-feeding caddisflies (Hydropsyche) in the unrestored reach to grazing mayflies (Baetis) in the restored reach. Using an in situ manipulation, we demonstrated that this trend was driven by increased substrate mobility that reduces the abundance of Hydropsyche and by decreased substrate heterogeneity that reduces the abundance of Baetis. Our studies suggest that geomorphic changes typical of reconstructed rivers can alter food webs in ways that may have important implications for supporting the focal species of restoration efforts.


title: Accurate realtime full-body motion capture using a single depth camera

abstract: We present a fast, automatic method for accurately capturing full-body motion data using a single depth camera. At the core of our system lies a realtime registration process that accurately reconstructs 3D human poses from single monocular depth images, even in the case of significant occlusions. The idea is to formulate the registration problem in a Maximum A Posteriori (MAP) framework and iteratively register a 3D articulated human body model with monocular depth cues via linear system solvers. We integrate depth data, silhouette information, full-body geometry, temporal pose priors, and occlusion reasoning into a unified MAP estimation framework. Our 3D tracking process, however, requires manual initialization and recovery from failures. We address this challenge by combining 3D tracking with 3D pose detection. This combination not only automates the whole process but also significantly improves the robustness and accuracy of the system. Our whole algorithm is highly parallel and is therefore easily implemented on a GPU. We demonstrate the power of our approach by capturing a wide range of human movements in real time and achieve state-of-the-art accuracy in our comparison against alternative systems such as Kinect [2012].


title: A Reduced Complexity Wallace Multiplier Reduction

abstract: Wallace high-speed multipliers use full adders and half adders in their reduction phase. Half adders do not reduce the number of partial product bits. Therefore, minimizing the number of half adders used in a multiplier reduction will reduce the complexity. A modification to the Wallace reduction is presented that ensures that the delay is the same as for the conventional Wallace reduction. The modified reduction method greatly reduces the number of half adders; producing implementations with 80 percent fewer half adders than standard Wallace multipliers, with a very slight increase in the number of full adders.


title: Model-based anthropometry: Predicting measurements from 3D human scans in multiple poses

abstract: Extracting anthropometric or tailoring measurements from 3D human body scans is important for applications such as virtual try-on, custom clothing, and online sizing. Existing commercial solutions identify anatomical landmarks on high-resolution 3D scans and then compute distances or circumferences on the scan. Landmark detection is sensitive to acquisition noise (e.g. holes) and these methods require subjects to adopt a specific pose. In contrast, we propose a solution we call model-based anthropometry. We fit a deformable 3D body model to scan data in one or more poses; this model-based fitting is robust to scan noise. This brings the scan into registration with a database of registered body scans. Then, we extract features from the registered model (rather than from the scan); these include, limb lengths, circumferences, and statistical features of global shape. Finally, we learn a mapping from these features to measurements using regularized linear regression. We perform an extensive evaluation using the CAESAR dataset and demonstrate that the accuracy of our method outperforms state-of-the-art methods.


title: Discourse as a lens for reframing consideration of learning progressions

abstract: Learning progressions -- descriptions of increasingly sophisticated ways of thinking about a topic - have generated much interest in the science education community, as a means of bridging the chasm between cognitive science research and methods for supporting and assessing student learning. In order to fulfill this promise, learning progressions must be developed and refined based upon a rigorous process that explores the match between the learning progression and available evidence of student learning and is complicated by the nature of students' thinking. This paper explores the implications of reframing consideration of learning progressions using Gee's notion of discourse. This reframing is posited as a means of making sense of students' use and interpretation of words that have both everyday and scientific meanings and their apparent inconsistency in responding to assessment items. Data collected about students' understandings relative to two learning progressions is used to explore this approach.


Forensics in the Year 2000: Competition versus Educational Values.


title: Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks

abstract: Retinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images. Though many approaches have been proposed, existing methods tend to miss fine vessels or allow false positives at terminal branches. Let alone under-segmentation, over-segmentation is also problematic when quantitative studies need to measure the precise width of vessels. In this paper, we present a method that generates the precise map of retinal vessels using generative adversarial training. Our methods achieve dice coefficient of 0.829 on DRIVE dataset and 0.834 on STARE dataset which is the state-of-the-art performance on both datasets.


title: Maximum Torque per Volt operation and stability improvement of PMSM in deep flux-weakening Region

abstract: The stability problem with current vector control in the deep flux-weakening region is analyzed and a new flux-weakening control scheme to operate the PMSM in the MTPV (Maximum Torque per Volt) region is proposed. The proposed control scheme significantly improves stability and increases speed range while maintaining maximum possible torque capability. The transition between different operation regions in the proposed control scheme proves to be smooth. Simulation and experimental tests are carried out to verify the theoretical analysis.


