title: OpenLensFlare: an open-source, lens flare designing and rendering framework

abstract: Lens flare rendering in computer games has always been lacking. While physically motivated solutions based on ::: real camera systems exist, their inherent complexity makes them inappropriate for widespread use. Developers are ::: not trained to work with these complicated imaging systems, and even if they were, artists and designers prefer ::: intuitive parameters over these complicated optical system descriptions. More support is needed to start adopting ::: to these advanced models, and to show that with an appropriate tool the situation can be vastly improved. This ::: paper describes OpenLensFlare, an open-source C++ framework for rendering convincing physically-based lens ::: flare effects. Additionally, a supporting optical system editor is also provided, which is capable of producing and ::: visualizing optical systems and showing an example usage of the run-time library. Together, these two systems can ::: be used to replace the existing naive algorithms, with small effort and low integration complexity.


title: Acoustic Event Detection and Classification

abstract: The human activity that takes place in meeting rooms or classrooms is reflected in a rich variety of acoustic events (AE), produced either by the human body or by objects handled by humans, so the determination of both the identity of sounds and their position in time may help to detect and describe that human activity. Indeed, speech is usually the most informative sound, but other kinds of AEs may also carry useful information, for example, clapping or laughing inside a speech, a strong yawn in the middle of a lecture, a chair moving or a door slam when the meeting has just started. Additionally, detection and classification of sounds other than speech may be useful to enhance the robustness of speech technologies like automatic speech recognition.


title: Soil moisture evaluation using multi-temporal synthetic aperture radar (SAR) in semiarid rangeland

abstract: Abstract There have been several efforts to utilize satellite-based synthetic aperture radar (SAR) measurements to determine surface soil moisture (to 5 cm) conditions of rangeland regions. The results have been mixed since the relation between the SAR signal and surface soil moisture is confounded by variations in topographic features, surface roughness and vegetation density. We designed an experiment to investigate the sensitivity of C-band SAR backscatter ( σ 0 ) to surface soil moisture ( θ s ) in a semiarid rangeland and to test a data-fusion approach based on both optical (Landsat TM) and radar (ERS-2 SAR) measurements to improve regional estimates of surface soil moisture content. The data-fusion approach [Sano, E.E. 1997. Sensitivity analysis of C- and Ku-band synthetic aperture radar data to soil moisture content in a semiarid regions. Ph.D. Dissertation. University of Arizona, AZ] utilized the difference between dry- and wet-season SAR σ 0 to normalize roughness effects, and utilized surface reflectance in optical wavelengths to account for differences in vegetation density. We focused the study on three flat, uniformly vegetated sites of known surface roughness, monitored variations in surface soil moisture, vegetation density and SAR signal over time, and obtained eight optical/SAR image pairs throughout the dry and wet seasons. For these sparsely vegetated sites during this dry year (1997), we found that the SAR signal was not significantly attenuated by sparse green vegetation cover ( green leaf area index ) and dense standing brown vegetation cover (brown leaf area index up to 1.5). Consequently, the optical data was not required for this application, and the approach could be implemented by simply taking the difference between the dry- and wet-season SAR σ 0 ( σ 0 − σ dry 0 ). For a data set of eight dates at three study sites, we confirmed that the relation between ERS-2 C-band SAR σ 0 and θ s was weak ( r 2 =0.27); yet for the same data set, that the relation between σ 0 − σ dry 0 and θ s was strong and significant ( r 2 =0.93). This study also raised two concerns: (1) the overall sensitivity of SAR σ 0 to θ s was relatively low, and (2) the approach required a high level of accuracy in the estimate of green leaf area level that may not be obtainable with standard optical remote sensing algorithms. In any case, the positive results from this study should encourage the use of a multi-temporal SAR and optical/SAR fusion for monitoring semiarid range conditions, and improving management of scarce resources.


title: Click Here: Human-Localized Keypoints as Guidance for Viewpoint Estimation

abstract: We motivate and address a human-in-the-loop variant of the monocular viewpoint estimation task in which the location and class of one semantic object keypoint is available at test time. In order to leverage the keypoint information, we devise a Convolutional Neural Network called Click-Here CNN (CH-CNN) that integrates the keypoint information with activations from the layers that process the image. It transforms the keypoint information into a 2D map that can be used to weigh features from certain parts of the image more heavily. The weighted sum of these spatial features is combined with global image features to provide relevant information to the prediction layers. To train our network, we collect a novel dataset of 3D keypoint annotations on thousands of CAD models, and synthetically render millions of images with 2D keypoint information. On test instances from PASCAL 3D+, our model achieves a mean class accuracy of 90.7%, whereas the state-of-the-art baseline only obtains 85.7% mean class accuracy, justifying our argument for human-in-the-loop inference.


title: CareToy: An Intelligent Baby Gym: Home-Based Intervention for Infants at Risk for Neurodevelopmental Disorders

abstract: This article describes the design of an innovative system for early intervention (EI) at home in infancy. The aim is to develop a smart device capable of promoting and measuring the actions of infants in the first months of life. The CareToy system, inspired by a commercially available gym for infants and equipped with a variety of sensors, can provide an intensive, individualized, home-based and familycentered EI program remotely telemonitored by clinicians. An array of sensors measures the activity of infants inside the gym. The sensor signals related to infant's movement, interaction with toys, and pressure distribution are acquired and processed in real time to classify the infant's activities and behavior. Rewards (feedback) are provided to the infants on the basis of their activity. Data are interpreted offline in terms of clinical meaning, and experimental tests are also reported.


title: Attribute-based People Search: Lessons Learnt from a Practical Surveillance System

abstract: We address the problem of attribute-based people search in real surveillance environments. The system we developed is capable of answering user queries such as "show me all people with a beard and sunglasses, wearing a white hat and a patterned blue shirt, from all metro cameras in the downtown area, from 2pm to 4pm last Saturday". In this paper, we describe the lessons we learned from practical deployments of our system, and how we made our algorithms achieve the accuracy and efficiency required by many police departments around the world. In particular, we show that a novel set of multimodal integral filters and proper normalization of attribute scores are critical to obtain good performance. We conduct a comprehensive experimental analysis on video footage captured from a large set of surveillance cameras monitoring metro chokepoints, in both crowded and normal activity periods. Moreover, we show impressive results using images from the recent Boston marathon bombing event, where our system can rapidly retrieve the two suspects based on their attributes from a database containing more than one thousand people present at the event.


LASER SCANNER BASED SLAM IN REAL ROAD AND TRAFFIC ENVIRONMENT


title: OPAL : Automated Form Understanding for the Deep Web ∗

abstract: Forms are our gates to the web. They enable us to access the deep content of web sites. Automatic form understanding unlocks this content for applications ranging from crawlers to meta-search engines and is essential for improving usability and accessibility of the web. Form understanding has received surprisingly little attention other than as component in specific applications such as crawlers. No comprehensive approach to form understanding exists and previous works disagree even in the definition of the problem. In this paper, we present OPAL, the first comprehensive approach to form understanding. We identify form labeling and form interpretation as the two main tasks involved in form understanding. On both problems OPAL pushes the state of the art: For form labeling, it combines signals from the text, structure, and visual rendering of a web page, yielding robust characterisations of common design patterns. In extensive experiments on the ICQ and TEL-8 benchmarks and a set of 200 modern web forms OPAL outperforms previous approaches by a significant margin. For form interpretation, we introduce a template language to describe frequent form patterns. These two parts of OPAL combined yield form understanding with near perfect accuracy (> 98%).


title: PANDA: Pose Aligned Networks for Deep Attribute Modeling

abstract: We propose a method for inferring human attributes (such as gender, hair style, clothes style, expression, action) from images of people under large variation of viewpoint, pose, appearance, articulation and occlusion. Convolutional Neural Nets (CNN) have been shown to perform very well on large scale object recognition problems. In the context of attribute classification, however, the signal is often subtle and it may cover only a small part of the image, while the image is dominated by the effects of pose and viewpoint. Discounting for pose variation would require training on very large labeled datasets which are not presently available. Part-based models, such as poselets [4] and DPM [12] have been shown to perform well for this problem but they are limited by shallow low-level features. We propose a new method which combines part-based models and deep learning by training pose-normalized CNNs. We show substantial improvement vs. state-of-the-art methods on challenging attribute classification tasks in unconstrained settings. Experiments confirm that our method outperforms both the best part-based methods on this problem and conventional CNNs trained on the full bounding box of the person.


title: An alignment-based framework to check the conformance of declarative process models and to preprocess event-log data

abstract: Process mining can be seen as the "missing link" between data mining and business process management. The lion's share of process mining research has been devoted to the discovery of procedural process models from event logs. However, often there are predefined constraints that (partially) describe the normative or expected process, e.g., "activity A should be followed by B" or "activities A and B should never be both executed". A collection of such constraints is called a declarative process model. Although it is possible to discover such models based on event data, this paper focuses on aligning event logs and predefined declarative process models. Discrepancies between log and model are mediated such that observed log traces are related to paths in the model. The resulting alignments provide sophisticated diagnostics that pinpoint where deviations occur and how severe they are. Moreover, selected parts of the declarative process model can be used to clean and repair the event log before applying other process mining techniques. Our alignment-based approach for preprocessing and conformance checking using declarative process models has been implemented in ProMand has been evaluated using both synthetic logs and real-life logs from a Dutch hospital.


title: People with Visual Impairment Training Personal Object Recognizers: Feasibility and Challenges

abstract: Blind people often need to identify objects around them, from packages of food to items of clothing. Automatic object recognition continues to provide limited assistance in such tasks because models tend to be trained on images taken by sighted people with different background clutter, scale, viewpoints, occlusion, and image quality than in photos taken by blind users. We explore personal object recognizers, where visually impaired people train a mobile application with a few snapshots of objects of interest and provide custom labels. We adopt transfer learning with a deep learning system for user-defined multi-label k-instance classification. Experiments with blind participants demonstrate the feasibility of our approach, which reaches accuracies over 90% for some participants. We analyze user data and feedback to explore effects of sample size, photo-quality variance, and object shape; and contrast models trained on photos by blind participants to those by sighted participants and generic recognizers.


