title: Small Cell Clustering for Efficient Distributed Fog Computing: A Multi-User Case

abstract: Ultra-dense deployment of radio access points is a key enabler for future 5G networks. It allows the network to cope with the ever increasing mobile data traffic. In addition, these radio access points can serve as an infrastructure for a local mobile cloud computing platform referred to as fog computing. The fog is a capillary edge cloud that enables joint optimization of communication and computational resources for maintaining an efficient and scalable network design. In this paper, we address the problem of radio access points clustering for fog computing applications. We focus on the case where multiple users require fog computing services. We formulate the distributed clustering problem as a joint optimization of the computation and communication resources. We transform the non-convex original problem into an equivalent convex one. Our simulation results show that the clustering solution derived from this problem yields high users' satisfaction ratio while keeping low the communication power consumption of the computation cluster.


Linux Kernel Infrastructure for User-Level Device Drivers


title: Deep matrix factorization models for recommender systems

abstract: Recommender systems usually make personalized recommendation with user-item interaction ratings, implicit feedback and auxiliary information. Matrix factorization is the basic idea to predict a personalized ranking over a set of items for an individual user with the similarities among users and items. In this paper, we propose a novel matrix factorization model with neural network architecture. Firstly, we construct a user-item matrix with explicit ratings and non-preference implicit feedback. With this matrix as the input, we present a deep structure learning architecture to learn a common low dimensional space for the representations of users and items. Secondly, we design a new loss function based on binary cross entropy, in which we consider both explicit ratings and implicit feedback for a better optimization. The experimental results show the effectiveness of both our proposed model and the loss function. On several benchmark datasets, our model outperformed other state-of-the-art methods. We also conduct extensive experiments to evaluate the performance within different experimental settings.


title: Too Proud to Let Go: Narcissistic Entitlement as a Barrier to Forgiveness.

abstract: Narcissistic entitlement impedes forgiveness in ways not captured by other robust predictors (e.g., offense severity, apology, relationship closeness, religiosity, Big Five personality factors), as demonstrated in 6 studies. Narcissistic entitlement involves expectations of special treatment and preoccupation with defending one's rights. In Study 1, entitlement predicted less forgiveness and greater insistence on repayment for a past offense. Complementary results emerged from Study 2, which used hypothetical transgressions, and Study 3, which assessed broad forgiveness dispositions. Study 4 examined associations with the Big Five, and Study 5 extended the findings to a laboratory context. Study 6 demonstrated that entitlement predicted diminished increases in forgiveness over time. Taken together, these results suggest that narcissistic entitlement is a robust, distinct predictor of unforgiveness.


GPS/INS Integration


title: Panoptic Studio: A Massively Multiview System for Social Motion Capture

abstract: We present an approach to capture the 3D structure and motion of a group of people engaged in a social interaction. The core challenges in capturing social interactions are: (1) occlusion is functional and frequent, (2) subtle motion needs to be measured over a space large enough to host a social group, and (3) human appearance and configuration variation is immense. The Panoptic Studio is a system organized around the thesis that social interactions should be measured through the perceptual integration of a large variety of view points. We present a modularized system designed around this principle, consisting of integrated structural, hardware, and software innovations. The system takes, as input, 480 synchronized video streams of multiple people engaged in social activities, and produces, as output, the labeled time-varying 3D structure of anatomical landmarks on individuals in the space. The algorithmic contributions include a hierarchical approach for generating skeletal trajectory proposals, and an optimization framework for skeletal reconstruction with trajectory re-association.


title: Following linguistic footprints: automatic deception detection in online communication

abstract: N-acetyl-para-aminophenyl N'acetylaminothioalkanoates I are new analgesic compounds with greatly reduced hepatotoxic effects, when taken in overdose, relative to N-acetyl-para-aminophenol. They are prepared by reacting an N-acetylaminothioalkanoic acid IV with a reactive organic chloride V to form a mixed anhydride II and then reacting the latter with N-acetyl-para-aminophenol. The mixed anhydrides II are new and useful intermediates. Alternatively the derivatives I may be prepared by reacting the acid IV with bis-(4-nitrophenyl) sulfite to form a para-nitrophenyl N-acetylaminothioalkanoic acid ester VIII, reducing the latter to a para-aminophenyl-N-acetylaminothioalkanoate VII, and acetylating this product. The esters VII and VIII are new and useful intermediates. Both reactions may pass through S-blocked intermediates, which are also new. Pharmaceutical compositions containing the derivatives I are disclosed, and also analgesic methods using them.


title: SSH Compromise Detection using NetFlow/IPFIX

abstract: Flow-based approaches for SSH intrusion detection have been developed to overcome the scalability issues of host-based alternatives. Although the detection of many SSH attacks in a flow-based fashion is fairly straightforward, no insight is typically provided in whether an attack was successful. We address this shortcoming by presenting a detection algorithm for the flow-based detection of compromises, i.e., hosts that have been compromised during an attack. Our algorithm has been implemented as part of our open-source IDS SSHCure and validated using almost 100 servers, workstations and honeypots, featuring an accuracy close to 100%.


title: DocuViz: Visualizing Collaborative Writing

abstract: Collaborative writing is on the increase. In order to write well together, authors often need to be aware of who has done what recently. We offer a new tool, DocuViz, that displays the entire revision history of Google Docs, showing more than the one-step-at-a-time view now shown in revision history and tracking changes in Word. We introduce the tool and present cases in which the tool has the potential to be useful: To authors themselves to see recent "seismic activity," indicating where in particular a co-author might want to pay attention, to instructors to see who has contributed what and which changes were made to comments from them, and to researchers interested in the new patterns of collaboration made possible by simultaneous editing capabilities.


title: IMAGE ENCRYPTION AND DECRYPTION USING BLOWFISH ALGORITHM IN MATLAB

abstract: With the progress in data exchange by electronic system, the need of information security has become a necessity. Due to growth of multimedia application, security becomes an important issue of communication and storage of images. This paper is about encryption and decryption of images using a secret-key block cipher called 64-bits Blowfish designed to increase security and to improve performance. This algorithm will be used as a variable key size up to 448 bits. It employs Feistel network which iterates simple function 16 times. The blowfish algorithm is safe against unauthorized attack and runs faster than the popular existing algorithms. The proposed algorithm is designed and realized using MATLAB.


title: Corporate Sustainability: First Evidence on Materiality

abstract: ABSTRACT: Using newly available materiality classifications of sustainability topics, we develop a novel dataset by hand-mapping sustainability investments classified as material for each industry ...


