title: Toward sustainable publishing and querying of distributed Linked Data archives

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to detail a low-cost, low-maintenance publishing strategy aimed at unlocking the value of Linked Data collections held by libraries, archives and museums (LAMs). ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: The shortcomings of commonly used Linked Data publishing approaches are identified, and the current lack of substantial collections of Linked Data exposed by LAMs is considered. To improve on the discussed status quo, a novel approach for publishing Linked Data is proposed and demonstrated by means of an archive of DBpedia versions, which is queried in combination with other Linked Data sources. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: The authors show that the approach makes publishing Linked Data archives easy and affordable, and supports distributed querying without causing untenable load on the Linked Data sources. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: ::: The proposed approach significantly lowers the barrier for publishing, maintaining, and making Linked Data collections queryable. As such, it offers the potential to substantially grow the distributed network of queryable Linked Data sources. Because the approach supports querying without causing unacceptable load on the sources, the queryable interfaces are expected to be more reliable, allowing them to become integral building blocks of robust applications that leverage distributed Linked Data sources. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: The novel publishing strategy significantly lowers the technical and financial barriers that LAMs face when attempting to publish Linked Data collections. The proposed approach yields Linked Data sources that can reliably be queried, paving the way for applications that leverage distributed Linked Data sources through federated querying.


title: Autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization for noisy speech recognition

abstract: Autocorrelation domain is a proper domain for clean speech signal and noise separation. In this paper, a method is proposed to decrease effects of noise on the clean speech signal, autocorrelation-based noise subtraction (ANS). Then to deal with the error introduced by assumption that noise and clean speech signal are uncorrelated, two methods are proposed. Also to improve recognition rate of speech recognition system, overestimation parameter is used. Finally, with the addition of energy and cepstral mean and variance normalization to features of speech, recognition rate has improved considerably in comparison to standard features and other correlation-based methods. The proposed methods are tested on the Aurora 2 database. Between different proposed methods, autocorrelation-based noise subtraction method with smoothing, overestimation, energy, and cepstral mean and variance normalization (ANSSOEMV) method has a best recognition rate improvement in average than MFCC features which is 64.91% on the Aurora 2 database.


title: Partitioned Variational Inference: A unified framework encompassing federated and continual learning

abstract: Variational inference (VI) has become the method of choice for fitting many modern probabilistic models. However, practitioners are faced with a fragmented literature that offers a bewildering array of algorithmic options. First, the variational family. Second, the granularity of the updates e.g. whether the updates are local to each data point and employ message passing or global. Third, the method of optimization (bespoke or blackbox, closed-form or stochastic updates, etc.). This paper presents a new framework, termed Partitioned Variational Inference (PVI), that explicitly acknowledges these algorithmic dimensions of VI, unifies disparate literature, and provides guidance on usage. Crucially, the proposed PVI framework allows us to identify new ways of performing VI that are ideally suited to challenging learning scenarios including federated learning (where distributed computing is leveraged to process non-centralized data) and continual learning (where new data and tasks arrive over time and must be accommodated quickly). We showcase these new capabilities by developing communication-efficient federated training of Bayesian neural networks and continual learning for Gaussian process models with private pseudo-points. The new methods significantly outperform the state-of-the-art, whilst being almost as straightforward to implement as standard VI.


Rendering with coherent layers


Cytogenetic investigations in colchicine induced tetraploid of Cosmos sulphureus (Asteraceae)


title: A 13-bit 160MS/s pipelined subranging-SAR ADC with low-offset dynamic comparator

abstract: A 13-bit 160MS/s hybrid ADC in 65 nm CMOS is presented in this paper. By combining the pipelined, flash and SAR architectures, a hybrid ADC architecture is proposed to improve the power efficiency. An input offset storage technique of dynamic comparator is proposed to increase the conversion linearity. A reference voltage buffer with the charge compensation is proposed to save power and reduce the decoupling capacitor. The achieved peak SNDR, SFDR, and FoM are 64.2 dB, 80.5 dBc, and 107.3 fJ/conversion-step, respectively.


title: A New Calibration Method for Current and Voltage Sensors Used in Power Quality Measurements

abstract: Sensor calibration plays an important role in instrument development and accuracy. This paper presents a method to calibrate current and voltage sensors specially dedicated to power quality measurements which can significantly reduce the distortions introduced by the sensors. The first approach to sensor calibration is based on the response of sensors to DC values imposed by a calibrator. The second method is an AC calibration, where a 50 Hz power signal is applied to the sensors also through a calibrator. The calibration coefficients are obtained by minimizing the output distortion. The minimized output distortion is assessed by the signal to noise and distortion ratio (SINAD) at the output of the sensor correction module.


title: Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments

abstract: Labeled Faces in the Wild (LFW) database has been widely utilized as the benchmark of unconstrained face verification and due to big data driven machine learning methods, the performance on the database approaches nearly 100%. However, we argue that this accuracy may be too optimistic because of some limiting factors. Besides different poses, illuminations, occlusions and expressions, cross-age face is another challenge in face recognition. Different ages of the same person result in large intra-class variations and aging process is unavoidable in real world face verification. However, LFW does not pay much attention on it. Thereby we construct a Cross-Age LFW (CALFW) which deliberately searches and selects 3,000 positive face pairs with age gaps to add aging process intra-class variance. Negative pairs with same gender and race are also selected to reduce the influence of attribute difference between positive/negative pairs and achieve face verification instead of attributes classification. We evaluate several metric learning and deep learning methods on the new database. Compared to the accuracy on LFW, the accuracy drops about 10%-17% on CALFW.


title: From Data Quality to Big Data Quality

abstract: This article investigates the evolution of data quality issues from traditional structured data managed in relational databases to Big Data. In particular, the paper examines the nature of the relationship between Data Quality and several research coordinates that are relevant in Big Data, such as the variety of data types, data sources and application domains, focusing on maps, semi-structured texts, linked open data, sensor & sensor networks and official statistics. Consequently a set of structural characteristics is identified and a systematization of the a posteriori correlation between them and quality dimensions is provided. Finally, Big Data quality issues are considered in a conceptual framework suitable to map the evolution of the quality paradigm according to three core coordinates that are significant in the context of the Big Data phenomenon: the data type considered, the source of data, and the application domain. Thus, the framework allows ascertaining the relevant changes in data quality emerging with the Big Data phenomenon, through an integrative and theoretical literature review.


title: A Wireless Emergency Telemedicine System for Patients Monitoring and Diagnosis

abstract: Recently, remote healthcare systems have received increasing attention in the last decade, explaining why intelligent systems with physiology signal monitoring for e-health care are an emerging area of development. Therefore, this study adopts a system which includes continuous collection and evaluation of multiple vital signs, long-term healthcare, and a cellular connection to a medical center in emergency case and it transfers all acquired raw data by the internet in normal case. The proposed system can continuously acquire four different physiological signs, for example, ECG, SpO2, temperature, and blood pressure and further relayed them to an intelligent data analysis scheme to diagnose abnormal pulses for exploring potential chronic diseases. The proposed system also has a friendly web-based interface for medical staff to observe immediate pulse signals for remote treatment. Once abnormal event happened or the request to real-time display vital signs is confirmed, all physiological signs will be immediately transmitted to remote medical server through both cellular networks and internet. Also data can be transmitted to a family member's mobile phone or doctor's phone through GPRS. A prototype of such system has been successfully developed and implemented, which will offer high standard of healthcare with a major reduction in cost for our society.


title: Secure k-nearest neighbor query over encrypted data in outsourced environments

abstract: For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by the cloud; otherwise, there would be no point to outsource the data at the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first present a basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns. Also, we empirically analyze the efficiency of our protocols through various experiments. These results indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query.


title: Hidden link prediction based on node centrality and weak ties

abstract: Link prediction has been widely used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. In this context, similarity-based algorithms have become the mainstream. However, most of them take into account the contributions of each common neighbor equally to the connection likelihood of two nodes. This paper proposes a model for link prediction, which is based on the node centrality of common neighbors. Three node centralities are discussed: degree, closeness and betweenness centrality. In our model, each common neighbor plays a different role to the node connection likelihood according to their centralities. Moreover, the weak-tie theory is considered for improving the prediction accuracy. Finally, extensive experiments on five real-world networks show that the proposed model can outperform the Common Neighbor (CN) algorithm and gives competitively good prediction of or even better than Adamic-Adar (AA) index and Resource Allocation (RA) index.