title: Beyond price: how does trust encourage online group's buying intention?

abstract: Purpose – The purpose of this paper is to investigate how online trust affects group shopping intention in the Ihergo web site.Design/methodology/approach – Samples from the Ihergo web site were collected by mailing a questionnaire survey to those who agreed to participate.Findings – Group‐buying operators need to understand their consumers and the scheduling shopping rules between internet shoppers and firms. Moreover, word‐of‐mouth (WOM) can be created online by offering web visitors the ability to access the opinions of satisfied customers.Practical implications – An online business may adopt different methods to enhance its customer satisfaction level. When people enter a significant amount of personal data at a web site, they are typically reluctant to change vendors and enter the data again.Social implications – Customers view a group‐buying operator as a shopping expert, and expect that the group‐buying operator can handle shopping problems before a dispute occurs.Originality/value – The findings o...


title: Occlusion horizons for driving through urban scenery

abstract: We present a rapid occlusion culling algorithm specifically designed for urban environments. For each frame, an occlusion horizon is being built on-the-fly during a hierarchical front-to-back traversal of the scene. All conservatively hidden objects are culled, while all the occluding impostors of all conservatively visible objects are added to the 2½D occlusion horizon. Our framework also supports levels-of-detail (LOD) rendering by estimating the visible area of the projection of an object in order to select the appropriate LOD for each object. This algorithm requires no substantial preprocessing and no excessive storage. In a test scene of 10,000 buildings, the cull phase took 11 ms on a PentiumII 333 MHz and 45 ms on an SGI Octane per frame on average. In typical views, the occlusion horizon culled away 80-90% of the objects that were within the view frustum, giving a 10 times speedup over view frustum culling alone. Combining the occlusion horizon with LOD rendering gave a 17 times speedup on an SGI Octane, and 23 times on a PII. (Less)


Factor-based Compositional Embedding Models


title: Initializing bayesian hyperparameter optimization via meta-learning

abstract: Model selection and hyperparameter optimization is crucial in applying machine learning to a novel dataset. Recently, a sub-community of machine learning has focused on solving this problem with Sequential Model-based Bayesian Optimization (SMBO), demonstrating substantial successes in many applications. However, for computationally expensive algorithms the overhead of hyperparameter optimization can still be prohibitive. In this paper we mimic a strategy human domain experts use: speed up optimization by starting from promising configurations that performed well on similar datasets. The resulting initialization technique integrates naturally into the generic SMBO framework and can be trivially applied to any SMBO method. To validate our approach, we perform extensive experiments with two established SMBO frameworks (Spearmint and SMAC) with complementary strengths; optimizing two machine learning frameworks on 57 datasets. Our initialization procedure yields mild improvements for low-dimensional hyperparameter optimization and substantially improves the state of the art for the more complex combined algorithm selection and hyperparameter optimization problem.


title: Accelerating vector graphics rendering using the graphics hardware pipeline

abstract: We describe our successful initiative to accelerate Adobe Illustrator with the graphics hardware pipeline of modern GPUs. Relying on OpenGL 4.4 plus recent OpenGL extensions for advanced blend modes and first-class GPU-accelerated path rendering, we accelerate the Adobe Graphics Model (AGM) layer responsible for rendering sophisticated Illustrator scenes. Illustrator documents render in either an RGB or CMYK color mode. While GPUs are designed and optimized for RGB rendering, we orchestrate OpenGL rendering of vector content in the proper CMYK color space and accommodate the 5+ color components required. We support both non-isolated and isolated transparency groups, knockout, patterns, and arbitrary path clipping. We harness GPU tessellation to shade paths smoothly with gradient meshes. We do all this and render complex Illustrator scenes 2 to 6x faster than CPU rendering at Full HD resolutions; and 5 to 16x faster at Ultra HD resolutions.


title: AC-grid generation using dual-input hybrid inverter circuit

abstract: Whilst a normal inverter converts the DC voltage into AC voltage, an off grid inverter occupies input from either solar panel or wind turbine. This paper is about the design and simulation of a bi-input inverter using PV and Wind power system. The overall design will result in a regulated AC output load for AC grid connected system. This paper also discusses on two circuits which is charge controller and inverter. It is found that regulated output AC voltage level of 110 V and current 13 A have been successfully simulated using MULTISIM Power Pro Edition 12.0 which is applicable for national 50-Hz transmission grid line.


