title: Condition-based Maintenance Optimization Using Neural Network-based Health Condition Prediction

abstract: Artificial neural network (ANN)-based methods have been extensively investigated for equipment health condition prediction. However, effective condition-based maintenance (CBM) optimization methods utilizing ANN prediction information are currently not available due to two key challenges: (i) ANN prediction models typically only give a single remaining life prediction value, and it is hard to quantify the uncertainty associated with the predicted value; (ii) simulation methods are generally used for evaluating the cost of the CBM policies, while more accurate and efficient numerical methods are not available, which is critical for performing CBM optimization. In this paper, we propose a CBM optimization approach based on ANN remaining life prediction information, in which the above-mentioned key challenges are addressed. The CBM policy is defined by a failure probability threshold value. The remaining life prediction uncertainty is estimated based on ANN lifetime prediction errors on the test set during the ANN training and testing processes. A numerical method is developed to evaluate the cost of the proposed CBM policy more accurately and efficiently. Optimization can be performed to find the optimal failure probability threshold value corresponding to the lowest maintenance cost. The effectiveness of the proposed CBM approach is demonstrated using two simulated degradation data sets and a real-world condition monitoring data set collected from pump bearings. The proposed approach is also compared with benchmark maintenance policies and is found to outperform the benchmark policies. The proposed CBM approach can also be adapted to utilize information obtained using other prognostics methods. Copyright © 2012 John Wiley & Sons, Ltd.


title: TaintART: A Practical Multi-level Information-Flow Tracking System for Android RunTime

abstract: Mobile operating systems like Android failed to provide sufficient protection on personal data, and privacy leakage becomes a major concern. To understand the security risks and privacy leakage, analysts have to carry out data-flow analysis. In 2014, Android upgraded with a fundamentally new design known as Android RunTime (ART) environment in Android 5.0. ART adopts ahead-of-time compilation strategy and replaces previous virtual-machine-based Dalvik. Unfortunately, many data-flow analysis systems like TaintDroid were designed for the legacy Dalvik environment. This makes data-flow analysis of new apps and malware infeasible. We design a multi-level information-flow tracking system for the new Android system called TaintART. TaintART employs a multi-level taint analysis technique to minimize the taint tag storage. Therefore, taint tags can be stored in processor registers to provide efficient taint propagation operations. We also customize the ART compiler to maximize performance gains of the ahead-of-time compilation optimizations. Based on the general design of TaintART, we also implement a multi-level privacy enforcement to prevent sensitive data leakage. We demonstrate that TaintART only incurs less than 15% overheads on a CPU-bound microbenchmark and negligible overhead on built-in or third-party applications. Compared to legacy Dalvik environment in Android 4.4, TaintART achieves about 99.7% faster performance for Java runtime benchmark.


title: Low cost and batteryless sensor-enabled radio frequency identification tag based approaches to identify patient bed entry and exit posture transitions

abstract: Abstract Introduction Falls in hospitals and residential care facilities commonly occur near the bed. The aim of this study was to investigate the accuracy of a continuously wearable, batteryless, low power and low cost monitoring device (Wearable Wireless Identification and Sensing Platform) with a single kinematic sensor capable of real-time monitoring to automatically detect bed entry and exit events. Materials and methods Three dimensional acceleration readings and the strength of the transmitted signal from the WISP was interpreted to identify bed exit events and sensitivity, specificity and Receiving Operator Curves (ROC) were determined. Results The sensor located over sternum method performed best with sensitivity and specificity values of 92.8% and 97.5% respectively for detecting bed entry and values of 90.4% and 93.80% respectively for bed exit. On the other hand, the sensor-on-mattress algorithm achieved sensitivity and specificity values of 84.2% and 97.4% respectively for bed entry and 79% and 97.4% for bed exit detection. Conclusion The WISP located over the sternum method is the preferred method to detect bed entry and exit. However, further work in frail older people is required to confirm the performance of this method.


title: "Differential Diagnosis: Is It Herpes or Aphthous?"

abstract: Recurrent aphthous stomatitis (RAS) and recurrent intraoral herpes (RIH) are the two most commonly presenting oral lesions in the dental setting. It is critical that the oral health professional be able to accurately discriminate between these disorders. To facilitate the differential diagnosis between RAS and RIH, important components of assessment are discussed. These include: prodromal signs and symptoms, lesion location, and appearance of the initial and mature lesion. The comparative etiology, prevalence, pathogenesis, and treatment considerations for these lesions are presented. A familial case report is provided.


title: Priority Tries for IP Address Lookup

abstract: High-speed IP address lookup is essential to achieve wire speed packet forwarding in Internet routers. The longest prefix matching for IP address lookup is more complex than exact matching because it involves dual dimensions: length and value. This paper presents a new formulation for IP address lookup problem using range representation of prefixes and proposes an efficient binary trie structure named a priority trie. In this range representation, prefixes are represented as ranges on a number line between 0 and 1 without expanding to the maximum length. The best match to a given input address is the smallest range that includes the input. The priority trie is based on the trie structure, with empty internal nodes in the trie replaced by the priority prefix which is the longest among those in the subtrie rooted by the empty nodes. The search ends when an input matches a priority prefix, which significantly improves the search performance. Performance evaluation using real routing data shows that the proposed priority trie is very good in performance metrics such as lookup speed, memory size, update performance, and scalability.


title: Dissociable effects of social context on song and doublecortin immunoreactivity in male canaries

abstract: Variation in environmental factors such as day length and social context greatly affects reproductive behavior and the brain areas that regulate these behaviors. One such behavior is song in songbirds, which males use to attract a mate during the breeding season. In these species the absence of a potential mate leads to an increase in the number of songs produced, while the presence of a mate greatly diminishes singing. Interestingly, although long days promote song behavior, producing song itself can promote the incorporation of new neurons in brain regions controlling song output. Social context can also affect such neuroplasticity in these song control nuclei. The goal of the present study was to investigate in canaries (Serinus canaria), a songbird species, how photoperiod and social context affect song and the incorporation of new neurons, as measured by the microtubule-associated protein doublecortin (DCX) in HVC, a key vocal production brain region of the song control system. We show that long days increased HVC size and singing activity. In addition, male canaries paired with a female for 2 weeks showed enhanced DCX-immunoreactivity in HVC relative to birds housed alone. Strikingly, however, paired males sang fewer songs that exhibited a reduction in acoustic features such as song complexity and energy, compared with birds housed alone, which sang prolifically. These results show that social presence plays a significant role in the regulation of neural and behavioral plasticity in songbirds and can exert these effects in opposition to what might be expected based on activity-induced neurogenesis.


title: A new approach to determine the effect of mismatches on kinetic parameters in DNA hybridization using an optical biosensor.

abstract: We have demonstrated a simple yet direct method for determining the kinetic parameters in DNA-DNA interactions using biosensor technology based on the surface plasmon resonance phenomenon; a technique that does not require complex DNA labeling. To determine the effect of mismatches on the kinetics involved in DNA-DNA interactions, DNA hybridization kinetics were monitored in real time using synthetic oligonucleotides less than 20 bases in length which contained either a complementary sequence or mismatched bases. Upon analysis of the kinetic parameters obtained in oligonucleotide hybridization, we found that they were significantly affected by the presence of mismatches as well as by their number and location in a DNA duplex. In addition, the presented biosensor method is sensitive enough to detect kinetic effects caused by the presence of a single-mismatched base pair. Our findings strongly suggest that analysis of kinetic parameters involved in DNA-DNA interactions is advantageous for detecting the presence of mismatch base pairs in a DNA duplex.


title: Case Report Minimally invasive corpectomy and posterior stabilization for lumbar burst fracture

abstract: BACKGROUND CONTEXT: Surgical indications for lumbar burst fracture remain controversial. Potential indications for surgery include 50% canal compromise, 50% loss of vertebral height, 30 of kyphosis, and posterior element fracture or disruption of the posterior ligamentous complex. Different surgical approaches are available depending on fracture characteristics. It is possible that a minimally invasive approach could allow for a safe and effective treatment with fewer comorbidities than the traditional open technique. PURPOSE: This is a report of an L3 burst fracture treated with a minimally invasive approach for anterior corpectomy and posterior pedicle screw fixation. STUDY DESIGN: Case report. PATIENT SAMPLE: Patient with L3 burst fracture. OUTCOME MEASURES: Radiographs and computed tomography scans to evaluate for fusion and evaluation of pain and neurologic function. METHODS: A 30-year-old male was involved in a head-on motor vehicle collision. Initial imaging revealed an L3 burst fracture with 60% canal compromise, 50% loss of vertebral body height, a large anteriorly displaced fragment consisting of 40% of the vertebral body depth, and a facet fracture. Surgical decompression and stabilization were recommended for this patient because of radiographic signs of instability. After medical clearance and consent, the patient underwent a minimally invasive L3 corpectomy and L2‐L4 interbody fusion through a direct lateral approach with placement of a titanium mesh cage filled with local autograft and allograft bone matrix. The patient then underwent a percutaneous stabilization with pedicle screw fixation from L2 to L4. RESULTS: The patient was ambulating on the first postoperative day, and pain was controlled with oral analgesics. Intraoperative blood loss was less than 100 cc. He was discharged to a rehabilitation facility on the second postoperative day. Postoperatively, he complained of some left lower extremity pain and numbness. The pain completely resolved by the 6-month follow-up visit. The numbness in the anterolateral left thigh was improved but not completely resolved at 12 months. He continued to have full strength in all extremities. CONCLUSION: The traditional approach to an anterior lumbar corpectomy and posterior pedicle screw fixation involves significant postoperative pain and frequent ileus. This minimally invasive approach allowed for early mobilization, resumption of diet, and discharge from the hospital on postoperative day two. 2011 Elsevier Inc. All rights reserved.