P.: Stanford tech report ctsr 2005-02 light field photography with a hand-held plenoptic camera


title: PWM/PDM dual mode controlled soft switching multi resonant high-frequency inverter

abstract: In this paper, a novel type auxiliary active resonant capacitor snubber assisted zero current soft switching pulse modulation single-ended push pull (SEPP) series load resonant inverter with two auxiliary resonant lossless inductor snubbers is proposed for consumer high-frequency induction heating (IH) appliances. Its operating principle in steady state is described by using each mode equivalent circuits. The new multi resonant high-frequency inverter can regulate its output power under a condition of a constant frequency zero current soft switching (ZCS) commutation principle on the basis of asymmetrical PWM control scheme. The consumer brand-new IH products using proposed ZCS-PWM series load resonant SEPP high-frequency inverter is evaluated and discussed as compared with conventional high-frequency inverter on the basis of experimental results. In order to extend ZCS operation ranges under a low power setting PWM, the pulse density modulation (PDM) strategy is demonstrated for high frequency multi resonant inverter. Its practical effectiveness is substantially proved from an application point of view


title: Integrating low-power IoT devices to a blockchain-based infrastructure: work-in-progress

abstract: The ever-increasing number of IoT devices necessitates a secure and scalable infrastructure to store and process generated data. Blockchain is an ideal choice with its decentralized, trustless architecture. However, low-power IoT end-devices do not possess enough horsepower to run a software client for intensive blockchain calculations. The purpose of this paper is to create a proof of concept to enable low-power, resource-constrained IoT end-devices accessing a blockchain-based infrastructure. To achieve this aim, an IoT gateway is configured as a blockchain node and an event-based messaging mechanism for low-power IoT end-devices is proposed. A demonstration of such a system is realized using LoRa nodes and gateway in a private Ethereum network.


title: Membrane distillation of high salinity wastewater from shale gas extraction: effect of antiscalants

abstract: AbstractAlthough shale gas has become an important source of natural gas, it has problems associated with water pollution by producing high salinity wastewater (i.e. TDS > 100,000 mg/L). Membrane distillation (MD) can be applied to treat such wastewater but may suffer from fouling due to scale formation. Accordingly, this study focused on the use of antiscalants to retard scale formation in MD process for the treatment of high salinity wastewater. Experiments were performed using a laboratory-scale direct contact MD (DCMD) system. Seven different antiscalants were applied to the simplified synthetic wastewater. The results were analyzed using a simple theoretical model. It was found that the abilities to retard scale formation were different for different antiscalants. The difference in chemical structures of the antiscalants was attributed to the changes in their effect on scale prevention. Moreover, the mechanisms of flux decline seem to be different in the presence of antiscalants. Not only surface blo...


title: Python in neuroscience

abstract: EDITORIALpublished:14April2015doi:10.3389/fninf.2015.00011FrontiersinNeuroinformatics|www.frontiersin.org 1 April2015|Volume9|Article11Editedandreviewedby:Sean L. Hill,International NeuroinformaticsCoordinating Facility, Sweden*Correspondence:Andrew P. Davison,andrew.davison@unic.cnrs-gif.frReceived:20 March 2015Accepted:28 March 2015Published:14 April 2015Citation:Muller E, Bednar JA, Diesmann M,Gewaltig M-O, Hines M and DavisonAP (2015) Python in neuroscience.Front. Neuroinform. 9:11.doi: 10.3389/fninf.2015.00011


title: Multi-Column Deep Neural Network for Traffic Sign Classification

abstract: We describe the approach that won the final phase of the German traffic sign recognition benchmark. Our method is the only one that achieved a better-than-human recognition rate of 99.46%. We use a fast, fully parameterizable GPU implementation of a Deep Neural Network (DNN) that does not require careful design of pre-wired feature extractors, which are rather learned in a supervised way. Combining various DNNs trained on differently preprocessed data into a Multi-Column DNN (MCDNN) further boosts recognition performance, making the system insensitive also to variations in contrast and illumination.


title: Expressive array constructs in an embedded GPU kernel programming language

abstract: Graphics Processing Units (GPUs) are powerful computing devices that with the advent of CUDA/OpenCL are becomming useful for general purpose computations. Obsidian is an embedded domain specific language that generates CUDA kernels from functional descriptions. A symbolic array construction allows us to guarantee that intermediate arrays are fused away. However, the current array construction has some drawbacks; in particular, arrays cannot be combined efficiently. We add a new type of push arrays to the existing Obsidian system in order to solve this problem. The two array types complement each other, and enable the definition of combinators that both take apart and combine arrays, and that result in efficient generated code. This extension to Obsidian is demonstrated on a sequence of sorting kernels, with good results. The case study also illustrates the use of combinators for expressing the structure of parallel algorithms. The work presented is preliminary, and the combinators presented must be generalised. However, the raw speed of the generated kernels bodes well.


title: Affirmative Cue Words in Task-Oriented Dialogue

abstract: We present a series of studies of affirmative cue words-a family of cue words such as "okay" or "alright" that speakers use frequently in conversation. These words pose a challenge for spoken dialogue systems because of their ambiguity: They may be used for agreeing with what the interlocutor has said, indicating continued attention, or for cueing the start of a new topic, among other meanings. We describe differences in the acoustic/prosodic realization of such functions in a corpus of spontaneous, task-oriented dialogues in Standard American English. These results are important both for interpretation and for production in spoken language applications. We also assess the predictive power of computational methods for the automatic disambiguation of these words. We find that contextual information and final intonation figure as the most salient cues to automatic disambiguation.


title: Topics and Label Propagation: Best of Both Worlds for Weakly Supervised Text Classification

abstract: We propose a Label Propagation based algorithm for weakly supervised text classification. We construct a graph where each document is represented by a node and edge weights represent similarities among the documents. Additionally, we discover underlying topics using Latent Dirichlet Allocation (LDA) and enrich the document graph by including the topics in the form of additional nodes. The edge weights between a topic and a text document represent level of "affinity" between them. Our approach does not require document level labelling, instead it expects manual labels only for topic nodes. This significantly minimizes the level of supervision needed as only a few topics are observed to be enough for achieving sufficiently high accuracy. The Label Propagation Algorithm is employed on this enriched graph to propagate labels among the nodes. Our approach combines the advantages of Label Propagation (through document-document similarities) and Topic Modelling (for minimal but smart supervision). We demonstrate the effectiveness of our approach on various datasets and compare with state-of-the-art weakly supervised text classification approaches.


title: Number of US Physicians Participating in Clinical Research Continuing to Drop (Association of Clinical Research Organizations Survey)

abstract: WASHINGTON, DC—The number of physicians participating in clinical trials in the United States has continued to decrease over the last 10 years, according to new data from the Association of Clinical Research Organizations (ACRO). And although countries in Western Europe are experiencing a similar de


title: Blogs, news and credibility

abstract: Purpose – The purpose of this paper is to examine the emergence of blogging in the news sphere. If blogs represent a genuinely new breed of news provision, then they should adhere to some of the founding principles of mainstream news and journalism. A key principle in this respect is news credibitility.Design/methodology/approach – This paper presents a review of recent literature about news blogging and assesses whether news blogs manifest many of the core attributes of mainstream news and journalism. The review considers the attributes that have previously been identified as defining good quality news and competent journalism and then applies some of these principles to “news” blogging.Findings – There is no doubt that blogs have emerged as news sources of increasing significance and there have been occasions when they can be influential in setting news agendas. The essential qualities of credibitiltiy and capturing public trust in the news sphere, however, often depends upon the established reputation ...


Update on Nasopharyngeal Carcinoma


title: A Survey of Euler Diagrams

abstract: Abstract Euler diagrams visually represent containment, intersection and exclusion using closed curves. They first appeared several hundred years ago, however, there has been a resurgence in Euler diagram research in the twenty-first century. This was initially driven by their use in visual languages, where they can be used to represent logical expressions diagrammatically. This work lead to the requirement to automatically generate Euler diagrams from an abstract description. The ability to generate diagrams has accelerated their use in information visualization, both in the standard case where multiple grouping of data items inside curves is required and in the area-proportional case where the area of curve intersections is important. As a result, examining the usability of Euler diagrams has become an important aspect of this research. Usability has been investigated by empirical studies, but much research has concentrated on wellformedness , which concerns how curves and other features of the diagram interrelate. This work has revealed the drawability of Euler diagrams under various wellformedness properties and has developed embedding methods that meet these properties. Euler diagram research surveyed in this paper includes theoretical results, generation techniques, transformation methods and the development of automated reasoning systems for Euler diagrams. It also overviews application areas and the ways in which Euler diagrams have been extended.


title: Genetic circuit building blocks for cellular computation, communications, and signal processing

abstract: In this paper, we review an emerging engineering discipline to program cell behaviors by embedding synthetic gene networks that perform computation, communications, and signal processing. To accomplish this goal, we begin with a genetic component library and a biocircuit design methodology for assembling these components into compound circuits. The main challenge in biocircuit design lies in selecting well-matched genetic components that when coupled, reliably produce the desired behavior. We use simulation tools to guide circuit design, a process that consists of selecting the appropriate components and genetically modifying existing components until the desired behavior is achieved. In addition to such rational design, we also employ directed evolution to optimize genetic circuit behavior. Building on Nature's fundamental principle of evolution, this unique process directs cells to mutate their own DNA until they find gene network configurations that exhibit the desired system characteristics. The integration of all the above capabilities in future synthetic gene networks will enable cells to perform sophisticated digital and analog computation, both as individual entities and as part of larger cell communities. This engineering discipline and its associated tools will advance the capabilities of genetic engineering, and allow us to harness cells for a myriad of applications not previously achievable.