Testing Theories of Happiness


title: Micropropagation of Eucalyptus grandis × E. urophylla AEC 224 clone

abstract: Genetic transformation systems require protocols that allow regenerating transgenic plants from transformed tissues. This study aimed to establish a protocol for indirect organogenesis in leaf explants of a Eucalyptus grandis × E. urophylla AEC 224 clone. During callogenesis stage, several concentrations of NAA and then NAA or 2,4-D combined with TDZ were tested in JADS culture medium for 30 days, followed by subculture of the explants in the regeneration medium, containing 5.0 µM BA and 0.5 µM NAA for another 30 days. In these media, the explant oxidation rate was high (95 %). Thus, in order to reduce oxidation, different culture media were compared: WPM, MS, JADS and modified QL, followed by explant transfer onto regeneration medium. The highest percentage of regeneration and the lowest oxidation rate were achieved on WPM medium. Then, NAA and 2,4-D were tested in combination with TDZ and also TDZ and BA combined with NAA in WPM medium. The most efficient culture media in terms of shoot regeneration were WPM supplemented with 0.25 µM TDZ and 0.1 µM NAA during 30 days for callus induction and then with 5.0 µM BA and 0.5 µM NAA for another 30 days. This protocol yielded a regeneration rate of 43 %, with a low oxidation of tissues. A rooting experiment was conducted using half strength MS medium and comparing three concentrations of IBA (2.46, 4.90 and 7.35 µM). The highest rooting percentage (35 %) was obtained on medium containing 2.46 µM IBA. Once the shoots were rooted, acclimatization in a greenhouse was not challenging and plant survival reached 100 %.


title: Data visualization sliders

abstract: Computer sliders are a generic user input mechanism for specifying a numeric value from a range. For data visualization, the effectiveness of sliders may be increased by using the space inside the slider as • an interactive color scale, • a barplot for discrete data, and • a density plot for continuous data. The idea is to show the selected values in relation to the data and its distribution. Furthermore, the selection mechanism may be generalized using a painting metaphor to specify arbitrary, disconnected intervals while maintaining an intuitive user-interface.


title: Spell Checking Techniques in NLP: A Survey

abstract: Spell checkers in Indian languages are the basic tools that need to be developed. A spell checker is a software tool that identifies and corrects any spelling mistakes in a text. Spell checkers can be combined with other applications or they can be distributed individually. In this paper the authors are discussing both the approaches and their roles in various applications.


Immersive Analytics for Clinical Neuroscience


title: AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks

abstract: ShuffleNet is a state-of-the-art light weight convolutional neural network architecture. Its basic operations include group, channel-wise convolution and channel shuffling. However, channel shuffling is manually designed empirically. Mathematically, shuffling is a multiplication by a permutation matrix. In this paper, we propose to automate channel shuffling by learning permutation matrices in network training. We introduce an exact Lipschitz continuous non-convex penalty so that it can be incorporated in the stochastic gradient descent to approximate permutation at high precision. Exact permutations are obtained by simple rounding at the end of training and are used in inference. The resulting network, referred to as AutoShuffleNet, achieved improved classification accuracies on CIFAR-10 and ImageNet data sets. In addition, we found experimentally that the standard convex relaxation of permutation matrices into stochastic matrices leads to poor performance. We prove theoretically the exactness (error bounds) in recovering permutation matrices when our penalty function is zero (very small). We present examples of permutation optimization through graph matching and two-layer neural network models where the loss functions are calculated in closed analytical form. In the examples, convex relaxation failed to capture permutations whereas our penalty succeeded.


title: A deep tree-based model for software defect prediction

abstract: Defects are common in software systems and can potentially cause various problems to software users. Different methods have been developed to quickly predict the most likely locations of defects in large code bases. Most of them focus on designing features (e.g. complexity metrics) that correlate with potentially defective code. Those approaches however do not sufficiently capture the syntax and different levels of semantics of source code, an important capability for building accurate prediction models. In this paper, we develop a novel prediction model which is capable of automatically learning features for representing source code and using them for defect prediction. Our prediction system is built upon the powerful deep learning, tree-structured Long Short Term Memory network which directly matches with the Abstract Syntax Tree representation of source code. An evaluation on two datasets, one from open source projects contributed by Samsung and the other from the public PROMISE repository, demonstrates the effectiveness of our approach for both within-project and cross-project predictions.