CRF-based Clinical Named Entity Recognition using clinical NLP Features


title: DSCarver: decompose-and-spiral-carve for subtractive manufacturing

abstract: We present an automatic algorithm for subtractive manufacturing of freeform 3D objects using high-speed CNC machining.


title: Interaction and outeraction: instant messaging in action

abstract: We discuss findings from an ethnographic study of instant messaging (IM) in the workplace and its implications for media theory. We describe how instant messaging supports a variety of informal communication tasks. We document the affordances of IM that support flexible, expressive communication. We describe some unexpected uses of IM that highlight aspects of communication which are not part of current media theorizing. They pertain to communicative processes people use to connect with each other and to manage communication, rather than to information exchange. We call these processes "outeraction". We discuss how outeractional aspects of communication affect media choice and patterns of media use.


title: A study on the fault diagnosis analysis of variable reluctance resolver for electric vehicle

abstract: Variable reluctance (VR) resolver is widely used in traction motor for battery electric vehicle as well as hybrid electric vehicle as a rotor position sensor. VR resolver generates absolute position signal by using resolver-to-digital converter (RDC) in order to deliver exact position of permanent magnets in a rotor of traction motor to motor controller. This paper deals with fault diagnosis of VR resolver by using co-simulation analysis with RDC for position angle detection. As fault conditions, eccentricity of VR resolver, short circuit condition of excitation coil and output signal coils, and material problem of silicon steel in a view point of permeability are considered. 2D FEM is used for the output signal waveforms of SIN, COS and these waveforms are converted into absolute position angle by using the algorithm of RDC. For the verification of proposed analysis results, experiment on fault conditions was conducted and compared with simulation ones.


title: Manual evaluation of the diaphragm muscle

abstract: The respiratory diaphragm is the most important muscle for breathing. It contributes to various processes such as expectoration, vomiting, swallowing, urination, and defecation. It facilitates the venous and lymphatic return and helps viscera located above and below the diaphragm to work properly. Its activity is fundamental in the maintenance of posture and body position changes. It can affect the pain perception and emotional state. Many authors reported on diaphragmatic training by using special instruments, whereas only a few studies focused on manual therapy approaches. To the knowledge of the authors, the existing scientific literature does not exhaustively examines the manual evaluation of the diaphragm in its different portions. A complete evaluation of the diaphragm is mandatory for several professional subjects, such as physiotherapists, osteopaths, and chiropractors not only to elaborate a treatment strategy but also to obtain information on the validity of the training performed on the patient. This article aims to describe a strategy of manual evaluation of the diaphragm, with particular attention to anatomical fundamentals, in order to stimulate further research on this less explored field.


title: Attention Alignment Multimodal LSTM for Fine-Gained Common Space Learning

abstract: We address the problem common space learning approach that maps all related multimodal information into a common space for multimodal data. To establish a fine-grained common space, the aligned relevant local information of different modalities is used to learn a common subspace where the projected fragmented information is further integrated according to intra-modal semantic relationships. Specifically, we propose a novel multimodal LSTM with an attention alignment mechanism, namely attention alignment multimodal LSTM (AAM-LSTM), which mainly includes attentional alignment recurrent network (AA-R) and hierarchical multimodal LSTM (HM-LSTM). Different from the traditional methods which operate on the full modal data directly, the proposed model exploits the inter-modal and intra-modal semantic relationships of local information, to jointly establish a uniform representation of multi-modal data. Specifically, AA-R automatically captures semantic-aligned local information to learn common subspace without the need of supervised labels, then HM-LSTM leverages the potential relationships of these local information to learn a fine-grained common space. The experimental results on Filker30K, Filker8K, and Filker30K entities verify the performance and effectiveness of our model, which compares favorably with the state-of-the-art methods. In particular, the experiment of phrase localization on AA-R with Filker30K entities shows the expected accurate attention alignment. Moreover, from the experiment results of image-sentence retrieval tasks, it can be concluded that the proposed AAM-LSTM outperforms benchmark algorithms by a large margin.


Law Enforcement and Digital Evidence


title: Postdural puncture headache following acupuncture

abstract: To the Editor: Acupuncture is a popular technique with worldwide application, particularly in clinical pain management. However, severe life-threatening acupuncture-related complications have been reported, even though it is considered a safe procedure [1]. Here, we report a case of postdural puncture headache (PDPH) that occurred following acupuncture for the treatment of a patient’s lower back pain. A 21-year-old male patient visited our hospital for a 2-day headache that commenced 2 h following acupuncture. The headache, accompanied by nausea, was located on the frontal region of his head, and sitting and standing exacerbated the discomfort. However, adopting the supine position allowed the pain to subside. The patient was healthy except for a previous complaint of 3 months of lower back pain, and myofascial pain was diagnosed. He had received medications and rehabilitation treatment, which, however, were ineffective. He then received a single acupuncture treatment. On admission, the physical examination showed no neurological disorder, except for seven acupoint wounds on his back, close to the fifth lumbar (L5) and first sacrum spine (S1). The patient was admitted to our hospital because of his severe headache. Magnetic resonance imaging of the brain revealed no intracranial pathology. PDPH, caused by acupuncture, was therefore diagnosed. Conventional treatments, including bed rest, fluid hydration, and oral analgesic (acetaminophen), were ineffective, however. Then, epidural blood patching with 10 ml autologous blood was injected into the L5–S1 epidural space. The headache subsided dramatically 15 min following the procedure, and the patient was duly discharged. Acupuncture was identified as the cause of cerebrospinal fluid leakage from puncture sites, which presented as headache; it is often associated with intracranial hypotension. Our patient’s postural headache had the same symptoms as PDPH, an adverse effect occasionally seen following spinal and epidural anesthesia. However, acupuncture-associated PDPH is rarely reported [2]. Aside from risk factors such as carelessness in performing acupuncture or poor operator skills, low body weight, resulting in less distance of the skin to the subarachnoid space, may have caused this patient be more vulnerable to this complication. The effective depth of an acupuncture needle insertion into the back, buttock, or leg ranges from 15 to 125 mm [3]. In our patient, a relatively long needle (3 inches) was used for acupuncture treatment. Thus, using a long acupuncture needle for a patient with low body weight might be the cause of the complication. In conclusion, PDPH should be considered following acupuncture for back pain treatment, and EBP can provide dramatic relief of the headache. We hope learning about this case will encourage clinicians to take more care when performing acupuncture. H.-C. Horng Department of Anesthesiology, Taichung Armed Forces General Hospital, Taichung, Taiwan


title: A new UHF anti-metal RFID tag antenna design with open-circuited stub feed

abstract: A new UHF RFID tag antenna used on the surface of a metallic object is proposed in this paper. In practical RFID applications, metals appear in many occasions. However, the UHF band is very sensitive to metal. When the tag antenna is near a metal, its impedance matching, radiation efficiency and directivity change and the reading distance is reduced so quickly that it cannot work. Therefore, it's necessary to employ some special handling or use a special tag so that it can be applied on the metal surface. After several rounds of design optimization and experimental validation, the designed tag antenna can work in the 920 MHz frequency band and is able to maintain a good performance on the metal surface. What's more, it has some advantages such as planar structure, small size and low cost. The proposed tag antenna provides an effective solution for metal identification applications in the UHF frequency band, which is validated by simulation results and practical test.


title: Classification with Noisy Labels by Importance Reweighting

abstract: In this paper, we study a classification problem in which sample labels are randomly corrupted. In this scenario, there is an unobservable sample with noise-free labels. However, before being observed, the true labels are independently flipped with a probability $\rho \in [0,0.5)$ , and the random label noise can be class-conditional. Here, we address two fundamental problems raised by this scenario. The first is how to best use the abundant surrogate loss functions designed for the traditional classification problem when there is label noise. We prove that any surrogate loss function can be used for classification with noisy labels by using importance reweighting, with consistency assurance that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. The other is the open problem of how to obtain the noise rate $\rho$ . We show that the rate is upper bounded by the conditional probability $P(\hat{Y}|X)$ of the noisy sample. Consequently, the rate can be estimated, because the upper bound can be easily reached in classification problems. Experimental results on synthetic and real datasets confirm the efficiency of our methods.