title: A wireless, minaturized multi-channel sEMG acquisition system for use in dynamic tasks

abstract: Nowadays, the detection of surface EMG (sEMG) signals is almost exclusively based on a single or a few electrode pairs. However, in the last two decades limitations of bipolar sEMG signals emerged. To increase the amount and reliability of information extracted from sEMG, linear electrode arrays and two-dimensional detection systems have been proposed. The aim of this work was the development of a wearable wireless, 32-channels sEMG acquisition system. The developed system performs the conditioning, sampling and wireless transmission of 32 monopolar sEMG channels and 3 auxiliary signals, sampled at 2.048ksps with 16 bit resolution. The system wirelessly transmits the acquired signals to either a mobile device (smartphone or tablet with Wi-Fi connectivity) or a personal computer for real time visualization and storage. The developed system has been tested in clinical and sport scenarios showing good performances in wearability and movement artefact robustness.


title: Mining the Web of Linked Data with RapidMiner

abstract: Lots of data from different domains are published as Linked Open Data (LOD). While there are quite a few browsers for such data, as well as intelligent tools for particular purposes, a versatile tool for deriving additional knowledge by mining the Web of Linked Data is still missing. In this system paper, we introduce the RapidMiner Linked Open Data extension. The extension hooks into the powerful data mining and analysis platform RapidMiner, and offers operators for accessing Linked Open Data in RapidMiner, allowing for using it in sophisticated data analysis workflows without the need for expert knowledge in SPARQL or RDF. The extension allows for autonomously exploring the Web of Data by following links, thereby discovering relevant datasets on the fly, as well as for integrating overlapping data found in different datasets. As an example, we show how statistical data from the World Bank on scientific publications, published as an RDF data cube, can be automatically linked to further datasets and analyzed using additional background knowledge from ten different LOD datasets.


WP 2004-10 Energy Options in an HJM Framework by Thomas Lyse


title: Sentence Selection and Weighting for Neural Machine Translation Domain Adaptation

abstract: Neural machine translation (NMT) has been prominent in many machine translation tasks. However, in some domain-specific tasks, only the corpora from similar domains can improve translation performance. If out-of-domain corpora are directly added into the in-domain corpus, the translation performance may even degrade. Therefore, domain adaptation techniques are essential to solve the NMT domain problem. Most existing methods for domain adaptation are designed for the conventional phrase-based machine translation. For NMT domain adaptation, there have been only a few studies on topics such as fine tuning, domain tags, and domain features. In this paper, we have four goals for sentence level NMT domain adaptation. First, the NMT's internal sentence embedding is exploited and the sentence embedding similarity is used to select out-of-domain sentences that are close to the in-domain corpus. Second, we propose three sentence weighting methods, i.e., sentence weighting, domain weighting, and batch weighting, to balance the data distribution during NMT training. Third, in addition, we propose dynamic training methods to adjust the sentence selection and weighting during NMT training. Fourth, to solve the multidomain problem in a real-world NMT scenario where the domain distributions of training and testing data often mismatch, we proposed a multidomain sentence weighting method to balance the domain distributions of training data and match the domain distributions of training and testing data. The proposed methods are evaluated in international workshop on spoken language translation (IWSLT) English-to-French/German tasks and a multidomain English-to-French task. Empirical results show that the sentence selection and weighting methods can significantly improve the NMT performance, outperforming the existing baselines.


title: Deep SNP: An End-to-end Deep Neural Network with Attention-based Localization for Break-point Detection in SNP Array Genomic data

abstract: Diagnosis and risk stratification of cancer and many other diseases require the detection of genomic breakpoints as a prerequisite of calling copy number alterations (CNA). This, however, is still challenging and requires time-consuming manual curation. As deep-learning methods outperformed classical state-of-the-art algorithms in various domains and have also been successfully applied to life science problems including medicine and biology, we here propose Deep SNP, a novel Deep Neural Network to learn from genomic data. Specifically, we used a manually curated dataset from 12 genomic single nucleotide polymorphism array (SNPa) profiles as truth-set and aimed at predicting the presence or absence of genomic breakpoints, an indicator of structural chromosomal variations, in windows of 40,000 probes. We compare our results with well-known neural network models as well as Rawcopy though this tool is designed to predict breakpoints and in addition genomic segments with high sensitivity. We show, that Deep SNP is capable of successfully predicting the presence or absence of a breakpoint in large genomic windows and outperforms state-of-the-art neural network models. Qualitative examples suggest that integration of a localization unit may enable breakpoint detection and prediction of genomic segments, even if the breakpoint coordinates were not provided for network training. These results warrant further evaluation of DeepSNP for breakpoint localization and subsequent calling of genomic segments.


title: An ultra-low power capacitor-less LDO for always-on domain in NB-IoT applications

abstract: This paper presents an ultra-low power 55 nm CMOS capacitor-less Low-Dropout (LDO) voltage regulator for power management of an Always-On domain module for implementation of NB-IoT SoC applications. Compared with traditional IoT SoC, the power consumption specification of NB-IoT SoC is lower. One effective way to achieve low power application is to reduce the power of the Always-On domain module in the SoC, where the LDO plays a critical role. According to the tape-out, results are validated with a 0.89V output voltage from 1.8V to 3.3V supply voltage, delivering a load current of 100uA. The peak-peak variance of output voltage is less than 100mV from 1.8V to 3.3V supply voltage. The quiescent current is lower than 61.86nA, including a 2-Transistor reference voltage, which can implement NB-IoT SoC application, with yield as high as 87.88%.


title: A 7.5mW 35–70MHz 4th-order semi-passive charge-sharing band-pass filter with programmable bandwidth and 72dB stop-band rejection in 65nm CMOS

abstract: This paper proposes a highly reconfigurable charge-domain switched-gm-C biquad band-pass filter (BPF) topology that uses a semi-passive charge-sharing technique. It uses only switches, capacitors, digital circuitry for 3-phase non-overlapping clock generation and linearity-enhanced gm-stages. A 4th-order BPF prototype operating at 1.2GS/s sampling rate is implemented using a cascade of two independent biquads in a 65nm LPE CMOS. A tunable center frequency of 35–70MHz is measured with programmable bandwidth and a maximum stop-band rejection of 72dB. The in-band 1-dB compression point is −2.4dBm, and the in-band IIP3 is +9dBm. The filter prototype consumes 7.5mW from a 1.2V supply, and occupies an active area of 0.17mm2.


title: Sensorless control of high-speed PM BLDC motor

abstract: In the paper were examined the possibilities to control a high-speed sensorless PM BLDC motor, with a target speed 100 000 rpm and power 1 kW, designed in the Department of Power Electronics, Electrical Drives and Robotics - KENER, Silesian University of Technology. The article describes methods based on phase voltages integration and the third harmonic, also presents a method of open-loop starting and ways of determining the position of the stopped rotor. Described sensorless control methods were examined by computer simulation, in order to test the possibilities of their implementation in the high-speed motor project.


title: A Closed-Form, Consistent and Robust Solution to Uncalibrated Photometric Stereo Via Local Diffuse Reflectance Maxima

abstract: Images of an object under different illumination are known to provide strong cues about the object surface. A mathematical formalization of how to recover the normal map of such a surface leads to the so-called uncalibrated photometric stereo problem. In the simplest instance, this problem can be reduced to the task of identifying only three parameters: the so-called generalized bas-relief (GBR) ambiguity. The challenge is to find additional general assumptions about the object, that identify these parameters uniquely. Current approaches are not consistent, i.e., they provide different solutions when run multiple times on the same data. To address this limitation, we propose exploiting local diffuse reflectance (LDR) maxima, i.e., points in the scene where the normal vector is parallel to the illumination direction (see Fig. 1). We demonstrate several noteworthy properties of these maxima: a closed-form solution, computational efficiency and GBR consistency. An LDR maximum yields a simple closed-form solution corresponding to a semi-circle in the GBR parameters space (see Fig. 2); because as few as two diffuse maxima in different images identify a unique solution, the identification of the GBR parameters can be achieved very efficiently; finally, the algorithm is consistent as it always returns the same solution given the same data. Our algorithm is also remarkably robust: It can obtain an accurate estimate of the GBR parameters even with extremely high levels of outliers in the detected maxima (up to 80 % of the observations). The method is validated on real data and achieves state-of-the-art results.


title: Multi-source deep learning for information trustworthiness estimation

abstract: In recent years, information trustworthiness has become a serious issue when user-generated contents prevail in our information world. In this paper, we investigate the important problem of estimating information trustworthiness from the perspective of correlating and comparing multiple data sources. To a certain extent, the consistency degree is an indicator of information reliability--Information unanimously agreed by all the sources is more likely to be reliable. Based on this principle, we develop an effective computational approach to identify consistent information from multiple data sources. Particularly, we analyze vast amounts of information collected from multiple review platforms (multiple sources) in which people can rate and review the items they have purchased. The major challenge is that different platforms attract diverse sets of users, and thus information cannot be compared directly at the surface. However, latent reasons hidden in user ratings are mostly shared by multiple sources, and thus inconsistency about an item only appears when some source provides ratings deviating from the common latent reasons. Therefore, we propose a novel two-step procedure to calculate information consistency degrees for a set of items which are rated by multiple sets of users on different platforms. We first build a Multi-Source Deep Belief Network (MSDBN) to identify the common reasons hidden in multi-source rating data, and then calculate a consistency score for each item by comparing individual sources with the reconstructed data derived from the latent reasons. We conduct experiments on real user ratings collected from Orbitz, Priceline and TripAdvisor on all the hotels in Las Vegas and New York City. Experimental results demonstrate that the proposed approach successfully finds the hotels that receive inconsistent, and possibly unreliable, ratings.