title: Modeling services to construct service-oriented healthcare architecture for digital home-care business

abstract: A returnable container for bottles. The container is formed as a tube having interconnected bottom, end and top walls. Bottles are side loaded into the tube and thereafter upper and lower side flaps on both sides of the container close the container around the bottles. A single thickness central partition is connected to at least two walls, e.g., an end wall and the bottom wall. The top wall is perforated so that substantially the entire portion of the top wall can be torn away to expose the bottle within. After the contents of the bottle have been consumed, they may be returned to the container and carried, by means of the central partition, back to the store where they were originally purchased.


title: AVEC 2012: the continuous audio/visual emotion challenge

abstract: We present the second Audio-Visual Emotion recognition Challenge and workshop (AVEC 2012), which aims to bring together researchers from the audio and video analysis communities around the topic of emotion recognition. The goal of the challenge is to recognise four continuously valued affective dimensions: arousal, expectancy, power, and valence. There are two sub-challenges: in the Fully Continuous Sub-Challenge participants have to predict the values of the four dimensions at every moment during the recordings, while for the Word-Level Sub-Challenge a single prediction has to be given per word uttered by the user. This paper presents the challenge guidelines, the common data used, and the performance of the baseline system on the two tasks.


Are You Asking the Right Questions? Teaching Machines to Ask Clarification Questions


title: Physical function of patients with implantable cardioverter-defibrillators.

abstract: BACKGROUND ::: Patients with implantable cardioverter-defibrillators (ICDs) face problems in physical function, but little is known about how mastery predicts physical function over time. ::: ::: ::: PURPOSE ::: The primary purpose of this study was to examine the influence of mastery (assessed at baseline) on physical function (observed distance walked and perceived function) over 12 months among ICD patients. Secondary purposes were to (1) examine the influence of aging, in interaction with mastery, on physical function and (2) determine predictors of 12-month physical function. ::: ::: ::: METHODS ::: The ICD patients (N = 122; 75% men; 88% white; mean age, 65 years) completed the baseline interviews; 100 patients completed the 12-month interviews. Repeated-measures 1-way analyses of variance were used to examine the influence of mastery and the interaction of mastery and aging on physical function over 12 months. Multiple regression analyses were conducted to determine predictors of 12-month physical function. ::: ::: ::: RESULTS ::: The ICD patients with higher mastery had better walk distances than those with lower mastery; and walk distances were improved over 12 months, but only in those with lower mastery (F = 5.40, P = .02). The ICD patients with higher mastery had better perceived physical function than those with lower mastery (F = 25.57, P < .0001), but those with both lower and higher mastery did not show significant improvement. Neither significant interaction of aging and mastery on physical function nor predictors of walk distances were found. Baseline depression significantly predicted 12-month perceived physical function (F = 8.94, P = .0042). ::: ::: ::: CONCLUSIONS ::: The ICD patients with lower mastery had considerable impairments in physical function compared with those with higher mastery over time. Depression is more likely to predict perceived physical function. Further prospective studies are needed to validate these findings in a larger sample and develop interventions to improve physical impairment in ICD patients.


title: Entropy based keyframe selection for Multi-Camera Visual SLAM

abstract: Although many state-of-the-art visual SLAM algorithms use keyframes to help alleviate the computational requirements of performing online bundle adjustment, little consideration is taken for specific keyframe selection. In this work, we propose two entropy based methods which aim to insert keyframes that will directly improve the system's ability to localize. The first approach inserts keyframes based on the cumulative point entropy reduction in the existing map, while the second approach uses the predicted point flow discrepancy to select keyframes which best initializes new features for the camera to track against in the future. We implement the proposed methods within the Multi-Camera Parallel Mapping and Tracking framework, and demonstrate the effectiveness of our methods using ground truth data collected using an indoor positioning system.


title: Extraction of high confidence minutiae points from fingerprint images

abstract: Fingerprint is one of the well accepted biometric traits for building automatic human recognition system. These systems mainly involves matching of two fingerprints based on their minutiae points. Therefore, extraction of reliable or true minutiae from the fingerprint image is critically important. This paper proposed an novel algorithm for automatic extraction of highly reliable minutiae points from a fingerprint image. It utilizes frequency domain image enhancement and heuristics. Experiments have been conducted on two databases. FVC2002 which is a publicly available fingerprint database containing 800 fingerprint images of 100 persons and IITK-Sel500FP which is an in-house database containing 1000 fingerprint images of 500 persons. Minutiae points extracted by the proposed algorithm are benchmarked against the manually marked ones and the comparison is done with an open source minutiae extractor mindtct of NIST. Experimental results have shown that the proposed algorithm has significantly higher confidence of extracted minutiae points being true.


title: Link prediction for partially observed networks

abstract: Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples of absent edges, which creates a difficulty for many existing supervised learning approaches. We develop a new method which treats the observed network as a sample of the true network with different sampling rates for positive and negative examples. We obtain a relative ranking of potential links by their probabilities, utilizing information on node covariates as well as on network topology. Empirically, the method performs well under many settings, including when the observed network is sparse. We apply the method to a protein-protein interaction network and a school friendship network.


Additive Markov processes


title: Smallify: Learning Network Size while Training

abstract: As neural networks become widely deployed in different applications and on different hardware, it has become increasingly important to optimize inference time and model size along with model accuracy. Most current techniques optimize model size, model accuracy and inference time in different stages, resulting in suboptimal results and computational inefficiency. In this work, we propose a new technique called Smallify that optimizes all three of these metrics at the same time. Specifically we present a new method to simultaneously optimize network size and model performance by neuron-level pruning during training. Neuron-level pruning not only produces much smaller networks but also produces dense weight matrices that are amenable to efficient inference. By applying our technique to convolutional as well as fully connected models, we show that Smallify can reduce network size by 35X with a 6X improvement in inference time with similar accuracy as models found by traditional training techniques.


title: A 2.7 to 4.6 GHz multi-phase high resolution and wide tuning range digitally-controlled oscillator in CMOS 65nm

abstract: In this paper, a digitally-controlled oscillator (DCO) with differential output and eight different phases is proposed. The DCO is based on two-path ring oscillator (RO) with cross-coupled structure. The proposed DCO structure consists of four differential stages and two 9-bit digitally-controlled current sources, which guarantee high resolution and monotonic behavior of the DCO. The circuit is simulated in 65nm standard CMOS technology. The simulation results indicate that frequency can change from 2.7GHz to 4.6GHz with 512 steps which leads to very high resolution DCO. The simulated cycle to cycle Peak-to-Peak jitter and RMS jitter in 4GHz frequency are 6.47ps and 0.53ps respectively. Power consumption at 1.2v supply, varies between 0.59mW to 1.39mW based on digital code word.