title: Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction

abstract: Close human-robot cooperation is a key enabler for new developments in advanced manufacturing and assistive applications. Close cooperation require robots that can predict human actions and intent, and understand human non-verbal cues. Recent approaches based on neural networks have led to encouraging results in the human action prediction problem both in continuous and discrete spaces. Our approach extends the research in this direction. Our contributions are three-fold. First, we validate the use of gaze and body pose cues as a means of predicting human action through a feature selection method. Next, we address two shortcomings of existing literature: predicting multiple and variable-length action sequences. This is achieved by introducing an encoder-decoder recurrent neural network topology in the discrete action prediction problem. In addition, we theoretically demonstrate the importance of predicting multiple action sequences as a means of estimating the stochastic reward in a human robot cooperation scenario. Finally, we show the ability to effectively train the prediction model on a action prediction dataset, involving human motion data, and explore the influence of the model's parameters on its performance. Source code repository: https://github.com/pschydlo/ActionAnticipation


title: Footwear bio-modelling: An industrial approach

abstract: There is a growing need within the footwear sector to customise the design of the last from which a specific footwear style is to be produced. This customisation is necessary for user comfort and health reasons, as the user needs to wear a suitable shoe. For this purpose, a relationship must be established between the user foot and the last with which the style will be made; up until now, no model has existed that integrates both elements. On the one hand, traditional customised footwear manufacturing techniques are based on purely artisanal procedures which make the process arduous and complex; on the other hand, geometric models proposed by different authors present the impossibility of implementing them in an industrial environment with limited resources for the acquisition of morphometric and structural data for the foot, apart from the fact that they do not prove to be sufficiently accurate given the non-similarity of the foot and last. In this paper, two interrelated geometric models are defined, the first, a bio-deformable foot model and the second, a deformable last model. The experiments completed show the goodness of the model, with it obtaining satisfactory results in terms of comfort, efficiency and precision, which make it viable for use in the sector.


title: Limitations of Learning Via Embeddings in Euclidean Half Spaces

abstract: This paper considers the embeddability of general concept classes in Euclidean half spaces. By embedding in half spaces we refer to a mapping from some concept class to half spaces so that the labeling given to points in the instance space is retained. The existence of an embedding for some class may be used to learn it using an algorithm for the class it is embedded into. The Support Vector Machines paradigm employs this idea for the construction of a general learning system. ::: ::: We show that an overwhelming majority of the family of finite concept classes of constant VC dimension d cannot be embedded in lowdimensional half spaces. (In fact, we show that the Euclidean dimension must be almost as high as the size of the instance space.) We strengthen this result even further by showing that an overwhelming majority of the family of finite concept classes of constant VC dimension d cannot be embedded in half spaces (of arbitrarily high Euclidean dimension) with a large margin. (In fact, the margin cannot be substantially larger than the margin achieved by the trivial embedding.) Furthermore, these bounds are robust in the sense that allowing each image half space to err on a small fraction of the instances does not imply a significant weakening of these dimension and margin bounds. ::: ::: Our results indicate that any universal learning machine, which transforms data into the Euclidean space and then applies linear (or large margin) classification, cannot enjoy any meaningful generalization guarantees that are based on either VC dimension or margins considerations.


title: Coded focal stack photography

abstract: We present coded focal stack photography as a computational photography paradigm that combines a focal sweep and a coded sensor readout with novel computational algorithms. We demonstrate various applications of coded focal stacks, including photography with programmable non-planar focal surfaces and multiplexed focal stack acquisition. By leveraging sparse coding techniques, coded focal stacks can also be used to recover a full-resolution depth and all-in-focus (AIF) image from a single photograph. Coded focal stack photography is a significant step towards a computational camera architecture that facilitates high-resolution post-capture refocusing, flexible depth of field, and 3D imaging.


title: Recognition of Daily Gestures with Wearable Inertial Rings and Bracelets

abstract: Recognition of activities of daily living plays an important role in monitoring elderly people and helping caregivers in controlling and detecting changes in daily behaviors. Thanks to the miniaturization and low cost of Microelectromechanical systems (MEMs), in particular of Inertial Measurement Units, in recent years body-worn activity recognition has gained popularity. In this context, the proposed work aims to recognize nine different gestures involved in daily activities using hand and wrist wearable sensors. Additionally, the analysis was carried out also considering different combinations of wearable sensors, in order to find the best combination in terms of unobtrusiveness and recognition accuracy. In order to achieve the proposed goals, an extensive experimentation was performed in a realistic environment. Twenty users were asked to perform the selected gestures and then the data were off-line analyzed to extract significant features. In order to corroborate the analysis, the classification problem was treated using two different and commonly used supervised machine learning techniques, namely Decision Tree and Support Vector Machine, analyzing both personal model and Leave-One-Subject-Out cross validation. The results obtained from this analysis show that the proposed system is able to recognize the proposed gestures with an accuracy of 89.01% in the Leave-One-Subject-Out cross validation and are therefore promising for further investigation in real life scenarios.