Pediatric Exercise Science:Back to the Future


title: Detection of Consistent Patterns from Process Enactment Data

abstract: Software process improvement has been a focus of industry for manyyears. To assist with the implementation of process improvement, we provide anapproach to recover process enactment data. The goal of our method is to uncoverthe actual process used and thereby provide evidence for improving thequality of a planned software process that is followed by an organization in thefuture. The recovered process model (or patterns) is presented at the same levelof abstraction as the planned process model. This allows an easy and clearmethod to identify the distance between a planned process model and the actualproject enactment. We investigate the enactment of a defined software processmodel from the view of understanding the opportunity for process model improvementfrom the viewpoint of the project managers in the context of a smallsoftware development organization. We collected data from one of our collaborationorganizations and then applied our method to a case study. The consistenciesbetween a planned process model and the project enactment were measured.The outcomes of our method provide precise information including qualitativeand quantitative data to assist project managers with process improvement in futurepractice. The main contribution of our work is to provide a novel approachto assist software process improvement by recovering a model from processenactment data.


title: Understanding the Sharing Economy: The Role of Response to Negative Reviews in the Peer-To-Peer Accommodation Sharing Network?

abstract: Recognizing the potentially ruinous effect of negative reviews on the reputation of the hosts as well as a subjective nature of the travel experience judgements, peer-to-peer accommodation sharing plat-forms, like Airbnb, have readily embraced the “response” option, empowering hosts with the voice to challenge, deny or at least apologize for the subject of critique. However, the effects of different re-sponse strategies on trusting beliefs towards the host remain unclear. To fill this gap, this study focus-es on understanding the impact of different response strategies and review negativity on trusting be-liefs towards the host in peer-to-peer accommodation sharing setting utilizing experimental methods. Examination of two different contexts, varying in the controllability of the subject of complaint, re-veals that when the subject of complaint is controllable by a host, such strategies as confession / apol-ogy and denial can improve trusting beliefs towards the host. However, when the subject of criticism is beyond the control of the host, denial of the issue does not yield guest’s confidence in the host, where-as confession and excuse have positive influence on trusting beliefs.


title: Inhibition-related Activation in the Right Inferior Frontal Gyrus in the Absence of Inhibitory Cues

abstract: The right inferior frontal gyrus (rIFG) has been hypothesized to mediate response inhibition. Typically response inhibition is signaled by an external stop cue, which provides a top-down signal to initiate the process. However, recent behavioral findings suggest that response inhibition can also be triggered automatically by bottom-up processes. In the present study, we evaluated whether rIFG activity would also be observed during automatic inhibition, in which no stop cue was presented and no motor inhibition was actually required. We measured rIFG activation in response to stimuli that were previously associated with stop signals but which required a response on the current trial (reversal trials). The results revealed an increase in rIFG (pars triangularis) activity, suggesting that it can be activated by associations between stimuli and stopping. Moreover, its role in inhibition tasks is not contingent on the presence of an external stop cue. We conclude that rIFG involvement in stopping is consistent with a role in reprogramming of action plans, which may comprise inhibition, and its activity can be triggered through automatic, bottom-up processing.


SU 2 : An Open-Source Suite for Multiphysics Simulation and Design


title: A virtual memory based runtime to support multi-tenancy in clusters with GPUs

abstract: Graphics Processing Units (GPUs) are increasingly becoming part of HPC clusters. Nevertheless, cloud computing services and resource management frameworks targeting heterogeneous clusters including GPUs are still in their infancy. Further, GPU software stacks (e.g., CUDA driver and runtime) currently provide very limited support to concurrency. In this paper, we propose a runtime system that provides abstraction and sharing of GPUs, while allowing isolation of concurrent applications. A central component of our runtime is a memory manager that provides a virtual memory abstraction to the applications. Our runtime is flexible in terms of scheduling policies, and allows dynamic (as opposed to programmer-defined) binding of applications to GPUs. In addition, our framework supports dynamic load balancing, dynamic upgrade and downgrade of GPUs, and is resilient to their failures. Our runtime can be deployed in combination with VM-based cloud computing services to allow virtualization of heterogeneous clusters, or in combination with HPC cluster resource managers to form an integrated resource management infrastructure for heterogeneous clusters. Experiments conducted on a three-node cluster show that our GPU sharing scheme allows up to a 28% and a 50% performance improvement over serialized execution on short- and long-running jobs, respectively. Further, dynamic inter-node load balancing leads to an additional 18-20% performance benefit.


title: Balanced Corpus of Contemporary Written Japanese

abstract: The balanced corpus of contemporary written Japanese (BCCWJ) is Japan's first 100 million words balanced corpus. It consists of three subcorpora (publication subcorpus, library subcorpus, and special-purpose subcorpus) and covers a wide range of text registers including books in general, magazines, newspapers, governmental white papers, best-selling books, an internet bulletin-board, a blog, school textbooks, minutes of the national diet, publicity newsletters of local governments, laws, and poetry verses. A random sampling technique is utilized whenever possible in order to maximize the representativeness of the corpus. The corpus is annotated in terms of dual POS analysis, document structure, and bibliographical information. The BCCWJ is currently accessible in three different ways including Chunagon a web-based interface to the dual POS analysis data. Lastly, results of some pilot evaluation of the corpus with respect to the textual diversity are reported. The analyses include POS distribution, word-class distribution, entropy of orthography, sentence length, and variation of the adjective predicate. High textual diversity is observed in all these analyses.


title: Who's Afraid of the Big Bad Whorf? Crosslinguistic Differences in Temporal Language and Thought

abstract: The idea that language shapes the way we think, often associated with Benjamin Whorf, has long been decried as not only wrong but also fundamentally wrong-headed. Yet, experimental evidence has reopened debate about the extent to which language influences nonlinguistic cognition, particularly in the domain of time. In this article, I will first analyze an influential argument against the Whorfian hypothesis and show that its anti-Whorfian conclusion is in part an artifact of conflating two distinct questions: Do we think in language? and Does language shape thought? Next, I will discuss crosslinguistic differences in spatial metaphors for time and describe experiments that demonstrate corresponding differences in nonlinguistic mental representations. Finally, I will sketch a simple learning mechanism by which some linguistic relativity effects appear to arise. Although people may not think in language, speakers of different languages develop distinctive conceptual repertoires as a consequence of ordinary and presumably universal neural and cognitive processes.


title: Development of next-generation system-on-package ( SOP ) technology based on silicon carriers with fine-pitch chip interconnection

abstract: System-on-Package (SOP) technology based on silicon carriers has the potential to provide modular design flexibility and high-performance integration of heterogeneous chip technologies and to support robust chip manufacturing with high-yield/low-cost chips for a wide range of two- and three-dimensional product applications. Key technology enablers include silicon through-vias, high-density wiring, high-I/O chip interconnection, and supporting test and assembly technologies. The silicon through-vias are a key feature permitting efficient area array signal, power, and ground interconnection through these thinned silicon packages. High-density wiring and high-density chip I/O interconnection can enable tight integration of heterogeneous chip technologies which approximate the performance of an integrated system-on-chip with a "virtual chip" using the silicon package for integration. Silicon carrier fabrication leverages existing manufacturing capability and mid-UV lithography to provide very dense package wiring following CMOS back-end-of-line design rules. Further, the thermal expansion of the silicon carrier package matches the chip, which helps maintain reliability even as the high-density chip microbump interconnections scale to smaller size. In addition to heterogeneous chip integration, SOP products may leverage the integration of passive components, active devices, and electro-optic structures to enhance system-level performance while also maintaining functional test capability and known good chips when needed. This paper describes the technical challenges and recent progress made in the development of silicon carrier technology for potential new applications.


Annotating and Automatically Tagging Constructions of Causal Language


title: Smartphone App Usage Prediction Using Points of Interest

abstract: In this paper we present the first population-level, city-scale analysis of application usage on smartphones. Using deep packet inspection at the network operator level, we obtained a geo-tagged dataset with more than 6 million unique devices that launched more than 10,000 unique applications across the city of Shanghai over one week. We develop a technique that leverages transfer learning to predict which applications are most popular and estimate the whole usage distribution based on the Point of Interest (POI) information of that particular location. We demonstrate that our technique has an 83.0% hitrate in successfully identifying the top five popular applications, and a 0.15 RMSE when estimating usage with just 10% sampled sparse data. It outperforms by about 25.7% over the existing state-of-the-art approaches. Our findings pave the way for predicting which apps are relevant to a user given their current location, and which applications are popular where. The implications of our findings are broad: it enables a range of systems to benefit from such timely predictions, including operating systems, network operators, appstores, advertisers, and service providers.


title: The World-as-Support: Embodied Exploration, Understanding and Meaning-Making of the Augmented World

abstract: Current technical capabilities of mobile technologies are consolidating the interest in developing context-aware Augmented/Mixed Reality applications. Most of these applications are designed based on the Window-on-the-World (WoW) interaction paradigm. A significant decrease in cost of projection technology and advances in pico-sized projectors have spurred applications of Projective Augmented Reality. This research has focused mainly on technological development. However, there is still a need to fully understand its communicational and expressive potential. Hence, we define a conceptual paradigm that we call World-as-Support (WaS). We compare the WaS and WoW paradigms by contrasting their assumptions and cultural values, as well as through a study of an application aimed at supporting the collaborative improvisation of site-specific narratives by children. Our analysis of children's understanding of the physical and social environment and of their imaginative play allowed us to identify the affordances, strengths and weaknesses of these two paradigms.