title: Automatic Cephalometric X-Ray Landmark Detection by Applying Game Theory and Random Forests

abstract: In this paper, we propose a novel framework for cephalometric X-ray landmark detection. The framework describes the appearance of each landmark by Haar-like features, and uses the theory of random forests (RFs) to combine them into a detector of landmark candidate points in the target image. The framework relies also on spatial relationships among landmarks, modeled by Gaussian kernel density estimation and RFs, and uses concepts from the game theory to determine the optimal landmark candidate points. After training the framework on 100 cephalometric X-ray images, it was validated on a different set of 100 images, and the obtained landmark detection accuracy was 1.82 ± 1.61 mm in terms of mean radial error and 72.7 % in terms of successful detec- tion rate (within 2 mm neighborhood). The results indicate that the proposed framework can be used for computerized cephalometry, moreover, the frame- work is universal and can be therefore applied to images of various anatomical structures acquired by different modalities.


title: Investigating Human Priors for Playing Video Games

abstract: What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors on human performance. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors. We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, e.g. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play. Videos and the game manipulations are available at https://rach0012.github.io/humanRL_website/


title: 3D printing of robotic soft actuators with programmable bioinspired architectures

abstract: Soft actuation allows robots to interact safely with humans, other machines, and their surroundings. Full exploitation of the potential of soft actuators has, however, been hindered by the lack of simple manufacturing routes to generate multimaterial parts with intricate shapes and architectures. Here, we report a 3D printing platform for the seamless digital fabrication of pneumatic silicone actuators exhibiting programmable bioinspired architectures and motions. The actuators comprise an elastomeric body whose surface is decorated with reinforcing stripes at a well-defined lead angle. Similar to the fibrous architectures found in muscular hydrostats, the lead angle can be altered to achieve elongation, contraction, or twisting motions. Using a quantitative model based on lamination theory, we establish design principles for the digital fabrication of silicone-based soft actuators whose functional response is programmed within the material's properties and architecture. Exploring such programmability enables 3D printing of a broad range of soft morphing structures. 3D-printed soft actuators have limited motion and are far from reaching the level of complexity found in biological systems. Here the authors present a multimaterial 3D printing platform for the fabrication of soft actuators displaying a wide range of motions that are programmable.


title: SR-IOV Networking in Xen: Architecture, Design and Implementation

abstract: SR-IOV capable network devices offer the benefits of direct I/O throughput and reduced CPU utilization while greatly increasing the scalability and sharing capabilities of the device. SR-IOV allows the benefits of the paravirtualized driver's throughput increase and additional CPU usage reductions in HVMs (Hardware Virtual Machines). SR-IOV uses direct I/O assignment of a network device to multiple VMs, maximizing the potential for using the full bandwidth capabilities of the network device, as well as enabling unmodified guest OS based device drivers which will work for different underlying VMMs. ::: ::: Drawing on our recent experience in developing an SR-IOV capable networking solution for the Xen hypervisor we discuss the system level requirements and techniques for SR-IOV enablement on the platform. We discuss PCI configuration considerations, direct MMIO, interrupt handling and DMA into an HVM using an IOMMU (I/O Memory Management Unit). We then explain the architectural, design and implementation considerations for SR-IOV networking in Xen in which the Physical Function has a driver running in the driver domain that serves as a "master" and each Virtual Function exposed to a guest VM has its own virtual driver.


title: Application of Deep Convolutional Neural Networks for Detecting Extreme Weather in Climate Datasets

abstract: Detecting extreme events in large datasets is a major challenge in climate science research. Current algorithms for extreme event detection are build upon human expertise in defining events based on subjective thresholds of relevant physical variables. Often, multiple competing methods produce vastly different results on the same dataset. Accurate characterization of extreme events in climate simulations and observational data archives is critical for understanding the trends and potential impacts of such events in a climate change content. This study presents the first application of Deep Learning techniques as alternative methodology for climate extreme events detection. Deep neural networks are able to learn high-level representations of a broad class of patterns from labeled data. In this work, we developed deep Convolutional Neural Network (CNN) classification system and demonstrated the usefulness of Deep Learning technique for tackling climate pattern detection problems. Coupled with Bayesian based hyper-parameter optimization scheme, our deep CNN system achieves 89\%-99\% of accuracy in detecting extreme events (Tropical Cyclones, Atmospheric Rivers and Weather Fronts


Safety of pedestrians and cyclists when interacting with self-driving vehicles : A case study of the WEpods (PPT)


title: Structured learning with constrained conditional models

abstract: Making complex decisions in real world problems often involves assigning values to sets of interdependent variables where an expressive dependency structure among these can influence, or even dictate, what assignments are possible. Commonly used models typically ignore expressive dependencies since the traditional way of incorporating non-local dependencies is inefficient and hence leads to expensive training and inference. ::: ::: The contribution of this paper is two-fold. First, this paper presents Constrained Conditional Models (CCMs), a framework that augments linear models with declarative constraints as a way to support decisions in an expressive output space while maintaining modularity and tractability of training. The paper develops, analyzes and compares novel algorithms for CCMs based on Hidden Markov Models and Structured Perceptron. The proposed CCM framework is also compared to task-tailored models, such as semi-CRFs. ::: ::: Second, we propose CoDL, a constraint-driven learning algorithm, which makes use of constraints to guide semi-supervised learning. We provide theoretical justification for CoDL along with empirical results which show the advantage of using declarative constraints in the context of semi-supervised training of probabilistic models.


title: Properly understanding the impacts of distributed resources on distribution systems

abstract: The subject paper discusses important impacts of distributed resources on distribution networks and feeders. These include capacity, line losses, voltage regulation, and central system support (such as volt/var control via central generators and substation) as the number, placement and penetration levels of distributed resources are varied. Typically, the impacts of distributed resources on the distribution system are studied by using steady-state rather than dynamic analysis tools. However, the response time and transient impacts of both system equipment (such as substation/feeder capacitors) and distributed resources needs to be taken into account and only dynamic analysis will provide the full impact results. ORNL is completing a study of distributed resources interconnected to a large distribution system considering the above variables. A report of the study and its results will be condensed into a paper for this panel session. The impact of distributed resources will vary as the penetration level reaches the capacity of the distribution feeder/system. The question is how high of a penetration of distributed resource can be accommodated on the distribution feeder/system without any major changes to system operation, system design and protection. The impacts will most surely vary depending upon load composition, distribution and level. Also, it is expected that various placement of distributed resources will impact the distribution system differently.


title: Representational flexibility and specificity following spatial descriptions of real-world environments

abstract: Abstract Current theories are mixed with regard to the nature of mental representations following spatial description reading. Whereas some findings argue that individuals’ representations are invariant following text-based, map-based, or first-person experience, other studies have suggested that representations can also exhibit considerable flexibility. In the current project we investigated the influences of spatial description perspectives and depictions on the nature of mental representations. In Experiment 1, participants exhibited more flexibility following survey, compared to route, spatial descriptions. With extended study time, though, flexibility following route descriptions increased. In Experiment 2, complementary maps further enhanced flexibility for route-based descriptions. Interestingly, increased exposure to these maps actually reduced flexibility following survey descriptions. These results demonstrate that the nature of our spatial mental representations depends upon a variety of factors; delineating these factors is critical for resolving debates concerning the malleable and invariant characteristics of spatial memory.