title: Dissociation of working memory impairments and attention-deficit/hyperactivity disorder in the brain

abstract: Prevailing neuropsychological models of attention-deficit/hyperactivity disorder (ADHD) propose that ADHD arises from deficits in executive functions such as working memory, but accumulating clinical evidence suggests a dissociation between ADHD and executive dysfunctions. This study examined whether ADHD and working memory capacity are behaviorally and neurobiologically separable using functional magnetic resonance imaging (fMRI). Participants diagnosed with ADHD in childhood who subsequently remitted or persisted in their diagnosis as adults were characterized at follow-up in adulthood as either impaired or unimpaired in spatial working memory relative to controls who never had ADHD. ADHD participants with impaired spatial working memory performed worse than controls and ADHD participants with unimpaired working memory during an n-back working memory task while being scanned. Both controls and ADHD participants with unimpaired working memory exhibited significant linearly increasing activation in the inferior frontal junction, precuneus, lingual gyrus, and cerebellum as a function of working-memory load, and these activations did not differ significantly between these groups. ADHD participants with impaired working memory exhibited significant hypoactivation in the same regions, which was significantly different than both control participants and ADHD participants with unimpaired working memory. These findings support both a behavioral and neurobiological dissociation between ADHD and working memory capacity.


title: Optimization of cutting tool geometry based on flank wear - DoE, PSO and SAA approach

abstract: In this analysis, an attempt has been made to study the effects of included angle, hardness, rake angle, tool approach angle and nose radius of cutting inserts on flank wear with the surface roughness as constraint. The selected parameters are varied through three levels. A new parametric study without considering any kind of benchmark problems in this area is performed and methodology has been developed to analyze the effects of selected parameters on the flank wear of cutting insert. As per the L18 orthogonal array, the values of flank wear after machining are measured and recorded. The best levels of selected parameters for the minimization of flank wear are identified by using Taguchi’s Design of Experiments. A validation experiment is conducted with the identified best levels of parameters, and the corresponding flank wear is recorded. Also this analysis inter-relates the performances of design of experiments (DoE), regression analysis, particle swarm optimization and simulated annealing algorithm (SAA) to obtain the best possible solution. The result of this analysis identifies the optimal values of selected parameters for effective and efficient machining. The experimental, optimized and predicted values of flank wear are compared and correlated for all the test conditions.


title: Facial landmark detection via pose-induced auto-encoder networks

abstract: Facial landmark detection is an important issue in a face recognition system. However, human faces in wild conditions often present large variations in shape due to different poses, occlusions or expressions, which makes it a difficult task. Instead of learning the same map for all images, we propose a Pose-Induced Auto-encoder Networks (PIAN) approach which uses different pose-induced networks for landmark estimation under different pose conditions. Firstly, we build a network to simultaneously get the initial landmark and pose estimation. Then, different networks which are induced by the estimated pose are built for local search where a component-based searching method is explored. By using the pose inducing strategy, the initial estimation is reliable and it helps reduce the variations in local patches. This makes the component-based search feasible and more accurate than previous searching methods. Experiments show that our method outperforms the state-of-the-art algorithms especially in terms of fine estimation of landmarks.


title: Incremental Maintenance of Regression Models over Joins

abstract: This paper introduces a principled incremental view maintenance (IVM) mechanism for in-database computation described by rings. We exemplify our approach by introducing the covariance matrix ring that we use for learning linear regression models over arbitrary equi-join queries. ::: Our approach is a higher-order IVM algorithm that exploits the factorized structure of joins and aggregates to avoid redundant computation and improve performance. We implemented it in DBToaster, which uses program synthesis to generate high-performance maintenance code. We experimentally show that it can outperform first-order and fully recursive higher-order IVM as well as recomputation by orders of magnitude while using less memory.


title: 60-GHz LTCC Differential-Fed Patch Antenna Array With High Gain by Using Soft-Surface Structures

abstract: This paper presents a 60-GHz $4 \times 4$ differential -fed patch antenna array using low-temperature cofired ceramic (LTCC) process. Wideband patch with L-shaped feeding scheme is adopted as antenna element, while differential substrate integrated waveguide feeding network with low insertion loss is applied for the integration of antenna array. The differential-fed structure improves the symmetry of radiation patterns and reduces the cross-polarization level significantly. To further suppress the surface wave and improve the gain of the antenna array, one kind of soft surface structure is proposed. The equivalent circuit model of the soft surface is developed for calculating its dispersion diagram and analyzing stopband characteristics. The simulated results indicate that the antenna gain enhancement can be up to 2 dB because of the proposed soft surface. For demonstration, one prototype using LTCC process is fabricated and measured. The measured 10-dB impedance bandwidth of the antenna array is 11.7%. The measured antenna peak gain of 18.62 dBi at 61.5 GHz and symmetrical radiation patterns with a low cross polarization of −25 dB across the whole operating frequency are achieved.


title: Highway long short-term memory RNNS for distant speech recognition

abstract: In this paper, we extend the deep long short-term memory (DLSTM) recurrent neural networks by introducing gated direct connections between memory cells in adjacent layers. These direct links, called highway connections, enable unimpeded information flow across different layers and thus alleviate the gradient vanishing problem when building deeper LSTMs. We further introduce the latency-controlled bidirectional LSTMs (BLSTMs) which can exploit the whole history while keeping the latency under control. Efficient algorithms are proposed to train these novel networks using both frame and sequence discriminative criteria. Experiments on the AMI distant speech recognition (DSR) task indicate that we can train deeper LSTMs and achieve better improvement from sequence training with highway LSTMs (HLSTMs). Our novel model obtains $43.9/47.7\%$ WER on AMI (SDM) dev and eval sets, outperforming all previous works. It beats the strong DNN and DLSTM baselines with $15.7\%$ and $5.3\%$ relative improvement respectively.


title: Mediating Effects of Supply Chain Integration: From IT Capability to Firm Performance

abstract: While information technology (IT) has been increasingly embedded in firms’ supply chain processes, the underlying mechanisms of how IT capability improves firm performance remain unclear. Drawing upon resource-based view and theory of swift and even flow, this study examines how IT capability (i.e., internally-focused IT capability, externally-focused IT capability, and IT business spanning capability) influences firm performance through supply chain integration (SCI) (i.e., internal and external integration). Using data collected from 222 firms in China, we find performance impacts of different IT capability are mediated by different SCI. Specifically, internal integration mediates the impact of internally-focused IT capability and external integration mediates the impact of externally-focused IT capability, while both internal and external integration mediate the impact of IT business spanning capability. We also find IT business spanning capability positively moderates the impact of internally-focused IT capability on internal integration. Theoretical contributions and implications of this research are also discussed.


title: Networked Distributed POMDPs: A Synergy of Distributed Constraint Optimization and POMDPs

abstract: In many real-world multiagent applications such as distributed sensor nets, a network of agents is formed based on each agent's limited interactions with a small number of neighbors. While distributed POMDPs capture the real-world uncertainty in multiagent domains, they fail to exploit such locality of interaction. Distributed constraint optimization (DCOP) captures the locality of interaction but fails to capture planning under uncertainty. This paper present a new model synthesized from distributed POMDPs and DCOPs, called Networked Distributed POMDPs (ND-POMDPs). Exploiting network structure enables us to present a distributed policy generation algorithm that performs local search.


title: Aircraft Fuselage Defect Detection using Deep Neural Networks

abstract: To ensure flight safety of aircraft structures, it is necessary to have regular maintenance using visual and nondestructive inspection (NDI) methods. In this paper, we propose an automatic image-based aircraft defect detection using Deep Neural Networks (DNNs). To the best of our knowledge, this is the first work for aircraft defect detection using DNNs. We perform a comprehensive evaluation of state-of-the-art feature descriptors and show that the best performance is achieved by vgg-f DNN as feature extractor with a linear SVM classifier. To reduce the processing time, we propose to apply SURF key point detector to identify defect patch candidates. Our experiment results suggest that we can achieve over 96% accuracy at around 15s processing time for a high-resolution (20-megapixel) image on a laptop.