title: SIFT Features Tracking for Video Stabilization

abstract: This paper presents a video stabilization algorithm based on the extraction and tracking of scale invariant feature transform features through video frames. Implementation of SIFT operator is analyzed and adapted to be used in a feature-based motion estimation algorithm. SIFT features are extracted from video frames and then their trajectory is evaluated to estimate interframe motion. A modified version of iterative least squares method is adopted to avoid estimation errors and features are tracked as they appear in nearby frames to improve video stability. Intentional camera motion is eventually filtered with adaptive motion vector integration. Results confirm the effectiveness of the method.


title: Power aware FinFET and lateral nanosheet FET targeting for 3nm CMOS technology

abstract: In this paper, we show how 5.5 tracks standard cells can be enabled at gate pitch 42 nm and metal pitch 21 nm and achieve 60% active power reduction from the 7nm node. A device downselection methodology driven by power and performance targets is introduced. This method demonstrates that three stacked nanosheets of 20 nm width are competitive with FinFETs made with two fins while relaxing the constraints on layout design rules.


title: Automated malaria diagnosis using convolutional neural networks in an on-field setting: The analysis of low quality smartphone based microscope images

abstract: This study focuses on automated malaria diagnosis in low quality blood smear images, captured by a low-cost smartphone based microscope system. The aim is to localize and classify the healthy and infected erythrocytes (red blood cells) in order to evaluate the parasitaemia in an infected blood smear. Due to the lower quality of the smartphone microscope system compared to traditional high-end light microscopes, conventional algorithms fail to process these images. We propose a framework using a convolutional neural network as a pixel classifier to localize the erythrocytes. Afterwards we classify them accordingly, using a convolutional neural network as an object classifier. Such a system can offer in the-field malaria diagnosis without human intervention or can act as an aid for human experts to lower workload and increase diagnosis accuracy. The algorithm successfully localizes the erythrocytes with an average sensitivity of 97.31% and precision of 92.21%. Classification performed inadequate, in terms of low agreement with two human experts. This can be due to the low image quality or the small amount of training data available at the time.


title: Answering Top-k Queries with Multi-Dimensional Selections: The Ranking Cube Approach

abstract: Observed in many real applications, a top-k query often consists of two components to reflect a user's preference: a selection condition and a ranking function. A user may not only propose ad hoc ranking functions, but also use different interesting subsets of the data. In many cases, a user may want to have a thorough study of the data by initiating a multi-dimensional analysis of the top-k query results. Previous work on top-k query processing mainly focuses on optimizing data access according to the ranking function only. The problem of efficient answering top-k queries with multi-dimensional selections has not been well addressed yet.This paper proposes a new computational model, called ranking cube, for efficient answering top-k queries with multi-dimensional selections. We define a rank-aware measure for the cube, capturing our goal of responding to multi-dimensional ranking analysis. Based on the ranking cube, an efficient query algorithm is developed which progressively retrieves data blocks until the top-k results are found. The curse of dimensionality is a well-known challenge for the data cube and we cope with this difficulty by introducing a new technique of ranking fragments. Our experiments on Microsoft's SQL Server 2005 show that our proposed approaches have significant improvement over the previous methods.


title: On Complex Valued Convolutional Neural Networks

abstract: Convolutional neural networks (CNNs) are the cutting edge model for supervised machine learning in computer vision. In recent years CNNs have outperformed traditional approaches in many computer vision tasks such as object detection, image classification and face recognition. CNNs are vulnerable to overfitting, and a lot of research focuses on finding regularization methods to overcome it. One approach is designing task specific models based on prior knowledge. ::: Several works have shown that properties of natural images can be easily captured using complex numbers. Motivated by these works, we present a variation of the CNN model with complex valued input and weights. We construct the complex model as a generalization of the real model. Lack of order over the complex field raises several difficulties both in the definition and in the training of the network. We address these issues and suggest possible solutions. ::: The resulting model is shown to be a restricted form of a real valued CNN with twice the parameters. It is sensitive to phase structure, and we suggest it serves as a regularized model for problems where such structure is important. This suggestion is verified empirically by comparing the performance of a complex and a real network in the problem of cell detection. The two networks achieve comparable results, and although the complex model is hard to train, it is significantly less vulnerable to overfitting. We also demonstrate that the complex network detects meaningful phase structure in the data.