Public / Private Partnership in Health Care Services in India


title: DNA fragments in the blood plasma of cancer patients: quantitations and evidence for their origin from apoptotic and necrotic cells.

abstract: Increased levels of DNA fragments have frequently been found in the blood plasma of cancer patients. Published data suggest that only a fraction of the DNA in blood plasma is derived from cancer cells. However, it is not known how much of the circulating DNA is from cancer or from noncancer cells. By quantitative methylation-specific PCR of the promoter region of the CDKN2A tumor suppressor gene, we were able to quantify the fraction of plasma DNA derived from tumor cells. In the plasma samples of 30 unselected cancer patients, we detected quantities of tumor DNA from only 3% to as much as 93% of total circulating DNA. We investigated possible origins of nontumor DNA in the plasma and demonstrate here a contribution of T-cell DNA in a few cases only. To investigate the possibility that plasma DNA originates from apoptotic or necrotic cells, we performed studies with apoptotic (staurosporine) and necrotic (staurosporine plus oligomycin) cells in vitro and with mice after induction of apoptotic (anti-CD95) or necrotic (acetaminophen) liver injury. Increasing amounts of DNA were found to be released in the supernatants of cells and in the blood plasma samples of treated animals. A clear discrimination of apoptotic and necrotic plasma DNA was possible by gel electrophoresis. The same characteristic patterns of DNA fragments could be identified in plasma derived from different cancer patients. The data are consistent with the possibility that apoptotic and necrotic cells are a major source for plasma DNA in cancer patients.


title: Automatic Sentence Ordering Assessment Based on Similarity

abstract: One of the tasks of text generation is sentence ordering since it is crucial for readability. Nevertheless, there is no common approach for evaluation of sentence ordering. The state-of-the art methods are based on the comparison with a human-provided order. However, in many cases it is impossible or time and resource consuming. Therefore, we propose three completely automatic approaches for sentence order assessment where the similarity between adjacent sentences is used as a measure of text coherence. We showed that the methods based on word and noun similarities have very high agreement with the human-provided judgment. We also propose an automatic evaluation framework for analysis of the metrics of sentence order that requires only a text collection.


title: Trunk movements in human locomotion

abstract: Trunk movements in the frontal and sagittal planes were studied in 10 healthy males (18-35 yrs) during normal walking (1.0-2.5 m/s) and running (2.0-6.0 m/s) on a treadmill. Movements were recorded ...


title: Lexicon Infused Phrase Embeddings for Named Entity Resolution

abstract: Most state-of-the-art approaches for named-entity recognition (NER) use semi supervised information in the form of word clusters and lexicons. Recently neural network-based language models have been explored, as they as a byproduct generate highly informative vector representations for words, known as word embeddings. In this paper we present two contributions: a new form of learning word embeddings that can leverage information from relevant lexicons to improve the representations, and the first system to use neural word embeddings to achieve state-of-the-art results on named-entity recognition in both CoNLL and Ontonotes NER. Our system achieves an F1 score of 90.90 on the test set for CoNLL 2003---significantly better than any previous system trained on public data, and matching a system employing massive private industrial query-log data.


title: Embree: a kernel framework for efficient CPU ray tracing

abstract: We describe Embree, an open source ray tracing framework for x86 CPUs. Embree is explicitly designed to achieve high performance in professional rendering environments in which complex geometry and incoherent ray distributions are common. Embree consists of a set of low-level kernels that maximize utilization of modern CPU architectures, and an API which enables these kernels to be used in existing renderers with minimal programmer effort. In this paper, we describe the design goals and software architecture of Embree, and show that for secondary rays in particular, the performance of Embree is competitive with (and often higher than) existing state-of-the-art methods on CPUs and GPUs.


title: Thought recognition: predicting and decoding brain activity using the zero-shot learning model

abstract: Machine learning algorithms have been successfully applied to learning classifiers in many domains such as computer vision, fraud detection, and brain image analysis. Typically, classifiers are trained to predict a class value given a set of labeled training data that includes all possible class values, and sometimes additional unlabeled training data. ::: Little research has been performed where the possible values for the class variable include values that have been omitted from the training examples. This is an important problem setting, especially in domains where the class value can take on many values, and the cost of obtaining labeled examples for all values is high. ::: We show that the key to addressing this problem is not predicting the held-out classes directly, but rather by recognizing the semantic properties of the classes such as their physical or functional attributes. We formalize this method as zero-shot learning and show that by utilizing semantic knowledge mined from large text corpora and crowd-sourced humans, we can discriminate classes without explicitly collecting examples of those classes for a training set. ::: As a case study, we consider this problem in the context of thought recognition, where the goal is to classify the pattern of brain activity observed from a non-invasive neural recording device. Specifically, we train classifiers to predict a specific concrete noun that a person is thinking about based on an observed image of that person's neural activity. ::: We show that by predicting the semantic properties of the nouns such as "is it heavy?" and "is it edible?", we can discriminate concrete nouns that people are thinking about, even without explicitly collecting examples of those nouns for a training set. Further, this allows discrimination of certain nouns that are within the same category with significantly higher accuracies than previous work. ::: In addition to being an important step forward for neural imaging and brain-computer-interfaces, we show that the zero-shot learning model has important implications for the broader machine learning community by providing a means for learning algorithms to extrapolate beyond their explicit training set.