title: Theory acquisition and the language of thought

abstract: Everyday knowledge about living things, physical objects and the beliefs and desires of other people appears to be organized into sophisticated systems that are often called intuitive theories. Two long term goals for psychological research are to understand how these theories are mentally represented, and how they are acquired. We argue that the language of thought hypothesis can help to address both questions. First, compositional languages can capture the content of intuitive theories. Second, any compositional language will generate an account of theory learning which predicts that theories with short descriptions tend to be preferred. We describe a computational framework that captures both ideas, and compare its predictions to behavioral data from a simple theory learning task. Any comprehensive account of human knowledge must acknowledge two principles. First, everyday knowledge is more than a list of isolated facts, and much of it appears to be organized into richly structured systems that are sometimes called intuitive theories. Even young children, for instance, have systematic beliefs about domains including folk physics, folk biology, and folk psychology [10]. Second, some aspects of these theories appear to be learned. Developmental psychologists have explored how intuitive theories emerge over the first decade of life, and at least some of these changes appear to result from learning. Although theory learning raises some challenging problems, two computational principles that may support this ability have been known for many years. First, a theory-learning system must be able to represent the content of any theory that it acquires. A learner that cannot represent a given system of concepts is clearly unable to learn this system from data. Second, there will always be many systems of concepts that are compatible with any given data set, and a learner must rely on some a priori ordering of the set of possible theories to decide which candidate is best [5, 9]. Loosely speaking, this ordering can be identified with a simplicity measure, or a prior distribution over the space of possible theories. There is at least one natural way to connect these two computational principles. Suppose that intuitive theories are represented in a “language of thought:” a language that allows complex concepts to be represented as combinations of simpler concepts [5]. A compositional language provides a straightforward way to construct sophisticated theories, but also provides a natural ordering over the resulting space of theories: the a priori probability of a theory can be identified with its length in this representation language [3, 7]. Combining this prior distribution with an engine for Bayesian inference immediately leads to a computational account of how theories might be learned. There may be other ways to explain how people represent and acquire complex systems of knowledge, but it is striking that the “language of thought” hypothesis can help to explain both knowledge representation and learning. This paper describes a computational framework that helps to explain how theories are acquired, and that can be used to evaluate different proposals about the language of thought. Our approach builds on previous discussions of concept learning that have explored the link between compositional representations and inductive inference. Two recent approaches propose that concepts are represented in a form of propositional logic, and that the a priori plausibility of an inductive hypothesis is related to the length of its representation in this language [4, 6]. Our approach is similar in spirit, but is motivated in part by the need for languages that are richer than propositional logic. The framework we present is extremely general, and is compatible with virtually any representation language, including various forms of predicate logic. Methods for learning predicate logic theories have previously been explored in the field of Inductive Logic Programming, and we recently proposed a theory-learning model that is inspired by this tradition [7]. Our current approach is motivated by similar goals, but is better able to account for the discovery of abstract theoretical laws. The next section describes our computational framework, and introduces a specific logical language that we will consider throughout. Our framework allows relatively sophisticated theories to be represented and learned, but we evaluate it here by applying it to a simple learning problem, and comparing its predictions with human inductive inferences. A Bayesian approach to theory discovery Suppose that a learner observes some of the relationships that hold among a fixed, finite set of entities, and wishes to discover a theory that accounts for these data. Suppose, for instance, that the entities are thirteen adults from a remote tribe (a through m), and that the data specify that the spouse relation (S(·, ·)) is true of some pairs (Figure 1). One candidate theory states that S(·, ·) is a symmetric relation, that some of the individuals are male (M(·)), that marriages are permitted only between males and non-males, and that males may take multiple spouses but non-males may have only one spouse (Figure 1b). Other theories are possible, including the theory which states only that S(·, ·) is symmetric. Accounts of theory learning should distinguish between at least three kinds of entities: theories, models, and data. A theory is a set of statements that captures constraints on the possible configurations of the world. For instance, the theory in Figure 1b rules out configurations where the spouse


title: t-FFD: free-form deformation by using triangular mesh

abstract: A new method of free-form deformation, t-FFD, is proposed. An original shape of large-scale polygonal mesh or point-cloud is deformed by using a control mesh, which is constituted of a set of triangles with arbitrary topology and geometry, including the cases of disconnection or self-intersection. For modeling purposes, a designer can handle the shape directly or indirectly, and also locally or globally. This method works on a simple mapping mechanism. First, each point of the original shape is parametrized by the local coordinate system on each triangle of the control mesh. After modifying the control mesh, the point is mapped according to each modified triangle. Finally, the mapped locations are blended as a new position of the original point, then a smoothly deformed shape is achieved. Details of the t-FFD are discussed and examples are shown.


title: Unsupervised Joint Object Discovery and Segmentation in Internet Images

abstract: We present a new unsupervised algorithm to discover and segment out common objects from large and diverse image collections. In contrast to previous co-segmentation methods, our algorithm performs well even in the presence of significant amounts of noise images (images not containing a common object), as typical for datasets collected from Internet search. The key insight to our algorithm is that common object patterns should be salient within each image, while being sparse with respect to smooth transformations across other images. We propose to use dense correspondences between images to capture the sparsity and visual variability of the common object over the entire database, which enables us to ignore noise objects that may be salient within their own images but do not commonly occur in others. We performed extensive numerical evaluation on established co-segmentation datasets, as well as several new datasets generated using Internet search. Our approach is able to effectively segment out the common object for diverse object categories, while naturally identifying images where the common object is not present.


title: Beam-Deflection Using Gradient Refractive-Index Media for 60-GHz End-Fire Antenna

abstract: This communication describes a beam-tilting technique for planar dipole antennas utilizing gradient refractive-index metamaterial (GRIM) unit-cells. Beam-deflection mechanism is based on the phase shift phenomena resulting from the interaction of the EM waves with media of different refractive-indices implemented using GRIM unit-cells. The GRIM unit-cell comprises of a stub-loaded I-shaped resonant structure that is directly integrated onto the dipole antenna. The simulation and experimental results show that an antenna with a ${5} \times {4}$ array of GRIM unit-cells can steer the main beam in the E-plane by $+ {26}^\circ $ with respect to the end-fire direction over 57–64 GHz. The antenna exhibits 4-dB gain enhancement and ${\text{S}_{{11}}}$ better than $- {10}\;\text{dB}$ from 57–64 GHz. It is also shown that a quad-feed dipole antenna with GRIM arrays can deflect the beam by $\pm{56}^\circ $ .


DEVELOPMENT OF A MECHATRONIC BLIND STICK


title: On the Approximation Capability of Recurrent Neural Networks

abstract: Abstract The capability of recurrent neural networks of approximating functions from lists of real vectors to a real vector space is examined. Any measurable function can be approximated in probability. Additionally, bounds on the resources sufficient for an approximation can be derived in interesting cases. On the contrary, there exist computable mappings on symbolic data which cannot be approximated in the maximum norm. For restricted input length, some continuous functions on real-valued sequences need a number of neurons increasing at least linearly in the input length. On unary sequences, any mapping with bounded range can be approximated in the maximum norm. Consequently, standard sigmoidal networks can compute any mapping on offline inputs as a computational model.


title: Stochastic Ratio Matching of RBMs for Sparse High-Dimensional Inputs

abstract: Sparse high-dimensional data vectors are common in many application domains where a very large number of rarely non-zero features can be devised. Unfortunately, this creates a computational bottleneck for unsupervised feature learning algorithms such as those based on auto-encoders and RBMs, because they involve a reconstruction step where the whole input vector is predicted from the current feature values. An algorithm was recently developed to successfully handle the case of auto-encoders, based on an importance sampling scheme stochastically selecting which input elements to actually reconstruct during training for each particular example. To generalize this idea to RBMs, we propose a stochastic ratio-matching algorithm that inherits all the computational advantages and unbiasedness of the importance sampling scheme. We show that stochastic ratio matching is a good estimator, allowing the approach to beat the state-of-the-art on two bag-of-word text classification benchmarks (20 Newsgroups and RCV1), while keeping computational cost linear in the number of non-zeros.


title: Review of emerging surgical robotic technology

abstract: BACKGROUND ::: The use of laparoscopic and robotic procedures has increased in general surgery. Minimally invasive robotic surgery has made tremendous progress in a relatively short period of time, realizing improvements for both the patient and surgeon. This has led to an increase in the use and development of robotic devices and platforms for general surgery. The purpose of this review is to explore current and emerging surgical robotic technologies in a growing and dynamic environment of research and development. ::: ::: ::: METHODS ::: This review explores medical and surgical robotic endoscopic surgery and peripheral technologies currently available or in development. The devices discussed here are specific to general surgery, including laparoscopy, colonoscopy, esophagogastroduodenoscopy, and thoracoscopy. Benefits and limitations of each technology were identified and applicable future directions were described. ::: ::: ::: RESULTS ::: A number of FDA-approved devices and platforms for robotic surgery were reviewed, including the da Vinci Surgical System, Sensei X Robotic Catheter System, FreeHand 1.2, invendoscopy E200 system, Flex® Robotic System, Senhance, ARES, the Single-Port Instrument Delivery Extended Research (SPIDER), and the NeoGuide Colonoscope. Additionally, platforms were reviewed which have not yet obtained FDA approval including MiroSurge, ViaCath System, SPORT™ Surgical System, SurgiBot, Versius Robotic System, Master and Slave Transluminal Endoscopic Robot, Verb Surgical, Miniature In Vivo Robot, and the Einstein Surgical Robot. ::: ::: ::: CONCLUSIONS ::: The use and demand for robotic medical and surgical platforms is increasing and new technologies are continually being developed. New technologies are increasingly implemented to improve on the capabilities of previously established systems. Future studies are needed to further evaluate the strengths and weaknesses of each robotic surgical device and platform in the operating suite.


title: A Lightweight Virtualization Solution for Android Devices

abstract: Mobile virtualization has emerged fairly recently and is considered a valuable way to mitigate security risks on Android devices. However, major challenges in mobile virtualization include runtime, hardware, resource overhead, and compatibility. In this paper, we propose a lightweight Android virtualization solution named Condroid , which is based on container technology. Condroid utilizes resource isolation based on namespaces feature and resource control based on cgroups feature. By leveraging them, Condroid can host multiple independent Android virtual machines on a single kernel to support mutilple Android containers. Furthermore, our implementation presents both a system service sharing mechanism to reduce memory utilization and a filesystem sharing mechanism to reduce storage usage. The evaluation results on Google Nexus 5 demonstrate that Condroid is feasible in terms of runtime, hardware resource overhead, and compatibility. Therefore, we find that Condroid has a higher performance than other virtualization solutions.


title: Mapping usability heuristics and design principles for touchscreen-based mobile devices

abstract: Touchscreen-based mobile devices (TMDs) are one of the most popular and widespread kind of electronic device. Many manufacturers have published its own design principles as a guideline for developers. Each platform has specific constrains and recommendations for software development; specially in terms of user interface. Four sets of design principles from iOS, Windows Phone, Android and Tizen OS has been mapped against a set of usability heuristics for TMDs. The map shows that the TMDs usability heuristics cover almost every design pattern with the addition of two new dimensions: user experience and cognitive load. These new dimensions will be considered when updating the proposal of usability heuristics for TMDs.