title: A Review of Semantic Similarity Measures in WordNet 1

abstract: Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. In recent years the measures based on WordNet have shown its talents and attracted great concern. Many measures have been proposed. The paper contains a review of the state of art measures, including path based measures, information based measures, feature based measures and hybrid measures. The features, performance, advantages, disadvantages and related issues of different measures are discussed. Finally the area of future research is described..


title: Implementation of back-stepping integral controller for a gesture driven quad-copter with human detection and auto follow feature

abstract: This work demonstrates building an ETH's back-stepping integral controller based quad-copter, and is driven by human gestures. A novel application of computer vision is introduced for easy operation of quad-copters. Furthermore a method for detecting and auto follow of human is devised in the quad-copter. The design and modeling have been successfully realized on the 450mm sized X-configured quad-copter built during this project. This paper also demonstrates a MATLAB framework, to simulate and analyze the performance of a quad-rotor designed with back-stepping integral controller. It gives a synopsis of the controller used in ETH OS4 quad-rotor and presents an easy to use GUI to tune the controller while designing the quad-rotor.


title: Trends in Supply of Lithium Resources and Demand of the Resources for Automobiles

abstract: A secondary battery contained in a lightweight and compact package is essential as the electric source to power hybrid vehicles (HVs) (that run on a combined system of a fossil fuel engine and an electric motor), plug-in HVs (PHVs) (for which the in-vehicle battery can be recharged using a commercial electric source), as well as electric vehicles (EVs). The development and introduction of such vehicles with secondary batteries and the subsequent substantial reduction of CO2 have become a challenge of urgent concern. These vehicles with secondary batteries have entered widespread use more rapidly than expected. Therefore, demand for high-power, large-capacity lithiumion batteries is expected to increase substantially by around 2030. A large amount of metallic lithium is required for electrode and electrolyte materials for secondary batteries. To meet rapidly increasing demand for metallic lithium, a stable supply of lithium raw materials is essential. Deposits of metallic lithium (a rare metal) are considered relatively abundant around the world. However, if demand for lithium for use in secondary batteries increases dramatically, the imbalance between supply and demand may become an issue depending on the resource policies of producing countries, their export restrictions, and the concentration of deposit areas. In Japan, the production of lithium minerals is slim to none. Japan is the world’s largest importer of lithium raw materials and heavily depends on South American countries. This article describes trends in the popularization of automobiles equipped with a secondary battery, estimates the amount of metallic lithium required for secondary batteries, and also introduces the current state of lithium supply and reserves, of resource 4


title: A Survey of Computational Semantics: Representation, Inference and Knowledge in Wide-Coverage Text Understanding

abstract: The aim of computational semantics is to capture the meaning of natural language expressions in representations suitable for performing inferences, in the service of understanding human language in written or spoken form. First-order logic is a good starting point, both from the representation and inference point of view. But even if one makes the choice of first-order logic as representation language, this is not enough: the computational semanticist needs to make further decisions on how to model events, tense, modal contexts, anaphora and plural entities. Semantic representations are usually built on top of a syntactic analysis, using unification, techniques from the lambda-calculus or linear logic, to do the book-keeping of variable naming. Inference has many potential applications in computational semantics. One way to implement inference is using algorithms from automated deduction dedicated to first-order logic, such as theorem proving and model building. Theorem proving can help in finding contradictions or checking for new information. Finite model building can be seen as a complementary inference task to theorem proving, and it often makes sense to use both procedures in parallel. The models produced by model generators for texts not only show that the text is contradiction-free; they also can be used for disambiguation tasks and linking interpretation with the real world. To make interesting inferences, often additional background knowledge is required (not expressed in the analysed text or speech parts). This can be derived (and turned into first-order logic) from raw text, semistructured databases or large-scale lexical databases such as WordNet. Promising future research directions of computational semantics are investigating alternative representation and inference methods (using weaker variants of first-order logic, reasoning with defaults), and developing evaluation methods measuring the semantic adequacy of systems and formalisms.


title: Guidelines for designing augmented reality games

abstract: The growing popularity of augmented reality (AR) games in both a research and more recently commercial context has led for a need to take a closer look at design related issues which impact on player experience. While issues relating to this area have been considered, to date most of the emphasis has been on the technology aspects. Furthermore it is almost always assumed that the augmented reality element in itself will provide a sufficient experience for the player. This has led to a need to evaluate what makes a successful augmented reality game. In this paper we present a set of design guidelines which are drawn from experiences of three mixed reality games. The guidelines provide specific guidance on relationships between real and virtual space, social interaction, use of AR technologies, maintaining consistent themes and implicitly address higher level aspects such as presence within a particular augmented reality place.


