title: User-Centered Design of a Mobile App for Weight and Health Management in Adolescents With Complex Health Needs: Qualitative Study

abstract: Background: Growing research has been conducted into the deployment and evaluation of mobile technology interventions for weight management in adolescents. However, no work has yet been conducted toward the development of these technologies for adolescents with complex health needs receiving specialized tertiary-level health care. Objective: The aim of this study was to conduct a user-centered needs assessment of adolescents interested in weight management with complex health needs requiring specialized health care services, their parents, and health care providers (HCPs) to inform the design and development of a mobile app for weight and health management. Methods: A qualitative study design was employed. Participants were recruited from two tertiary health care centers. Separate audiotaped focus group interviews were conducted with adolescents aged 12 to 18 years, parents, and HCPs. Interviews were transcribed, and field notes were collected by research staff. Iterative simple content analysis was performed independently by 4 research team members using computer software NVivo (QSR International) 10.0. Results: A total of 19 adolescents, 16 parents, and 21 HCPs were interviewed. Qualitative analysis revealed seven major themes related to app functionality: healthy eating, social support, self-monitoring, communicating with HCPs, supporting mental health, gamification and incentives, and user interface (UI) design. Adolescents provided several ideas related to each feature, whereas parents’ views focused on assistance with meal planning and greater access to HCPs. HCPs viewed the app as a novel and more acceptable platform to connect remotely with adolescents than conventional methods. They also strongly endorsed the value of social support capabilities and the ability to connect with an HCP. Conclusions: This is the first study to conduct a qualitative needs assessment in adolescents receiving specialized health care services toward the design of a mobile app for weight and health management. Our results indicate that core components of the app should include tailored meal recommendations and assistance with meal planning, social networking for peer support, customized and convenient tracking, remote access to HCPs, features to support mental health, and an attractive and engaging UI. These findings will be used to develop and evaluate a mobile app targeting adolescents with complex health needs. [JMIR Formativ Res 2018;2(1):e7]


title: Learning Fine-Grained Spatial Models for Dynamic Sports Play Prediction

abstract: We consider the problem of learning predictive models for in-game sports play prediction. Focusing on basketball, we develop models for anticipating near-future events given the current game state. We employ a latent factor modeling approach, which leads to a compact data representation that enables efficient prediction given raw spatiotemporal tracking data. We validate our approach using tracking data from the 2012-2013 NBA season, and show that our model can make accurate in-game predictions. We provide a detailed inspection of our learned factors, and show that our model is interpretable and corresponds to known intuitions of basketball game play.


title: Brain Tumor Detection And Localization In Magnetic Resonance Imaging

abstract: A tumor also known as neoplasm is a growth in the abnormal tissue which can be differentiated from the surrounding tissue by its structure. A tumor may lead to cancer, which is a major leading cause of death and responsible for around 13% of all deaths world-wide. Cancer incidence rate is growing at an alarming rate in the world. Great knowledge and experience on radiology are required for accurate tumor detection in medical imaging. Automation of tumor detection is required because there might be a shortage of skilled radiologists at a time of great need. We propose an automatic brain tumor detection and localization framework that can detect and localize brain tumor in magnetic resonance imaging. The proposed brain tumor detection and localization framework comprises five steps: image acquisition, pre-processing, edge detection, modified histogram clustering and morphological operations. After morphological operations, tumors appear as pure white color on pure black backgrounds. We used 50 neuroimages to optimize our system and 100 out-of-sample neuroimages to test our system. The proposed tumor detection and localization system was found to be able to accurately detect and localize brain tumor in magnetic resonance imaging. The preliminary results demonstrate how a simple machine learning classifier with a set of simple image-based features can result in high classification accuracy. The preliminary results also demonstrate the efficacy and efficiency of our five-step brain tumor detection and localization approach and motivate us to extend this framework to detect and localize a variety of other types of tumors in other types of medical imagery.


title: Pilates-based therapeutic exercise: effect on subjects with nonspecific chronic low back pain and functional disability: a randomized controlled trial.

abstract: STUDY DESIGN ::: A randomized controlled trial, prestest-posttest design, with a 3-, 6-, and 12-month follow-up. ::: ::: ::: OBJECTIVES ::: To investigate the efficacy of a therapeutic exercise approach in a population with chronic low back pain (LBP). ::: ::: ::: BACKGROUND ::: Therapeutic approaches developed from the Pilates method are becoming increasingly popular; however, there have been no reports on their efficacy. ::: ::: ::: METHODS AND MEASURES ::: Thirty-nine physically active subjects between 20 and 55 years old with chronic LBP were randomly assigned to 1 of 2 groups. The specific-exercise-training group participated in a 4-week program consisting of training on specialized (Pilates) exercise equipment, while the control group received the usual care, defined as consultation with a physician and other specialists and healthcare professionals, as necessary. Treatment sessions were designed to train the activation of specific muscles thought to stabilize the lumbar-pelvic region. Functional disability outcomes were measured with The Roland Morris Disability Questionnaire (RMQ/RMDQ-HK) and average pain intensity using a 101-point numerical rating scale. ::: ::: ::: RESULTS ::: There was a significantly lower level of functional disability (P = .023) and average pain intensity (P = .002) in the specific-exercise-training group than in the control group following the treatment intervention period. The posttest adjusted mean in functional disability level in the specific-exercise-training group was 2.0 (95% CI, 1.3 to 2.7) RMQ/RMDQ-HK points compared to a posttest adjusted mean in the control group of 3.2 (95% CI, 2.5 to 4.0) RMQ/RMDQ-HK points. The posttest adjusted mean in pain intensity in the specific-exercise-training group was 18.3 (95% CI, 11.8 to 24.8), as compared to 33.9 (95% CI, 26.9 to 41.0) in the control group. Improved disability scores in the specific-exercise-training group were maintained for up to 12 months following treatment intervention. ::: ::: ::: CONCLUSIONS ::: The individuals in the specific-exercise-training group reported a significant decrease in LBP and disability, which was maintained over a 12-month follow-up period. Treatment with a modified Pilates-based approach was more efficacious than usual care in a population with chronic, unresolved LBP.


Addressing Noise in Multidialectal Word Embeddings


Conditional Kronecker Batch Normalization for Compositional Reasoning


title: Skin Disease Recognition Method Based on Image Color and Texture Features

abstract: Skin diseases have a serious impact on people’s life and health. Current research proposes an efficient approach to identify singular type of skin diseases. It is necessary to develop automatic methods in order to increase the accuracy of diagnosis for multitype skin diseases. In this paper, three type skin diseases such as herpes, dermatitis, and psoriasis skin disease could be identified by a new recognition method. Initially, skin images were preprocessed to remove noise and irrelevant background by filtering and transformation. Then the method of grey-level co-occurrence matrix (GLCM) was introduced to segment images of skin disease. The texture and color features of different skin disease images could be obtained accurately. Finally, by using the support vector machine (SVM) classification method, three types of skin diseases were identified. The experimental results demonstrate the effectiveness and feasibility of the proposed method.


title: A document level neural model integrated domain knowledge for chemical-induced disease relations

abstract: BackgroundThe effective combination of texts and knowledge may improve performances of natural language processing tasks. For the recognition of chemical-induced disease (CID) relations which may span sentence boundaries in an article, although existing CID systems explored the utilization for knowledge bases, the effects of different knowledge on the identification of a special CID haven’t been distinguished by these systems. Moreover, systems based on neural network only constructed sentence or mention level models.ResultsIn this work, we proposed an effective document level neural model integrated domain knowledge to extract CID relations from biomedical articles. Basic semantic information of an article with respect to a special CID candidate pair was learned from the document level sub-network module. Furthermore, knowledge attention depending on the representation of the article was proposed to distinguish the influences of different knowledge on the special CID pair and then the final representation of knowledge was formed by aggregating weighed knowledge. Finally, the integrated representations of texts and knowledge were passed to a softmax classifier to perform the CID recognition. Experimental results on the chemical-disease relation corpus proposed by BioCreative V show that our proposed system integrated knowledge achieves a good overall performance compared with other state-of-the-art systems.ConclusionsExperimental analyses demonstrate that the introduced attention mechanism on domain knowledge plays a significant role in distinguishing influences of different knowledge on the judgment for a special CID relation.


title: GANs for Medical Image Analysis

abstract: Generative Adversarial Networks (GANs) and their extensions have carved open many exciting ways to tackle well known and challenging medical image analysis problems such as medical image de-noising, reconstruction, segmentation, data simulation, detection or classification. Furthermore, their ability to synthesize images at unprecedented levels of realism also gives hope that the chronic scarcity of labeled data in the medical field can be resolved with the help of these generative models. In this review paper, a broad overview of recent literature on GANs for medical applications is given, the shortcomings and opportunities of the proposed methods are thoroughly discussed and potential future work is elaborated. We review the most relevant papers published until the submission date. For quick access, important details such as the underlying method, datasets and performance are tabulated. An interactive visualization categorizes all papers to keep the review alive.


title: Improving Term Weighting for Community Question Answering Search Using Syntactic Analysis

abstract: Query term weighting is a fundamental task in information retrieval and most popular term weighting schemes are primarily based on statistical analysis of term occurrences within the document collection. In this work we study how term weighting may benefit from syntactic analysis of the corpus. Focusing on community question answering (CQA) sites, we take into account the syntactic function of the terms within CQA texts as an important factor affecting their relative importance for retrieval. We analyze a large log of web queries that landed on Yahoo Answers site, showing a strong deviation between the tendencies of different document words to appear in a landing (click-through) query given their syntactic function. To this end, we propose a novel term weighting method that makes use of the syntactic information available for each query term occurrence in the document, on top of term occurrence statistics. The relative importance of each feature is learned via a learning to rank algorithm that utilizes a click-through query log. We examine the new weighting scheme using manual evaluation based on editorial data and using automatic evaluation over the query log. Our experimental results show consistent improvement in retrieval when syntactic information is taken into account.