title: Sample Selection Bias as a Specification Error

abstract: Sample selection bias as a specification error This paper discusses the bias that results from using non-randomly selected samples to estimate behavioral relationships as an ordinary specification error or «omitted variables» bias. A simple consistent two stage estimator is considered that enables analysts to utilize simple regression methods to estimate behavioral functions by least squares methods. The asymptotic distribution of the estimator is derived.


title: Who but not where: The effect of social play on immersion in digital games

abstract: The majority of digital games available today offer a variety of multi-player settings including co-located and mediated play between opponents. Immersion, the sense of being ''in the game,'' is one of the key components of the gaming experience but existing literature suggests that social play provides more fun but less immersion. There is however little empirical support for this. This paper therefore addresses the question: how does playing digital games in a social situation alter the sense of immersion felt by the individuals playing? This paper presents three experiments that test the relationship between social setting and immersion. The three experiments aim to manipulate the social setting in which players play, be it against a computer, against a person online or against a co-located person. Overall the three experiments show that players are more immersed when playing against another person rather than playing against a computer but there is no significant difference in immersion whether the other person is online or in the same room. This refutes previous claims about social play reducing immersion and indeed that social play enhances the sense of being in the game where interaction is through the game.


title: Psychological universals: What are they and how can we know

abstract: Psychological universals, or core mental attributes shared by humans everywhere, are a foundational postulate of psychology, yet explicit analysis of how to identify such universals is lacking. This article offers a conceptual and methodological framework to guide the investigation of genuine universals through empirical analysis of psychological patterns across cultures. Issues of cross-cultural generalizability of psychological processes and 3 cross-cultural research strategies to probe universals are considered. Four distinct levels of hierarchically organized universals are possible: From strongest to weakest claims for universality, they are accessibility universals, functional universals, existential universals, and nonuniversals. Finally, universals are examined in relation to the questions of levels of analysis, evolutionary explanations of psychological processes, and management of cross-cultural relations.


title: Fast PatchMatch stereo matching using cross-scale cost fusion for automotive applications

abstract: Due to recent developments of low-cost image sensors and high-performance embedded processing hardware, future cars and automotive systems will increasingly use binocular stereo vision for environmental perception. However, research and development in stereo vision is still ongoing since there are many challenges unsolved. In this paper, we propose a fast and accurate stereo matching algorithm, designed for automotive applications. It convincingly handles real-world scenes containing complex, textureless, and slanted surfaces. To achieve that, we propose an improved PatchMatch stereo algorithm that combines a census-based cost function with Semi-Global Matching optimization integrated in a cross-scale fusion processing scheme. To further accelerate the algorithm, we propose a novel enhancement approach for PatchMatch-based approximation which allows us to skip the random search or at least significantly reduce the number of iterations. Our method is ranked in the upper third of the KITTI benchmark and among the top performers in terms of processing time.


title: Mixed signal SoC based Bio-Sensor Node for long term health monitoring

abstract: Major requirements of wearable biosensor nodes include small size, light weight and low power consumption. It can be fulfilled by exploring various high integration technologies such as Application Specific Integrated Circuit (ASIC) or System on Chip (SoC) implementation. This paper presents SoC prototype of a Biosensor Node. Electrocardiogram (ECG), a vital body parameter, has been considered for prototype implementation. It is based on PSoC (Programmable System on Chip), a reconfigurable platform for SoC implementation. The main feature of PSoC is the programmable analog and digital blocks, which allow the implementation of a mixed signal circuit in single chip. Biosensor node comprises of data acquisition module implemented in PSoC 5 LP interfaced with Bluetooth module. It continuously acquires ECG data and wirelessly transmits the data to central node, which essentially is a smart phone. An android application has been used to plot received ECG signal data for monitoring over the long duration.


title: Light trapping limits in plasmonic solar cells: an analytical investigation.

abstract: We analytically investigate the light trapping performance in plasmonic solar cells with Si/metallic structures. We consider absorption enhancements for surface plasmon polaritons (SPPs) at planar Si/metal interfaces and localized surface plasmon resonances (LSPRs) for metallic spheres in a Si matrix. We discover that the enhancement factors at Si/metal interfaces are not bound to the conventional Lambertian limit, and strong absorption can be achieved around plasmonic resonant frequencies. In addition, those enhancements are greatly reduced as the fields decay away from the Si/metal interfaces. Therefore, localized plasmonic resonances can be used as efficient light trapping schemes for ultrathin Si solar cells (< 50 nm), while photonic guided mode enhancement is more appropriate for thicker films.


title: Tracking onscreen gender and role bias over time

abstract: Film and television play an important role in popular culture. However studies that require watching and annotating video are time-consuming and expensive to run at scale. We explore information mined from media database cast lists to explore the evolution of different roles over time. We focus on the gender distribution of those roles and how this changes over time. We compare real-life em- ployment gender distributions to our web-mediated onscreen gender data. Finally, we investigate how gender role biases differ between film and television. We propose these methodologies are a useful adjunct to traditional analysis that allow researchers to explore the relationship between online and on- screen gender depictions.


title: Intelligent traffic recommender system

abstract: In this paper we present our solution of using ICT and ubiquitous computing with the goal of reducing traffic congestion during the peek hours. The solution is in presenting intelligent recommender system that is based on the combination of several machine learning techniques used for traffic density estimation, and recommending the optimal period for accessing the crossroad. We describe the design and implementation of that system, and also perform a simulation that shows its usefulness.


title: Bare-Bone Particle Swarm Optimisation for Simultaneously Discretising and Selecting Features for High-Dimensional Classification

abstract: Feature selection and discretisation have shown their effectiveness for data preprocessing especially for high-dimensional data with many irrelevant features. While feature selection selects only relevant features, feature discretisation finds a discrete representation of data that contains enough information but ignoring some minor fluctuation. These techniques are usually applied in two stages, discretisation and then selection since many feature selection methods work only on discrete features. Most commonly used discretisation methods are univariate in which each feature is discretised independently; therefore, the feature selection stage may not work efficiently since information showing feature interaction is not considered in the discretisation process. In this study, we propose a new method called PSO-DFS using bare-bone particle swarm optimisation (BBPSO) for discretisation and feature selection in a single stage. The results on ten high-dimensional datasets show that PSO-DFS obtains a substantial dimensionality reduction for all datasets. The classification performance is significantly improved or at least maintained on nine out of ten datasets by using the transformed “small” data obtained from PSO-DFS. Compared to applying the two-stage approach which uses PSO for feature selection on the discretised data, PSO-DFS achieves better performance on six datasets, and similar performance on three datasets with a much smaller number of features selected.


title: Fingerprint Combination for Privacy Protection

abstract: We propose here a novel system for protecting fingerprint privacy by combining two different fingerprints into a new identity. In the enrollment, two fingerprints are captured from two different fingers. We extract the minutiae positions from one fingerprint, the orientation from the other fingerprint, and the reference points from both fingerprints. Based on this extracted information and our proposed coding strategies, a combined minutiae template is generated and stored in a database. In the authentication, the system requires two query fingerprints from the same two fingers which are used in the enrollment. A two-stage fingerprint matching process is proposed for matching the two query fingerprints against a combined minutiae template. By storing the combined minutiae template, the complete minutiae feature of a single fingerprint will not be compromised when the database is stolen. Furthermore, because of the similarity in topology, it is difficult for the attacker to distinguish a combined minutiae template from the original minutiae templates. With the help of an existing fingerprint reconstruction approach, we are able to convert the combined minutiae template into a real-look alike combined fingerprint. Thus, a new virtual identity is created for the two different fingerprints, which can be matched using minutiae-based fingerprint matching algorithms. The experimental results show that our system can achieve a very low error rate with FRR = 0.4% at FAR = 0.1%. Compared with the state-of-the-art technique, our work has the advantage in creating a better new virtual identity when the two different fingerprints are randomly chosen.


title: Pitch and Roll Camera Orientation from a Single 2D Image Using Convolutional Neural Networks

abstract: In this paper, we propose using convolutional neural networks (CNNs) to automatically determine the pitch and roll of a camera using a single, scene agnostic, 2D image. We compared a linear regressor, a two-layer neural network, and two CNNs. We show the CNNs produce high levels of accuracy in estimating the ground truth orientations which can be used in various computer vision tasks where calculating the camera orientation is necessary or useful. By utilizing accelerometer data in an existing image dataset, we were able to provide the large camera orientation ground truth dataset needed to train such a network with approximately correct values. The trained network is then fine-tuned to smaller datasets with exact camera orientation labels. Additionally, the network is fine-tuned to a dataset with different intrinsic camera parameters to demonstrate the transferability of the network.


title: Coupling analysis and equivalent circuit model of the IC stripline method

abstract: As portable electronic devices are widely used in wireless communication, analysis of RF interference becomes an essential step for IC designers. In order to test electromagnetic compatibility (EMC) of IC operating at high frequencies, IC stripline method is proposed in IEC standard. This method can be applied up to 3 GHz and covers the testing of ICs and small component. This paper represents simulation results of the open version of IC stripline in 3D EM solver. Also, the coupling effect of IC stripline method is analyzed with S-parameter results. The distributed lumped-element equivalent model is presented for explaining coupling relation between IC stripline and package. This model can be used for quick analysis for EMC of ICs.


title: Smartphone Apps as a Source of Cancer Information: Changing Trends in Health Information-Seeking Behavior

abstract: There is an increased interest in smartphone applications as a tool for delivery of health-care information. There have been no studies which evaluated the availability and content of cancer-related smartphone applications. This study aims to identify and analyze cancer-related applications available on the Apple iTunes platform. The Apple iTunes store was searched for cancer-related smartphone applications on July 29, 2011. The content of the applications was analyzed for cost, type of information, validity, and involvement of health-care agencies. A total of 77 relevant applications were identified. There were 24.6 % apps uploaded by health-care agencies, and 36 % of the apps were aimed at health-care workers. Among the apps, 55.8 % provided scientifically validated data. The difference in scientific validity between the apps aimed at general population versus health-care professionals was statistically significant (P < 0.01). Seventy-nine percent of the apps uploaded by health-care agencies were found to be backed by scientific data. There is lack of cancer-related applications with scientifically backed data. There is a need to improve the accountability and reliability of cancer-related smartphone applications and encourage participation by health-care agencies to ensure patient safety.