title: Generating a shoe last shape using Laplacian deformation

abstract: Body-shape data from 3D digitizers are utilized to design various products. We present the method generating a shoe last shape from 3D scanned foot data. To generate a producible last shape that fit to subject's foot, we apply designing method originated from a shoemaker to the foot shape. Then the constraints on last shape are derived. We deform a template surface with Laplacian deformation under the constraints. As a result of that, we try to obtain a custom last shape.


title: A mathematical model of the finding of usability problems

abstract: For 11 studies, we find that the detection of usability problems as a function of number of users tested or heuristic evaluators employed is well modeled as a Poisson process. The model can be used to plan the amount of evaluation required to achieve desired levels of thoroughness or benefits. Results of early tests can provide estimates of the number of problems left to be found and the number of additional evaluations needed to find a given fraction. With quantitative evaluation costs and detection values, the model can estimate the numbers of evaluations at which optimal cost/benefit ratios are obtained and at which marginal utility vanishes. For a “medium” example, we estimate that 16 evaluations would be worth their cost, with maximum benefit/cost ratio at four.


title: DRASH: A Data Replication-Aware Scheduler in Geo-Distributed Data Centers

abstract: Driven by the trends of BigData and Cloud computing, there is a growing demand for processing and analyzing data that are generated and stored across geo-distributed data centers. However, due to the limited network bandwidth between data centers and the growing data volume spread across different locations, it has become increasingly inefficient to aggregate data and to perform computations at a single data center. An approach that has been commonly used by data-intensive cluster computation systems, like Hadoop, is to distribute computations based on data locality so that data can be processed locally to reduce the network overhead and improve performance. But limited work has been done to adapt and evaluate such technique for geo-distributed data centers. In this paper, we proposed DRASH (Data-Replication Aware Scheduler), a job scheduling algorithm that enforces data locality to prevent data transfer, and exploits data replications to improve overall system performance. Our evaluation using simulations with realistic workload traces shows that DRASH can outperform other existing approaches by 16% to 60% in average job completion time, and achieve greater improvements under higher data replication factors.


title: Emotion regulation and culture: Are the social consequences of emotion suppression culture-specific?

abstract: Emotional suppression has been associated with generally negative social consequences (Butler et al., 2003; Gross & John, 2003). A cultural perspective suggests, however, that these consequences may be moderated by cultural values. We tested this hypothesis in a two-part study, and found that, for Americans holding Western-European values, habitual suppression was associated with self-protective goals and negative emotion. In addition, experimentally elicited suppression resulted in reduced interpersonal responsiveness during face-to-face interaction, along with negative partner-perceptions and hostile behavior. These deleterious effects were reduced when individuals with more Asian values suppressed, and these reductions were mediated by cultural differences in the responsiveness of the suppressors. These findings suggest that many of suppression's negative social impacts may be moderated by cultural values.


title: Discriminative coupled dictionary hashing for fast cross-media retrieval

abstract: Cross-media hashing, which conducts cross-media retrieval by embedding data from different modalities into a common low-dimensional Hamming space, has attracted intensive attention in recent years. The existing cross-media hashing approaches only aim at learning hash functions to preserve the intra-modality and inter-modality correlations, but do not directly capture the underlying semantic information of the multi-modal data. We propose a discriminative coupled dictionary hashing (DCDH) method in this paper. In DCDH, the coupled dictionary for each modality is learned with side information (e.g., categories). As a result, the coupled dictionaries not only preserve the intra-similarity and inter-correlation among multi-modal data, but also contain dictionary atoms that are semantically discriminative (i.e., the data from the same category is reconstructed by the similar dictionary atoms). To perform fast cross-media retrieval, we learn hash functions which map data from the dictionary space to a low-dimensional Hamming space. Besides, we conjecture that a balanced representation is crucial in cross-media retrieval. We introduce multi-view features on the relatively ``weak'' modalities into DCDH and extend it to multi-view DCDH (MV-DCDH) in order to enhance their representation capability. The experiments on two real-world data sets show that our DCDH and MV-DCDH outperform the state-of-the-art methods significantly on cross-media retrieval.


title: Onix: A Distributed Control Platform for Large-scale Production Networks

abstract: Computer networks lack a general control paradigm, as traditional networks do not provide any network-wide management abstractions. As a result, each new function (such as routing) must provide its own state distribution, element discovery, and failure recovery mechanisms. We believe this lack of a common control platform has significantly hindered the development of flexible, reliable and feature-rich network control planes. ::: ::: To address this, we present Onix, a platform on top of which a network control plane can be implemented as a distributed system. Control planes written within Onix operate on a global view of the network, and use basic state distribution primitives provided by the platform. Thus Onix provides a general API for control plane implementations, while allowing them to make their own trade-offs among consistency, durability, and scalability.