Amount and Diversity of Digital Emotional Expression Predicts Happiness


title: AUTOMATIC BEHAVIOUR-BASED ANALYSIS AND CLASSIFICATION SYSTEM FOR MALWARE DETECTION

abstract: Malware is any kind of program explicitly designed to harm, such as viruses, trojan horses or worms. Since the amount of malware is growing exponentially, it already poses a serious security threat. Therefore, every incoming code must be analysed in order to classify it as malware or benign software. These tests commonly combine static and dynamic analysis techniques in order to extract the major amount of information from distrustful files. Moreover, the increment of the number of attacks hinders manually testing the thousands of suspicious archives that every day reach antivirus laboratories. Against this background, we address here an automatised system for malware behaviour analysis based on emulation and simulation techniques. Hence, creating a secure and reliable sandbox environment allows us to test the suspicious code retrieved without risk. In this way, we can also generate evidences and classify the samples with several machine-learning algorithms. We have developed the proposed solution, testing it with real malware. Finally, we have evaluated it in terms of reliability and time performance, two of the main aspects for such a system to work.


title: Common and Distinct Neural Substrates for the Perception of Speech Rhythm and Intonation

abstract: The present study examines the neural substrates for the perception of speech rhythm and into- nation. Subjects listened passively to synthesized speech stimuli that contained no semantic and phono- logical information, in three conditions: (1) continuous speech stimuli with fixed syllable duration and fundamental frequency in the standard condition, (2) stimuli with varying vocalic durations of syllables in the speech rhythm condition, and (3) stimuli with varying fundamental frequency in the intonation condition. Compared to the standard condition, speech rhythm activated the right middle superior tem- poral gyrus (mSTG), whereas intonation activated the bilateral superior temporal gyrus and sulcus (STG/ STS) and the right posterior STS. Conjunction analysis further revealed that rhythm and intonation acti- vated a common area in the right mSTG but compared to speech rhythm, intonation elicited additional activations in the right anterior STS. Findings from the current study reveal that the right mSTG plays an important role in prosodic processing. Implications of our findings are discussed with respect to neuro- cognitive theories of auditory processing. Hum Brain Mapp 31:1106-1116, 2010. V C 2010 Wiley-Liss, Inc.


title: A Single-Chip CMOS-Based Parallel Optical Transceiver Capable of 240-Gb/s Bidirectional Data Rates

abstract: We report here on the design, fabrication, and high-speed performance of a parallel optical transceiver based on a single CMOS amplifier chip incorporating 16 transmitter and 16 receiver channels. The optical interfaces to the chip are provided by 16-channel photodiode (PD) and VCSEL arrays that are directly flip-chip soldered to the CMOS IC. The substrate emitting/illuminated VCSEL/PD arrays operate at 985 nm and include integrated lenses. The complete transceivers are low-cost, low-profile, highly integrated assemblies that are compatible with conventional chip packaging technology such as direct flip-chip soldering to organic circuit boards. In addition, the packaging approach, dense hybrid integration, readily scales to higher channel counts, supporting future massively parallel optical data buses. All transmitter and receiver channels operate at speeds up to 15 Gb/s for an aggregate bidirectional data rate of 240 Gb/s. Interchannel crosstalk was extensively characterized and the dominant source was found to be between receiver channels, with a maximum sensitivity penalty of 1 dB measured at 10 Gb/s for a victim channel completely surrounded by active aggressor channels. The transceiver measures 3.25times5.25 mm and consumes 2.15 W of power with all channels fully operational. The per-bit power consumption is as low as 9 mW/Gb/s, and this is the first single-chip optical transceiver capable of channel rates in excess of 10 Gb/s. The area efficiency of 14 Gb/s/mm2 per link is the highest ever reported for any parallel optical transmitter, receiver, or transceiver reported to-date.


Design of a Hybrid Intrusion Detection System using Snort and Hadoop Prathibha.P.G


title: The toronto mindfulness scale: Development and validation

abstract: In this study, the authors both developed and validated a self-report mindfulness measure, the Toronto Mindfulness Scale (TMS). In Study 1, participants were individuals with and without meditation experience. Results showed good internal consistency and two factors, Curiosity and Decentering. Most of the expected relationships with other constructs were as expected. The TMS scores increased with increasing mindfulness meditation experience. In Study 2, criterion and incremental validity of the TMS were investigated on a group of individuals participating in 8-week mindfulness-based stress reduction programs. Results showed that TMS scores increased following treatment, and Decentering scores predicted improvements in clinical outcome. Thus, the TMS is a promising measure of the mindfulness state with good psychometric properties and predictive of treatment outcome. © 2006 Wiley Periodicals, Inc. J Clin Psychol 62: 1445–1467, 2006.


title: A Survey of Predictive Modeling on Imbalanced Domains

abstract: Many real-world data-mining applications involve obtaining predictive models using datasets with strongly imbalanced distributions of the target variable. Frequently, the least-common values of this target variable are associated with events that are highly relevant for end users (e.g., fraud detection, unusual returns on stock markets, anticipation of catastrophes, etc.). Moreover, the events may have different costs and benefits, which, when associated with the rarity of some of them on the available training data, creates serious problems to predictive modeling techniques. This article presents a survey of existing techniques for handling these important applications of predictive analytics. Although most of the existing work addresses classification tasks (nominal target variables), we also describe methods designed to handle similar problems within regression tasks (numeric target variables). In this survey, we discuss the main challenges raised by imbalanced domains, propose a definition of the problem, describe the main approaches to these tasks, propose a taxonomy of the methods, summarize the conclusions of existing comparative studies as well as some theoretical analyses of some methods, and refer to some related problems within predictive modeling.


title: Formal Distributional Semantics: Introduction to the Special Issue

abstract: Formal Semantics and Distributional Semantics are two very influential semantic frameworks in Computational Linguistics. Formal Semantics is based on a symbolic tradition and centered around the inferential properties of language. Distributional Semantics is statistical and data-driven, and focuses on aspects of meaning related to descriptive content. The two frameworks are complementary in their strengths, and this has motivated interest in combining them into an overarching semantic framework: a "Formal Distributional Semantics." Given the fundamentally different natures of the two paradigms, however, building an integrative framework poses significant theoretical and engineering challenges. The present issue of Computational Linguistics advances the state of the art in Formal Distributional Semantics; this introductory article explains the motivation behind it and summarizes the contributions of previous work on the topic, providing the necessary background for the articles that follow.