title: Cloud Computing Through Mobile-Learning

abstract: Cloud computing is the new technology that has various advantages and it is an adoptable technology in this present scenario. The main advantage of the cloud computing is that this technology reduces the cost effectiveness for the implementation of the Hardware, software and License for all. This is the better peak time to analyze the cloud and its implementation and better use it for the development of the quality and low cost education for all over the world. In this paper, we discuss how to influence on cloud computing and influence on this technology to take education to a wider mass of students over the country. We believe cloud computing will surely improve the current system of education and improve quality at an affordable cost.


title: Delegation in role-based access control

abstract: User delegation is a mechanism for assigning access rights available to a user to another user. A delegation operation can either be a grant or transfer operation. Delegation for role-based access control models have extensively studied grant delegations. However, transfer delegations for role-based access control have largely been ignored. This is largely because enforcing transfer delegation policies is more complex than grant delegation policies. This paper, primarily, studies transfer delegations for role-based access control models. We also include grant delegations in our model for completeness. We present various mechanisms that authorise delegations in our model. In particular, we show that the use of administrative scope for authorising delegations is more efficient than using relations. We also discuss the enforcement and revocation of delegations. Finally, we compare our work with relevant work in the literature.


title: Modeling Non-Stationary Asymmetric Lens Blur by Normal Sinh-Arcsinh Model

abstract: Images acquired by a camera show lens blur due to imperfection in the optical system even when images are properly focused. Lens blur is non-stationary in a sense that the amount of blur depends on pixel locations in a sensor. Lens blur is also asymmetric in a sense that the amount of blur is different in the radial and tangential directions, and also in the inward and outward radial directions. This paper presents parametric blur kernel models based on the normal sinh-arcsinh distribution function. The proposed models can provide flexible shapes of blur kernels with a different symmetry and skewness to model complicated lens blur due to optical aberration in a properly focused images accurately. Blur of single focal length lenses is estimated, and the accuracy of the models is compared with the existing parametric blur models. An advantage of the proposed models is demonstrated through deblurring experiments.


Friend Recommendation System for Online Social Networks


title: 10T SRAM Using Half- $V_{\text {DD}}$ Precharge and Row-Wise Dynamically Powered Read Port for Low Switching Power and Ultralow RBL Leakage

abstract: We present, in this paper, a new 10T static random access memory cell having single ended decoupled read-bitline (RBL) with a 4T read port for low power operation and leakage reduction. The RBL is precharged at half the cell’s supply voltage, and is allowed to charge and discharge according to the stored data bit. An inverter, driven by the complementary data node (QB), connects the RBL to the virtual power rails through a transmission gate during the read operation. RBL increases toward the $V_{\text {DD}}$ level for a read-1, and discharges toward the ground level for a read-0. Virtual power rails have the same value of the RBL precharging level during the write and the hold mode, and are connected to true supply levels only during the read operation. Dynamic control of virtual rails substantially reduces the RBL leakage. The proposed 10T cell in a commercial 65 nm technology is $2.47\times $ the size of 6T with $\beta = 2$ , provides $2.3\times $ read static noise margin, and reduces the read power dissipation by 50% than that of 6T. The value of RBL leakage is reduced by more than 3 orders of magnitude and $({I_{\mathrm{\scriptscriptstyle ON}}}/{I_{\mathrm{\scriptscriptstyle OFF}}})$ is greatly improved compared with the 6T BL leakage. The overall leakage characteristics of 6T and 10T are similar, and competitive performance is achieved.


title: Accelerating Face Detection on Programmable SoC Using C-Based Synthesis

abstract: High-level synthesis (HLS) enables designing at a higher level of abstraction to effectively cope with design complexity of emerging applications on modern programmable system-on-chip (SoC). While HLS continues to evolve with a growing set of algorithms, methodologies, and tools to efficiently map software designs onto optimized hardware architectures, there continues to lack realistic benchmark applications with sufficient complexity and enforceable constraints. In this paper we present a case study of accelerating face detection based on the Viola Jones algorithm on a programmable SoC using a C-based HLS flow. We also share our insights in porting a software-based design into a synthesizable implementation with HLS-specific data structures and optimizations. Our design is able to achieve a frame rate of 30 frames per second which is suitable for realtime applications. Our performance and quality of results are comparable to those of many traditional RTL implementations.


Renal Transporters for Organic Anions and Organic Cations. Structural Requirements for Substrates


Classification and Regression Trees


title: iPrevention: towards a novel real-time smartphone-based fall prevention system

abstract: Falling remains one of the leading causes of hospitalization and death for the elderly all around the world. The considerable risk of falls and the substantial increase of the elderly population have stimulated scientific research on smartphone-based fall detection systems recently. Even though these systems are helpful for fall detection, the best way to reduce the number of falls and their consequences is to prevent them from happening in the first place. Therefore, our focus is on fall prevention rather than fall detection. To address the issue of fall prevention, in this paper, we propose a smartphone-based fall prevention system that can alert the user about their abnormal walking pattern. Most current systems merely detect a fall whereas our approach attempts to identify high-risk gait patterns and alert the user to save them from an imminent fall. Our system uses a gait analysis approach that couples cycle detection with feature extraction to detect gait abnormality. We validated our approach using a decision tree with 10-fold cross validation and found 99.8% accuracy in gait abnormality detection. To the best of our knowledge, we are the first to use the built-in accelerometer and gyroscope of the smartphone to identify abnormal gaits in users for fall prevention.


title: A Deep Neural Network Model using Random Forest to Extract Feature Representation for Gene Expression Data Classification

abstract: In predictive model development, gene expression data is associated with the unique challenge that the number of samples (n) is much smaller than the amount of features (p). This “n ≪ p” property has prevented classification of gene expression data from deep learning techniques, which have been proved powerful under “n > p” scenarios in other application fields, such as image classification. Further, the sparsity of effective features with unknown correlation structures in gene expression profiles brings more challenges for classification tasks. To tackle these problems, we propose a newly developed classifier named Forest Deep Neural Network (fDNN), to integrate the deep neural network architecture with a supervised forest feature detector. Using this built-in feature detector, the method is able to learn sparse feature representations and feed the representations into a neural network to mitigate the overfitting problem. Simulation experiments and real data analyses using two RNA-seq expression datasets are conducted to evaluate fDNN’s capability. The method is demonstrated a useful addition to current predictive models with better classification performance and more meaningful selected features compared to ordinary random forests and deep neural networks.


title: Revision of Neoclassical Facial Canons in Young Adult Afro-Americans

abstract: The validity of seven neoclassical facial canons was tested in 100 young, adult, Afro-American (A-A) males and females, and the results were compared with 103 North American Caucasians, producing the first reliable information about the differences of facial proportions in the two populations. Although the canons should not be regarded as strict directives in reconstructive and esthetic surgery, the data obtained from the study offered the first information about the facial proportion qualities of Afro-Americans in relation to the white population. In the absence of a detailed facial proportion study in A-As, the findings in canons can assist in planning the surgery. Comparison of the two populations revealed that the three sections of the facial profile were not equal in either population. In an A-A sample it showed the prevalence of the long lower face height also in relation to the height of the forehead. In horizontal canons of the orbital regions, the similarity between the two populations was demonstrated by the great frequency of intercanthal spaces to be wider than the length of the palpebral fissures [1]. The facial canons, including the nose width, relatively wider [2] in A-A than in white subjects, prompted the difference between A-A and Caucasians in frequency and degree in the dominant canon variations. The greater inclination of the nasal bridge than that of the medial longitudinal axis of the ear was a very frequent canon variation in both populations. Generally, the frequency of valid canons was greatly surpassed by their variations.


title: Phone classification using HMM/SVM system and normalization technique

abstract: Support vector machines (SVM) were originally developed for binary classification and extended for multi-class classification. Due to their powerfulness and adaptation to hard classification problems, we have chosen them for automatic speech recognition (ASR). The aim of this paper is to investigate the use of SVM multi-class classification coupled with HMM for TIMIT phones. SVM requires that all data samples for training and test to have the same features vector size. Due to the variability in length of phone signals even for the same phone, we have used a normalization technique: zero padding and resampling on all data samples to get them have features vector with the same size. After mapping the 61 TIMIT phones in 46 phones and conducting tests using LibSVM and HTK, we have obtained a classification accuracy rate of 91.26% with the hybrid HMM/SVM system and 71.41% with the HMM-based system. These results show that the hybrid HMM/SVM system using the normalization technique overcomes an HMM-based system and improves the recognition accuracy by 19.8%. Therefore, our experiments result encouraged us to use this hybrid system and normalization technique for the next work in the context of spoken dialogue system.


title: Wrist pulse signals analysis based on Deep Convolutional Neural Networks

abstract: Concerning computer aided analysis of the Traditional Chinese Medicine Pulse Diagnosis, the recognition effect of wrist pulse signals is undesirable because of its morphology complexity and the features ambiguity. To solve the problem, we propose a new methodology based on classifier using Shannon Energy Envelope, Hilbert Transform (SEEHT) and Deep Convolutional Neural Networks (DCNN). In this paper, we demonstrate the pulse wave extractor: SEEHT, which is better than traditional one in case of wider, small pulse wave or sudden changes in wave amplitude. Then DCNN is trained by adding noise to increase the sample size for excavating potential features. The proposed methodology is validated using data from Shanghai University of Traditional Chinese Medicine. Various experimental results show that the proposed method significantly outperforms other well-known methods in case of feature ambiguity.


title: Automatic recognition of clothes pattern and motifs empowering online fashion shopping

abstract: Automatic recognition of clothes pattern and styles have broad application in consumer online fashion shopping. In this research, we propose a fast and accurate method of recognizing a clothes textile design and pattern. We used a modified 6-channel co-occurrence matrix with a random forest classifier. We tested the accuracy of recognizing clothing of fashion models and obtained results of 93%.