title: Thermal Contact Properties of 2-Yarn Fleece Knitted Fabrics

abstract: When a person touches a garment, heat flow occurs between the human body and the fab ric, with the resulting warm-cool feeling as the first sensation. This transient and dynamic thermal-contact feeling should be carefully investigated, since it strongly affects people’s first impressions and subsequent choices when buying garments. It is possible to find papers on the thermal contact properties of various fabrics, from woven shirt fabrics to underwear types; in this paper, the warm-cool feeling of 2-yarn fleece knitted fabrics, which are widely used for outdoor garments, have been investigated, and to the best of our knowledge this has not been studied before. Four different fleecy fabrics were selected with varying ground and loop yarn combination, which is 100% cotton or PET/cotton blend in accordance with the most common, commercially available fleece-knitted fabric types. The fabrics are rinsed and subjected to raising treatment to produce typical fleecy fabric, and the influence of yarn type and raising treatment on warm-cool feeling is objectively determined by measurements performed by an instrument developed at the end of the 1990s. The measurements are taken in the dry and wet state of the fabrics. It is found that raising treatment is the primary factor determining the thermal contact feeling of fleecy fabrics; the yarn type and fibre blend is less important.


title: Generic User Modeling Systems

abstract: The paper reviews the development of generic user modeling systems over the past twenty years. It describes their purposes, their services within user-adaptive systems, and the different design requirements for research prototypes and commercially deployed servers. It discusses the architectures that have been explored so far, namely shell systems that form part of the application, central server systems that communicate with several applications, and possible future user modeling agents that physically follow the user. Several implemented research prototypes and commercial systems are briefly described.


title: Will the Pedestrian Cross? A Study on Pedestrian Path Prediction

abstract: Future vehicle systems for active pedestrian safety will not only require a high recognition performance but also an accurate analysis of the developing traffic situation. In this paper, we present a study on pedestrian path prediction and action classification at short subsecond time intervals. We consider four representative approaches: two novel approaches (based on Gaussian process dynamical models and probabilistic hierarchical trajectory matching) that use augmented features derived from dense optical flow and two approaches as baseline that use positional information only (a Kalman filter and its extension to interacting multiple models). In experiments using stereo vision data obtained from a vehicle, we investigate the accuracy of path prediction and action classification at various time horizons, the effect of various errors (image localization, vehicle egomotion estimation), and the benefit of the proposed approaches. The scenario of interest is that of a crossing pedestrian, who might stop or continue walking at the road curbside. Results indicate similar performance of the four approaches on walking motion, with near-linear dynamics. During stopping, however, the two newly proposed approaches, with nonlinear and/or higher order models and augmented motion features, achieve a more accurate position prediction of 10-50 cm at a time horizon of 0-0.77 s around the stopping event.


title: M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search

abstract: Learning to walk over a graph towards a target node for a given query and a source node is an important problem in applications such as knowledge base completion (KBC). It can be formulated as a reinforcement learning (RL) problem with a known state transition model. To overcome the challenge of sparse rewards, we develop a graph-walking agent called M-Walk, which consists of a deep recurrent neural network (RNN) and Monte Carlo Tree Search (MCTS). The RNN encodes the state (i.e., history of the walked path) and maps it separately to a policy and Q-values. In order to effectively train the agent from sparse rewards, we combine MCTS with the neural policy to generate trajectories yielding more positive rewards. From these trajectories, the network is improved in an off-policy manner using Q-learning, which modifies the RNN policy via parameter sharing. Our proposed RL algorithm repeatedly applies this policy-improvement step to learn the model. At test time, MCTS is combined with the neural policy to predict the target node. Experimental results on several graph-walking benchmarks show that M-Walk is able to learn better policies than other RL-based methods, which are mainly based on policy gradients. M-Walk also outperforms traditional KBC baselines.


Bidirectional Learning for Time-series Models with Hidden Units.


title: The Complexity of Finding Cycles in Periodic Functions

abstract: Given a function f over a finite domain D and an arbitrary starting point x, the sequence $f^0 (x),f^1 (x),f^2 (x), \cdots $ is ultimately periodic. Such sequences are typically the output of random number generators. The cycle problem is to determine the first repeated element $f^n (x)$ in the sequence. Previous algorithms for this problem have required $3n + O(1)$ operations. In this paper we show that $n(1 + \Theta (1/\sqrt M ))$ steps are both necessary and sufficient, if M memory cells are available to store values of the function. We explicitly consider the performance of the algorithm as a function of the amount of memory available and the relative cost of evaluating f and comparing sequence elements for equality.


title: Deep Self-Organizing Map for visual classification

abstract: We proposed a Deep Self-Organizing Map (DSOM) algorithm which is completely different from the existing multi-layers SOM algorithms, such as SOINN. It consists of layers of alternating self-organizing map and sampling operator. The self-organizing layer is made up of certain numbers of SOMs, with each map only looking at a local region block on its input. The winning neuron's index value from every SOM in self-organizing layer is then organized in the sampling layer to generate another 2D map, which could then be fed to a second self-organizing layer. In this way, local information is gathered together, forming more global information in higher layers. The construction method of the DSOM is unique and will be introduced in this paper. Experiments were carried out to discuss how the DSOM architecture parameters affect the performance. We evaluate our proposed DSOM on MNIST and CASIA-HWDB1.1 dataset. Experimental results show that DSOM outperforms the original supervised SOM by 7:17% on MNIST and 7:25% on CASIA-HWDB1.1.


title: Capacitive Micromachined Ultrasonic Transducers (CMUTs) for Underwater Imaging Applications

abstract: A capacitive micromachined ultrasonic transducer structure for use in underwater imaging is designed, fabricated and tested in this paper. In this structure, a silicon dioxide insulation layer is inserted between the top electrodes and the vibration membrane to prevent ohmic contact. The capacitance-voltage (C-V) characteristic curve shows that the transducer offers suitable levels of hysteresis and repeatability performance. The −6 dB center frequency is 540 kHz and the transducer has a bandwidth of 840 kHz for a relative bandwidth of 155%. Underwater pressure of 143.43 Pa is achieved 1 m away from the capacitive micromachined ultrasonic transducer under 20 excitation. Two-dimensional underwater ultrasonic imaging, which is able to prove that a rectangular object is present underwater, is achieved. The results presented here indicate that our work will be highly beneficial for the establishment of an underwater ultrasonic imaging system.


title: Modulations of mirroring activity by desire for social connection and relevance of movement

abstract: Mirroring neurons fire both when an individual moves and observes another move in kind. This simulation of others’ movements is thought to effortlessly and ubiquitously support empathetic connection and social understanding. However, at times this could be maladaptive. How could a boxer mirror a losing opponent’s expressions of fatigue, feeling his weariness, precisely when strength is required? Clearly, the boxer must emotionally disconnect from his opponent and those expressions of fatigue must become irrelevant and not mirrored. But, movements that inform of his opponent’s intentions to deliver an incoming blow are quite relevant and still should require mirroring. We tested these dimensions of emotional connectedness and relevance of movement in an electroencephalography experiment, where participants’ desires to socially connect with a confederate were manipulated. Before manipulation, all participants mirrored the confederate’s purely kinematic (a hand opening and closing) and goal-directed (a hand opening and closing around a token that the participant desired) hand movements. After manipulation, unfairly treated subjects ceased to mirror the purely kinematic movements but continued to mirror goal-relevant movements. Those treated fairly continued to mirror all movements. The results suggest that social mirroring can be adaptive in order to meet the demands of a varied social environment.


title: Intelligent book positioning for library using RFID and book spine matching

abstract: In library, the management of books is very complication and timing costing. The location of books could be altered by librarian, students, teachers and any one around the library. Therefore, allocating a book is not an easy task in big library. Indoor positioning is an important technology to help storage management and customer services providing. RFID provides a good wireless platform to facilitate indoor positioning. However, duo to the small width of each book spine, adopting positioning based RFID alone is not enough to locate books in a library. In this work, we combine image matching with L-GEM based RBFNN to enhance the accuracy and robustness of the book locating system. We apply this new method in a library environment to position the certain books. Experimental results show that the proposed method is highly accurate and robust to white noise of RFID signals.


title: Don't follow me: Spam detection in Twitter

abstract: The rapidly growing social network Twitter has been infiltrated by large amount of spam. In this paper, a spam detection prototype system is proposed to identify suspicious users on Twitter. A directed social graph model is proposed to explore the “follower” and “friend” relationships among Twitter. Based on Twitter's spam policy, novel content-based features and graph-based features are also proposed to facilitate spam detection. A Web crawler is developed relying on API methods provided by Twitter. Around 25K users, 500K tweets, and 49M follower/friend relationships in total are collected from public available data on Twitter. Bayesian classification algorithm is applied to distinguish the suspicious behaviors from normal ones. I analyze the data set and evaluate the performance of the detection system. Classic evaluation metrics are used to compare the performance of various traditional classification methods. Experiment results show that the Bayesian classifier has the best overall performance in term of F-measure. The trained classifier is also applied to the entire data set. The result shows that the spam detection system can achieve 89% precision.