A Soft-label Method for Noise-tolerant Distantly Supervised Relation Extraction


title: ROFF/RON ratio of nano-well fluidic memristor sensor towards hydroxide based liquid detection

abstract: This paper reports a new design of TiO2 thin film memristor sensor with nano-well implementation. Three nano-wells with diameters of 0.5, 1 and 2 mm fabricated using lift-off techniques. 30 nm TiO2 thin film deposited using RF sputtering process. The behavior of the nano-well memristor sensor was being observed through ROFF/RON ratios to represent the hysteresis loop area of the memristor sensor. The ROFF/RON ratio was being observed in two conditions; free liquid and with liquid. For free liquid, the nano-well memristor was exposed to air while with liquid condition, three glucose concentrations of 10, 20 and 40 mM were used. Experiment shows that ROFF/RON ratio increased as the nano-well diameter increased. The existence of glucose liquid also contributed to the increase in the ratio.


title: Pharmacophore modeling for COX-1 and -2 inhibitors with LigandScout in comparison to Discovery Studio.

abstract: BACKGROUND ::: Pharmacophore modeling has become an integrated tool in drug discovery. However, no prospective study compares the performance of the available software. ::: ::: ::: METHODS ::: The two widely used pharmacophore modeling and screening software programs Discovery Studio and LigandScout were used to generate, validate, and prospectively apply COX-1 and -2 models. Selected virtual hits were tested in cell-free enzymatic assays. The correct retrieval of active compounds was compared. ::: ::: ::: RESULTS ::: In the enzymatic testing, 10.5% of the tested hits for COX-2 and 6.6% of the predicted compounds for COX-1 were active. To directly compare the two models, both based on the same PDB entry, were selected for virtual screening. The two programs yielded vastly different hit lists, but both predicted active compounds. ::: ::: ::: CONCLUSION ::: To obtain a comprehensive selection of active compounds, more than one program should be used for modeling.


title: Both ciliary and non-ciliary functions of Foxj1a confer Wnt/β-catenin signaling in zebrafish left-right patterning

abstract: The Wnt/β-catenin pathway is implicated in left-right (LR) axis determination; however, the underlying mechanism remains elusive. Prompted by our recent discovery that Wnt signaling regulates ciliogenesis in the zebrafish Kupffer's vesicle (KV) via Foxj1a, a ciliogenic transcription factor, we decided to elucidate functions of Foxj1a in Wnt-regulated LR pattern formation. We showed that targeted injection of wnt8a mRNA into a single cell at the 128-cell stage is sufficient to induce ectopic foxj1a expression and ectopic cilia. By interrogating the transcription circuit of foxj1a regulation, we found that both Lef1 and Tcf7 bind to a consensus element in the foxj1a promoter region. Depletion of Lef1 and Tcf7 inhibits foxj1a transcription in the dorsal forerunner cells, downregulates cilia length and number in KV, and randomizes LR asymmetry. Targeted overexpression of a constitutively active form of Lef1 also induced an ectopic protrusion that contains ectopic transcripts for sox17, foxj1a, and charon, and ectopic monocilia. Further genetic studies using this ectopic expression platform revealed two distinct functions of Foxj1a; mediating Wnt-governed monocilia length elongation as well as charon transcription. The novel Foxj1a-charon regulation is conserved in KV, and importantly, it is independent of the canonical role of Foxj1a in the biosynthesis of motile cilia. Together with the known function of motile cilia movement in generating asymmetric expression of charon, our data put forward a hypothesis that Foxj1a confers both ciliary and non-ciliary functions of Wnt signaling, which converge on charon to regulate LR pattern formation.


title: Development of transverse flux linear motor with permanent-magnet excitation for direct drive applications

abstract: This paper presents the development of a transverse flux linear motor (TFLM) with permanent-magnet excitation for Stocker system, which is used for transferring glasses in the liquid crystal display industry. Magnetic field is analyzed by three-dimensional equivalent magnetic circuit network method and two-dimensional finite-element analysis is used to determine the influence of the end effect of a mover on the performance of TFLM. The experimental results of a prototyped motor are in good agreement with the analysis ones.


title: Big data: A new challenge for tourism

abstract: This paper proposes the foundations for a definition of digital tourist survey field based on the study of social networks. Several social network provide essential information on the perceptions of tourist destination. Nevertheless to operate correctly, several points should be discussed. This article provides multiple ideas for reflections on the challenges and opportunities offered by these new media.


title: EARTh: An Environmental Application Reference Thesaurus in the Linked Open Data cloud

abstract: The paper aims at providing a description of EARTh, the Environmental Application Reference Thesaurus. It represents a general-purpose thesaurus for the environment, which has been published as a SKOS dataset in the Linked Open Data cloud. It promises to become a core tool for indexing and discovery environmental resources by refining and extending GEMET, which is considered the de facto standard when speaking of general-purpose thesaurus for the environment in Europe, besides it has been interlinked to popular LOD datasets as AGROVOC, EUROVOC, DBPEDIA and UMTHES. The paper illustrates the main characteristics of EARTh as a guide to its usage. It clarifies i the methodology adopted to define the EARTh content; ii the design and technological choices made when publishing EARTh as Linked Data; iii the information pertaining to its access and maintenance. Descriptions of EARTh applications and future relevance are highlighted.


title: Latent Normalizing Flows for Discrete Sequences

abstract: Normalizing flows are a powerful class of generative models for continuous random variables, showing both strong model flexibility and the potential for non-autoregressive generation. These benefits are also desired when modeling discrete random variables such as text, but directly applying normalizing flows to discrete sequences poses significant additional challenges. We propose a VAE-based generative model which jointly learns a normalizing flow-based distribution in the latent space and a stochastic mapping to an observed discrete space. In this setting, we find that it is crucial for the flow-based distribution to be highly multimodal. To capture this property, we propose several normalizing flow architectures to maximize model flexibility. Experiments consider common discrete sequence tasks of character-level language modeling and polyphonic music generation. Our results indicate that an autoregressive flow-based model can match the performance of a comparable autoregressive baseline, and a non-autoregressive flow-based model can improve generation speed with a penalty to performance.


title: InfoBot: Transfer and Exploration via the Information Bottleneck

abstract: A central challenge in reinforcement learning is discovering effective policies for tasks where rewards are sparsely distributed. We postulate that in the absence of useful reward signals, an effective exploration strategy should seek out {\it decision states}. These states lie at critical junctions in the state space from where the agent can transition to new, potentially unexplored regions. We propose to learn about decision states from prior experience. By training a goal-conditioned policy with an information bottleneck, we can identify decision states by examining where the model actually leverages the goal state. We find that this simple mechanism effectively identifies decision states, even in partially observed settings. In effect, the model learns the sensory cues that correlate with potential subgoals. In new environments, this model can then identify novel subgoals for further exploration, guiding the agent through a sequence of potential decision states and through new regions of the state space.


title: Pronunciation and silence probability modeling for ASR.

abstract: In this paper we evaluate the WER improvement from modeling pronunciation probabilities and word-specific silence probabilities in speech recognition. We do this in the context of Finite State Transducer (FST)-based decoding, where pronunciation and silence probabilities are encoded in the lexicon (L) transducer. We describe a novel way to model word-dependent silence probabilities, where in addition to modeling the probability of silence following each individual word, we also model the probability of each word appearing after silence. All of these probabilities are estimated from aligned training data, with suitable smoothing. We conduct our experiments on four commonly used automatic speech recognition datasets, namely Wall Street Journal, Switchboard, TED-LIUM, and Librispeech. The improvement from modeling pronunciation and silence probabilities is small but fairly consistent across datasets.


title: Comparative Study on Email Spam Classifier using Data Mining Techniques

abstract: In this e-world, most of the transactions and business is taking place through e-mails. Nowadays, email becomes a powerful tool for communication as it saves a lot of time and cost. But, due to social networks and advertisers, most of the emails contain unwanted information called spam. Even though lot of algorithms has been developed for email spam classification, still none of the algorithms produces 100% accuracy in classifying spam emails. In this paper, spam dataset is analyzed using TANAGRA data mining tool to explore the efficient classifier for email spam classification. Initially, feature construction and feature selection is done to extract the relevant features. Then various classification algorithms are applied over this dataset and cross validation is done for each of these classifiers. Finally, best classifier for email spam is identified based on the error rate, precision and recall.


title: Time-series prediction with applications to traffic and moving objects databases

abstract: In this paper we explore the application of travel-speed prediction to query processing in Moving Objects Databases. We propose to revise the motion plans of moving objects using the predicted travel-speeds. This revision occurs before answering queries. We develop three methods of doing this. These methods differ in the time when the motion plans are revised, and which of them are revised. We analyze the three methods theoretically and experimentally.


title: Evaluating the security of logic encryption algorithms

abstract: Contemporary integrated circuits are designed and manufactured in a globalized environment leading to concerns of piracy, overproduction and counterfeiting. One class of techniques to combat these threats is logic encryption. Logic encryption modifies an IC design such that it operates correctly only when a set of newly introduced inputs, called key inputs, are set to the correct values. In this paper, we use algorithms based on satisfiability checking (SAT) to investigate the security of logic encryption. We present a SAT-based algorithm which allows an attacker to “decrypt” an encrypted netlist using a small number of carefully-selected input patterns and their corresponding output observations. We also present a “partial-break” algorithm that can reveal some of the key inputs even when the attack is not fully successful. We conduct a thorough evaluation of our attack by examining six proposals for logic encryption from the literature. We find that all of these are vulnerable to our attack. Among the 441 encrypted circuits we examined, we were able to decrypt 418 (95%). We discuss the strengths and limitations of our attack and suggest directions that may lead to improved logic encryption algorithms.


title: Knock-Knock: The unbearable lightness of Android Notifications

abstract: Android Notifications can be considered as essential parts in Human-Smartphone interaction and inextricable modules of modern mobile applications that can facilitate User Interaction and improve User Experience. This paper presents how this well-crafted and thoroughly documented mechanism, provided by the OS can be exploited by an adversary. More precisely, we present attacks that result either in forging smartphone application notifications to lure the user in disclosing sensitive information, or manipulate Android Notifications to launch a Denial of Service attack to the users' device, locally and remotely, rendering them unusable. This paper concludes by proposing generic countermeasures for the discussed security threats.