title: Decentralized discovery of free parking places

abstract: This paper proposes a topology independent, scalable information dissemination algorithm for spatio-temporal traffic information such as parking place availability using vehicular ad hoc networks (VANET) based on Wireless-LAN IEEE 802.11.The algorithm uses periodic broadcasts for information dissemination. Broadcast redundancy is minimized by evaluation of application layer information and aggregation. Due to the spatio-temporal characteristics of parking place information, the spatial distribution of information is limited by utilizing techniques, which take the local relevance and age of information into account.Based on a realistic model of a german city with up to 10000 vehicles, our results show that a decentralized parking place information system works efficiently even with 5% of vehicles equipped with Wireless-LAN and without the help of any message relaying infrastructure. This fact makes such a non-safety driver assistance application very interesting, especially in the rollout phase of vehicular ad hoc networks.


title: Confirmation Bias: Roles of Search Engines and Search Contexts

abstract: Prior work shows that confirmation bias, defined as the tendency to seek confirming evidence, is prevalent on the Web as well. While this has been attributed to individuals' psychological needs or cognitive limitations, the roles of search engines and search contexts have largely been neglected. The goals of this study are to examine how search contexts may change the composition of search results, and how – if at all – search engines may contribute to confirmation bias. Results of two studies show that search engines may exacerbate confirmation bias by generating results that consist only of confirming evidence for search contexts where disconfirming evidence is identified using different terms or phrases. This induces individuals to make biased decisions. Findings of this study deepen our understanding of the ways in which confirmation bias unfolds on the Web when individuals use search engines.


title: Complex tourism systems: a visibility graph approach

abstract: Purpose – The social responsibility of a tourism destination results from the combined efforts of the single stakeholders. This needs coordination and harmonization that cannot be achieved without a deep understanding of the structural and dynamic characteristics of the destination. A tourism destination is a complex dynamic system and requires specific methods to be analyzed and understood in order to better tailor governance actions for steering it along an evolutionary growth path, respectful of the social responsibility towards the community. Many methodological recommendations exist that allow to assess these features, and some have been successfully applied to tourism destinations. This paper aims to explore a new proposal: the visibility graph algorithm (VGA), which is able to provide the required level of information in a fast and simple way. Design/methodology/approach – VGA is a technique to map a time series into a network. The method and its implementation are relatively simple and straightfor...


title: Design of Area-Efficient and Highly Reliable RHBD 10T Memory Cell for Aerospace Applications

abstract: In this brief, based on upset physical mechanism together with reasonable transistor size, a robust 10T memory cell is first proposed to enhance the reliability level in aerospace radiation environment, while keeping the main advantages of small area, low power, and high stability. Using Taiwan Semiconductor Manufacturing Company 65-nm CMOS commercial standard process, simulations performed in Cadence Spectre demonstrate the ability of the proposed radiation-hardened-by-design 10T cell to tolerate both $0~\rightarrow ~1$ and $1~\rightarrow ~0$ single node upsets, with the increased read/write access time.


title: Seeking the best blend for deep learning in a flipped classroom – viewing student perceptions through the Community of Inquiry lens

abstract: We describe a case study of a third-year undergraduate class in Enterprise Education. A blended learning design in the form of a flipped classroom with a duration of one semester, was explored in two cohorts. The question was to explore how students experienced the flipped class for learning and how this approach presented the different presences in the Community of Inquiry (CoI), and its revisions. The online learning components represented the individual learning space, where the main resource was bespoke videos that replaced lectures and complemented the textbook and other learning material. The classroom hosted a business school-style seminar where students in small groups engaged in solving a new business case study, going through phases of developing a concept to presenting the group solutions to the class. It aimed at fostering active learning both inside and outside the class. Students participated in the activities to apply the theory in new cases. The teacher facilitated the sessions, provided direction and correction as needed. The research used mixed methods consisting of trace data, quantitative and qualitative student feedback to explore how suitable the flipped classroom in undergraduate education was towards developing deep learning. The online individual learning space yielded highly salient Teaching Presences, accompanied by evidence of Agency Presence, characterised by independent activity and personal learning preferences. Online videos and ICT resources helped with understanding the theory ahead of class meetings. Seminars in the collaborative space fostered deep learning of the theory, and enabled students to apply the prepared theory in case studies and solve problems. Integration and particularly Resolution in Cognitive Presence of CoI featured in the seminars, while Social Presence was the weakest. Suggestions are made to implement the flipped class principles in an online class.