Increasing Student Engagement in Secondary Classrooms


title: Integrated WiFi/PDR/Smartphone Using an Unscented Kalman Filter Algorithm for 3D Indoor Localization

abstract: Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals.


title: Considerations for planning esthetic treatment with veneers involving no or minimal preparation.

abstract: BACKGROUND ::: Recently, some manufacturers, dental laboratories and practitioners have been marketing their "no-preparation" porcelain veneer systems as ones that will achieve results the same as or better than those seen with traditional veneers accomplished with tooth reduction. This case report describes case selection and esthetic treatment planning considerations for veneers involving no or minimal preparation. ::: ::: ::: CASE DESCRIPTION ::: A 30-year-old woman with erosion on the facial surfaces of her maxillary teeth sought treatment for esthetic improvement of her smile. The author evaluated the patient's goals, midline position and cant, lip position and fullness, color, incisal edge position, desired tooth contours and occlusion. Once the author determined the ideal final position of the teeth and compared it with their current position, he decided that the patient was a perfect candidate for no-preparation veneers. ::: ::: ::: CLINICAL IMPLICATIONS ::: To optimize the outcome, dentists should perform a complete esthetic examination before selecting and planning treatment for patients receiving porcelain veneers involving no or minimal preparation.


title: Can a mind have two time lines? Exploring space–time mapping in Mandarin and English speakers

abstract: Spatial representations of time are a ubiquitous feature of human cognition. Nevertheless, interesting sociolinguistic variations exist with respect to where in space people locate temporal constructs. For instance, while in English time metaphorically flows horizontally, in Mandarin an additional vertical dimension is employed. Noting that the bilingual mind can flexibly accommodate multiple representations, the present work explored whether Mandarin–English bilinguals possess two mental time lines. Across two experiments, we demonstrated that Mandarin–English bilinguals do indeed employ both horizontal and vertical representations of time. Importantly, subtle variations to cultural context were seen to shape how these time lines were deployed.


title: High-Isolation X-Band Marine Radar Antenna Design

abstract: This paper presents a high-isolation printed antenna array for marine radar applications. The antenna array is composed of 32 identical square microstrip patches operated at a center frequency of 9.35 GHz and includes a 100-MHz bandwidth (subject to a 1.5:1 voltage standing wave ratio [VSWR]). The patch antennas are arranged in four arms, each of which contains eight elements and is series-fed using Chebyshev tapering (25 dB side-lobe level). To apply the antenna in marine radar applications, an antenna with horizontal polarization was employed because, in comparison with vertical polarization, it can relatively reduce the sea clutter reflectivity. Therefore, a slit was carved on each patch element to change the current path, thereby enabling horizontal polarization. The antenna gain, 3-dB beamwidth, side-lobe level, and front-to-back ratio were 22 dBi, 5.3 °, 26.4 dB, and 38.5 dB, respectively. Additionally, metallic baffles were introduced for increasing the isolation between the transmitting and receiving antennas to 60 dB .


title: Structured Sparse Canonical Correlation Analysis

abstract: In this paper, we propose to apply sparse canonical correlation analysis (sparse CCA) to an important genome-wide association study problem, eQTL mapping. Existing sparse CCA models do not incorporate structural information among variables such as pathways of genes. This work extends the sparse CCA so that it could exploit either the pre-given or unknown group structure via the structured-sparsity-inducing penalty. Such structured penalty poses new challenge on optimization techniques. To address this challenge, by specializing the excessive gap framework, we develop a scalable primal-dual optimization algorithm with a fast rate of convergence. Empirical results show that the proposed optimization algorithm is more efficient than existing state-of-the-art methods. We also demonstrate the effectiveness of the structured sparse CCA on both simulated and genetic datasets.


title: Combinatorial Coverage Measurement Concepts and Applications

abstract: Empirical data demonstrate the value of t-way coverage, but in some testing situations, it is not practical to use covering arrays. However any set of tests covers at least some proportion of t-way combinations. This paper describes a variety of measures of combinatorial coverage that can be used in evaluating aspects of t-way coverage of a test suite. We also provide lower bounds on t-way coverage of several widely-used testing strategies, and describe a tool that analyzes test suites using the measures discussed in the paper.


Determinants of Implementation and Adoption of Customer Relationship Management ( CRM ) System


ARQuake: the outdoor augmented reality gaming system


title: Spectrotemporal dynamics of the EEG during working memory encoding and maintenance predicts individual behavioral capacity.

abstract: We investigated the effect of memory load on encoding and maintenance of information in working memory. Electroencephalography (EEG) signals were recorded while participants performed a modified Sternberg visual memory task. Independent component analysis (ICA) was used to factorise the EEG signals into distinct temporal activations to perform spectrotemporal analysis and localisation of source activities. We found ‘encoding’ and ‘maintenance’ operations were correlated with negative and positive changes in a-band power, respectively. Transient activities were observed during encoding of information in the bilateral cuneus, precuneus, inferior parietal gyrus and fusiform gyrus, and a sustained activity in the inferior frontal gyrus. Strong correlations were also observed between changes in a-power and behavioral performance during both encoding and maintenance. Furthermore, it was also found that individuals with higher working memory capacity experienced stronger neural oscillatory responses during the encoding of visual objects into working memory. Our results suggest an interplay between two distinct neural pathways and different spatiotemporal operations during the encoding and maintenance of information which predict individual differences in working memory capacity observed at the behavioral level.


title: Study on partial discharge model of solid insulator

abstract: Considering the effect of the surface resistance of void in the actual discharge process, on the basis of the Partial discharge (PD) model based on the theory of plasma, the physical model of PD in voids was modified in this paper. The influence of the change to surface resistance in the void discharge process has been considered in this model. Based on this model, the simulations of PD were investigated by using SIMULINK. The discharge waveform obtained by simulation is very similar to the measured ones in the same condition. It can be seen from the results that the modified model simulation waveform is superior to the traditional proposed, and closer to the actual waveform. This indicates that the simulation model is effective and the simulation results are reliable.


title: How to move towards digital era governance: the case of VDAB

abstract: This paper takes our research work with VDAB (Vlaamse Dienst voor Arbeidsbemiddeling en Beroepsopleiding), the public employment service for the Flemish region in Belgium, as a starting point to study the transformation of government from New Public Management (NPM) to Digital Era Governance (DEG). This study focuses on how to work towards disruptive DEG innovation in a turbulent strategic context by employing a strategy of simple rules. Together with VDAB we apply an Action Design Research (ADR) approach to develop a set of "boundary breaking rules". Coining these simple rules represents a first significant step in VDAB's journey towards achieving a radical business innovation. In addition to the main artifact designed using our ADR approach in the VDAB context, i.e. the "boundary breaking rules", we derive lessons from this approach concerning the nature of this artifact specific for the VDAB case. Although this paper represents an early stage of the research and has not yet reached the final ADR stage of formalization of learning, we aim for it to lay the foundations for a more broadly applicable design theory of simple rules, useful in contexts generalizable from the specific VDAB context.


title: A survey of current paradigms in machine translation

abstract: Abstract This paper is a survey of the current machine translation research in the US, Europe and Japan. A short history of machine translation is presented first, followed by an overview of the current research work. Representative examples of a wide range of different approaches adopted by machine translation researchers are presented. These are described in detail along with a discussion of the practicalities of scaling up these approaches for operational environments. In support of this discussion, issues in, and techniques for, evaluating machine translation systems are addressed.