title: The rostral migratory stream in adult squirrel monkeys: contribution of new neurons to the olfactory tubercle and involvement of the antiapoptotic protein Bcl‐2

abstract: The subventricular zone (SVZ) lying along the ependymal layer of lateral ventricle is known to generate neural progenitor cells throughout adulthood in specific areas of the mammalian brain. In rodents, the anterior region of the SVZ produces neuroblasts that migrate in chain toward the olfactory bulb along the so-called rostral migratory stream (RMS). In the present study, the organization of the RMS in a representative of New World primates - the squirrel monkey (Saimiri sciureus) - was studied by using bromodeoxyuridine (BrdU), a thymidine analogue that incorporates itself into the DNA of cells undergoing mitotic division. Double and triple immunofluorescence labelling with a confocal microscope served to visualize cells that expressed BrdU as well as molecular markers of neurogenesis. Numerous newborn (BrdU+) cells, many ensheated in glial (GFAP+) tubes, were scattered along the entire RMS in squirrel monkeys. Some of these BrdU+ cells expressed molecular markers for early committed neurons (TuJ1), postmitotic granular neuroblasts (TUC-4) or mature neurons (MAP-2, NeuN), and virtually all of them expressed the antiapoptotic protein Bcl-2. A significant number of BrdU+ cells were found to deviate from the main stream of the RMS. Instead of reaching the olfactory bulb, these cells migrated ventrally into the olfactory tubercle, where they expressed a mature neuronal phenotype (MAP-2). These findings reveal that the RMS in New World monkeys is mitotically robust and markedly extended and suggest that Bcl-2 might play a role in the survival and/or differentiation of newborn neurons destined to olfactory bulb and olfactory tubercle in primates.


title: Familia Adelante: A Multi-Risk Prevention Intervention for Latino Families

abstract: A comprehensive approach for providing behavioral health services to youth is becoming increasingly emphasized. Latino youth are at increased risk for substance abuse, mental health concerns, unsafe sexual practices and HIV, and these outcomes have been empirically connected to individual, family and community-based stress. Despite this knowledge, there is a lack of evidence-based approaches that target these negative outcomes by reducing stress in Latino families in a culturally relevant manner. The current study examined the use of research-based strategies for reducing multiple risk behaviors in a predominantly Mexican–American sample of families. Through a modular approach, participants engaged in a psycho-educational curriculum to enhance communication and psychosocial coping, increase substance abuse and HIV knowledge and perception of harm, and improve school behavior. Over 12 sessions, the curriculum aimed to achieve these outcomes through an overall decrease in family and community-based stress by focusing on acculturative stress. Findings indicate that communication and perception of substance use harm were significantly enhanced, while social norms regarding sexual behavior, HIV anxiety and past use of marijuana and other illegal drugs were significantly reduced. While many of measures were reliable (α > .80), further changes are necessary to improve the accuracy of future studies. Despite these limitations, Familia Adelante improves many areas of participant’s family life, and points toward the feasibility of multi-risk reduction behavioral health prevention approaches.


title: 16-Way Radial Divider/Combiner for Solid State Power Amplifiers in the K Band

abstract: This paper presents the design and measurement results of a 16-way radial power divider/combiner for solid state power amplifiers in the K band using an original assembly technique of the combined ways. The design is based on a radial non-resonant cavity connected to 8 peripheral rectangular waveguides with two waveguide-to-microstrip transitions inserted in each peripheral rectangular waveguide. Each combined way includes on the same tray the input and output waveguide-to-microstrip transitions and the MMIC amplifier location. The trays can be individually tested before mounting in the 16-way divider/combiner. The low insertion loss of the 16-way power divider/combiner leads to an estimated combining efficiency better than 86.5% from 16.5 GHz to 21 GHz with a maximum value about 88% from 17.2 to 19.2 GHz.


title: A Twofold Siamese Network for Real-Time Object Tracking

abstract: Observing that Semantic features learned in an image classification task and Appearance features learned in a similarity matching task complement each other, we build a twofold Siamese network, named SA-Siam, for real-time object tracking. SA-Siam is composed of a semantic branch and an appearance branch. Each branch is a similaritylearning Siamese network. An important design choice in SA-Siam is to separately train the two branches to keep the heterogeneity of the two types of features. In addition, we propose a channel attention mechanism for the semantic branch. Channel-wise weights are computed according to the channel activations around the target position. While the inherited architecture from SiamFC [3] allows our tracker to operate beyond real-time, the twofold design and the attention mechanism significantly improve the tracking performance. The proposed SA-Siam outperforms all other real-time trackers by a large margin on OTB-2013/50/100 benchmarks.


title: BodyChat: autonomous communicative behaviors in avatars

abstract: Although avatars may resemble animated communicating interface agents, they have for the most part not profited from recent research into autonomous systems. In particular, even though avatars function within conversational environments (for example, chat or games), and even though they often resemble humans (with a head, hands, and a body) they are incapable of representing the kinds of knowledge that humans have about how to use the body during communication. Their appearance does not translate into increased communicative bandwidth. Face-to-face conversation among humans, however, does make extensive use of the visual channel for interaction management where many subtle and even involuntary cues are read from stance, gaze and gesture. We argue that the modeling and animation of such fundamental behavior is crucial for the credibility and effectiveness of the virtual interaction in chat. By treating the avatar as a communicative agent, we propose a method to automate the animation of important communicative behavior, deriving from work in context analysis and discourse theory. BodyChat is a system that allows users to communicate via text while their avatars automatically animate attention, salutations, turn taking, back-channel feedback and facial expression, as well as simple body functions such as the blinking of the eyes. 1. BEHAVIORS IN AVATARS One type of embodied agent that has received much airplay but little serious research attention in the agent community, is the avatar in a graphical chat. An avatar represents a user in a distributed virtual environment, but has until now not been autonomous. That is, it has not had knowledge to act in the absence of explicit control on the part of the user. In most current graphical chat systems the user is obliged to switch between controlling the avatar behavior and typing messages to other users. While the user is creating the message for her interlocutor, her avatar stands motionless or repeats a selected animation sequence. This fails to reflect the natural relationship between the body and the conversation that is taking place, potentially giving misleading or even conflicting visual


title: Towards an automation of the mutation analysis dedicated to model transformation

abstract: A benefit of model-driven engineering relies on the automatic generation of artefacts from high-level models through intermediary levels using model transformations. In such a process, the input must be well designed, and the model transformations should be trustworthy. Because of the specificities of models and transformations, classical software test techniques have to be adapted. Among these techniques, mutation analysis has been ported, and a set of mutation operators has been defined. However, it currently requires considerable manual work and suffers from the test data set improvement activity. This activity is a difficult and time-consuming job and reduces the benefits of the mutation analysis. This paper addresses the test data set improvement activity. Model transformation traceability in conjunction with a model of mutation operators and a dedicated algorithm allow to automatically or semi-automatically produce improved test models. The approach is validated and illustrated in two case studies written in Kermeta.Copyright © 2014 John Wiley & Sons, Ltd.


title: Matryoshka Peek: Toward Learning Fine-Grained, Robust, Discriminative Features for Product Search

abstract: In sharp contrast to the traditional category/subcategory level image retrieval, product image search aims to find the images containing the exact same product. This is a challenging problem because in addition to being robust under different imaging conditions such as varying viewpoints and illumination changes, the features should also be able to distinguish the specific product among many similar products. Consequently, it is important to utilize a large dataset, containing many product classes, to learn a strongly discriminative representation. Building such a dataset requires laborious manual annotation. Toward learning fine-grained, robust, discriminative features for product image search, we present a novel paradigm that can construct the required dataset without any human annotation. Unlike other fine-grained recognition works that rely on high-quality annotated datasets and are very narrowly focused on a specific object category, our method handles multiple object classes and requires minimum human effort. First, an ImageNet pretrained model is used to generate product clusters. As the original features from ImageNet are not discriminative, the clusters generated by this unsupervised procedure contain much noise. We alleviate noise by explicitly modeling noise distribution and automatically detecting errors during learning. The proposed paradigm is general, requires minimum human efforts, and is applicable to any deep learning task where fine-grained discriminative features are desired. Extensive experiments on the ALISC dataset have demonstrated that our approach is sound and effective, surpassing the baseline GoogleNet model by 15.09%.


title: Bluetooth worm propagation: mobility pattern matters!

abstract: The alarm that worms start to spread on increasingly popular mobile devices calls for an in-depth investigation of their propagation dynamics. In this paper, we study how mobility patterns affect Bluetooth worm spreading speeds. We find that the impact of mobility patterns is substantial over a large set of of changing Bluetooth and worm parameters. For instance, a mobility model under which devices move among a fixed set of activity locations can result in worm propagation speeds four times faster than a classical mobility model such as the random walk model. Our investigation reveals that the key factors affecting Bluetooth worm propagation speeds include spatial distributions of nodes, link duration distributions, degrees to which devices are mixed together, and even the burstiness of successive links.