A Performance-oriented Operating System Approach to Fast Communications in a Low-cost Network of Workstations


title: Multimodal Attribute Extraction

abstract: The broad goal of information extraction is to derive structured information from unstructured data. However, most existing methods focus solely on text, ignoring other types of unstructured data such as images, video and audio which comprise an increasing portion of the information on the web. To address this shortcoming, we propose the task of multimodal attribute extraction. Given a collection of unstructured and semi-structured contextual information about an entity (such as a textual description, or visual depictions) the task is to extract the entity's underlying attributes. In this paper, we provide a dataset containing mixed-media data for over 2 million product items along with 7 million attribute-value pairs describing the items which can be used to train attribute extractors in a weakly supervised manner. We provide a variety of baselines which demonstrate the relative effectiveness of the individual modes of information towards solving the task, as well as study human performance.


title: Effects of yeast culture supplementation on growth performance, intestinal health, and immune response of nursery pigs.

abstract: A total of 216 weaning pigs were used in 2 experiments to determine the effects of dietary supplementation of yeast culture (YC) at different dose levels on the growth performance, nutrient digestibility, intestinal morphology, intestinal microflora, and immune response in weanling pigs and to determine whether YC can be a candidate to replace antibiotic growth promoters (AGP). In Exp. 1, 192 pigs (7.5 +/- 0.2 kg of BW) weaned at 28 d of age were randomly allotted to 6 treatments: 1) control (without AGP or YC); 2) AGP (chlortetracycline, 80 mg/kg); 3) 2.5 g/kg of YC (Diamond V XP Yeast Culture); 4) 5 g/kg of YC; 5) 10 g/kg of YC; and 6) 20 g/kg of YC. Each treatment had 8 replicated pens with 4 pigs per pen. Pigs were fed the experimental diets for 21 d. Average daily gain of pigs fed 5 g/kg of YC was greater (P < 0.05) than that of pigs in the control and other YC groups. However, there was no difference between the YC and AGP group. Pigs supplemented with 5 g/kg of YC, 10 g/kg of YC, and AGP had a greater (P < 0.01) ADFI than the control; however, G:F was not affected by treatment. Thus, 5 g/kg of YC supplementation level was chosen for Exp. 2. In Exp. 2, to elucidate the mode of action of YC, 24 nursery pigs (5.8 +/- 0.1 kg of BW; 21 d of age) were randomly allotted into 3 treatments for a 21-d trial. Treatments consisted of 1) control (without AGP or YC), 2) AGP, and 3) 5 g/kg of YC. Blood samples were collected weekly to measure CD4(+), CD8(+) percentage, and blood cytokine content. All pigs were harvested to determine treatment effects on gut microbiota, morphology, and immune function. Dietary supplementation of 5 g/kg of YC improved (P < 0.05) ADG of pigs compared with the control group, but performance of pigs fed YC was similar to those fed AGP. Pigs receiving 5 g/kg of YC had greater (P < 0.05) digestibility of DM, CP, GE, and jejunal villus height and villus height:crypt depth ratio (P < 0.05) compared with pigs fed the control diet. However, no differences in performance, digestibility, or gut morphology were observed between pigs fed YC and AGP. Gut interferon (IFN)-gamma concentrations were greater (P < 0.01) for pigs supplemented with YC compared with control pigs and pigs supplemented with AGP on d 21. However, plasma IFN-gamma concentrations were decreased (P < 0.01) in pigs supplemented with YC and AGP compared with control pigs on d 7, and CD4(+) was decreased (P < 0.01) in pigs supplemented with YC and AGP on d 14. Results indicate that dietary YC supplementation at 5 g/kg had a positive effect on growth performance of nursery pigs by improving jejunal villus height and villus height:crypt depth ratio and by modulating gut immune response. The comparable effect of 5 g/kg of YC supplementation and AGP on the growth performance of nursery pigs indicates that YC may be a good candidate as an antibiotic alternative.


title: Glenohumeral Internal Rotation Deficit : Pathogenesis and Response to Acute Throwing

abstract: Overhand throwing places high loads and stresses on the joints and tissues of the shoulder and arm. As a result, throwing athletes regularly demonstrate altered shoulder internal and external ranges of motion where internal rotation (IR) is decreased and external rotation is increased in the dominant arm when compared with the nondominant arm. This alteration can exist as a result of alterations to the bones (humeral retroversion), capsule (posterior thickening), or muscle (passive stiffness known as thixotropy). When the amount of IR or total arc of motion difference reaches a certain threshold (typically 20 or more degrees of IR or 8 degrees total arc difference), it is known as glenohumeral internal rotation deficit or total arc of motion deficit. Glenohumeral internal rotation deficit and total arc of motion deficit can cause alterations in biomechanics such as scapular "wind-up" or alteration of glenohumeral joint kinematics, which can in turn lead to clinical findings of impingement and labral pathology. This study will review the causes of motion alteration, effects of altered motion on the throwing motion, provide definitions for the various types of rotation deficits, and how to evaluate and treat rotational deficits.


title: Techniques of measurement for Parkinson's tremor highlighting advantages of embedded IMU over EMG

abstract: For Parkinson's disease (PD) detection at its best, early diagnosis of the disorder is the key criteria to be focused [5]. Tremors are one of the primary characteristics of PD [11][13]. The paper presents some of the effective biomechanical techniques that can aid in measuring PD tremors with a high grade of accuracy and sensitivity. The paper describes the application of salient tracking tools like magnetic trackers and optical markers, electrical diagnostic practices like Electromyogram (EMG) and the use of Microelectromechanical systems (MEMS) based inertial sensor modules(a combination of 3 axes accelerometer + 3 axes gyroscope) to record movement. The IMU and electrodes were suitably mounted on the hands of subjects who were made to perform certain pre-defined actions to record the wrist movement and electrical activity simultaneously. The ability of a subject to precisely follow a pre-defined task's motion trajectory is a crucial indicator of tremor. In this paper we study two techniques of trajectory measurement, in terms of the accuracy of measurement of the slightest deviations from a prescribed trajectory. It is shown that an IMU based motion tracking is more accurate than that tracked by EMG sensors.


An Efficient Medical Image Segmentation Using Conventional OTSU Method


title: Service Providers of the Sharing Economy: Who Joins and Who Benefits?

abstract: Many "sharing economy" platforms, such as Uber and Airbnb, have become increasingly popular, providing consumers with more choices and suppliers a chance to make profit. They, however, have also brought about emerging issues regarding regulation, tax obligation, and impact on urban environment, and have generated heated debates from various interest groups. Empirical studies regarding these issues are limited, partly due to the unavailability of relevant data. Here we aim to understand service providers of the sharing economy, investigating who joins and who benefits, using the Airbnb market in the United States as a case study. We link more than 211 thousand Airbnb listings owned by 188 thousand hosts with demographic, socio-economic status (SES), housing, and tourism characteristics. We show that income and education are consistently the two most influential factors that are linked to the joining of Airbnb, regardless of the form of participation or year. Areas with lower median household income, or higher fraction of residents who have Bachelor's and higher degrees, tend to have more hosts. However, when considering the performance of listings, as measured by number of newly received reviews, we find that income has a positive effect for entire-home listings; listings located in areas with higher median household income tend to have more new reviews. Our findings demonstrate empirically that the disadvantage of SES-disadvantaged areas and the advantage of SES-advantaged areas may be present in the sharing economy.


title: Ordinal hyperplanes ranker with cost sensitivities for age estimation

abstract: In this paper, we propose an ordinal hyperplane ranking algorithm called OHRank, which estimates human ages via facial images. The design of the algorithm is based on the relative order information among the age labels in a database. Each ordinal hyperplane separates all the facial images into two groups according to the relative order, and a cost-sensitive property is exploited to find better hyperplanes based on the classification costs. Human ages are inferred by aggregating a set of preferences from the ordinal hyperplanes with their cost sensitivities. Our experimental results demonstrate that the proposed approach outperforms conventional multiclass-based and regression-based approaches as well as recently developed ranking-based age estimation approaches.


title: Dynamic synchronization of learning contents of distributed learning management systems over band limited network — Contents sharing between distributed Moodle 2.0 series

abstract: Sharing learning contents over distributed learning management systems (LMSs) is an important issue in order to reduce the burden for building well-designed contents. As a lecturer, regular updates of contents for on-going courses are really important because contents should reflect the learners' attributes as well as for corrections and additions of contents. Although this kind of dynamics is preferred for actual situation, differential updates of contents suitable for distributed LMSs are not implemented on popular open source LMS, Moodle. This paper introduces the dynamic synchronization between distributed Moodle systems and reports the synchronization results between Moodle 2.0 series systems. The proposed method has been implemented on Moodle for versions 2.0 series up to 2.3 as the extension of the previously proposed method developed for Moodle 1.9.


title: Trajectory analysis and prediction for improved pedestrian safety: Integrated framework and evaluations

abstract: This paper presents a monocular and purely vision based pedestrian trajectory tracking and prediction framework with integrated map-based hazard inference. In Advanced Driver Assistance systems research, a lot of effort has been put into pedestrian detection over the last decade, and several pedestrian detection systems are indeed showing impressive results. Considerably less effort has been put into processing the detections further. We present a tracking system for pedestrians, which based on detection bounding boxes tracks pedestrians and is able to predict their positions in the near future. The tracking system is combined with a module which, based on the car's GPS position acquires a map and uses the road information in the map to know where the car can drive. Then the system warns the driver about pedestrians at risk, by combining the information about hazardous areas for pedestrians with a probabilistic position prediction for all observed pedestrians.