title: A Martingale Framework for Detecting Changes in Data Streams by Testing Exchangeability

abstract: In a data streaming setting, data points are observed sequentially. The data generating model may change as the data are streaming. In this paper, we propose detecting this change in data streams by testing the exchangeability property of the observed data. Our martingale approach is an efficient, nonparametric, one-pass algorithm that is effective on the classification, cluster, and regression data generating models. Experimental results show the feasibility and effectiveness of the martingale methodology in detecting changes in the data generating model for time-varying data streams. Moreover, we also show that: (1) An adaptive support vector machine (SVM) utilizing the martingale methodology compares favorably against an adaptive SVM utilizing a sliding window, and (2) a multiple martingale video-shot change detector compares favorably against standard shot-change detection algorithms.


title: Fully integrated silicon probes for high-density recording of neural activity

abstract: New silicon probes known as Neuropixels are shown to record from hundreds of neurons simultaneously in awake and freely moving rodents.


title: Joint Bayesian removal of impulse and background noise

abstract: We present a method for the removal of noise including non-Gaussian impulses from a signal. Impulse noise is removed jointly a homogenous Gaussian noise floor using a Gabor regression model [1]. The problem is formulated in a joint Bayesian framework and we use a Gibbs MCMC sampler to estimate parameters. We show how to deal with variable magnitude impulses using a shifted inverse gamma distribution for their variance. Our results show improved signal to noise ratios and perceived audio quality by explicitly modelling impulses with a discrete switching process and a new heavy-tailed amplitude model.


title: Cache-, hash-, and space-efficient bloom filters

abstract: A Bloom filter is a very compact data structure that supports approximate membership queries on a set, allowing false positives. We propose several new variants of Bloom filters and replacements with similar functionality. All of them have a better cache-efficiency and need less hash bits than regular Bloom filters. Some use SIMD functionality, while the others provide an even better space efficiency. As a consequence, we get a more flexible trade-off between false-positive rate, space-efficiency, cache-efficiency, hash-efficiency, and computational effort. We analyze the efficiency of Bloom filters and the proposed replacements in detail, in terms of the false-positive rate, the number of expected cache-misses, and the number of required hash bits. We also describe and experimentally evaluate the performance of highly tuned implementations. For many settings, our alternatives perform better than the methods proposed so far.


title: Visually-aware Collaborative Food Recommendation

abstract: Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, user's personal preference on food, and various contexts like what had been eaten in past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network-based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual image. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from this http URL. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference on food.


title: Centralized Control for Optimizing Microgrids Operation

abstract: Microgrids are Low Voltage distribution networks comprising various distributed generators (DG), storage devices and controllable loads that can operate either interconnected or isolated from the main distribution grid as a controlled entity. This paper describes the operation of a Central Controller for Microgrids. The controller aims to optimize the operation of the Microgrid during interconnected operation, i.e. maximize its value by optimizing production of the local DGs and power exchanges with the main distribution grid. Two market policies are assumed including Demand Side Bidding options for controllable loads. The developed optimization algorithms are applied on a typical LV study case network operating under various market policies and assuming realistic spot market prices and DG bids reflecting realistic operational costs. The effects on the Microgrid and the Distribution network operation are presented and discussed.


title: Alzheimer’s Disease: Analyzing the Missing Heritability

abstract: Alzheimer’s disease (AD) is a complex disorder influenced by environmental and genetic factors. Recent work has identified 11 AD markers in 10 loci. We used Genome-wide Complex Trait Analysis to analyze >2 million SNPs for 10,922 individuals from the Alzheimer’s Disease Genetics Consortium to assess the phenotypic variance explained first by known late-onset AD loci, and then by all SNPs in the Alzheimer’s Disease Genetics Consortium dataset. In all, 33% of total phenotypic variance is explained by all common SNPs. APOE alone explained 6% and other known markers 2%, meaning more than 25% of phenotypic variance remains unexplained by known markers, but is tagged by common SNPs included on genotyping arrays or imputed with HapMap genotypes. Novel AD markers that explain large amounts of phenotypic variance are likely to be rare and unidentifiable using genome-wide association studies. Based on our findings and the current direction of human genetics research, we suggest specific study designs for future studies to identify the remaining heritability of Alzheimer’s disease.


title: An inquiry into money laundering tools in the Bitcoin ecosystem

abstract: We provide a first systematic account of opportunities and limitations of anti-money laundering (AML) in Bitcoin, a decentralized cryptographic currency proliferating on the Internet. Our starting point is the observation that Bitcoin attracts criminal activity as many say it is an anonymous transaction system. While this claim does not stand up to scrutiny, several services offering increased transaction anonymization have emerged in the Bitcoin ecosystem - such as Bitcoin Fog, BitLaundry, and the Send Shared functionality of Blockchain.info. Some of these services routinely handle the equivalent of 6-digit dollar amounts. In a series of experiments, we use reverse-engineering methods to understand the mode of operation and try to trace anonymized transactions back to our probe accounts. While Bitcoin Fog and Blockchain.info successfully anonymize our test transactions, we can link the input and output transactions of BitLaundry. Against the backdrop of these findings, it appears unlikely that a Know-Your-Customer principle can be enforced in the Bitcoin system. Hence, we sketch alternative AML strategies accounting for imperfect knowledge of true identities but exploiting public information in the transaction graph, and discuss the implications for Bitcoin as a decentralized currency.


title: Learning Deep Features via Congenerous Cosine Loss for Person Recognition

abstract: Person recognition aims at recognizing the same identity across time and space with complicated scenes and similar appearance. In this paper, we propose a novel method to address this task by training a network to obtain robust and representative features. The intuition is that we directly compare and optimize the cosine distance between two features - enlarging inter-class distinction as well as alleviating inner-class variance. We propose a congenerous cosine loss by minimizing the cosine distance between samples and their cluster centroid in a cooperative way. Such a design reduces the complexity and could be implemented via softmax with normalized inputs. Our method also differs from previous work in person recognition that we do not conduct a second training on the test subset. The identity of a person is determined by measuring the similarity from several body regions in the reference set. Experimental results show that the proposed approach achieves better classification accuracy against previous state-of-the-arts.


title: Ant Colony Optimization Applied to Route Planning Using Link Travel Time Predictions

abstract: Finding the shortest path in a road network is a well known problem. Various proven static algorithms such as Dijkstra and A* are extensively evaluated and implemented. When confronted with dynamic costs, such as link travel time predictions, alternative route planning algorithms have to be applied. This paper applies Ant Colony Optimization combined with link travel time predictions to find routes that reduce the time spend by travels by taking into account link travel time predictions. The presented algorithm is evaluated using simulations in real world traffic networks.