Knowledge and practice of infection control among health workers in a tertiary hospital in Edo state , Nigeria


title: Improved Ring Oscillator PUF: An FPGA-friendly Secure Primitive

abstract: In this paper, we analyze ring oscillator (RO) based physical unclonable function (PUF) on FPGAs. We show that the systematic process variation adversely affects the ability of the RO-PUF to generate unique chip-signatures, and propose a compensation method to mitigate it. Moreover, a configurable ring oscillator (CRO) technique is proposed to reduce noise in PUF responses. Our compensation method could improve the uniqueness of the PUF by an amount as high as 18%. The CRO technique could produce nearly 100% error-free PUF outputs over varying environmental conditions without post-processing while consuming minimum area.


title: Did you hear that? Adversarial Examples Against Automatic Speech Recognition

abstract: Speech is a common and effective way of communication between humans, and modern consumer devices such as smartphones and home hubs are equipped with deep learning based accurate automatic speech recognition to enable natural interaction between humans and machines. Recently, researchers have demonstrated powerful attacks against machine learning models that can fool them to produceincorrect results. However, nearly all previous research in adversarial attacks has focused on image recognition and object detection models. In this short paper, we present a first of its kind demonstration of adversarial attacks against speech classification model. Our algorithm performs targeted attacks with 87% success by adding small background noise without having to know the underlying model parameter and architecture. Our attack only changes the least significant bits of a subset of audio clip samples, and the noise does not change 89% the human listener's perception of the audio clip as evaluated in our human study.


title: Selecting the best VM across multiple public clouds: a data-driven performance modeling approach

abstract: Users of cloud services are presented with a bewildering choice of VM types and the choice of VM can have significant implications on performance and cost. In this paper we address the fundamental problem of accurately and economically choosing the best VM for a given workload and user goals. To address the problem of optimal VM selection, we present PARIS, a data-driven system that uses a novel hybrid offline and online data collection and modeling framework to provide accurate performance estimates with minimal data collection. PARIS is able to predict workload performance for different user-specified metrics, and resulting costs for a wide range of VM types and workloads across multiple cloud providers. When compared to sophisticated baselines, including collaborative filtering and a linear interpolation model using measured workload performance on two VM types, PARIS produces significantly better estimates of performance. For instance, it reduces runtime prediction error by a factor of 4 for some workloads on both AWS and Azure. The increased accuracy translates into a 45% reduction in user cost while maintaining performance.


title: Modeling and Validating BPMN Diagrams

abstract: The ever increasing number of adoptions from companies and the growing interest upon BPMN entails the need of having one more structured underlying conceptual model. In the past different proposals to equip BPMN with a conceptual model have been defined, but the solution currently adopted remains unclear and too complex. Also the proposals submitted to the BPMN 2.0 RFP, which calls for a single notation to define notation, metamodel and interchange format addressing BPDM concepts, are still - in part - unsatisfactory. We developed a new model starting from scratch, and the result of this work is a complete conceptual model for BPMN with a clear metamodel and its related XML-based serialization which provides a self-validating mechanism for checking BP syntax and semantics.


Adaptive Thresholding for Edge Detection in Gray Scale Images


title: Papert's prison fab lab: implications for the maker movement and education design

abstract: For three years, the author collaborated with Seymour Papert in the planning, design, operation, teaching and documentation of the Constructionist Learning Laboratory at the Maine Youth Center. This work is significant as it represents Dr. Papert's last institutional research project and marks his first attempt to design an educational environment based on the theory of constructionism from scratch. The implications for education reform and school reform are numerous. However, in the context of the 2013 Interaction Design and Children focus on DIY/maker culture, the overlooked work of the Constructionist Learning Laboratory the work of Papert, Stager and their colleagues is particularly pertinent. More than a decade before maker culture and "fab labs" emerged as a popular addition to formal education, Papert succeeded in creating a school built entirely upon the ideals of that movement.


title: Emotional display rules and emotional labor : The moderating role of commitment

abstract: The authors examined whether commitment to emotional display rules is a necessary condition for emotional display rules to affect behavior at work. Results using structural equation modeling revealed that display rule commitment moderated the relationships of emotional display rule perceptions with surface acting, deep acting, and positive affective delivery at work, such that the relationships were strong and positive when commitment to display rules was high and weak when commitment to display rules was low. These findings suggest that motivation plays a role in the emotional labor process in that individuals must be committed to display rules for these rules to affect behavior.


title: A Neural Classifier with Fraud Density Map for Effective Credit Card Fraud Detection

abstract: In this paper, we propose a way of effective fraud detection to improve the detection efficiency. We focus on the bias of the training dataset, which is typically caused by the skewed distribution and highly overlapped classes of credit card transaction data and leads to lots of mis-detections. To reduce mis-detections, we take the fraud density of real transaction data as a confidence value and generate the weighted fraud score in the proposed scheme. The effectiveness of our proposed scheme is examined with experimental results on real data.


title: Long-Term Consequences of Childhood Sexual Abuse by Gender of Victim

abstract: Background: Childhood sexual abuse (CSA) is a worldwide problem. Although most studies on the long-term consequences of CSA have focused on women, sexual abuse of both boys and girls is common. Thus, a comparison of the long-term effects of CSA by gender of the victim will provide perspective on the need for future research, prevention activities, and treatment of survivors. Methods: A retrospective cohort study was conducted from 1995 to 1997 among 17,337 adult HMO members in San Diego, California. Participants completed a survey about abuse or household dysfunction during childhood, and multiple other health-related issues. Multivariate logistic regression was used to examine the relationships between severity of CSA (intercourse vs no intercourse) and long-term health and social problems (substance use and abuse, mental illness, and current problems with marriage and family) by gender of victim. Models controlled for exposure to other forms of adverse childhood experiences that co-occur with CSA. Among men, the relationship between the gender of the CSA perpetrator to the outcomes was also examined. Results: Contact CSA was reported by 16% of males and 25% of females. Men reported female perpetration of CSA nearly 40% of the time, and women reported female perpetration of CSA 6% of the time. CSA significantly increased the risk of the outcomes. The magnitude of the increase was similar for men and women. For example, compared to reporting no sexual abuse, a history of suicide attempt was more than twice as likely among both men and women who experienced CSA (p 0.05). Compared with those who did not report CSA, men and women exposed to CSA were at a 40% increased risk of marrying an alcoholic, and a 40% to 50% increased risk of reporting current problems with their marriage (p 0.05). Conclusions: In this cohort of adult HMO members, experiencing CSA was common among both men and women. The long-term impact of CSA on multiple health and social problems was similar for both men and women. These findings strongly indicate that boys and girls are vulnerable to this form of childhood maltreatment; the similarity in the likelihood for multiple behavioral, mental, and social outcomes among men and women suggests the need to identify and treat all adults affected by CSA. (Am J Prev Med 2005;28(5):430‐438) © 2005 American Journal of Preventive Medicine


title: Supply chain risk management: review, classification and future research directions

abstract: In order to be more efficient, firms have adopted strategies such as outsourcing, global partnerships and lean practices. Although such strategies have tremendous abilities to improve the efficiencies but simultaneously they make the firms vulnerable to market uncertainties, dependencies and disruptions. Moreover, natural calamities and manmade crises have also put negative impact on strategic, operational and tactical performance of supply chains. These factors have triggered the interest of academia and industry to consider the risk issues as prime concerns. To capture the more fine-grained elements of diversified risk issues related to the supply chain we employ a multi-layered top town taxonomy to classify and codify the literature and put forward the probable dimensions for future research. We further study the pool of SCRM literature focusing on coordination, decision making and sector-wise SCRM implementation issues and derive relevant propositions.


title: Caregiver status affects medication adherence among older home care clients with heart failure

abstract: Background and Aims: For older individuals living in the community with chronic diseases such as heart failure (HF), caregivers may play an important role in medication adherence. This role may be increasingly important as cognition declines. This study aimed to 1) examine the role of caregivers in medication adherence in a complex population of older home care clients with mild cognitive impairment (MCI) and 2) examine the effect of caregiver stress on medication non-adherence. Methods: The interRAI Resident Assessment Instrument — Home Care (RAI-HC) instrument collects comprehensive information about all individuals receiving long-term home care services in the Canadian province of Ontario. This analysis of secondary data utilized this database to examine the relationship between caregiver residence and stress on medication adherence among a subset of clients with MCI who were over age 75. Results: The prevalence of HF among the sample was 15.5%, while MCI was present in 42.3% of the sample. Among individuals with MCI, having a caregiver at the same residence reduced medication non-adherence. Additionally, caregiver stress was significantly associated with higher rates of non-adherence. Conclusions: MCI can impair medication adherence. The presence of a caregiver at home significantly improves medication adherence in patients with HF and MCI. Supporting caregivers is an important strategy in allowing clinically complex older adults to remain safely at home.


title: AI-Assisted Game Debugging with Cicero

abstract: We present Cicero, a mixed-initiative application for prototyping two-dimensional sprite-based games across different genres such as shooters, puzzles, and action games. Cicero provides a host of features which can offer assistance in different stages of the game development process. Noteworthy features include AI agents for gameplay simulation, a game mechanics recommender system, a playtrace aggregator, heatmap-based game analysis, a sequential replay mechanism, and a query system that allows searching for particular interaction patterns. In order to evaluate the efficacy and usefulness of the different features of Cicero, we conducted a user study in which we compared how users perform in game debugging tasks with different kinds of assistance.


title: Towards the Development of an Interactive 3D Coach Training Serious Game

abstract: Today serious games, i.e. software applications developed with game technology and design principles for a primary purpose other than pure entertainment, include games used for educational, persuasive, pedagogical, political, or health and training purposes. This paper describes the work-in-progress development of an interactive 3D training application generated using components of the Unreal game engine that facilitate the learning of various fundamental coaching methods via the interactive completion of a variety of interrelated tasks. Apart from highlighting the contribution and innovation of this approach, a synopsis of the coach training content of the serious game and its purpose is also presented here, as well as an architecture overview of the implementation of the prototype application. Finally, future directions for the completion and evaluation of this serious game are also discussed.


title: An obligation model bridging access control policies and privacy policies

abstract: In this paper, we present a novel obligation model for the Core Privacy-aware Role Based Access Control (P-RBAC), and discuss some design issues in detail. Pre-obligations, post-obligations, conditional obligations, and repeating obligations are supported by the obligation model. Interaction between permissions and obligations is discussed, and efficient algorithms are provided to detect undesired effects.