title: Deep Active Learning Through Cognitive Information Parcels

abstract: In deep learning scenarios, a lot of labeled samples are needed to train the models. However, in practical application fields, since the objects to be recognized are complex and non-uniformly distributed, it is difficult to get enough labeled samples at one time. Active learning can actively improve the accuracy with fewer training labels, which is one of the promising solutions to tackle this problem. Inspired by human being's cognition process to acquire additional knowledge gradually, we propose a novel deep active learning method through Cognitive Information Parcels (CIPs) based on the analysis of model's cognitive errors and expert's instruction. The transformation of the cognitive parcels is defined, and the corresponding representation feature of the objects is obtained to identify the model's cognitive error information. Experiments prove that the samples, selected based on the CIPs, can benefit the target recognition and boost the deep model's performance efficiently. The characterization of cognitive knowledge can avoid the other samples' disturbance to the cognitive property of the model effectively. We believe that our work could provide a trial of thought about the cognitive knowledge used in deep learning field.


title: Effects of random food deprivation on body mass, behavior and serum leptin levels in Eothenomys miletus (Mammalia: Rodentia: Cricetidae)

abstract: AbstractThe value of phenotypic plasticity in energy metabolism and behavioral patterns in response to variations of food availability was handled in adult male voles (Eothenomys miletus) acclimated to random food deprivation (FD) for 4 weeks. During this period, in which voles were fed ad libitum for a randomly assigned 3 days each week, changes in body mass, food intake, resting metabolic rate (RMR), non-shivering thermogenesis (NST) and serum leptin levels were measured. Behavioral observations were made to examine behavioral patterns including activity, feeding, grooming and resting behaviors. The results showed that food intake and gastrointestinal mass increased significantly, but RMR and NST decreased significantly in the FD group compared to the control group. Moreover, the FD group had a significantly higher percentage of feeding behavior and lower activity than those in control group. In addition, serum leptin levels were positively correlated with body fat mass, but showed no significant differ...


Instance-Based Learning: Nearest Neighbour with Generalisation


title: Using genetic algorithms to parameters (d,r) estimation for threshold autoregressive models

abstract: Threshold autoregressive model (TAR model) has certain characteristics due to which linear models fail to fit a nonlinear time series, while the problem of how to find an appropriate threshold value still attracts many researchers' attention. In this paper, we apply the genetic algorithms to estimate the threshold and lag parameters r and d for TAR models. The selection operator is formulated following Darwin's principle of survival of the fittest to guide the trek through a search space. The crossover and mutation operators have been inspired by the mechanisms of gene mutation and chromosome recombination.


title: Assembly development of 1.3 Tb/s full duplex optical module

abstract: A unique optoelectronic assembly has been developed for an optical module (OM) which allows for better than 1.3 Tb/sec full duplex data transfer into and out of a single ASIC die. The assembly process and initial system results are presented. The Optical Module assembly development focused on using standard semiconductor processing equipment and materials, wherever it could be done. Reliability tests, such as accelerated aging and thermal shock treatment, were performed successfully. The Optical Module components - an organic interposer (substrate), ASIC die, two 168-channel 1000nm optical dies (VCSELs and photodiodes) and two Micro Lens Arrays (MLA's) of 168 pixels each - are all assembled using flip-chip technology, allowing for a very high density data rate transfer. The assembly consists of five different flip-chip steps, followed by reflow or gluing and underfill. Flip-chip accuracy ranges from one to ten microns, depending on assembly step. Complete Optical Modules have been routinely assembled and tested in a small series production scale for more than a year now. These Optical Modules are a key component in the Compass-EOS all-optical backplane system. The Optical Module allows for total data transfer rate density of 67 Gb/s/mm2 with Bit Error Rate (BER) <; 1E-12. This is the highest optical data rate transfer and density that has been reported to-date for a single ASIC die.


title: The Impact of Suicide on the Family

abstract: Little research has examined the consequences of a suicide for social or family networks. Because suicide occurs within families, the focus on the aftermath of suicide within families is an important next step to determine exactly how to help survivors. In this article, we review and summarize the research on the impact of suicide on individuals within families and on family and social networks. We begin with a discussion of family changes following suicide. Next, we discuss the effects of suicide on social networks overall and responses of children and the elderly to a suicide in the family. Finally, we identify key issues that remain to be resolved in family survivor research and make recommendations for future studies.


Higher Order Beliefs and Sequential Reciprocity by Lianjie Jiang


title: FPGA Vendor Agnostic True Random Number Generator

abstract: This paper describes a solution for the generation of true random numbers in a purely digital fashion; making it suitable for any FPGA type, because no FPGA vendor specific features (e.g., like phase-locked loop) or external analog components are required. Our solution is based on a framework for a provable secure true random number generator recently proposed by Sunar, Martin and Stinson. It uses a large amount of ring oscillators with identical ring lengths as a fast noise source ? but with some deterministic bits ? and eliminates the non-random samples by appropriate post-processing based on resilient functions. This results in a slower bit stream with high entropy. Our FPGA implementation achieves a random bit throughput of more than 2 Mbps, remains fairly compact (needing minimally 110 ring oscillators of 3 inverters) and is highly portable.


title: Antibiotic growth promoter olaquindox increases pathogen susceptibility in fish by inducing gut microbiota dysbiosis

abstract: Low dose antibiotics have been used as growth promoters in livestock and fish. The use of antibiotics has been associated with reduced pathogen infections in livestock. In contrast, antibiotic growth promoter has been suspected of leading to disease outbreaks in aquaculture. However, this phenomenon is circumstantial and has not been confirmed in experimental conditions. In this study, we showed that antibiotic olaquindox increased the susceptibility of zebrafish to A. hydrophila infection. Olaquindox led to profound alterations in the intestinal microbiota of zebrafish, with a drastic bloom of Enterobacter and diminishing of Cetobacterium. Moreover, the innate immune responses of zebrafish were compromised by olaquindox (P<0.05). Transfer of microbiota to GF zebrafish indicated that while the immuo-suppression effect of olaquindox is a combined effect mediated by both OLA-altered microbiota and direct action of the antibiotic (P<0.05), the increased pathogen susceptibility was driven by the OLA-altered microbiota and was not dependent on direct antibiotic effect. Taken together, these data indicate that low level of OLA induced gut microbiota dysbiosis in zebrafish, which led to increased pathogen susceptibility.


title: Absorption and efflux of glyphosate by cell suspensions

abstract: of controlling both annual and perennial weeds (Grossbard and Atkinson, 1985). Researchers have The absorption and efflux of [14C]-glyphosate (N-[phos- investigated the uptake, translocation and metabolism of phonomethyl]glycine) was studied in maize (Zea mays glyphosate at the whole plant level, revealing gross interL. cv. Aussie) and soybean (Glycine max L. Merr. cv. specific diVerences in its eYcacy (Atkinson, 1985; Terry, Maple Arrow) cell suspensions. Glyphosate absorption 1985; Bovey 1985). Such studies have shown that a was complex: at low external herbicide concentrations variable amount of the glyphosate dose gets absorbed (3‐250 mM) there was evidence for a single active which is often less than 60% (Ahmadi et al., 1980; Devine uptake system with an apparent K m of 31 mM and V max and Bandeen, 1983; Caseley and Coupland, 1985). Also, of 11 nmol g’1 fr. wt. 2 h’1. The system was inhibited only a small fraction of the applied dose (around 5%) by carbonylcyanide m-chlorophenyl hydrazone (CCCP), gets translocated from the treated leaves (Sandberg et al., orthovanadate, diethylstilbestrol (DES), phosphate, 1980). The mode of action of glyphosate is by inhibition and phosphonoformic acid (PFA) suggesting the of the enzyme 5-enolpyruvylshikimate 3-phosphate syn


title: The Role of Pheromones in Termite Caste Differentiation

abstract: Publisher Summary This chapter discusses the role of pheromones in termite caste differentiation. Termite polymorphism appears to be a complication of post-embryonic development processes observed in non-social insects; the environmental, nutritional, and neurohormonal individual factors are of major importance. However, the harmonious development of the colony involves a social regulation which is ensured by chemical communication and especially by the distribution of pheromones. Caste pheromones have certainly an important role in these mechanisms. The pheromonal stimulation put forward for the reproductive and soldier production remains entirely hypothetical. The inhibition exerted by the reproductives on the supplementary reproductive differentiation is pheromonal in nature. The chemical composition of these pheromones is not yet known. Evidence exists for an inhibitory pheromone of soldier production. In Nasutitermes lujae, the rate of soldier production is reduced by organic extracts of soldier frontal glands. Olfaction is not involved in this phenomenon.


title: MOSES: A Framework for QoS Driven Runtime Adaptation of Service-Oriented Systems

abstract: Architecting software systems according to the service-oriented paradigm and designing runtime self-adaptable systems are two relevant research areas in today's software engineering. In this paper, we address issues that lie at the intersection of these two important fields. First, we present a characterization of the problem space of self-adaptation for service-oriented systems, thus providing a frame of reference where our and other approaches can be classified. Then, we present MOSES, a methodology and a software tool implementing it to support QoS-driven adaptation of a service-oriented system. It works in a specific region of the identified problem space, corresponding to the scenario where a service-oriented system architected as a composite service needs to sustain a traffic of requests generated by several users. MOSES integrates within a unified framework different adaptation mechanisms. In this way it achieves greater flexibility in facing various operating environments and the possibly conflicting QoS requirements of several concurrent users. Experimental results obtained with a prototype implementation of MOSES show the effectiveness of the proposed approach.