Understanding the Dynamic Interplay of Social Buzz and Contribution Behavior within and between Online Platforms – Evidence from Crowdfunding


title: Cbica: Correlation based incremental clustering algorithm, a new approach

abstract: With progress in the area of computer science, it is achievable to read, process, store and generate information out of the available data. Humongous amount of data is generated, which is of mixed type, including time-series, Boolean, spatial-temporal and alpha-numeric data. This data is generated at a very giant speed and volume, which makes difficult for the traditional clustering algorithms to create and maintain the desired clusters. Thus, the proposed system encourages incremental clustering using a non-probability based similarity measure. The experimental results, of Correlation Based Incremental Clustering Algorithm (CBICA), which are obtained using the Pearson's coefficient of correlation, are compared with the experimental results of the Closeness-Factor Based Algorithm (CFBA), which uses the probability based similarity measures. The threshold computation is done to decide the cluster members in the post clustering phase, to adapt influx of new data. Wherein the new data is accommodated in the available clusters or new clusters are formed, depending upon the threshold values.


title: Motivating Factors Influencing College Students' Choice of Academic Major

abstract: Objectives. To assess the factors, motivations, and nonacademic influences that affected the choice of major among pharmacy and nonpharmacy undergraduate students. Methods. A survey was administered to 618 pharmacy and nonpharmacy majors to assess background and motivational factors that may have influenced their choice of major. The sample consisted of freshman and sophomore students enrolled in a required speech course. Results. African-American and Hispanic students were less likely to choose pharmacy as a major than Caucasians, whereas Asian-Americans were more likely to choose pharmacy as a major. Pharmacy students were more likely to be interested in science and math than nonpharmacy students. Conclusion. Students' self-reported racial/ethnic backgrounds influence their decision of whether to choose pharmacy as their academic major. Results of this survey provide further insight into developing effective recruiting strategies and enhancing the marketing efforts of academic institutions.


Software Architecture Quality Measurement Stability and


title: Congenital pouch colon in females

abstract: From January 1995 to March 1998, congenital pouch colon (CPC) with anorectal agenesis was diagnosed in ten girls who were classified into four groups based on the length of normal colon proximal to the colonic pouch. Of six girls with little or no normal colon, one had a cloacal anomaly while five had a colovesical or colovestibular fistula along with a completely bifid uterus and cervix and a septate vagina. Initial surgery consisted of ligation of the fistula and subtotal pouch excision with tubularization of the remaining colon in four girls, ileostomy after excision of a gangrenous pouch in one, and window colostomy in another. None of the patients had a sacral abnormality or associated major malformation. Definitive surgery using the posterior sagittal approach consisted of a pull-through of the tubularized colon in three girls (including one in whom one-stage cloacal reconstruction was performed), the ileum in one, and the proximal colon in another. The four girls with a longer length of normal colon had an associated vestibular fistula. In these, a colostomy was constructed just proximal to the pouch with definitive surgery in two patients consisting of excision of the pouch and pull-through of the proximal colon. Definitive surgery was well tolerated in all seven patients. The embryogenesis of this condition and the related genitourinary tract abnormalities are discussed.


title: Fast Calculation of Histogram of Oriented Gradient Feature by Removing Redundancy in Overlapping Block

abstract: In order to improve pedestrian detection accuracy, histogram of oriented gradient (HOG) feature is widely used in many applications. Although HOG feature can provide high detection accuracy, fast detection time is hardly achieved due to its computational complexity. Therefore, this paper describes a novel algorithm for fast calculation of HOG feature. In the proposed algorithm, HOG feature is calculated based on cells instead of overlapping blocks to avoid redundancy. Furthermore, by identifying key rules and sharing common operations in trilinear interpolation, the number of required operations in HOG feature calculation is reduced up to 60.5% while detection accuracy is not degraded at all. Therefore, the proposed method is applicable to many applications such as intelligent vehicles, robots, and surveillance systems in which both high detection rate and fast detection time are strongly required.


title: CLASSIFICATION OF WAVELET TRANSFORMED EEG SIGNALS WITH NEURAL NETWORK FOR IMAGINED MENTAL AND MOTOR TASKS

abstract: Brain-computer interfaces (BCI) are devices that enable communication between a computer and humans by using brain activity as input signals. Brain imaging technology used in a BCI system is usually electroencephalography (EEG). In order to properly interpret brain activity, acquired signals from the brain have to be classified correctly. In this paper EEG signals are transformed by means of discrete wavelet transform. Thus the obtained signal features are used as inputs for a neural network classifier that should separate five different sets of EEG signals representing various mental tasks. Mean classification accuracy for the recognition of all five tasks was 90.75% and mean classification accuracy for the recognition of two tasks (baseline and any other mental task) was 99.87%. The same procedure was also used on the motor imagery dataset. A mean classification accuracy of 68.21% suggests alternative methods of feature extraction for motor imagery tasks.


title: Power management in virtualized data centers: state of the art

abstract: Cloud computing is an emerging technology in the field of computing that provides access to a wide range of shared resources. The rapid growth of cloud computing has led to establishing numerous data centers around the world. As data centers consume huge amounts of power, enhancing their power efficiency has become a major challenge in cloud computing. This paper surveys previous studies and researches that aimed to improve power efficiency of virtualized data centers. This survey is a valuable guide for researchers in the field of power efficiency in virtualized data centers following the cloud computing model.


title: Improving the power cycling performance of the emitter contact of IGBT modules: Implementation and evaluation of stitch bond layouts

abstract: Abstract The emitter (front metallization) of IGBTs is contacted by wire bonding. In this study, the influence of the wirebond layout on the power cycling performance of IGBT modules is investigated. Stitch bonding is implemented to modify the wirebond layout of the emitter contact. The different layouts are subjected to power cycling tests. For a better understanding of the experimental results, electrical and thermo-mechanical FEM simulations are run and the current distribution and induced mechanical stress is discussed. Based on the results of this study, the emitter contact of the new HiPak module platform is designed.


title: Dream Worlds: Production Design for Animation

abstract: Foreword by Don Hahn, Producer of Beauty and the Beast and The Lion King Introduction Film Analysis Beauty and the Beast Visual Development and Research Aladdin Inspiration Camera Rules The Lion King Composition Hercules Staging and Formats Mulan Rhythm and Syle Variety Lilo and Stitch Value and Color Brother Bear Unfinished Disney Projects Own Projects The Masters - Bambi


Author name disambiguation


title: The Gender Paradox in Suicide

abstract: In most Western countries females have higher rates of suicidal ideation and behavior than males, yet mortality from suicide is typically lower for females than for males. This article explores the gender paradox of suicidal behavior, examines its validity, and critically examines some of the explanations, concluding that the gender paradox of suicidal behavior is a real phenomenon and not a mere artifact of data collection. At the same time, the gender paradox in suicide is a more culture-bound phenomenon than has been traditionally assumed; cultural expectations about gender and suicidal behavior strongly determine its existence. Evidence from the United States and Canada suggests that the gender gap may be more prominent in communities where different suicidal behaviors are expected of females and males. These divergent expectations may affect the scenarios chosen by females and males, once suicide becomes a possibility, as well as the interpretations of those who are charged with determining whether a particular behavior is suicidal (e.g., coroners). The realization that cultural influences play an important role in the gender paradox of suicidal behaviors holds important implications for research and for public policy.


title: SNAP: A General-Purpose Network Analysis and Graph-Mining Library

abstract: Large networks are becoming a widely used abstraction for studying complex systems in a broad set of disciplines, ranging from social-network analysis to molecular biology and neuroscience. Despite an increasing need to analyze and manipulate large networks, only a limited number of tools are available for this task. Here, we describe the Stanford Network Analysis Platform (SNAP), a general-purpose, high-performance system that provides easy-to-use, high-level operations for analysis and manipulation of large networks. We present SNAP functionality, describe its implementational details, and give performance benchmarks. SNAP has been developed for single big-memory machines, and it balances the trade-off between maximum performance, compact in-memory graph representation, and the ability to handle dynamic graphs in which nodes and edges are being added or removed over time. SNAP can process massive networks with hundreds of millions of nodes and billions of edges. SNAP offers over 140 different graph algorithms that can efficiently manipulate large graphs, calculate structural properties, generate regular and random graphs, and handle attributes and metadata on nodes and edges. Besides being able to handle large graphs, an additional strength of SNAP is that networks and their attributes are fully dynamic; they can be modified during the computation at low cost. SNAP is provided as an open-source library in C++ as well as a module in Python. We also describe the Stanford Large Network Dataset, a set of social and information real-world networks and datasets, which we make publicly available. The collection is a complementary resource to our SNAP software and is widely used for development and benchmarking of graph analytics algorithms.