title: Full-Frame Scene Coordinate Regression for Image-Based Localization

abstract: Image-based localization, or camera relocalization, is a fundamental problem in computer vision and robotics, and it refers to estimating camera pose from an image. Recent state-of-the-art approaches use learning based methods, such as Random Forests (RFs) and Convolutional Neural Networks (CNNs), to regress for each pixel in the image its corresponding position in the scene's world coordinate frame, and solve the final pose via a RANSAC-based optimization scheme using the predicted correspondences. In this paper, instead of in a patch-based manner, we propose to perform the scene coordinate regression in a full-frame manner to make the computation efficient at test time and, more importantly, to add more global context to the regression process to improve the robustness. To do so, we adopt a fully convolutional encoder-decoder neural network architecture which accepts a whole image as input and produces scene coordinate predictions for all pixels in the image. However, using more global context is prone to overfitting. To alleviate this issue, we propose to use data augmentation to generate more data for training. In addition to the data augmentation in 2D image space, we also augment the data in 3D space. We evaluate our approach on the publicly available 7-Scenes dataset, and experiments show that it has better scene coordinate predictions and achieves state-of-the-art results in localization with improved robustness on the hardest frames (e.g., frames with repeated structures).


title: Design and dynamics model of a lightweight series elastic tendon-driven robot arm

abstract: This paper presents the design of a lightweight robot arm intended for safe physical human-robot interaction. The robot arm design combines tendon actuation with elasticity in the tendons to achieve a significant reduction in mass and passive compliant behavior. The use of elastic tendons in all joints in order to gain maximum safety and performance properties, however, results in a significant increase of the model complexity with oscillatory behavior and kinematic coupling of the joint equilibrium positions. Therefore, special effort needs to be made to model the robot arm dynamics, which is an essential basis for model-based algorithms utilizing and fully exploiting the particular properties of the robot arm. This paper therefore derives the full dynamics model of the robot arm with focus on the nonlinear elastic tendon actuators, the kinematic tendon coupling, and modeling complexity reduction by reflecting all model parameters to the joint space. The resulting model is validated by comparing the identified simulation model with experimental data of an application-related pick-and-place trajectory and a trajectory with undamped oscillating motions of the robot arm.


title: Simulating gait assistance of a hip exoskeleton: Case studies for ankle pathologies

abstract: We propose a simulation framework for gait assistance with ankle pathologies. We first construct the neu-romuscular walking model, then design the parameters for assistance torques for stance and swing legs. The parameter values are determined by performing dynamic optimizations which takes into account the human-exoskeleton interactive dynamics. The simulated energy expenditure and kinematic data are compared with the real data. Case studies involve abnormal gaits with 1) foot drop, 2) foot drop and plantarflexion failure. We evaluate the gait efficiency and walking speed for the different gait types. Our result shows that each gait type should have a different assistance strategy (timing and magnitude) compared to the assistance strategy of a normal gait.


title: Small interfering RNA directed against CTMP reduces acute traumatic brain injury in a mouse model by activating Akt.

abstract: OBJECTIVE ::: Protein kinase B (PKB/Akt), which is phosphorylated and activated by upstream activators, exerts critical neuroprotective effects by phosphorylating downstream targets after traumatic brain injury (TBI). Studies on the regulation of Akt will be crucial for our understanding of neuronal survival. The goal of this study is to investigate the effects of carboxyl-terminal modulator protein (CTMP) on phosphorylation of Akt and neurological function in a mouse model of TBI. ::: ::: ::: METHODS ::: Traumatic brain injury in mice was performed by a controlled cortical impact device. The expression of Akt, phospho-Akt, and CTMP was examined in the injured cortices by immunohistochemistry and Western blot analysis. To determine the effects of CTMP, small interfering RNAs (siRNAs) directed against CTMP were injected in mice with TBI, and the expression of phosphorylated Akt and neurological function were evaluated. ::: ::: ::: RESULTS ::: Phospho-Akt significantly increased at 4 hours post-TBI in the nucleus (P < 0.01) and remained at high levels until 72 hours after TBI, as shown by Western blot analysis. In the cytosol, the expression of phospho-Akt reached its peak at 4 hours post-TBI, but decreased markedly at 24 hours and maintained below pre-TBI levels until 72 hours post-TBI. Interestingly, the expression of CTMP significantly increased 4 hours after TBI (P < 0.01) and sustained those levels until 72 hours without dramatic changes. Treatment with CTMP siRNA effectively augmented the phosphorylation of Akt and significantly improved the neurological functional recovery up to 28 days post-TBI. ::: ::: ::: CONCLUSION ::: We conclude that Akt is phosphorylated and translocated to nucleus after TBI to exert neuroprotective effects. However, CTMP is simultaneously triggered to inhibit the phosphorylation of Akt. Inhibition of CTMP by siRNA improves the recovery of neurological functions after TBI.


title: Cell phone use while driving and attributable crash risk.

abstract: OBJECTIVES ::: Prior research has estimated that crash risk is 4 times higher when talking on a cell phone versus not talking. The objectives of this study were to estimate the extent to which drivers talk on cell phones while driving and to compute the implied annual number of crashes that could have been avoided if driver cell phone use were restricted. ::: ::: ::: METHODS ::: A national survey of approximately 1200 U.S. drivers was conducted. Respondents were asked to approximate the amount of time spent driving during a given day, number of cell phone calls made or received, and amount of driving time spent talking on a cell phone. Population attributable risk (PAR) was computed for each combination of driver gender, driver age, day of week, and time of day. These were multiplied by the corresponding crash counts to estimate the number of crashes that could have been avoided. ::: ::: ::: RESULTS ::: On average, drivers were talking on cell phones approximately 7 percent of the time while driving. Rates were higher on weekdays (8%), in the afternoon and evening (8%), and for drivers younger than 30 (16%). Based on these use rates, restricting cell phones while driving could have prevented an estimated 22 percent (i.e., 1.3 million) of the crashes in 2008. ::: ::: ::: CONCLUSIONS ::: Although increased rates of cell phone use while driving should be leading to increased crash rates, crash rates have been declining. Reasons for this paradox are unclear. One possibility is that the increase in cell phone use and crash risk due to cell phone use have been overestimated. Another possibility is that cell phone use has supplanted other driving distractions that were similarly hazardous.


title: A Semantic Space for Music Derived from Social Tags

abstract: In this paper we investigate social tags as a novel highvolume source of semantic metadata for music, using techniques from the fields of information retrieval and multivariate data analysis. We show that, despite the ad hoc and informal language of tagging, tags define a low-dimensional semantic space that is extremely well-behaved at the track level, in particular being highly organised by artist and musical genre. We introduce the use of Correspondence Analysis to visualise this semantic space, and show how it can be applied to create a browse-by-mood interface for a psychologically-motivated two-dimensional subspace representing musical emotion.


title: Iterative entity alignment via joint knowledge embeddings

abstract: Entity alignment aims to link entities and their counterparts among multiple knowledge graphs (KGs). Most existing methods typically rely on external information of entities such as Wikipedia links and require costly manual feature construction to complete alignment. In this paper, we present a novel approach for entity alignment via joint knowledge embeddings. Our method jointly encodes both entities and relations of various KGs into a unified low-dimensional semantic space according to a small seed set of aligned entities. During this process, we can align entities according to their semantic distance in this joint semantic space. More specifically, we present an iterative and parameter sharing method to improve alignment performance. Experiment results on realworld datasets show that, as compared to baselines, our method achieves significant improvements on entity alignment, and can further improve knowledge graph completion performance on various KGs with the favor of joint knowledge embeddings.


title: Measuring praise and criticism: Inference of semantic orientation from association

abstract: The evaluative character of a word is called its semantic orientation. Positive semantic orientation indicates praise (e.g., "honest", "intrepid") and negative semantic orientation indicates criticism (e.g., "disturbing", "superfluous"). Semantic orientation varies in both direction (positive or negative) and degree (mild to strong). An automated system for measuring semantic orientation would have application in text classification, text filtering, tracking opinions in online discussions, analysis of survey responses, and automated chat systems (chatbots). This article introduces a method for inferring the semantic orientation of a word from its statistical association with a set of positive and negative paradigm words. Two instances of this approach are evaluated, based on two different statistical measures of word association: pointwise mutual information (PMI) and latent semantic analysis (LSA). The method is experimentally tested with 3,596 words (including adjectives, adverbs, nouns, and verbs) that have been manually labeled positive (1,614 words) and negative (1,982 words). The method attains an accuracy of 82.8p on the full test set, but the accuracy rises above 95p when the algorithm is allowed to abstain from classifying mild words.


title: Towards optimal design of solar assisted industrial processes: Case study of a dairy

abstract: Pinch analysis is a valuable tool for industrial process integration and optimization. Based on heat cascading and Mixed Integer Linear Programming (MILP) the optimal utility system can be found, includ- ing traditional heat sources (burners, boilers) and more innovative components, such as compression and absorption heat pumps. Investigation of the potential and design of renewable sources, such as solar thermal or photovoltaics, imposes several additional elements: time-dependence of the source, storage inertia and losses, and the system intrinsic non-linearity are difficult to represent by linearized time-invariant equations. In this work, a set of multi-period MILP equations for solar components are presented as well as a superstructure for optimization of heat pump cycles. The presented approach offers a comprehensive methodology that allows simultaneous optimization of the process’ refrigeration and renewable utility system. The proposed methodology is illustrated on the basis of a dairy plant where the different solar components are compared and evaluated based on cost and emissions.