title: A printed dipole antenna with tapered slot feed for ultrawide-band applications

abstract: In this paper a planar antenna is studied for ultrawide-band (UWB) applications. This antenna consists of a wide-band tapered-slot feeding structure, curved radiators and a parasitic element. It is a modification of the conventional dual exponential tapered slot antenna and can be viewed as a printed dipole antenna with tapered slot feed. The design guideline is introduced, and the antenna parameters including return loss, radiation patterns and gain are investigated. To demonstrate the applicability of the proposed antenna to UWB applications, the transfer functions of a transmitting-receiving system with a pair of identical antennas are measured. Transient waveforms as the transmitting-receiving system being excited by a simulated pulse are discussed at the end of this paper.


title: 6 Seconds of Sound and Vision: Creativity in Micro-Videos

abstract: The notion of creativity, as opposed to related concepts such as beauty or interestingness, has not been studied from the perspective of automatic analysis of multimedia content. Meanwhile, short online videos shared on social media platforms, or micro-videos, have arisen as a new medium for creative expression. In this paper we study creative micro-videos in an effort to understand the features that make a video creative, and to address the problem of automatic detection of creative content. Defining creative videos as those that are novel and have aesthetic value, we conduct a crowdsourcing experiment to create a dataset of over 3, 800 micro-videos labelled as creative and non-creative. We propose a set of computational features that we map to the components of our definition of creativity, and conduct an analysis to determine which of these features correlate most with creative video. Finally, we evaluate a supervised approach to automatically detect creative video, with promising results, showing that it is necessary to model both aesthetic value and novelty to achieve optimal classification accuracy.


title: Research Article EFFECTS OF FEAR AND ANGER ON PERCEIVED RISKS OF TERRORISM A National Field Experiment

abstract: The aftermath of September 11th highlights the need to un- derstand how emotion affects citizens' responses to risk. It also provides an opportunity to test current theories of such effects. On the basis of appraisal-tendency theory, we predicted opposite effects for anger and fear on risk judgments and policy preferences. In a nationally representative sample of Americans ( N � 973, ages 13-88), fear increased risk esti- mates and plans for precautionary measures; anger did the opposite. These patterns emerged with both experimentally induced emotions and naturally occurring ones. Males had less pessimistic risk estimates than did females, emotion differences explaining 60 to 80% of the gender dif- ference. Emotions also predicted diverging public policy preferences. Dis- cussion focuses on theoretical, methodological, and policy implications. Terrorist attacks on the United States intensely affected many individu- als and institutions, well beyond those directly harmed. Financial markets dropped, consumer spending declined, air travel plummeted, and public opinion toward government shifted. These responses reflected intense thought—and emotion. The attacks—and prospect of sustained conflict with a diffuse, unfamiliar enemy—created anger, fear, and sadness.


Difficult Endotracheal Intubation—Scales and Causes


title: Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms

abstract: In this paper, we establish max-flow min-cut theorems for several important classes of multicommodity flow problems. In particular, we show that for any n-node multicommodity flow problem with uniform demands, the max-flow for the problem is within an O(log n) factor of the upper bound implied by the min-cut. The result (which is existentially optimal) establishes an important analogue of the famous 1-commodity max-flow min-cut theorem for problems with multiple commodities. The result also has substantial applications to the field of approximation algorithms. For example, we use the flow result to design the first polynomial-time (polylog n-times-optimal) approximation algorithms for well-known NP-hard optimization problems such as graph partitioning, min-cut linear arrangement, crossing number, VLSI layout, and minimum feedback arc set. Applications of the flow results to path routing problems, network reconfiguration, communication in distributed networks, scientific computing and rapidly mixing Markov chains are also described in the paper. Categories and Subject Descriptors: F.2.2 (Analysis of Algorithms and Problem Complexity):


title: Efficient and robust deep networks for semantic segmentation

abstract: This paper explores and investigates deep convolutional neural network architectures to increase the efficiency and robustness of semantic segmentation tasks. The proposed solutions are based on up-convolutional networks. We introduce three different architectures in this work. The first architecture, called Part-Net, is designed to tackle the specific problem of human body part segmentation and to provide robustness to overfitting and body part occlusion. The second network, called Fast-Net, is a network specifically designed to provide the smallest computation load without losing representation power. Such an architecture is capable of being run on mobile GPUs. The last architecture, called M-Net, aims to maximize the robustness characteristics of deep semantic segmentation approaches through multiresolution fusion. The networks achieve state-of-the-art performance on the PASCAL Parts dataset and competitive results on the KITTI dataset for road and lane segmentation. Moreover, we introduce a new part s...


title: BPEmb: Tokenization-free Pre-trained Subword Embeddings in 275 Languages

abstract: We present BPEmb, a collection of pre-trained subword unit embeddings in 275 languages, based on Byte-Pair Encoding (BPE). In an evaluation using fine-grained entity typing as testbed, BPEmb performs competitively, and for some languages bet- ter than alternative subword approaches, while requiring vastly fewer resources and no tokenization. BPEmb is available at this https URL


title: Automated design technique for constant-gm rail-to-rail for OTA input stage

abstract: This paper presents a novel technique for an automated design to produce a constant transconductance (gm). This constant gm is valide over the entire common-mode input range for input stage of Low Voltage (LV) operational transconductance amplifier (OTA) based on DC level shifter. The issue lies at the input sage so, this automated design technique is created to rail-to-rail input stage of OTA. The key parameter is the bias current in DC level shifter (Ish). The proposed technique is responsible for finding the optimal Ish to obtain minimum variation on the gm of the OTA input stage. This technique is based on a script written on Linux, operating system, to be connected to the BSIM MOST model and netlist of the desired topology. Utility of the physics-based gm/ID characteristics, this technique is more suitable for short channel transistors in submicron processes. This work allows the design problem to be cast as a program. So, it offers an efficient, reliable, and fast way to implement high-performance of analog integrated circuits. The results demonstrate that gm variation can be restricted within ±1.25 %. The circuit is simulated in IBM 0.13μ CMOS technology with a single power supply 1.5-V.


title: Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

abstract: The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.


title: Node localization in 3-D by magnetic-induction communications in wireless sensor networks

abstract: This paper proposes a localization method for wireless sensor nodes that utilize tri-directional coils for near-field magnetic induction (MI) communications. Taking advantage of magnetic field measurements of the tri-directional coils, the proposed localization algorithm uses only two anchor nodes to locate a sensor node in the 3-D space. Assuming each anchor node sequentially transmits the communication signal by three orthogonal Tx coils, and the sensor node receives the signals at the three orthogonal Rx coils simultaneously, the communication distance and the polar angles of transmission are estimated in a local coordinate system of the anchor node. These estimates from the two anchor nodes yield two sets of 8 possible locations of the sensor node. Then a Rotation Matrix (RM) between the transmitter and receiver is derived to narrow down to two possible location vectors with the opposite directions in each anchor node. Finally, we use the maximum likelihood method to estimate the accurate location from the two sets of two location vectors. Simulations results show that the proposed RM-based method can achieve high localization accuracy because the derived closed-form formula of distance estimation achieves good accuracy under large measurement errors.


title: Guiding Deep Learning System Testing Using Surprise Adequacy

abstract: Deep Learning (DL) systems are rapidly being adopted in safety and security critical domains, urgently calling for ways to test their correctness and robustness. Testing of DL systems has traditionally relied on manual collection and labelling of data. Recently, a number of coverage criteria based on neuron activation values have been proposed. These criteria essentially count the number of neurons whose activation during the execution of a DL system satisfied certain properties, such as being above predefined thresholds. However, existing coverage criteria are not sufficiently fine grained to capture subtle behaviours exhibited by DL systems. Moreover, evaluations have focused on showing correlation between adversarial examples and proposed criteria rather than evaluating and guiding their use for actual testing of DL systems. We propose a novel test adequacy criterion for testing of DL systems, called Surprise Adequacy for Deep Learning Systems (SADL), which is based on the behaviour of DL systems with respect to their training data. We measure the surprise of an input as the difference in DL system's behaviour between the input and the training data (i.e., what was learnt during training), and subsequently develop this as an adequacy criterion: a good test input should be sufficiently but not overtly surprising compared to training data. Empirical evaluation using a range of DL systems from simple image classifiers to autonomous driving car platforms shows that systematic sampling of inputs based on their surprise can improve classification accuracy of DL systems against adversarial examples by up to 77.5% via retraining.


title: Irrigation management system with micro-controller application

abstract: This paper presents optimized irrigation scheme using automatic water control for performing crop specific irrigation activities. Level of irrigation depends on soil moisture content and type of crop considered. Different crops need different soil moisture for optimum crop yield. For optimum crop yield, smart irrigation, knowledge of soil condition, and water requirement of crop is essential. Optimal usage of water reduces overall power consumption and optimizes usage of water reserves. Smart usage of irrigation system optimizes land area productivity and conserves nature by reducing overall involved emission & losses in conversion components. Low cost microcontroller like Arduino based interfacing unit, has been used in this paper to obtain low cost smart irrigation solution.