title: A 4Ω 2.65W Class-D Audio Amplifier With Embedded DC-DC Boost Converter, Current Sensing ADC and DSP for Adaptive Speaker Protection

abstract: In this paper a class-D smart speaker driver is presented that can deliver 2.65 W at 1% THD into a 4Ω load. Maximal output power is maintained at low battery voltage by supplying the class-D amplifier from a DC-DC boost converter. Speaker damage is avoided by a speaker protection algorithm that runs on an embedded DSP. The protection algorithm estimates the membrane excursion and voice coil temperature using a speaker model that tracks the speaker impedance which is determined by measuring the speaker current with less than 2% relative error. A sample & hold technique is presented that rejects the load current ripple by sampling at the moments where the instantaneous load current equals the average current. At full output power the combined efficiency of the class-D amplifier and DC-DC boost converter is higher than 80%. The complete system is implemented on a single chip that measures 6.6 mm2 and is fabricated in a 0.14 μm CMOS technology with a 5 V gate-oxide option.


An investigation of the intellectual structure of opinion mining research


title: Assortment Auctions: A Myersonian Characterization for Markov Chain based Choice Models

abstract: We introduce the assortment auction optimization problem, defined as follows. A seller has a set of substitute products with exogenously-given prices. Each buyer has a ranked list from which she would like to purchase at most one product. The buyers report their lists to the seller, who then allocates products to the buyers using a truthful mechanism, subject to constraints on how many products can be allocated. The seller collects revenues equal to the prices of the products allocated, and would like to design an auction to maximize total revenue, when the buyers' lists are drawn independently from known distributions. ::: If there is a single buyer, then our problem reduces to the assortment optimization problem, which is solved for Markov Chain choice models. We extend this result and compute the optimal assortment auction when each buyer's list distribution arises from its own Markov chain. Moreover, we show that the optimal auction is structurally ``Myersonian'', in that each buyer is assigned a virtual valuation based on her list and Markov chain, and then the mechanism maximizes virtual surplus. Since Markov Chain choice models capture valuation distributions, our optimal assortment auction generalizes the classical Myerson's auction. Markov chains also capture the commonly used MNL choice model. We show that without the Markov chain assumption, the optimal assortment auction may be structurally non-Myersonian. ::: Finally, we apply the concept of an assortment auction in online assortment problems. We show that any personalized assortment policy is a special case of a truthful assortment auction, and that moreover, the optimal auction provides a tighter relaxation for online policies than the commonly-used ``deterministic LP''. Using this fact, we improve many online assortment policies, and derive the first approximation guarantees that strictly exceed 1-1/e.


title: Analytical calculation of air-gap magnetic field distribution in transverse-flux machines

abstract: Analytical methods are useful tools in the design of rotating electric machines because the simulation times tend to be low and they provide a good insight. However, in the analysis of non-traditional machines, like the transverse-flux machine (TFM), the most common approach is to use finite element analysis (FEA). In this paper we show how to use the complex permeance (CP) methodology to obtain the magnetic field distribution on the air-gap of a homopolar TFM produced by the stator windings. The properties of the CP function are based on conformal mapping theory. Also, we propose an algorithm to estimate the parameters of the CP function after evaluating the function in a set of randomly generated number of points, which reduces the computation time significantly. The proposed methodology is applied to a single-sided TFM showing that the results are consistent with FEA calculations.


title: D.L.D., Dynamic Lighting Design: Parametric Interactive Lighting Software in Urban Public Space

abstract: D.L.D., Dynamic Lighting Design, is a three year, E.U. funded, ongoing research project, concerning ways of designing dynamic luminous environments for pedestrians in open public spaces. This paper presents a set of ideas about how space and the surface of architecture are defined in a dynamic luminous environment, which enhance pedestrian lighting and encourage people to walk and visit public spaces. The D.L.D., parametric interactive lighting software in urban public space, will also be outlined in this paper.


title: The Economic Position of Women in Asia

abstract: This article surveys the literature on the economic position of women in Asian countries. Statistics show that the relative position of women improves with economic development. A negative income effect on women's economic participation in the paid workforce results in an inverse U‐shaped relationship between female participation rate and economic development. Cultural influences affect the degree of discrimination against women, and these vary between countries. China and Australia illustrate the potentially moderating role of government policy on gender discrimination.


title: Effects of hapten epitope structure and hapten-self conjugation pattern on T cell specificity and Ir gene control in hapten-self cytotoxic and helper T cell responses.

abstract: Mouse strains of H-2b haplotype exhibit much weaker cytotoxic T lymphocyte (CTL) responses to haptens reactive with amino groups of cell surface (NH2-reactive haptens) compared with H-2k strains. However, H-2b strains can generate high CTL responses to haptens reactive with sulfhydryl groups of cell surface (SH-reactive haptens). The present study investigates the role of haptenic structure and hapten-cell surface reaction patterns in influencing the generation of the T cell specificity as well as the H-2-linked genetic control. CTL and helper T cell responses were generated against two structurally related haptens, N-iodoacetyl-N'-(5-sulfonic-1-naphthyl) ethylene-diamine (SH-reactive AEDANS; AED-SH) and 5-sulfo-1-naphthoxy acetic acid N-hydroxysuccinimide ester (NH2-reactive form of AEDANS; AED-NH2) by immunizing C57BL/6N (H-2b) mice with these hapten-modified syngeneic spleen cells. Spleen cells from primed C57BL/6N mice generated strong CTL and helper T cell activities upon in vitro restimulation with the respective hapten-modified self. The generation of potent anti-AED-NH2 CTL and helper T cell responses in C57BL/6N mice sharply contrasted with the failure of NH2-reactive haptens studied thus far to generate strong anti-hapten cytotoxic responses in H-2b mice. Antibodies induced against the above two haptens exhibited extensive cross-reactivity detected by hemagglutination, whereas CTL and helper T cells clearly discriminated the structural difference between AED-NH2 and AED-SH haptens. The hapten specificity in T cell recognition was also observed between AED-NH2 and trinitrophenyl (TNP) haptens, which were demonstrated to functionally modify similar cell surface sites. These results indicate that hapten epitope structure and hapten-cell membrane conjugation patterns influence the generation of H-2-linked genetic control and T cell specificity in anti-hapten self cytotoxic as well as helper T cell responses.


title: Design considerations of sub-mW indoor light energy harvesting for wireless sensor systems

abstract: For most wireless sensor networks, one common and major bottleneck is the limited battery lifetime. The frequent maintenance efforts associated with battery replacement significantly increase the system operational and logistics cost. Unnoticed power failures on nodes will degrade the system reliability and may lead to system failure. In building management applications, to solve this problem, small energy sources such as indoor light energy are promising to provide long-term power to these distributed wireless sensor nodes. This article provides comprehensive design considerations for an indoor light energy harvesting system for building management applications. Photovoltaic cells characteristics, energy storage units, power management circuit design, and power consumption pattern of the target mote are presented. Maximum power point tracking circuits are proposed which significantly increase the power obtained from the solar cells. The novel fast charge circuit reduces the charging time. A prototype was then successfully built and tested in various indoor light conditions to discover the practical issues of the design. The evaluation results show that the proposed prototype increases the power harvested from the PV cells by 30% and also accelerates the charging rate by 34% in a typical indoor lighting condition. By entirely eliminating the rechargeable battery as energy storage, the proposed system would expect an operational lifetime 10--20 years instead of the current less than 6 months battery lifetime.


title: Automated Machine Vision System for Liquid Particle Inspection of Pharmaceutical Injection

abstract: The particle matter inspection for pharmaceutical injection is inevitable in the field of pharmaceutical manufacturing, as it has the direct impact on the quality of the drugs. It is a challenge to inspect the contaminated injection online using an imaging system. This paper introduces a novel and effective inspection machine consisting of three modules, a mechanical system with 120 carousel grips, an image acquisition system with multihigh resolution cameras and a multilight sources station, and a distributed industrial electrical computer control system. Particle visual inspection machine first acquires image sequence using the high-speed image acquisition system. The image capture process at each camera module is alternately synchronized with different LED illumination techniques (light transmission method and light reflection method), enabling independent capture of particle images from the same container. Then, a set of novel algorithms for image registration and fast segmentation are proposed to minimize false rejections even in sensitive conditions, which enable the identification of all the tiny potential defects. Finally, a particle tracking and classification algorithm based on an adaptive local weighted-collaborative sparse model is also presented. The experiments demonstrate that the proposed inspection system can effectively detect the particles in the pharmaceutical infusion solution online, and achieve a performance rate of above 97% average accuracy.