title: Selective Transfer Learning for Cross Domain Recommendation

abstract: Collaborative filtering (CF) aims to predict users’ ratings on items according to historical user-item preference data. In many realworld applications, preference data are usually sparse, which would make models overfit and fail to give accurate predictions. Recently, several research works show that by transferring knowledge from some manually selected source domains, the data sparseness problem could be mitigated. However for most cases, parts of source domain data are not consistent with the observations in the target domain, which may misguide the target domain model building. In this paper, we propose a novel criterion based on empirical prediction error and its variance to better capture the consistency across domains in CF settings. Consequently, we embed this criterion into a boosting framework to perform selective knowledge transfer. Comparing to several state-of-the-art methods, we show that our proposed selective transfer learning framework can significantly improve the accuracy of rating prediction on several realworld recommendation tasks.


title: Editorial: BeAware!-Situation awareness, the ontology-driven way

abstract: Information overload is a severe problem for human operators of large-scale control systems as, for example, encountered in the domain of road traffic management. Operators of such systems are at risk to lack situation awareness, because existing systems focus on the mere presentation of the available information on graphical user interfaces-thus endangering the timely and correct identification, resolution, and prevention of critical situations. In recent years, ontology-based approaches to situation awareness featuring a semantically richer knowledge model have emerged. However, current approaches are either highly domain-specific or have, in case they are domain-independent, shortcomings regarding their reusability. In this paper, we present our experience gained from the development of BeAware!, a framework for ontology-driven information systems aiming at increasing an operator's situation awareness. In contrast to existing domain-independent approaches, BeAware!'s ontology introduces the concept of spatio-temporal primitive relations between observed real-world objects thereby improving the reusability of the framework. To show its applicability, a prototype of BeAware! has been implemented in the domain of road traffic management. An overview of this prototype and lessons learned for the development of ontology-driven information systems complete our contribution.


title: Going Dark: Social Factors in Collective Action Against Platform Operators in the Reddit Blackout

abstract: This paper describes how people who lead communities on online platforms join together in mass collective action to influence platform operators. I investigate this by analyzing a protest against the social news platform reddit by moderators of 2,278 subreddit communities in July 2015. These moderators collectively disabled their subreddits, preventing millions of readers from accessing major parts of reddit and convincing the company to negotiate over their demands. This paper offers a descriptive analysis of the protest, combining qualitative content analysis, interviews, and quantitative analysis with the population of 52,735 active subreddits. Through participatory hypotheses testing with moderators, this study reveals social factors including the grievances of moderators, relations with platform operators, relations among moderators, subreddit resources, subreddit isolation, and moderators' relations with their subreddits that can lead to participation in mass collective action against a platform.


title: NELSON lung cancer screening study

abstract: The Dutch-Belgian Randomized Lung Cancer Screening Trial (Dutch acronym: NELSON study) was designed to investigate whether screening for lung cancer by low-dose multidetector computed tomography (CT) in high-risk subjects will lead to a decrease in 10-year lung cancer mortality of at least 25% compared with a control group without screening. Since the start of the NELSON study in 2003, 7557 participants underwent CT screening, with scan rounds in years 1, 2, 4 and 6. In the current review, the design of the NELSON study including participant selection and the lung nodule management protocol, as well as results on validation of CT screening and first results on lung cancer screening are described.


title: Incremental Sequence Learning

abstract: Deep learning research over the past years has shown that by increasing the scope or difficulty of the learning problem over time, increasingly complex learning problems can be addressed. We study incremental learning in the context of sequence learning, using generative RNNs in the form of multi-layer recurrent Mixture Density Networks. While the potential of incremental or curriculum learning to enhance learning is known, indiscriminate application of the principle does not necessarily lead to improvement, and it is essential therefore to know which forms of incremental or curriculum learning have a positive effect. This research contributes to that aim by comparing three instantiations of incremental or curriculum learning. ::: ::: We introduce Incremental Sequence Learning, a simple incremental approach to sequence learning. Incremental Sequence Learning starts out by using only the first few steps of each sequence as training data. Each time a performance criterion has been reached, the length of the parts of the sequences used for training is increased. ::: ::: We introduce and make available a novel sequence learning task and data set: predicting and classifying MNIST pen stroke sequences. We find that Incremental Sequence Learning greatly speeds up sequence learning and reaches the best test performance level of regular sequence learning 20 times faster, reduces the test error by 74%, and in general performs more robustly; it displays lower variance and achieves sustained progress after all three comparison methods have stopped improving. The other instantiations of curriculum learning do not result in any noticeable improvement. A trained sequence prediction model is also used in transfer learning to the task of sequence classification, where it is found that transfer learning realizes improved classification performance compared to methods that learn to classify from scratch.


title: Minimizers of cost-functions involving non-smooth data-fidelity terms. Application to the processing of outliers

abstract: We present a theoretical study of the recovery of an unknown vector $x\in{\mathbb R}^p$ (such as a signal or an image) from noisy data $y\in{\mathbb R}^q$ by minimizing with respect to $x$ a regularized cost-function ${\cal F}(x,y)=\Psi(x,y)+\alpha\Phi(x)$, where $\Psi$ is a data-fidelity term, $\Phi$ is a smooth regularization term, and $\alpha>0$ is a parameter. Typically, $\Psi(x,y)=\|Ax-y \|^2$, where A is a linear operator. The data-fidelity terms $\Psi$ involved in regularized cost-functions are generally smooth functions; only a few papers make an exception to this and they consider restricted situations. Nonsmooth data-fidelity terms are avoided in image processing. In spite of this, we consider both smooth and nonsmooth data-fidelity terms. Our goal is to capture essential features exhibited by the local minimizers of regularized cost-functions in relation to the smoothness of the data-fidelity term. ::: In order to fix the context of our study, we consider $\Psi(x,y)=\sum_i\psi(a_i^Tx-y_i)$, where $a_i^T$ are the rows of $A$ and $\psi$ is ${\cal C}^m$ on ${\mathbb R}\setminus \{0\}$. We show that if $\psi\1(0^-)<\psi\1(0^+)$, then typical data y give rise to local minimizers $\hat x$ of ${\cal F}(.,y)$ which fit exactly a certain number of the data entries: there is a possibly large set $\hat h$ of indexes such that $a_i^T\hat x=y_i$ for every $i\in\hat h$. In contrast, if $\psi$ is smooth on ${\mathbb R}$, for almost every y, the local minimizers of ${\cal F}(.,y)$ do not fit any entry of y. Thus, the possibility that a local minimizer fits some data entries is due to the nonsmoothness of the data-fidelity term. This is a strong mathematical property which is useful in practice. By way of application, we construct a cost-function allowing aberrant data (outliers) to be detected and to be selectively smoothed. Our numerical experiments advocate the use of nonsmooth data-fidelity terms in regularized cost-functions for special purposes in image and signal processing.


title: Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations

abstract: In this work we explore the generalization characteristics of unsupervised representation learning by leveraging disentangled VAE's to learn a useful latent space on a set of relational reasoning problems derived from Raven Progressive Matrices. We show that the latent representations, learned by unsupervised training using the right objective function, significantly outperform the same architectures trained with purely supervised learning, especially when it comes to generalization.


title: Reasoning over RDF Knowledge Bases using Deep Learning

abstract: Semantic Web knowledge representation standards, and in particular RDF and OWL, often come endowed with a formal semantics which is considered to be of fundamental importance for the field. Reasoning, i.e., the drawing of logical inferences from knowledge expressed in such standards, is traditionally based on logical deductive methods and algorithms which can be proven to be sound and complete and terminating, i.e. correct in a very strong sense. For various reasons, though, in particular, the scalability issues arising from the ever-increasing amounts of Semantic Web data available and the inability of deductive algorithms to deal with noise in the data, it has been argued that alternative means of reasoning should be investigated which bear high promise for high scalability and better robustness. From this perspective, deductive algorithms can be considered the gold standard regarding correctness against which alternative methods need to be tested. In this paper, we show that it is possible to train a Deep Learning system on RDF knowledge graphs, such that it is able to perform reasoning over new RDF knowledge graphs, with high precision and recall compared to the deductive gold standard.


title: Combining RGB and Depth Images for Indoor Scene Classification Using Deep Learning

abstract: There has been significant progress in recognition of outdoor scenes but indoor scene recognition is still an challenge. This is due to the high appearance fluctuation of indoor situations. With the recent developments in indoor and mobile robotics, identifying the indoor scenes has gained importance. Many approaches have been proposed to detect scenes using object detection and geotags. In contrast, the proposal of this paper uses the convolutional neural network which has gained importance with advancement in machine learning methodologies. Our method has higher efficiency than the existing models as we try to classify the environment as a whole rather than using object identification for the same. We test this approach on our dataset which consists of RGB and also depth images of common locations present in academic environments such as class rooms, labs etc. The proposed approach performs better than previous ones with accuracy up to 98%.


title: Bacterial spoilage of meat and cured meat products

abstract: The influence of environmental factors (product composition and storage conditions) on the selection, growth rate and metabolic activity of the bacterial flora is presented for meat (pork and beef) and cooked, cured meat products. The predominant bacteria associated with spoilage of refrigerated beef and pork, are Brochothrix thermosphacta, Carnobacterium spp., Enterobacteriaceae, Lactobacillus spp., Leuconostoc spp., Pseudomonas spp. and Shewanella putrefaciens. The main defects in meat are off-odours and off-flavours, but discolouration and gas production also occur. Bacteria associated with the spoilage of refrigerated meat products, causing defects such as sour off-flavours, discolouration, gas production, slime production and decrease in pH, consist of B. thermosphacta, Carnobacterium spp, Lactobacillus spp, Leuconostoc spp. and Weissella spp. Analysis of spoilage as measured by bacterial and chemical indicators is discussed. It is concluded that a multivariate approach based on spectra of chemical compounds, may be helpful in order to analyse spoilage, at least for spoilage caused by lactic acid bacteria. The consequences of bacteria-bacteria interactions should be evaluated more.