title: Optimal Design and Tradeoff Analysis of Planar Transformer in High-Power DC–DC Converters

abstract: The trend toward high power density, high operating frequency, and low profile in power converters has exposed a number of limitations in the use of conventional wire-wound magnetic component structures. A planar magnetic is a low-profile transformer or inductor utilizing planar windings, instead of the traditional windings made of Cu wires. In this paper, the most important factors for planar transformer (PT) design including winding loss, core loss, leakage inductance, and stray capacitance have individually been investigated. The tradeoffs among these factors have to be analyzed in order to achieve optimal parameters. Combined with an application, four typical winding arrangements have been compared to illustrate their advantages and disadvantages. An improved interleaving structure with optimal behaviors is proposed, which constructs the top layer paralleling with the bottom layer and then in series with the other turns of the primary, so that a lower magnetomotive force ratio m can be obtained, as well as minimized ac resistance, leakage inductance, and even stray capacitance. A 1.2-kW full-bridge dc-dc converter prototype employing the improved PT structure has been constructed, over 96% efficiency is achieved, and a 2.7% improvement, compared with the noninterleaving structure, is obtained.


title: A Disruption Analysis in the Mobile Payment Market

abstract: Mobile payments are predicted to have a bright future as m-commerce becomes more popular. However, this promising application has not been as successful as anticipated. This can be partially explained by the infancy of the market and a lack of standards. Moreover, technological and business issues create a market uncertainty that mobile network operators and financial institutions have difficulties managing. Thus, the evolution of the mobile payment market is subject to many speculative scenarios. This paper analyzes two possible disruptions of the mobile payment market.


title: e-Government and Cyber Security: The Role of Cyber Security Exercises

abstract: e-Government operations are increasing with citizen demand for timely and cost effective services. Security associated with individual systems is similar to many e-commerce solutions. The span of control of e-government and its impact across a community defines a system that is more than a sum of just single systems. To test security issues across the entire system requires a new method of analysis, a community based cyber security exercise. Results from recent community based exercises have provided insight into opportunities for improvement and has demonstrated the value of these events. Information gained from community based exercises permits local governmental entities to better prepare their e-government systems to serve their citizens needs. Although some actions discovered in the exercise may be difficult and resource intensive, numerous low-hanging fruit opportunities for improvement can be discovered and used to improve e-government systems.


title: A Decision Framework for Cloud Computing

abstract: Cloud computing technology is garnering success and wisdom-like stories of savings, ease of use, and increased flexibility in controlling how resources are used at any given time to deliver computing capability. This paper develops a preliminary decision framework to assist managers who are determining which cloud solution matches their specific requirements and evaluating the numerous commercial claims (in many cases unsubstantiated) of a cloud's value. This decision framework and research helps managers allocate investments and assess cloud alternatives that now compete with in-house data centers that previously stored, accessed, and processed data or with another company's (outsourced) data center resources. The hypothetically newly captured corporate value (from cloud) is that resources are no longer idle most of the time, and are now much more fully utilized (with lower unit costs). This reduces high ownership and support costs, improves capital leverage, and delivers increased flexibility in the use of resources.


title: Development of a Self-Correcting CMOS-Based Clock Generator for Wireless Sensor Network in 90nm Process Technology

abstract: The trend in today’s technology is continuously evolving and resolving some of the nature’s problems through environmental monitoring. Sensor platforms are acquainted to these functions, for it is one of the devices that enables the reading of data from the environment and interprets to a function that allows monitoring. In every sensor platform, a stable clock generator circuit is a necessity to synchronize the input data from the output results and action. A self-correcting clock generator fixes the process, voltage, and temperature variation issues that affect the outputs of almost all clock generator circuit. The Five-stage Prescriptive Model was used to have a proper procedure in designing the clock generator while pairwise comparison chart and decision matrix were used for the evaluation of alternatives. Current starved ring oscillator was the main circuit block that was used as clock generator which produced the desired frequency. Different circuit blocks such as frequency to voltage converter, analog to digital converter, limiter circuit, trimming circuit, and oscillator are put together as the feedback circuit. The clock automatically corrects itself with a frequency variation of ±10% even if the process, voltage, and temperature change. This study focused on having a low power of 1.12mW for the whole circuit, with a voltage supply of 1.2V. It has three different output frequencies, 26MHz, 13MHz, and 1.08MHz, that were obtained using frequency dividers. The design was implemented using Synopsys tool in 90nm CMOS process technology.


title: A Framework for Efficient and Composable Oblivious Transfer

abstract: We propose a simple and general framework for constructing oblivious transfer (OT) protocols that are efficient, universally composable, and generally realizableunder any one of a variety of standard number-theoretic assumptions, including the decisional Diffie-Hellman assumption, the quadratic residuosity and decisional composite residuosity assumptions, and worst-caselattice assumptions. ::: ::: Our OT protocols are round-optimal (one message each way), quite efficient in computation and communication, and can use a single common string for an unbounded number of executions between the same sender and receiver. Furthermore, the protocols can provide statisticalsecurity to either the sender or the receiver, simply by changing the distribution of the common string. For certain instantiations of the protocol, even a common uniformly randomstring suffices. ::: ::: Our key technical contribution is a simple abstraction that we call a dual-modecryptosystem. We implement dual-mode cryptosystems by taking a unified view of several cryptosystems that have what we call "messy" public keys, whose defining property is that a ciphertext encrypted under such a key carries no information(statistically) about the encrypted message. ::: ::: As a contribution of independent interest, we also provide a multi-bit amortizedversion of Regev's lattice-based cryptosystem (STOC 2005) whose time and space complexity are improved by a linear factor in the security parameter n. The resulting amortized encryption and decryption times are only $\tilde{O}(n)$ bit operations per message bit, and the ciphertext expansion can be made as small as a constant; the public key size and underlying lattice assumption remain essentially the same.


title: A blended learning approach for teaching computer programming: design for large classes in Sub-Saharan Africa

abstract: The challenge of teaching programming in higher education is complicated by problems associated with large class teaching, a prevalent situation in many developing countries. This paper reports on an investigation into the use of a blended learning approach to teaching and learning of programming in a class of more than 200 students. A course and learning environment was designed by integrating constructivist learning models of Constructive Alignment, Conversational Framework and the Three-Stage Learning Model. Design science research is used for the course redesign and development of the learning environment, and action research is integrated to undertake participatory evaluation of the intervention. The action research involved the Students’ Approach to Learning survey, a comparative analysis of students’ performance, and qualitative data analysis of data gathered from various sources. The paper makes a theoretical contribution in presenting a design of a blended learning solution for large class teachi...


title: Heisenbyte: Thwarting Memory Disclosure Attacks using Destructive Code Reads

abstract: Vulnerabilities that disclose executable memory pages enable a new class of powerful code reuse attacks that build the attack payload at runtime. In this work, we present Heisenbyte, a system to protect against memory disclosure attacks. Central to Heisenbyte is the concept of destructive code reads -- code is garbled right after it is read. Garbling the code after reading it takes away from the attacker her ability to leverage memory disclosure bugs in both static code and dynamically generated just-in-time code. By leveraging existing virtualization support, Heisenbyte's novel use of destructive code reads sidesteps the problem of incomplete binary disassembly in binaries, and extends protection to close-sourced COTS binaries, which are two major limitations of prior solutions against memory disclosure vulnerabilities. Our experiments demonstrate that Heisenbyte can tolerate some degree of imperfect static analysis in disassembled binaries, while effectively thwarting dynamic code reuse exploits in both static and JIT code, at a modest 1.8% average runtime overhead due to virtualization and 16.5% average overhead due to the destructive code reads.


title: Comet: Code offload by migrating execution transparently

abstract: In this paper we introduce a runtime system to allow unmodified multi-threaded applications to use multiple machines. The system allows threads to migrate freely between machines depending on the workload. Our prototype, COMET (Code Offload by Migrating Execution Transparently), is a realization of this design built on top of the Dalvik Virtual Machine. COMET leverages the underlying memory model of our runtime to implement distributed shared memory (DSM) with as few interactions between machines as possible. Making use of a new VM-synchronization primitive, COMET imposes little restriction on when migration can occur. Additionally, enough information is maintained so one machine may resume computation after a network failure. ::: ::: We target our efforts towards augmenting smartphones or tablets with machines available in the network. We demonstrate the effectiveness of COMET on several real applications available on Google Play. These applications include image editors, turn-based games, a trip planner, and math tools. Utilizing a server-class machine, COMET can offer significant speed-ups on these real applications when run on a modern smartphone. With WiFi and 3G networks, we observe geometric mean speed-ups of 2.88× and 1.27× relative to the Dalvik interpreter across the set of applications with speed-ups as high as 15× on some applications.


title: Histologic Analysis of Clinical Biopsies Taken 6 Months and 3 Years after Maxillary Sinus Floor Augmentation with 80% Bovine Hydroxyapatite and 20% Autogenous Bone Mixed with Fibrin Glue

abstract: BACKGROUND: Bovine hydroxyapatite (Bio-Oss, Geistlich Pharmaceutical, Wollhausen, Switzerland) has been suggested to be used in maxillary sinus floor augmentation procedures prior to or in conjunct ...


title: A ridged cross-junction power divider for small blockage and symmetrical slot arrangement in the center-feed single-layer slotted waveguide array

abstract: We have proposed a ridged cross-junction power divider for the feed waveguide in a center-feed single-layer waveguide slot array antenna. This feed waveguide is advantageous in terms of reducing blocking area and forming a symmetrical slot array to obtain high isolation. Furthermore, reflection is less than -20dB at the design frequency. The calculated sidelobe level of a ridged cross-junction model is almost -13 dB in the H-plane at 25.3GHz.


title: Isolation of ACC deaminase-producing habitat-adapted symbiotic bacteria associated with halophyte Limonium sinense (Girard) Kuntze and evaluating their plant growth-promoting activity under salt stress

abstract: Background and aims ::: Many plant growth-promoting endophytes (PGPE) possessing 1-aminocyclopropane-1-carboxylate (ACC) deaminase activity can reduce the level of stress ethylene and assist their host plants cope with various biotic and abiotic stresses. However, information about the endophytic bacteria colonizing in the coastal halophytes is still very scarce. This study aims at isolating efficient ACC deaminase-producing plant growth-promoting (PGP) bacterial strains from the inner tissues of a traditional Chinese folk medicine Limonium sinense (Girard) Kuntze, a halophyte which has high economic and medicinal values grown in the coastal saline soils. Their PGP activity and effects on host seed germination and seedling growth under salinity stress were also evaluated.