title: Desulfobacter psychrotolerans sp. nov., a new psychrotolerant sulfate-reducing bacterium and descriptions of its physiological response to temperature changes

abstract: A psychrotrolerant acetate-oxidizing sulfate-reducing bacterium (strain akvb T ) was isolated from sediment from the northern part of The North Sea with annual temperature fluctuations between 8 and 14 � C. Of the various substrates tested, strain akvb T grew exclusively by the oxidation of acetate coupled to the reduction of sulfate. The cells were motile, thick rods with round ends and grew in dense aggregates. Strain akvb T grew at temperatures ranging from )3.6 to 26.3 � C. Optimal growth was observed at 20 � C. The highest cell specific sulfate reduction rate of 6.2 fmol cell )1 d )1 determined by the 35 SO 2� 4 method was measured at 26 � C. The temperature range of short-term sulfate reduction rates exceeded the temperature range of growth by 5 � C. The Arrhenius relationship for the temperature dependence of growth and sulfate reduction was linear, with two distinct slopes below the optimum temperatures of both processes. The critical temperature was 6.4 � C. The highest growth yield (4.3–4.5 g dry weight mol )1 acetate) was determined at temperatures between 5 and 15 � C. The cellular fatty acid composition was determined with cultures grown at 4 and 20 � C, respectively. The relative proportion of cellular unsaturated fatty acids (e.g. 16:1x7c) was higher in cells grown at 4 � C than in cells grown at 20 � C. The physiological responses to temperature changes showed that strain akvb T was well adapted to the temperature regime of the environment from which it was isolated. Phylogenetic analysis showed that strain akvb T is closest related to Desulfobacter hydrogenophilus, with a 16S rRNA gene sequence similarity of 98.6%. DNA–DNA-hybridization showed a similarity of 32% between D. hydrogenophilus and strain akvb T . Based on phenotypic and DNA-based characteristics we propose that strain akvb T is a member of a new species, Desulfobacter psychrotolerans


title: An analysis of NP-completeness in novelty and diversity ranking

abstract: A useful ability for search engines is to be able to rank objects with novelty and diversity: the top k documents retrieved should cover possible intents of a query with some distribution, or should contain a diverse set of subtopics related to the user's information need, or contain nuggets of information with little redundancy. Evaluation measures have been introduced to measure the effectiveness of systems at this task, but these measures have worst-case NP-hard computation time. The primary consequence of this is that there is no ranking principle akin to the Probability Ranking Principle for document relevance that provides uniform instruction on how to rank documents for novelty and diversity. We use simulation to investigate the practical implications of this for optimization and evaluation of retrieval systems.


title: CitySee: not only a wireless sensor network

abstract: CitySee, an environment monitoring system with 1196 sensor nodes and 4 mesh nodes in an urban area, is mainly motivated by the needs of precise carbon emission measurement and real-time surveillance for CO2 management in cities. Being one of the largest working wireless sensor networks, CitySee faces several challenges such as hardware design, software development, platforms, network protocols, and, most important, satisfactory services to users. We share some early lessons learned from this project, illustrate the potential benefits and risks of current solutions, and discuss the possible extensions of CitySee applications.


title: Primary angle closure glaucoma: What we know and what we don't know

abstract: Primary angle-closure glaucoma (PACG) is a common cause of blindness. Angle closure is a fundamental pathologic process in PAGC. With the development of imaging devices for the anterior segment of the eye, a better understanding of the pathogenesis of angle closure has been reached. Aside from pupillary block and plateau iris, multiple-mechanisms are more common contributors for closure of the angle such as choroidal thickness and uveal expansion, which may be responsible for the presenting features of PACG. Recent Genome Wide Association Studies identified several new PACG loci and genes, which may shed light on the molecular mechanisms of PACG. The current classification systems of PACG remain controversial. Focusing the anterior chamber angle is a principal management strategy for PACG. Treatments to open the angle or halt the angle closure process such as laser peripheral iridotomy and/or iridoplasty, as well as cataract extraction, are proving their effectiveness. PACG may be preventable in the early stages if future research can identify which kind of angles and/or persons are more likely to benefit from prophylactic treatment. New treatment strategies like adjusting the psychological status and balancing the sympathetic-parasympathetic nerve activity, and innovative medicines are needed to improve the prognosis of PACG. In this review, we intend to describe current understanding and unknown aspects of PACG, and to share the clinical experience and viewpoints of the authors.


title: The Random Neural Network: A Survey

abstract: The random neural network (RNN) is a recurrent neural network model inspired by the spiking behaviour of biological neuronal networks. Contrary to most artificial neural network models, neurons in the RNN interact by probabilistically exchanging excitatory and inhibitory spiking signals. The model is described by analytical equations, has a low complexity supervised learning algorithm and is a universal approximator for bounded continuous functions. The RNN has been applied in a variety of areas including pattern recognition, classification, image processing, combinatorial optimization and communication systems. It has also inspired research activity in modelling interacting entities in various systems such as queueing and gene regulatory networks. This paper presents a review of the theory, extension models, learning algorithms and applications of the RNN.


title: IMAGINATION: a robust image-based CAPTCHA generation system

abstract: We propose IMAGINATION (IMAge Generation for INternet AuthenticaTION), a system for the generation of attack-resistant, user-friendly, image-based CAPTCHAs. In our system, we produce controlled distortions on randomly chosen images and present them to the user for annotation from a given list of words. The distortions are performed in a way that satisfies the incongruous requirements of low perceptual degradation and high resistance to attack by content-based image retrieval systems. Word choices are carefully generated to avoid ambiguity as well as to avoid attacks based on the choices themselves. Preliminary results demonstrate the attack-resistance and user-friendliness of our system compared to text-based CAPTCHAs.


title: A System for Efficient High-Recall Retrieval

abstract: The goal of high-recall information retrieval (HRIR) is to find all or nearly all relevant documents for a search topic. In this paper, we present the design of our system that affords efficient high-recall retrieval. HRIR systems commonly rely on iterative relevance feedback. Our system uses a state-of-the-art implementation of continuous active learning (CAL), and is designed to allow other feedback systems to be attached with little work. Our system allows users to judge documents as fast as possible with no perceptible interface lag. We also support the integration of a search engine for users who would like to interactively search and judge documents. In addition to detailing the design of our system, we report on user feedback collected as part of a 50 participants user study. While we have found that users find the most relevant documents when we restrict user interaction, a majority of participants prefer having flexibility in user interaction. Our work has implications on how to build effective assessment systems and what features of the system are believed to be useful by users.


title: Self-Improving Visual Odometry

abstract: We propose a self-supervised learning framework that uses unlabeled monocular video sequences to generate large-scale supervision for training a Visual Odometry (VO) frontend, a network which computes pointwise data associations across images. Our self-improving method enables a VO frontend to learn over time, unlike other VO and SLAM systems which require time-consuming hand-tuning or expensive data collection to adapt to new environments. Our proposed frontend operates on monocular images and consists of a single multi-task convolutional neural network which outputs 2D keypoints locations, keypoint descriptors, and a novel point stability score. We use the output of VO to create a self-supervised dataset of point correspondences to retrain the frontend. When trained using VO at scale on 2.5 million monocular images from ScanNet, the stability classifier automatically discovers a ranking for keypoints that are not likely to help in VO, such as t-junctions across depth discontinuities, features on shadows and highlights, and dynamic objects like people. The resulting frontend outperforms both traditional methods (SIFT, ORB, AKAZE) and deep learning methods (SuperPoint and LF-Net) in a 3D-to-2D pose estimation task on ScanNet.


title: Learning Montezuma's Revenge from a Single Demonstration

abstract: We propose a new method for learning from a single demonstration to solve hard exploration tasks like the Atari game Montezuma's Revenge. Instead of imitating human demonstrations, as proposed in other recent works, our approach is to maximize rewards directly. Our agent is trained using off-the-shelf reinforcement learning, but starts every episode by resetting to a state from a demonstration. By starting from such demonstration states, the agent requires much less exploration to learn a game compared to when it starts from the beginning of the game at every episode. We analyze reinforcement learning for tasks with sparse rewards in a simple toy environment, where we show that the run-time of standard RL methods scales exponentially in the number of states between rewards. Our method reduces this to quadratic scaling, opening up many tasks that were previously infeasible. We then apply our method to Montezuma's Revenge, for which we present a trained agent achieving a high-score of 74,500, better than any previously published result.


title: Depth Resolution Enhancement in Time-of-Flight Cameras Using Polarization State of the Reflected Light

abstract: Time-of-Flight (ToF) cameras are nowadays commonly used for depth measurements and are useful in applications, such as robotic navigation, gaming, and others. The depth measurements by the ToF camera are influenced by the systematic errors of the camera and the nonsystematic errors of the environment. This paper presents a novel method for reduction of depth measurement error using polarization state of the reflected light. Using polarization state of the reflected light, the nonsystematic errors introduced due to multipath interference, material properties, and color of the target object reduces, which reduces the depth errors. The idea is tested using SR-4000 3-D sensor and a linear polarizer in front of it. The transmission angle of the linear polarizer is varied for a fixed object distance from the camera. The depth error has a sinusoidal variation with clear maxima and minima. It is observed that the depth error is minimum when the polarization angle is equal to the ToF phase shift. The experimental results show a maximum of 87% reduction in depth error at 3000 mm distance using polarization angle equal to ToF phase shift. The reduction in the nonsystematic error in the ToF-based distance measurements using polarization of the reflected light will enable new applications, such as diffuser imaging, detection of transparent objects, and among others.