title: To treat or not to treat: puberty suppression in childhood-onset gender dysphoria

abstract: Puberty suppression using gonadotropin-releasing-hormone analogues (GnRHa) has become increasingly accepted as an intervention during the early stages of puberty (Tanner stage 2-3) in individuals with clear signs of childhood-onset gender dysphoria. However, lowering the age threshold for using medical intervention for children with gender dysphoria is still a matter of contention, and is more controversial than treating the condition in adolescents and adults, as children with gender dysphoria are more likely to express an unstable pattern of gender variance. Furthermore, concerns have been expressed regarding the risks of puberty suppression, which are poorly understood, and the child's ability to make decisions and provide informed consent. However, even if the limited data available mean that it is not possible to make a conclusive treatment recommendation, some safety criteria for puberty suppression can be identified and applied.


title: Frequency discrimination as a function of frequency, measured in several ways.

abstract: Frequency discrimination was measured for a wide range of center frequencies (0.25-8 kHz) using three different tasks. In the first (difference limens for frequency, DLFs) subjects were required to indicate which of two successive tone pulses was higher in frequency. In the second (difference limens for change, DLCs), two successive pairs of tone pulses were presented; one pair had the same frequency and the other pair differed in frequency. Subjects were required to indicate which pair differed in frequency. In the third (frequency-modulation difference limens, FMDLs), subjects were required to indicate which of two successive tone pulses was frequency modulated. Modulation rates were 2, 5, or 10 Hz. For frequencies up to 2 kHz, DLFs and DLCs were small (less than 0.6% of the center frequency) and were similar to one another. For frequencies of 4 kHz and above, both DLFs and DLCs increased markedly, but the increase was greater for DLFs. Thus the worsening of performance at high frequencies is greater when subjects are required to indicate the direction of a frequency change than when they just have to detect any change. FMDLs, when expressed relative to the carrier frequency, varied much less with frequency than DLFs or DLCs. At 2 kHz and below, FMDLs were larger than DLFs or DLCs. Above 4 kHz, FMDLs were smaller than DLFs or DLCs. At 2 kHz and below, FMDLs usually worsened with increasing modulation frequency. Above 4 kHz, FMDLs improved with increasing modulation frequency.(ABSTRACT TRUNCATED AT 250 WORDS)


title: An online chess game designed for people with dyslexia

abstract: Teaching chess to students with learning disabilities has been shown to benefit their school performance in unrelated domains. At the same time, chess involves skills that are highly correlated with dyslexia, such as visuospatial and calculation abilities. In this paper, we created a online chess game designed for people with dyslexia and seek to understand whether people with dyslexia learn and play chess online in ways that differ from other students and whether such differences may be leveraged to improve classroom performance. To test how people with dyslexia learn to play chess we carried out a within-subject experiment with 62 participants, 31 of them with diagnosed dyslexia. Participants used an instrumented web-based chess learning platform that we developed to (i) complete lessons on how to play chess and about chess theory, (ii) work through exercises designed to test and reaffirm their skills, and (iii) play chess against a computer opponent. We could not find significant differences on four dependent measures out of the twelve measures we collected. Therefore, dyslexia might have an impact on how people learn and play chess using a computer, suggesting that chess may be useful as a fun way to help people with dyslexia improve their abilities.


title: A Simple, Low Cost Optical Tilt Sensor

abstract: For the purpose of kinematic monitoring system a prototype relatively simple and low cost optical Tilt (or inclination) sensor with sensitivity of 50mV/o and a working range of ±50o is presented in this paper. The sensor consists of a cylindrical shaped transparent container with half filled color liquid and an optical source and detector. The sensor principle based on temporal changes in light intensity due to variation of optical path length through a liquid medium with the tilt angel of sensor module. This change of light intensity detected by an optical detector & eventually processed by a signal processing circuit (SPC) to display the tilting (or inclination) value digitally. Proposed sensor's exhibits an accuracy of 0.8o and resolution of 0.09o respectively.


title: Precoding for Full Duplex Multiuser MIMO Systems: Spectral and Energy Efficiency Maximization

abstract: We consider data transmissions in a full duplex (FD) multiuser multiple-input multiple-output (MU-MIMO) system, where a base station (BS) bidirectionally communicates with multiple users in the downlink (DL) and uplink (UL) channels on the same system resources. The system model of consideration has been thought to be impractical due to the self-interference (SI) between transmit and receive antennas at the BS. Interestingly, recent advanced techniques in hardware design have demonstrated that the SI can be suppressed to a degree that possibly allows for FD transmission. This paper goes one step further in exploring the potential gains in terms of the spectral efficiency (SE) and energy efficiency (EE) that can be brought by the FD MU-MIMO model. Toward this end, we propose low-complexity designs for maximizing the SE and EE, and evaluate their performance numerically. For the SE maximization problem, we present an iterative design that obtains a locally optimal solution based on a sequential convex approximation method. In this way, the nonconvex precoder design problem is approximated by a convex program at each iteration. Then, we propose a numerical algorithm to solve the resulting convex program based on the alternating and dual decomposition approaches, where analytical expressions for precoders are derived. For the EE maximization problem, using the same method, we first transform it into a concave-convex fractional program, which then can be reformulated as a convex program using the parametric approach. We will show that the resulting problem can be solved similarly to the SE maximization problem. Numerical results demonstrate that, compared to a half duplex system, the FD system of interest with the proposed designs achieves a better SE and a slightly smaller EE when the SI is small.


title: Free gait generation with reinforcement learning for a six-legged robot

abstract: In this paper the problem of free gait generation and adaptability with reinforcement learning are addressed for a six-legged robot. Using the developed free gait generation algorithm the robot maintains to generate stable gaits according to the commanded velocity. The reinforcement learning scheme incorporated into the free gait generation makes the robot choose more stable states and develop a continuous walking pattern with a larger average stability margin. While walking in normal conditions with no external effects causing unstability, the robot is guaranteed to have stable walk, and the reinforcement learning only improves the stability. The adaptability of the learning scheme is tested also for the abnormal case of deficiency in one of the rear-legs. The robot gets a negative reinforcement when it falls, and a positive reinforcement when a stable transition is achieved. In this way the robot learns to achieve a continuous pattern of stable walk with five legs. The developed free gait generation with reinforcement learning is applied in real-time on the actual robot both for normal walking with different speeds and learning of five-legged walking in the abnormal case.


title: A Comparison Of Corpus-Based Techniques For Restoring Accents In Spanish And French Text

abstract: This chapter will explore and compare three corpus-based techniques for lexical ambiguity resolution, focusing on the problem of restoring missing accents to Spanish and French text. Many of the ambiguities created by missing accents are differences in part of speech: hence one of the methods considered is an N-gram tagger using Viterbi decoding, such as is found in stochastic part-of-speech taggers. A second technique, Bayesian classification, has been successfully applied to word-sense disambiguation and is well suited for some of the semantic ambiguities which arise from missing accents. The third approach, based on decision lists, combines the strengths of the two other methods, incorporating both local syntactic patterns and more distant collocational evidence, and outperforms them both. The problem of accent restoration is particularly well suited for demonstrating and testing the capabilities of the given algorithms because it requires the resolution of both semantic and syntactic ambiguity, and offers an objective ground truth for automatic evaluation. The problem is also a practical one with immediate application.


title: Predicting Bankruptcy with Robust Logistic Regression

abstract: Using financial ratio data from 2006 and 2007, this study uses a three-fold cross validation scheme to compare the classification and pre- diction of bankrupt firms by robust logistic regression with the Bianco and Yohai (BY) estimator versus maximum likelihood (ML) logistic regression. With both the 2006 and 2007 data, BY robust logistic regression improves both the classification of bankrupt firms in the training set and the prediction of bankrupt firms in the testing set. In an out of sample test, the BY robust logistic regression correctly predicts bankruptcy for Lehman Brothers; however, the ML logistic regression never predicts bankruptcy for Lehman Brothers with either the 2006 or 2007 data. Our analysis indicates that if the BY robust logistic regression significantly changes the estimated regression coefficients from ML logistic regression, then the BY robust logistic regression method can significantly improve the classification and prediction of bankrupt firms. At worst, the BY robust logistic regression makes no changes in the estimated regression coefficients and has the same classification and prediction results as ML logistic regression. This is strong evidence that BY robust logistic regression should be used as a robustness check on ML logistic regression, and if a difference exists, then BY robust logistic regression should be used as the primary classifier.


title: Evaluation of the hamstring muscle complex following acute injury

abstract: OBJECTIVE ::: To evaluate the imaging findings following acute hamstring injury. ::: ::: ::: DESIGN AND PATIENTS ::: We retrospectively reviewed the imaging findings of hamstring muscle complex (HMC) strain in 170 patients referred to our institution over a 3-year period. A total of 179 injuries to the HMC were demonstrated in 170 patients (154 male, 16 female, mean age 28.2 years). The mean duration of symptoms was 4.7 days (range 1-10 days). MR imaging was performed in 97 cases and sonography in 102 cases (both modalities were performed in 20 examinations). Attention was directed to the frequency of muscle involvement, the location of the injury within the muscle-tendon unit, the extent of the injury and discriminating avulsion from muscle injury. ::: ::: ::: RESULTS AND CONCLUSIONS ::: Twenty-one patients had proximal tendon injury, with sixteen avulsions and five partial tears. Sixteen of these patients had surgical confirmation of hamstring avulsion from the ischial tuberosity (14 conjoint, 2 biceps femoris alone) and all were reliably diagnosed with MR imaging (16/16), but less so with sonography (7/12). Four distal tendon avulsions were also observed (three semitendinosus, one biceps femoris). With respect to muscle injury, the biceps femoris was most commonly injured (124/154). Semimembranosus was an uncommon muscle injury (21/154) and semitendinosus rare (9/154). Imaging can discriminate a hamstring tendon avulsion from musculotendinous strain and helps identify which patients necessitate surgical management as opposed to conservative treatment.