Dmdedup : Device Mapper Target for Data Deduplication


The Role of “Jigsaw” Method in Enhancing Indonesian Prospective Teachers’ Pedagogical Knowledge and Communication Skill


title: Effects of rubber/filler interactions on deformation behavior of silica filled SBR systems

abstract: Abstract Effect of rubber/filler interactions on the stress–strain behavior for silica filled styrene-butadiene rubber (SBR) vulcanizates were studied in relation to the chain scissions of rubber molecules during deformation. The rubber/filler interactions were controlled by the modification of silica surface using several kinds of silane coupling agents. Information on the chain scission was obtained by the electron spin resonance (ESR) measurements. The combination of ESR results and stress–strain data revealed that, at a given strain, the tensile stress increased with increasing the interfacial interactions between rubber molecules and silica surface, and simultaneously the chain scission became remarkable. Further, the degree of chain scission was closely related to the mechanical energy applied to the vulcanizates. The cyclic tests of stress–strain measurements suggested that the chain scissions contributed to the Mullins effect.


title: An Effective CU Size Decision Method for HEVC Encoders

abstract: The emerging high efficiency video coding standard (HEVC) adopts the quadtree-structured coding unit (CU). Each CU allows recursive splitting into four equal sub-CUs. At each depth level (CU size), the test model of HEVC (HM) performs motion estimation (ME) with different sizes including 2N × 2N, 2N × N, N × 2N and N × N. ME process in HM is performed using all the possible depth levels and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency but requires a very high computational complexity. In this paper, we propose a fast CU size decision algorithm for HM. Since the optimal depth level is highly content-dependent, it is not efficient to use all levels. We can determine CU depth range (including the minimum depth level and the maximum depth level) and skip some specific depth levels rarely used in the previous frame and neighboring CUs. Besides, the proposed algorithm also introduces early termination methods based on motion homogeneity checking, RD cost checking and SKIP mode checking to skip ME on unnecessary CU sizes. Experimental results demonstrate that the proposed algorithm can significantly reduce computational complexity while maintaining almost the same RD performance as the original HEVC encoder.


title: Performance evaluation of solid state transformer based microgrid in FREEDM systems

abstract: A new concept of solid state transformer based microgrid system is presented in this paper. By utilizing 400V DC bus generated from Gen-I solid state transformer proposed by FREEDM systems center, integration issues of DC microgrid and solid state transformer are analyzed. Zonal DC microgrid concept is applied to this novel system with the consideration of burden minimization to the existing AC grid. The future grid architecture is described by using this solid state transformer based integrated microgrid system.


title: A survey on evaluation methods for image segmentation

abstract: Abstract This paper studies different methods proposed so far for segmentation evaluation. Most methods can be classified into three groups: the analytical, the empirical goodness and the empirical discrepancy groups. Each group has its own characteristics. After a brief description of each method in every group, some comparative discussions about different method groups are first carried out. An experimental comparison for some empirical (goodness and discrepancy) methods commonly used is then performed to provide a rank of their evaluation abilities. In addition, some special methods are also discussed. This study is helpful for an appropriate use of existing evaluation methods and for improving their performance as well as for systematically designing new evalution methods.


title: The effects of lowering LDL cholesterol with simvastatin plus ezetimibe in patients with chronic kidney disease (Study of Heart and Renal Protection): a randomised placebo-controlled trial

abstract: Background Lowering LDL cholesterol with statin regimens reduces the risk of myocardial infarction, ischaemic stroke, and the need for coronary revascularisation in people without kidney disease, but its effects in people with moderate-to-severe kidney disease are uncertain. The SHARP trial aimed to assess the efficacy and safety of the combination of simvastatin plus ezetimibe in such patients. Methods This randomised double-blind trial included 9270 patients with chronic kidney disease (3023 on dialysis and 6247 not) with no known history of myocardial infarction or coronary revascularisation. Patients were randomly assigned to simvastatin 20 mg plus ezetimibe 10 mg daily versus matching placebo. The key prespecified outcome was first major atherosclerotic event (non-fatal myocardial infarction or coronary death, non-haemorrhagic stroke, or any arterial revascularisation procedure). All analyses were by intention to treat. This trial is registered at ClinicalTrials.gov, NCT00125593, and I SRCTN54137607. Findings 4650 patients were assigned to receive simvastatin plus ezetimibe and 4620 to placebo. Allocation to simvastatin plus ezetimibe yielded an average LDL cholesterol difference of 0.85 mmol/L (SE 0.02; with about two-thirds compliance) during a median follow-up of 4.9 years and produced a 17% proportional reduction in major atherosclerotic events (526 [11.3%] simvastatin plus ezetimibe vs 619 [13.4%] placebo; rate ratio [RR] 0.83, 95% CI 0.74-0.94; log-rank p=0.0021). Non-significantly fewer patients allocated to simvastatin plus ezetimibe had a non-fatal myocardial infarction or died from coronary heart disease (213 [4.6%] vs 230 [5.0%]; RR 0.92,95% CI 0.76-1.11; p=0.37) and there were significant reductions in non-haemorrhagic stroke (131 [2.8%] vs 174 [3.8%]; RR 0.75,95% CI 0.60-0.94; p=0.01) and arterial revascularisation procedures (284 [6.1%] vs 352 [7.6%]; RR 0.79, 95% CI 0.68-0.93; p=0.0036). After weighting for subgroup-specific reductions in LDL cholesterol, there was no good evidence that the proportional effects on major atherosclerotic events differed from the summary rate ratio in any subgroup examined, and, in particular, they were similar in patients on dialysis and those who were not. The excess risk of myopathy was only two per 10 000 patients per year of treatment with this combination (9 [0.2%] vs 5 [0.1%]). There was no evidence of excess risks of hepatitis (21 [0.5%] vs 18 [0.4%]), gallstones (106 [2.3%] vs 106 [2.3%]), or cancer (438 [9.4%] vs 439 [9.5%], p=0.89) and there was no significant excess of death from any non-vascular cause (668 [14.4%] vs 612 [13.2%], p=0.13). Interpretation Reduction of LDL cholesterol with simvastatin 20 mg plus ezetimibe 10 mg daily safely reduced the incidence of major atherosclerotic events in a wide range of patients with advanced chronic kidney disease.


title: Attention and anticipation in fast visual-inertial navigation

abstract: We study a Visual-Inertial Navigation (VIN) problem in which a robot needs to estimate its state using an on-board camera and an inertial sensor, without any prior knowledge of the external environment. We consider the case in which the robot can allocate limited resources to VIN, due to tight computational constraints. Therefore, we answer the following question: under limited resources, what are the most relevant visual cues to maximize the performance of visual-inertial navigation? Our approach has four key ingredients. First, it is task-driven, in that the selection of the visual cues is guided by a metric quantifying the VIN performance. Second, it exploits the notion of anticipation, since it uses a simplified model for forward-simulation of robot dynamics, predicting the utility of a set of visual cues over a future time horizon. Third, it is efficient and easy to implement, since it leads to a greedy algorithm for the selection of the most relevant visual cues. Fourth, it provides formal performance guarantees: we leverage submodularity to prove that the greedy selection cannot be far from the optimal (combinatorial) selection. Simulations and real experiments on agile drones show that our approach ensures state-of-the-art VIN performance while maintaining a lean processing time. In the easy scenarios, our approach outperforms appearance-based feature selection in terms of localization errors. In the most challenging scenarios, it enables accurate visual-inertial navigation while appearance-based feature selection fails to track robot's motion during aggressive maneuvers.