Leisure Boredom , Sensation Seeking , Self-esteem , Addiction Symptoms and Patterns of Mobile Phone Use


title: A game-theoretic and dynamical-systems analysis of selection methods in coevolution

abstract: We use evolutionary game theory (EGT) to investigate the dynamics and equilibria of selection methods in coevolutionary algorithms. The canonical selection method used in EGT is equivalent to the standard "fitness-proportional" selection method used in evolutionary algorithms. All attractors of the EGT dynamic are Nash equilibria; we focus on simple symmetric variable-sum games that have polymorphic Nash-equilibrium attractors. Against the dynamics of proportional selection, we contrast the behaviors of truncation selection, (/spl mu/,/spl lambda/),(/spl mu/+/spl lambda/), linear ranking, Boltzmann, and tournament selection. Except for Boltzmann selection, each of the methods we test unconditionally fail to achieve polymorphic Nash equilibrium. Instead, we find point attractors that lack game-theoretic justification, cyclic dynamics, or chaos. Boltzmann selection converges onto polymorphic Nash equilibrium only when selection pressure is sufficiently low; otherwise, we obtain attracting limit-cycles or chaos. Coevolutionary algorithms are often used to search for solutions (e.g., Nash equilibria) of games of strategy; our results show that many selection methods are inappropriate for finding polymorphic Nash solutions to variable-sum games. Another application of coevolution is to model other systems; our results emphasize the degree to which the model's behavior is sensitive to implementation details regarding selection-details that we might not otherwise believe to be critical.


title: Hidden Markov Random Field model and BFGS algorithm for Brain Image Segmentation

abstract: Brain MR images segmentation has attracted a particular focus in medical imaging. The automatic image analysis and interpretation became a necessity. Segmentation is one of the key operations to provide a crucial decision support to physicians. Its goal is to simplify the representation of an image into items meaningful and easier to analyze. Hidden Markov Random Fields (HMRF) provide an elegant way to model the segmentation problem. This model leads to the minimization problem of a function. BFGS (Broyden-Fletcher-Goldfarb-Shanno algorithm) is one of the most powerful methods to solve unconstrained optimization problem. This paper presents how we combine HMRF and BFGS to achieve a good segmentation. Brain image segmentation results are evaluated on ground-truth images, using the Dice coefficient.


title: A precision 140MHz relaxation oscillator in 40nm CMOS with 28ppm/°C frequency stability for automotive SoC applications

abstract: The need for high-frequency, low-power, wide temperature range, precision on-chip reference clock generation makes relaxation oscillator topology an attractive solution for various automotive applications. This paper presents for the first time a 140MHz relaxation oscillator with robust-against-process-variation temperature compensation scheme. The high-frequency relaxation oscillator achieves 28 ppm/°C frequency stability over the automotive temperature range from −40 to 175°C. The circuit is fabricated in 40nm CMOS technology, occupies 0.009 mm2 and consumes 294µW from 1.2V supply.


Real-Time Editing , Synthesis , and Rendering of Infinite Landscapes on GPUs


MEMS Based Broadband Piezoelectric Ultrasonic Energy Harvester (PUEH) for Enabling Self-Powered Implantable Biomedical Devices


"Industrie 4.0" and Smart Manufacturing - A Review of Research Issues and Application Examples


High Dense Crowd Pattern and Anomaly Detection Using Statistical Model


title: On the Impact of Knowledge Discovery and Data Mining

abstract: Knowledge Discovery and Data Mining are powerful automated data analysis tools and they are predicted to become the most frequently used analytical tools in the near future. The rapid dissemination of these technologies calls for an urgent examination of their social impact. This paper identifies social issues arising from Knowledge Discovery (KD) and Data Mining (DM). An overview of these technologies is presented, followed by a detailed discussion of each issue. The paper's intention is to primarily illustrate the cultural context of each issue and, secondly, to describe the impact of KD and DM in each case. Existing solutions specific to each issue are identified and examined for feasibility and effectiveness, and a solution that provides a suitably contextually sensitive means for gathering and analysing sensitive data is proposed and briefly outlined. The paper concludes with a discussion of topics for further consideration.


title: Design of the 2015 ChaLearn AutoML challenge

abstract: ChaLearn is organizing the Automatic Machine Learning (AutoML) contest for IJCNN 2015, which challenges participants to solve classification and regression problems without any human intervention. Participants' code is automatically run on the contest servers to train and test learning machines. However, there is no obligation to submit code; half of the prizes can be won by submitting prediction results only. Datasets of progressively increasing difficulty are introduced throughout the six rounds of the challenge. (Participants can enter the competition in any round.) The rounds alternate phases in which learners are tested on datasets participants have not seen, and phases in which participants have limited time to tweak their algorithms on those datasets to improve performance. This challenge will push the state of the art in fully automatic machine learning on a wide range of real-world problems. The platform will remain available beyond the termination of the challenge.


title: The value of IT-enabled retailer learning: personalized product recommendations and customer store loyalty in electronic markets

abstract: Recent research has acknowledged the key role of information technology in helping build stronger and more enduring customer relationships. Personalized product recommendations (PPRs) adapted to individual customers' preferences and tastes are one IT-enabled strategy that has been widely adopted by online retailers to enhance customers' shopping experience. Although many online retailers have implemented PPRs on their electronic storefronts to improve customer retention, empirical evidence for the effects of PPRs on retention is sparse, and the limited anecdotal evidence is contradictory. We draw upon the household production function model in the consumer economics literature to develop a theoretical framework that explains the mechanisms through which PPRs influence customer store loyalty in electronic markets. We suggest that retailer learning that occurs as a result of customer knowledge obtained to enable personalization influences the efficiency of the online product brokering activity. Data collected from a two-phase lab experiment with 253 student subjects where the quality of PPRs was manipulated are used to empirically test the predictions of the theoretical model. Empirical analyses of the data indicate that retailer learning reflected in higher quality PPRs is associated with lower product screening cost, but higher product evaluation cost. We further find that higher quality PPRs are associated with greater value derived by consumers from the online product brokering activity in terms of higher decision making quality, which is positively associated with repurchase intention. The paper presents the implications, limitations, and contributions of this study along with areas for future research.


title: 77 GHz automotive digital beamforming radar with SiGe chipset

abstract: In this paper, we describe the design and layout of an automotive radar sensor demonstrator for 77 GHz with a SiGe chipset and a fully parallel receiver architecture which is capable of digital beamforming and superresolution direction of arrival estimation methods in azimuth. Additionally, we show measurement results of this radar sensor mounted on a test vehicle.


title: Position Control of DC Motor Using Genetic Algorithm Based PID Controller

abstract: The aim of this paper is to design a position controller of a DC motor by selection of a PID parameters using genetic algorithm. The model of a DC motor is considered as a third order system. And this paper compares two kinds of tuning methods of parameter for PID controller. One is the controller design by the genetic algorithm, second is the controller design by the Ziegler and Nichols method. It was found that the proposed PID parameters adjustment by the genetic algorithm is better than the Ziegler & Nichols' method. The proposed method could be applied to the higher order system also.


title: Tumor Detection in Automated Breast Ultrasound Using 3-D CNN and Prioritized Candidate Aggregation

abstract: Automated whole breast ultrasound (ABUS) has been widely used as a screening modality for examination of breast abnormalities. Reviewing hundreds of slices produced by ABUS, however, is time consuming. Therefore, in this paper, a fast and effective computer-aided detection system based on 3-D convolutional neural networks (CNNs) and prioritized candidate aggregation is proposed to accelerate this reviewing. First, an efficient sliding window method is used to extract volumes of interest (VOIs). Then, each VOI is estimated the tumor probability with a 3-D CNN, and VOIs with higher estimated probability are selected as tumor candidates. Since the candidates may overlap each other, a novel scheme is designed to aggregate the overlapped candidates. During the aggregation, candidates are prioritized based on estimated tumor probability to alleviate over-aggregation issue. The relationship between the sizes of VOI and target tumor is optimally exploited to effectively perform each stage of our detection algorithm. On evaluation with a test set of 171 tumors, our method achieved sensitivities of 95% (162/171), 90% (154/171), 85% (145/171), and 80% (137/171) with 14.03, 6.92, 4.91, and 3.62 false positives per patient (with six passes), respectively. In summary, our method is more general and much faster than preliminary works and demonstrates promising results.


Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences


A Spline Theory of Deep Networks.


title: ML-Plan: Automated machine learning via hierarchical planning

abstract: Automated machine learning (AutoML) seeks to automatically select, compose, and parametrize machine learning algorithms, so as to achieve optimal performance on a given task (dataset). Although current approaches to AutoML have already produced impressive results, the field is still far from mature, and new techniques are still being developed. In this paper, we present ML-Plan, a new approach to AutoML based on hierarchical planning. To highlight the potential of this approach, we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn, and TPOT. In an extensive series of experiments, we show that ML-Plan is highly competitive and often outperforms existing approaches.


title: Hybrid de-anonymization across real-world heterogeneous social networks

abstract: To enjoy various utility and services, people are active in multiple social networks nowadays. With tons of data generated on platforms, multiple accounts of the same user in different social networks can be used to de-anonymize the user in a large scale. The aggregation of user profiles poses a threat to user privacy. With a concern of privacy leakage, de-anonymization techniques, including graph based approaches and profile based approaches, are widely studied in recent years. However, few works throw light on the deanonymization between real-world heterogeneous social networks. In this paper, we propose a Hybrid De-anonymization Scheme (HDS) aiming at de-anonymizing heterogeneous social networks. HDS firstly leverages the network graph structure to significantly reduce the size of candidate set, then exploits user profile information to identify the correct mapping users with a high confidence. Performance evaluation on real-world social network datasets shows that HDS has considerable accuracy on de-anonymization and significantly outperforms the prior schemes.


title: Investigation on topology for Type-4 LLC resonant Dc-Dc converter

abstract: All kinds of basic topologies dedicating to the type-4 LLC series resonant converter have been explored and researched. These topologies, which varying from the input and output structures with different isolation requirements, have their own potential useful properties while maintaining the same characteristics and possessing all merits of the original type-4 LLC converter. Consequently, each of the presented topologies could be a candidate for the specific application in which it is suitable for. Furthermore, by being appropriately modified, the presented basic topologies can flexibly change to some applicable variations that are favorable for rigorous applications such as ultra-high input and high efficiency cost-effective multi-output occasions. Finally, from theory to practical, an example verified the validity and high applicability of type-4 LLC resonant converter and its variations for practical applications in industry.


title: Freeway speed limits and traffic fatalities in Washington State.

abstract: BACKGROUND ::: In 1987 individual states in the USA were allowed to raise speed limits on rural freeways from 55 to 65 mph. Analyses of the impact of the increased speed limits on highway safety have produced conflicting results. ::: ::: ::: OBJECTIVE ::: To determine if the 1987 speed limit increase on Washington State's rural freeways affected the incidence of fatal crashes or all crashes on rural freeways, or affected average vehicle speeds or speed variance. ::: ::: ::: DESIGN ::: An ecological study of crashes and vehicle speeds on Washington State freeways from 1974 through 1994. ::: ::: ::: RESULTS ::: The incidence of fatal crashes more than doubled after 1987, compared with what would have been expected if there had been no speed limit increase, rate ratio 2.1 (95% confidence interval (CI), 1.6-2.7). This resulted in an excess of 26.4 deaths per year on rural freeways in Washington State. The total crash rate did not change substantially, rate ratio 1.1 (95% CI, 1.0-1.3). Average vehicle speed increased by 5.5 mph. Speed variance was not affected by the speed limit increase. ::: ::: ::: CONCLUSIONS ::: The speed limit increase was associated with a higher fatal crash rate and more deaths on freeways in Washington State.


title: Inference and learning in probabilistic logic programs using weighted Boolean formulas

abstract: Probabilistic logic programs are logic programs in which some of the facts are annotated with probabilities. This paper investigates how classical inference and learning tasks known from the graphical model community can be tackled for probabilistic logic programs. Several such tasks such as computing the marginals given evidence and learning from (partial) interpretations have not really been addressed for probabilistic logic programs before. The rst contribution of this paper is a suite of ecient algorithms for various inference tasks. It is based on a conversion of the program and the queries and evidence to a weighted Boolean formula. This allows us to reduce the inference tasks to well-studied tasks such as weighted model counting, which can be solved using state-of-the-art methods known from the graphical model and knowledge compilation literature. The second contribution is an algorithm for parameter estimation in the learning from interpretations setting. The algorithm employs Expectation Maximization, and is built on top of the developed inference algorithms. The proposed approach is experimentally evaluated. The results show that the inference algorithms improve upon the state-of-the-art in probabilistic logic programming and that it is indeed possible to learn the parameters of a probabilistic logic program from interpretations.


title: Yield estimation in vineyards by visual grape detection

abstract: The harvest yield in vineyards can vary significantly from year to year and also spatially within plots due to variations in climate, soil conditions and pests. Fine grained knowledge of crop yields can allow viticulturists to better manage their vineyards. The current industry practice for yield prediction is destructive, expensive and spatially sparse - during the growing season sparse samples are taken and extrapolated to determine overall yield. We present an automated method that uses computer vision to detect and count grape berries. The method could potentially be deployed across large vineyards taking measurements at every vine in a non-destructive manner. Our berry detection uses both shape and visual texture and we can demonstrate detection of green berries against a green leaf background. Berry detections are counted and the eventual harvest yield is predicted. Results are presented for 224 vines (over 450 meters) of two different grape varieties and compared against the actual harvest yield as groundtruth. We calibrate our berry count to yield and find that we can predict yield of individual vineyard rows to within 9.8% of actual crop weight.


title: Microduplication of 4p16.3 due to an unbalanced translocation resulting in a mild phenotype

abstract: With the widespread clinical use of comparative genomic hybridization chromosomal microarray technology, several previously unidentified clinically significant submicroscopic chromosome abnormalities have been discovered. Specifically, there have been reports of clinically significant microduplications found in regions of known microdeletion syndromes. In general, these microduplications have distinct features from those described in the corresponding microdeletion syndromes. We present a 5 1 /2-year-old patient with normal growth, borderline normal IQ, borderline hypertelorism, and speech and language delay who was found to have a submicroscopic 2.3Mb terminal duplication involving the two proposed Wolf– Hirschhorn syndrome (WHS) critical regions at chromosome 4p16.3. This duplication was the result of a maternally inherited reciprocal translocation involving the breakpoints 4p16.3 and 17q25.3. Our patient’s features aredistinctfromthosedescribed inWHSandarenotassevereasthosedescribedinpartialtrisomy 4p. There are two other patients in the medical literature with 4p16.3microduplications ofsimilarsizealso involvingtheWHS criticalregions.Ourpatientshowsclinicaloverlapwiththesetwo patients, although overall her features are milder than what has been previously described. Our patient’s features expand the knowledge of the clinical phenotype of a 4p16.3 microduplication and highlight the need for further information about it. � 2011 Wiley-Liss, Inc.


title: Multiple view semi-supervised dimensionality reduction

abstract: Multiple view data, together with some domain knowledge in the form of pairwise constraints, arise in various data mining applications. How to learn a hidden consensus pattern in the low dimensional space is a challenging problem. In this paper, we propose a new method for multiple view semi-supervised dimensionality reduction. The pairwise constraints are used to derive embedding in each view and simultaneously, the linear transformation is introduced to make different embeddings from different pattern spaces comparable. Hence, the consensus pattern can be learned from multiple embeddings of multiple representations. We derive an iterating algorithm to solve the above problem. Some theoretical analyses and out-of-sample extensions are also provided. Promising experiments on various data sets, together with some important discussions, are also presented to demonstrate the effectiveness of the proposed algorithm.


title: Efficient hardware voxelization

abstract: This paper presentes a novel algorithm for the voxelization of surface models of arbitrary topology. Our algorithm uses the depth and stencil buffers, available in most commercial graphics hardware, to achieve high performance. It is suitable for both polygonal meshes and parametric surfaces. Experiments highlight the advantages and limitations of our approach.


title: Building robust prediction models for defective sensor data using Artificial Neural Networks

abstract: Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges. The primary aim of such predictive systems is to use the high-dimensional data acquired from different sensors and predict the state-of-health of a particular component, e.g., brake pad. The classical approach involves selecting a smaller set of relevant sensor signals using feature selection and using them to train a machine learning algorithm. However, this fails to address two prominent problems: (1) sensors are susceptible to failure when exposed to extreme conditions over a long periods of time; (2) sensors are electrical devices that can be affected by noise or electrical interference. Using the failed and noisy sensor signals as inputs largely reduce the prediction accuracy. To tackle this problem, it is advantageous to use the information from all sensor signals, so that the failure of one sensor can be compensated by another. In this work, we propose an Artificial Neural Network (ANN) based framework to exploit the information from a large number of signals. Secondly, our framework introduces a data augmentation approach to perform accurate predictions in spite of noisy signals. The plausibility of our framework is validated on real life industrial application from Robert Bosch GmbH.


title: Thermal, Spectroscopic and Chemical Characterization of Biofield Energy Treated Anisole

abstract: The objective of the present study was to evaluate the impact of biofield energy treatment on the thermal, spectroscopic, and chemical properties of anisole by various analytical methods such as gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography (HPLC), differential scanning calorimetry (DSC), Fourier transform infrared (FT-IR) spectroscopy, and ultraviolet-visible (UV-Vis) spectroscopy. The anisole sample was divided into two parts, control and treated. The control part was remained same while the other part was treated with Mr. Trivedi’s unique biofield energy treatment. Mass spectra showed the molecular ion peak with five fragmented peaks in control and all treated samples. The isotopic abundance ratio of 2H/1H, and 13C/12C [(PM+1)/PM] in treated sample was increased by 154.47% (T1) as compared to the control [where, PM- primary molecule, (PM+1)-isotopic molecule either for 13C or 2H]. The HPLC chromatogram showed retention time of treated anisole was slightly decreased as compared to the control. Moreover, the heat change in the sharp endothermic transition of treated anisole was increased by 389.07% in DSC thermogram as compared to the control. Further, C-C aromatic stretching frequency of treated sample was shifted by 2 cm-1 to low energy region in FT-IR spectroscopy. The UV-Vis spectra of control sample showed characteristic absorption peaks at 325 nm, which was red shifted and appeared as shoulder in the treated sample. These results suggested that biofield treatment has significantly altered the physical and spectroscopic properties of anisole, which could make them stable solvent for organic synthesis and as a suitable reaction intermediate in industrial applications.