title: Design of intelligent fire alarm system based on GSM network

abstract: In order to solve the problem of complex cabling, misdeclaration and missing alarm of traditional fire alarm system, an intelligent fire alarm system based on GSM network is designed. MSP430F149 is adopted as main control chip, and the remote alarming and data exchanging are achieved by using GSM module TC35I. By adopting smoke detector and temperature detector and using a variable threshold alert algorithm with temperature compensation, the accuracy of fire alarm is improved. The result of test shows this design has characters of real-time and good reliability, and will be widely used.


title: Clustering Algorithms for Categorical Data

abstract: Categorical datasets in many domains, such as biology or software analysis, have a rich underlying cluster structure. Categorical clustering methods that are motivated by uncovering interesting local cluster structure could produce high clustering quality, and potentially help analysts to study hidden roles of objects in a dataset. ::: This thesis presents several clustering algorithms for categorical data. First, we introduce the HIERDENC algorithm for hierarchical density-based clustering of categorical data. Then, we present the MULIC algorithm, which is a faster simplification of HIERDENC. MULIC is designed for categorical datasets with a multi-layered structure, such as protein interaction data. Our experimental evaluation of MULIC on such datasets shows that it can uncover their underlying structure better than other algorithms and has comparable runtimes. ::: Next, we present the MULICsoft algorithm for clustering large software systems. MULICsoft is an extension of MULIC that incorporates in the clustering process information on a software system's runtime execution. We evaluate MULICsoft on a large open-source system. MULICsoft is able to produce decompositions that are close to those created by system experts. ::: We continue with the BILCOM algorithm which is an extension of MULIC. BILCOM is used for clustering mixed categorical and numerical biomedical data. We apply BILCOM to datasets of mixed types, such as hepatitis, thyroid disease and yeast gene expression data with Gene Ontology annotations. The results show that BILCOM can partition these datasets significantly better than using just categorical or numerical type. ::: Finally, we present the M-BILCOM algorithm, which is an extension of BILCOM for clustering mixed numerical and low quality categorical data. M-BILCOM incorporates in the clustering process the confidence on the categorical values' correctness. We apply M-BILCOM to yeast gene expression data with Gene Ontology-annotations and GO Evidence codes representing evidence on the annotations' correctness.


title: Actively transfer domain knowledge

abstract: When labeled examples are not readily available, active learning and transfer learning are separate efforts to obtain labeled examples for inductive learning. Active learning asks domain experts to label a small set of examples, but there is a cost incurred for each answer. While transfer learning could borrow labeled examples from a different domain without incurring any labeling cost, there is no guarantee that the transferred examples will actually help improve the learning accuracy. To solve both problems, we propose a framework to actively transfer the knowledge across domains, and the key intuition is to use the knowledge transferred from other domain as often as possible to help learn the current domain, and query experts only when necessary. To do so, labeled examples from the other domain (out-of-domain) are examined on the basis of their likelihood to correctly label the examples of the current domain (in-domain). When this likelihood is low, these out-of-domain examples will not be used to label the in-domain example, but domain experts are consulted to provide class label. We derive a sampling error bound and a querying bound to demonstrate that the proposed method can effectively mitigate risk of domain difference by transferring domain knowledge only when they are useful, and query domain experts only when necessary. Experimental studies have employed synthetic datasets and two types of real world datasets, including remote sensing and text classification problems. The proposed method is compared with previously proposed transfer learning and active learning methods. Across all comparisons, the proposed approach can evidently outperform the transfer learning model in classification accuracy given different out-of-domain datasets. For example, upon the remote sensing dataset, the proposed approach achieves an accuracy around 94.5%, while the comparable transfer learning model drops to less than 89% in most cases. The software and datasets are available from the authors.


title: The Illustrated Guide to Assistive Technology and Devices: Tools and Gadgets for Living Independently

abstract: Foreword Preface 1 What Is Assistive Technology? 2 History of Assistive Technology 3 Technologies for People with Visual Disabilities 4 Technologies for People with Hearing Disabilities 5 Technologies for People with Physical Disabilities 6 Technologies for People with Cognitive Disabilities 7 Technologies for People with Communications Disabilities 8 Assistive Technology and the ADA 9 How to Pay for Assistive Technology 10 The Future of Assistive Technology Resources Index


title: jamSheets: thin interfaces with tunable stiffness enabled by layer jamming

abstract: This works introduces layer jamming as an enabling technology for designing deformable, stiffness-tunable, thin sheet interfaces. Interfaces that exhibit tunable stiffness properties can yield dynamic haptic feedback and shape deformation capabilities. In comparison to the particle jamming, layer jamming allows for constructing thin and lightweight form factors of an interface. We propose five layer structure designs and an approach which composites multiple materials to control the deformability of the interfaces. We also present methods to embed different types of sensing and pneumatic actuation layers on the layer-jamming unit. Through three application prototypes we demonstrate the benefits of using layer jamming in interface design. Finally, we provide a survey of materials that have proven successful for layer jamming.


Anatomy of online hate: Developing a taxonomy and machine learning models for identifying and classifying hate in online news media


title: WARP: a flexible platform for clean-slate wireless medium access protocol design

abstract: The flexible interface between the medium access layer and the custom physical layer of the Rice University Wireless Open-Access Research Platform (WARP) provides a high performance research tool for clean-slate cross layer designs. As we target a community platform, we have implemented various basic PHY and MAC technologies over WARP. Moreover, we are implementing cross-layer schemes such as rate adaptation and crosslayer MIMO MAC protocols. In this demo, we demonstrate the flexibility of the interaction between the the WARP PHY and MAC layers by showing the capability to instantaneously change the modulation scheme, disabling/enabling MAC features such as carrier sensing or RTS/CTS 4-way handshake, and different multi-rate schemes.


title: Fine-Grained Recognition as HSnet Search for Informative Image Parts

abstract: This work addresses fine-grained image classification. Our work is based on the hypothesis that when dealing with subtle differences among object classes it is critical to identify and only account for a few informative image parts, as the remaining image context may not only be uninformative but may also hurt recognition. This motivates us to formulate our problem as a sequential search for informative parts over a deep feature map produced by a deep Convolutional Neural Network (CNN). A state of this search is a set of proposal bounding boxes in the image, whose informativeness is evaluated by the heuristic function (H), and used for generating new candidate states by the successor function (S). The two functions are unified via a Long Short-Term Memory network (LSTM) into a new deep recurrent architecture, called HSnet. Thus, HSnet (i) generates proposals of informative image parts and (ii) fuses all proposals toward final fine-grained recognition. We specify both supervised and weakly supervised training of HSnet depending on the availability of object part annotations. Evaluation on the benchmark Caltech-UCSD Birds 200-2011 and Cars-196 datasets demonstrate our competitive performance relative to the state of the art.


title: Randomized controlled trial of yoga and exercise in multiple sclerosis

abstract: OBJECTIVE ::: To determine the effect of yoga and of aerobic exercise on cognitive function, fatigue, mood, and quality of life in multiple sclerosis (MS). ::: ::: ::: METHODS ::: Subjects with clinically definite MS and Expanded Disability Status Score less than or equal to 6.0 were randomly assigned to one of three groups lasting 6 months: weekly Iyengar yoga class along with home practice, weekly exercise class using a stationary bicycle along with home exercise, or a waiting-list control group. Outcome assessments performed at baseline and at the end of the 6-month period included a battery of cognitive measures focused on attention, physiologic measures of alertness, Profile of Mood States, State-Trait Anxiety Inventory, Multi-Dimensional Fatigue Inventory (MFI), and Short Form (SF)-36 health-related quality of life. ::: ::: ::: RESULTS ::: Sixty-nine subjects were recruited and randomized. Twelve subjects did not finish the 6-month intervention. There were no adverse events related to the intervention. There were no effects from either of the active interventions on either of the primary outcome measures of attention or alertness. Both active interventions produced improvement in secondary measures of fatigue compared to the control group: Energy and Fatigue (Vitality) on the SF-36 and general fatigue on the MFI. There were no clear changes in mood related to yoga or exercise. ::: ::: ::: CONCLUSION ::: Subjects with MS participating in either a 6-month yoga class or exercise class showed significant improvement in measures of fatigue compared to a waiting-list control group. There was no relative improvement of cognitive function in either of the intervention groups.


Striving for work-life balance.


title: Lane change and path planning of autonomous vehicles using GIS

abstract: This study aims to develop an autonomous vehicle to have the efficient lane change and path planning using GIS information. Segmentalizing the global path data, established in advance and adding information on roads to those segments would establish GIS data. GIS data should include information on road lanes, speed limits, traffic lights, crosswalks, speed bumps and traffic signs. Using GIS data, autonomous vehicles would determine whether to change its lane or not when they detect an obstacle. If the autonomous vehicle decides to change its lane, a new path would be generated. On the other hand, if the autonomous vehicle cannot decide whether to change its lane or not using GIS information, it would collide with the obstacle. This study expects that GIS information could substitute several roles of a local sensor, in turn enhancing the autonomous driving of an autonomous vehicle.


title: Neural Rating Regression with Abstractive Tips Generation for Recommendation

abstract: Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings. Jointly modeling these two facets is helpful for designing a better recommendation system. While some existing models integrate text information such as item specifications or user reviews into user and item latent factors for improving the rating prediction, no existing works consider tips for improving recommendation quality. We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. For abstractive tips generation, gated recurrent neural networks are employed to "translate'' user and item latent representations into a concise sentence. Extensive experiments on benchmark datasets from different domains show that NRT achieves significant improvements over the state-of-the-art methods. Moreover, the generated tips can vividly predict the user experience and feelings.