Epoxy Vitrimers: The Effect of Transesterification Reactions on the Network Structure


title: An improved Particle Swarm Optimization algorithm

abstract: An improved Particle Swarm Optimization (IPSO) algorithm is proposed in this paper. In the algorithm, a premature estimate mechanism is introduced to judge whether the particles accumulate in a small region and tell the probability whether the swarm is trapped in a local optimum. If the estimate criterion is satisfied, the chaotic mutation operation, which makes use of the chaos search strategy and the “uphill” movement of Simulated Annealing algorithm, is performed to increase the diversity of the swarm and to guide the algorithm to escape from the local optimum. Simulation results show that the searching properties including searching efficiency, precision and robustness of IPSO algorithm are obviously better than that of the standard PSO(SPSO) algorithm.


title: HGABAC: Towards a Formal Model of Hierarchical Attribute-Based Access Control

abstract: Attribute-based access control (ABAC) is a promising alternative to traditional models of access control (i.e. discretionary access control (DAC), mandatory access control (MAC) and role-based access control (RBAC)) that is drawing attention in both recent academic literature and industry application. However, formalization of a foundational model of ABAC and large scale adoption are still lacking. This paper seeks to aid in the transition by providing a formal model of hierarchical ABAC, called Hierarchical Group and Attribute-Based Access Control (or HGABAC), which includes attribute inheritance through user and object groups as well as environment, connection and administrative attributes. A formal specification and an attribute-based policy language are provided. Finally, several example configurations (which demonstrate the versatility of the model) are presented and evaluated.


title: Structure of Concentrated-Flux-Type Interior Permanent-Magnet Synchronous Motors Using Ferrite Permanent Magnets

abstract: Interior permanent magnet synchronous motors (IPMs) usually use NdFeB for achieving high-torque and high-efficiency performance. This paper reviews the performance of concentrated-flux-type ferrite magnet motors (CFFMs) that use ferrite, as an alternative to the general type of IPMs that use NdFeB. A structure to reduce the motor volume and improve the torque density of CFFMs is suggested in this paper. The characteristics of CFFMs for different rotor types are analyzed. The motor parameters are analyzed. Using 2-D finite element analysis (FEA), the efficiency and size of the CFFMs are compared with each other. This paper discusses the results of stress analysis for each rotor type using 2-D FEA. Finally, it suggests a CFFM structure to replace the general type of IPMs that use NdFeB. The proposed LC-type has the highest torque density among all the ferrite rotor types. It is seen that the proposed LC-type ferrite-model replacement for the Nd model is the best choice of all the types.


title: CMSN: Continuous Multi-stage Network and Variable Margin Cosine Loss for Temporal Action Proposal Generation

abstract: Accurately locating the start and end time of an action in untrimmed videos is a challenging task. One of the important reasons is the boundary of action is not highly distinguishable, and the features around the boundary are difficult to discriminate. To address this problem, we propose a novel framework for temporal action proposal generation, namely Continuous Multi-stage Network (CMSN), which divides a video that contains a complete action instance into six stages, namely Backgroud, Ready, Start, Confirm, End, Follow. To distinguish between Ready and Start, End and Follow more accurately, we propose a novel loss function, Variable Margin Cosine Loss (VMCL), which allows for different margins between different categories. Our experiments on THUMOS14 show that the proposed method for temporal proposal generation performs better than the state-of-the-art methods using the same network architecture and training dataset.


title: Satellite Image Classification with Deep Learning

abstract: Satellite imagery is important for many applications including disaster response, law enforcement, and environmental monitoring. These applications require the manual identification of objects and facilities in the imagery. Because the geographic expanses to be covered are great and the analysts available to conduct the searches are few, automation is required. Yet traditional object detection and classification algorithms are too inaccurate and unreliable to solve the problem. Deep learning is a family of machine learning algorithms that have shown promise for the automation of such tasks. It has achieved success in image understanding by means of convolutional neural networks. In this paper we apply them to the problem of object and facility recognition in high-resolution, multi-spectral satellite imagery. We describe a deep learning system for classifying objects and facilities from the IARPA Functional Map of the World (fMoW) dataset into 63 different classes. The system consists of an ensemble of convolutional neural networks and additional neural networks that integrate satellite metadata with image features. It is implemented in Python using the Keras and TensorFlow deep learning libraries and runs on a Linux server with an NVIDIA Titan X graphics card. At the time of writing the system is in 2nd place in the fMoW TopCoder competition. Its total accuracy is 83%, the F 1 score is 0.797, and it classifies 15 of the classes with accuracies of 95% or better.


title: Analysing Customer Engagement of Turkish Airlines Using Big Social Data

abstract: Companies started taking advantage of the unlocked potential of Big Social Data, however, research on airlines’ use of social media is limited. This research aims to investigate to what extent Turkish Airlines can utilize their Facebook page to improve performance metrics. This study will exploit the concepts of Big Social Data, customer satisfaction, sentiment analysis to answer the research questions by employing dataand text mining, machine learning. The results showed a weak relationship between the business data and Facebook data, however, the findings provided explanations to customer behavior and showed that most of the company’s Facebook users were likely to purchase a Turkish Airline ticket. Therefore, Turkish Airlines could utilize their Facebook page in the short-term to improve revenue-generating indicators such as customer satisfaction and likelihood of purchase.


title: Cooperative Control of Dynamical Systems With Application to Autonomous Vehicles

abstract: In this paper, a new framework based on matrix theory is proposed to analyze and design cooperative controls for a group of individual dynamical systems whose outputs are sensed by or communicated to others in an intermittent, dynamically changing, and local manner. In the framework, sensing/communication is described mathematically by a time-varying matrix whose dimension is equal to the number of dynamical systems in the group and whose elements assume piecewise-constant and binary values. Dynamical systems are generally heterogeneous and can be transformed into a canonical form of different, arbitrary, but finite relative degrees. Utilizing a set of new results on augmentation of irreducible matrices and on lower triangulation of reducible matrices, the framework allows a designer to study how a general local-and-output-feedback cooperative control can determine group behaviors of the dynamical systems and to see how changes of sensing/communication would impact the group behaviors over time. A necessary and sufficient condition on convergence of a multiplicative sequence of reducible row-stochastic (diagonally positive) matrices is explicitly derived, and through simple choices of a gain matrix in the cooperative control law, the overall closed-loop system is shown to exhibit cooperative behaviors (such as single group behavior, multiple group behaviors, adaptive cooperative behavior for the group, and cooperative formation including individual behaviors). Examples, including formation control of nonholonomic systems in the chained form, are used to illustrate the proposed framework.


title: Reading Between the Lies Identifying Concealed and Falsified Emotions in Universal Facial Expressions

abstract: The widespread supposition that aspects of facial communication are uncontrollable and can betray a deceiver's true emotion has received little empirical attention. We examined the presence of inconsistent emotional expressions and “microexpressions” (1/25–1/5 of a second) in genuine and deceptive facial expressions. Participants viewed disgusting, sad, frightening, happy, and neutral images, responding to each with a genuine or deceptive (simulated, neutralized, or masked) expression. Each 1/30-s frame (104,550 frames in 697 expressions) was analyzed for the presence and duration of universal expressions, microexpressions, and blink rate. Relative to genuine emotions, masked emotions were associated with more inconsistent expressions and an elevated blink rate; neutralized emotions showed a decreased blink rate. Negative emotions were more difficult to falsify than happiness. Although untrained observers performed only slightly above chance at detecting deception, inconsistent emotional leakage occurred ...


title: Fast simulation of equity-linked life insurance contracts with a surrender option

abstract: In this paper, we consider equity-linked life insurance contracts that give their holder the possibility to surrender their policy before maturity. Such contracts can be valued using simulation methods proposed for the pricing of American options, but the mortality risk must also be taken into account when pricing such contracts. Here, we use the least-squares Monte Carlo approach of Longstaff and Schwartz coupled with quasi-Monte Carlo sampling and a control variate in order to construct efficient estimators for the value of such contracts. We also show how to incorporate the mortality risk into these pricing algorithms without explicitly simulating it.


title: Hinging hyperplanes for regression, classification, and function approximation

abstract: A hinge function y=h(x) consists of two hyperplanes continuously joined together at a hinge. In regression (prediction), classification (pattern recognition), and noiseless function approximation, use of sums of hinge functions gives a powerful and efficient alternative to neural networks with computation times several orders of magnitude less than is obtained by fitting neural networks with a comparable number of parameters. A simple and effective method for finding good hinges is presented. >


title: Applying the Stetler model of research utilization in staff development: revitalizing a preceptor program.

abstract: Using a model for the utilization of research can aid and direct staff development educators in the appropriate application of existing research-based knowledge and help prevent the pitfalls of using research inaccurately. The Stetler Model of Research Utilization was applied to obtain support for and revitalize a preceptor program at one large metropolitan tertiary medical center. Applying the Stetler Model resulted in the improvement of nurses' satisfaction with their preceptors and a reduction in turnover rate.


title: A virtual translation technique to improve current regulator for salient-pole AC machines

abstract: This paper presents a synchronous frame current regulator for salient-pole AC machines, which is insensitive to both parameters and synchronous frequencies. The transformation from stationary to synchronous frame is known to produce a frame-dependent cross-coupling term, which results in degradation in current response at high speeds and is represented as an asymmetric pole in the complex vector form of such systems. The asymmetric pole close to right half plane (RHP) in the complex vector s-domain cannot be perfectly canceled by parameter-based design of PI controllers due to parameter variation. Complex vector design synchronous frame PI current regulator with a virtually translated asymmetric pole is shown in this paper to reduce the parameter sensitivity and to improve disturbance rejection. The current regulator with the virtually translated pole have been analyzed in complex vector form for nonsalient-pole AC machine and in scalar form for salient-pole AC machines. It has been shown to yield comparable performances in both cases. This paper includes experimental verification of the performance of the proposed current regulator for an IPMSM.


title: Unsupervised learning of maritime traffic patterns for anomaly detection

abstract: Maritime anomaly detection requires an efficient representation and consistent knowledge of vessel behaviour. Automatic Identification System (AIS) data provides ships state vector and identity information that is here used to automatically derive knowledge of maritime traffic in an unsupervised way. The proposed approach only utilises AIS data, historical or real-time, and is aimed at incrementally learning motion patterns without any specific a priori contextual description. This can be applied to a single AIS terrestrial receiver, to regional networks or to global scale tracking. The maritime traffic representation underpins low- likelihood behaviour detection and supports enhanced Maritime Situational Awareness by providing a characterisation of vessels traffic.


title: A Systematic Review of Business and Information Technology Alignment

abstract: Business organizations have become heavily dependent on information technology (IT) services. The process of alignment is defined as the mutual synchronization of business goals and IT services. However, achieving mature alignment between business and IT is difficult due to the rapid changes in the business and IT environments. This article provides a systematic review of studies on the alignment of business and IT. The research articles reviewed are based on topics of alignment, the definition of alignment, history, alignment challenges, phases of alignment, alignment measurement approaches, the importance of alignment in business industries, how software engineering helps in better alignment, and the role of the business environment in aligning business with IT. It aims to present a thorough understanding of business-IT alignment and to provide a list of future research directions regarding alignment. To perform the systematic review, we used the guidelines developed by Kitchenham for reviewing the available research papers relevant to our topic.


title: Performance comparison of different SiC-MOSFETs for high-frequency high-power DC-DC converters

abstract: Wide-band-gap (WBG) semiconductor devices represent an opportunity for reaching higher power densities for power electronic converters due to higher switching speeds, and thus, a size reduction of the passive components is enabled while the total efficiency of the converter is not compromised. In this paper, four SiC-MOSFETs from CREE/Wolfspeed are compared regarding their losses and performance in an application with a high-frequency DC-DC converter. For this purpose, the on-state resistances and body-diode voltage drops of every MOSFET are measured for different drain currents to evaluate the semiconductors static losses. The switching loss of every MOSFET is obtained with a double-pulse test. Three of these MOSFETs are further evaluated in a two-phase interleaved DC-DC converter prototype to investigate the system efficiency. In the end, device C2M0040120D shows the highest potential for high-power high-frequency operation, due to a balance between electrical and thermal properties.


title: NeuroLinear: From neural networks to oblique decision rules

abstract: Abstract We present NeuroLinear, a system for extracting oblique decision rules from neural networks that have been trained for classification of patterns. Each condition of an oblique decision rule corresponds to a partition of the attribute space by a hyperplane that is not necessarily axisparallel. Allowing a set of such hyperplanes to form the boundaries of the decision regions leads to a significant reduction in the number of rules generated while maintaining the accuracy rates of the networks. We describe the components of NeuroLinear in detail by way of two examples using artificial datasets. Our experimental results on real-world datasets show that the system is effective in extracting compact and comprehensible rules with high predictive accuracy from neural networks.


title: Local Neighborhood Intensity Pattern: A new texture feature descriptor for image retrieval

abstract: Abstract In this paper, a new texture descriptor based on the local neighborhood intensity difference is proposed for content based image retrieval (CBIR). For computation of texture features like Local Binary Pattern (LBP), the center pixel in a 3 × 3 window of an image is compared with all the remaining neighbors, one pixel at a time to generate a binary bit pattern. It ignores the effect of the adjacent neighbors of a particular pixel for its binary encoding and also for texture description. The proposed method is based on the concept that neighbors of a particular pixel hold significant amount of texture information that can be considered for efficient texture representation for CBIR. The main impact of utilizing the mutual relationship among adjacent neighbors is that we do not rely on the sign of the intensity difference between central pixel and one of its neighbors (Ii) only, rather we take into account the sign of difference values between Ii and its adjacent neighbors along with the central pixels and same set of neighbors of Ii. This makes our pattern more resistant to illumination changes. Moreover, most of the local patterns including LBP concentrates mainly on the sign information and thus ignores the magnitude. The magnitude information which plays an auxiliary role to supply complementary information of texture descriptor, is integrated in our approach by considering the mean of absolute deviation about each pixel Ii from its adjacent neighbors. Taking this into account, we develop a new texture descriptor, named as Local Neighborhood Intensity Pattern (LNIP) which considers the relative intensity difference between a particular pixel and the center pixel by considering its adjacent neighbors and generate a sign and a magnitude pattern. Finally, the sign pattern (LNIPS) and the magnitude pattern (LNIPM) are concatenated into a single feature descriptor to generate a more effective feature descriptor. The proposed descriptor has been tested for image retrieval on four databases, including three texture image databases - Brodatz texture image database, MIT VisTex database and Salzburg texture database and one face database - AT&T face database. The precision and recall values observed on these databases are compared with some state-of-art local patterns. The proposed method showed a significant improvement over many other existing methods.


title: Why are teleworkers stressed? An empirical analysis of the causes of telework-enabled stress

abstract: Despite the wide dissemination and acceptance of teleworking, there are negative effects for employees. Literature shows that these negative effects of telework lead to undesired outcomes for teleworkers such as telework- exhaustion. The present research, however, goes one step further by identifying three significant telework-characteristics and hypothesising the relationship be- tween telework-characteristics and telework-enabled stressors. By doing so, one can understand the reason why telework-enabled stressors are perceived and identify the source of these stressors, which can then be counteracted to main- tain the advantages of teleworking. Therefore, this research investigates the in- fluence of the following telework-characteristics - information undersupply, au- tonomy, and isolation - on telework-enabled stressors and accordingly on tele- work-exhaustion and on the intention to continue teleworking. Results of an analysis on 310 teleworkers show that telework-characteristics influence tele- work-enabled stressors and accordingly increase the extent of telework- exhaustion and decrease the intention to continue teleworking.


title: A Survey on Position-Based Routing in Mobile Ad-Hoc Networks

abstract: We present an overview of ad hoc routing protocols that make forwarding decisions based on the geographical position of a packet's destination. Other than the destination's position, each node need know only its own position and the position of its one-hop neighbors in order to forward packets. Since it is not necessary to maintain explicit routes, position-based routing does scale well even if the network is highly dynamic. This is a major advantage in a mobile ad hoc network where the topology may change frequently. The main prerequisite for position-based routing is that a sender can obtain the current position of the destination. Therefore, previously proposed location services are discussed in addition to position-based packet forwarding strategies. We provide a qualitative comparison of the approaches in both areas and investigate opportunities for future research.


title: Multi-label Learning of Part Detectors for Heavily Occluded Pedestrian Detection

abstract: Detecting pedestrians that are partially occluded remains a challenging problem due to variations and uncertainties of partial occlusion patterns. Following a commonly used framework of handling partial occlusions by part detection, we propose a multi-label learning approach to jointly learn part detectors to capture partial occlusion patterns. The part detectors share a set of decision trees via boosting to exploit part correlations and also reduce the computational cost of applying these part detectors. The learned decision trees capture the overall distribution of all the parts. When used as a pedestrian detector individually, our part detectors learned jointly show better performance than their counterparts learned separately in different occlusion situations. The learned part detectors can be further integrated to better detect partially occluded pedestrians. Experiments on the Caltech dataset show state-of-the-art performance of our approach for detecting heavily occluded pedestrians.


title: Planning over Chain Causal Graphs for Variables with Domains of Size 5 Is NP-Hard

abstract: Recently, considerable focus has been given to the problem of determining the boundary between tractable and intractable planning problems. In this paper, we study the complexity of planning in the class Cn of planning problems, characterized by unary operators and directed path causal graphs. Although this is one of the simplest forms of causal graphs a planning problem can have, we show that planning is intractable for Cn (unless P = NP), even if the domains of state variables have bounded size. In particular, we show that plan existence for Ckn is NP-hard for k ≥ 5 by reduction from CNF-SAT. Here, k denotes the upper bound on the size of the state variable domains. Our result reduces the complexity gap for the class Ckn to cases k = 3 and k = 4 only, since C2n is known to be tractable.


title: Blind One-microphone Speech Separation: A Spectral Learning Approach

abstract: We present an algorithm to perform blind, one-microphone speech separation. Our algorithm separates mixtures of speech without modeling individual speakers. Instead, we formulate the problem of speech separation as a problem in segmenting the spectrogram of the signal into two or more disjoint sets. We build feature sets for our segmenter using classical cues from speech psychophysics. We then combine these features into parameterized affinity matrices. We also take advantage of the fact that we can generate training examples for segmentation by artificially superposing separately-recorded signals. Thus the parameters of the affinity matrices can be tuned using recent work on learning spectral clustering [1]. This yields an adaptive, speech-specific segmentation algorithm that can successfully separate one-microphone speech mixtures.


title: Qualitative Research Part II: Participants, Analysis, and Quality Assurance

abstract: This is the second of a two-part series on qualitative research. Part 1 in the December 2011 issue of Journal of Graduate Medical Education provided an introduction to the topic and compared characteristics of quantitative and qualitative research, identified common data collection approaches, and briefly described data analysis and quality assessment techniques. Part II describes in more detail specific techniques and methods used to select participants, analyze data, and ensure research quality and rigor. ::: ::: If you are relatively new to qualitative research, some references you may find especially helpful are provided below. The two texts by Creswell 2008 and 2009 are clear and practical.1,2 In 2008, the British Medical Journal offered a series of short essays on qualitative research; the references provided are easily read and digested.3–,8 For those wishing to pursue qualitative research in more detail, a suggestion is to start with the appropriate chapters in Creswell 2008,1 and then move to the other texts suggested.9–,11 ::: ::: To summarize the previous editorial, while quantitative research focuses predominantly on the impact of an intervention and generally answers questions like “did it work?” and “what was the outcome?”, qualitative research focuses on understanding the intervention or phenomenon and exploring questions like “why was this effective or not?” and “how is this helpful for learning?” The intent of qualitative research is to contribute to understanding. Hence, the research procedures for selecting participants, analyzing data, and ensuring research rigor differ from those for quantitative research. The following sections address these approaches. table 1 provides a comparative summary of methodological approaches for quantitative and qualitative research. ::: ::: ::: ::: TABLE 1 ::: ::: A Comparison of Qualitative and Quantitative Methodological Approaches ::: ::: ::: ::: Data collection methods most commonly used in qualitative research are individual or group interviews (including focus groups), observation, and document review. They can be used alone or in combination. While the following sections are written in the context of using interviews or focus groups to collect data, the principles described for sample selection, data analysis, and quality assurance are applicable across qualitative approaches.