title: The role of big data in improving power system operation and protection

abstract: This paper focuses on the use of extremely large data sets in power system operation, control, and protection, which are difficult to process with traditional database tools and often termed big data. We will discuss three aspects of using such data sets: feature extraction, systematic integration for power system applications, and examples of typical applications in the utility industry. The following analytics tasks based on big data methodology are elaborated upon: corrective, predictive, distributed and adaptive. The paper also outlines several research topics related to asset management, operation planning, realtime monitoring and fault detection/protection that present new opportunities but require further investigation.


title: Relationship between the cayley-dickson fourier transform and the hartley transform of multidimensional real signals

abstract: The paper shows relations between two different frequency representations of n-dimensional signals (n = 1, 2, 3): the Cayley-Dickson Fourier transform and the Hartley transform. The formulas relating the n-D complex Fourier transform and the n-D Hartley transform are presented. New formulas relating the Quaternion and Octonion Fourier transforms and respectively the 2-D and 3-D Hartley transforms are developed. The paper is illustrated with examples of Hartley transforms of 1-D, 2-D and 3-D Gaussian signals.


Montmorillonite : An efficient , heterogeneous and green catalyst for organic synthesis


title: Learning Word Vectors for 157 Languages

abstract: Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredient to the successful application of these representations is to train them on very large corpora, and use these pre-trained models in downstream tasks. In this paper, we describe how we trained such high quality word representations for 157 languages. We used two sources of data to train these models: the free online encyclopedia Wikipedia and data from the common crawl project. We also introduce three new word analogy datasets to evaluate these word vectors, for French, Hindi and Polish. Finally, we evaluate our pre-trained word vectors on 10 languages for which evaluation datasets exists, showing very strong performance compared to previous models.


title: Identity, location, disease and more: inferring your secrets from android public resources

abstract: The design of Android is based on a set of unprotected shared resources, including those inherited from Linux (e.g., Linux public directories). However, the dramatic development in Android applications (app for short) makes available a large amount of public background information (e.g., social networks, public online services), which can potentially turn such originally harmless resource sharing into serious privacy breaches. In this paper, we report our work on this important yet understudied problem. We discovered three unexpected channels of information leaks on Android: per-app data-usage statistics, ARP information, and speaker status (on or off). By monitoring these channels, an app without any permission may acquire sensitive information such as smartphone user's identity, the disease condition she is interested in, her geo-locations and her driving route, from top-of-the-line Android apps. Furthermore, we show that using existing and new techniques, this zero-permission app can both determine when its target (a particular application) is running and send out collected data stealthily to a remote adversary. These findings call into question the soundness of the design assumptions on shared resources, and demand effective solutions. To this end, we present a mitigation mechanism for achieving a delicate balance between utility and privacy of such resources.


title: Condition-based maintenance policies for systems with multiple dependent components: A review

abstract: Condition-based maintenance (CBM) has received increasing attention in the literature over the past years. The application of CBM in practice, however, is lagging behind. This is, at least in part, explained by the complexity of real-life systems as opposed to the stylized ones studied most often. To overcome this issue, research is focusing more and more on complex systems, with multiple components subject to various dependencies. Existing classifications of these dependencies in the literature are no longer sufficient. Therefore, we provide an extended classification scheme. Besides the types of dependencies identified in the past (economic, structural, and stochastic), we add resource dependence, where multiple components are connected through, e.g., shared spares, tools, or maintenance workers. Furthermore, we extend the existing notion of structural dependence by distinguishing between structural dependence from a technical point of view and structural dependence from a performance point of view (e.g., through a series or parallel setting). We review the advances made with respect to CBM. Our main focus is on the implications of dependencies on the structure of the optimal CBM policy. We link our review to practice by providing real-life examples, thereby stressing current gaps in the literature.


title: Provable Tensor Factorization with Missing Data

abstract: We study the problem of low-rank tensor factorization in the presence of missing data. We ask the following question: how many sampled entries do we need, to efficiently and exactly reconstruct a tensor with a low-rank orthogonal decomposition? We propose a novel alternating minimization based method which iteratively refines estimates of the singular vectors. We show that under certain standard assumptions, our method can recover a three-mode $n\times n\times n$ dimensional rank-$r$ tensor exactly from $O(n^{3/2} r^5 \log^4 n)$ randomly sampled entries. In the process of proving this result, we solve two challenging sub-problems for tensors with missing data. First, in the process of analyzing the initialization step, we prove a generalization of a celebrated result by Szemer\'edie et al. on the spectrum of random graphs. Next, we prove global convergence of alternating minimization with a good initialization. Simulations suggest that the dependence of the sample size on dimensionality $n$ is indeed tight.


Significance of Reduced Features for Subcellular Bioimage Classification


title: A Fast and Parametric Torque Distribution Strategy for Four-Wheel-Drive Energy-Efficient Electric Vehicles

abstract: Electric vehicles (EVs) with four individually controlled drivetrains are over-actuated systems, and therefore, the total wheel torque and yaw moment demands can be realized through an infinite number of feasible wheel torque combinations. Hence, an energy-efficient torque distribution among the four drivetrains is crucial for reducing the drivetrain power losses and extending driving range. In this paper, the optimal torque distribution is formulated as the solution of a parametric optimization problem, depending on the vehicle speed. An analytical solution is provided for the case of equal drivetrains, under the experimentally confirmed hypothesis that the drivetrain power losses are strictly monotonically increasing with the torque demand. The easily implementable and computationally fast wheel torque distribution algorithm is validated by simulations and experiments on an EV demonstrator, along driving cycles and cornering maneuvers. The results show considerable energy savings compared to alternative torque distribution strategies.


title: AToM3: A Tool for Multi-formalism and Meta-modelling

abstract: This article introduces the combined use of multiformalism modelling and meta-modelling to facilitate computer assisted modelling of complex systems. The approach allows one to model different parts of a system using different formalisms. Models can be automatically converted between formalisms thanks to information found in a Formalism Transformation Graph (FTG), proposed by the authors. To aid in the automatic generation of multi-formalism modelling tools, formalisms are modelled in their own right (at a meta-level) within an appropriate formalism. This has been implemented in the interactive tool AToM3. This tool is used to describe formalisms commonly used in the simulation of dynamical systems, as well as to generate custom tools to process (create, edit, transform, simulate, optimise, ...) models expressed in the corresponding formalism. AToM3 relies on graph rewriting techniques and graph grammars to perform the transformations between formalisms as well as for other tasks, such as code generation and operational semantics specification.


title: Deep meta-modelling with METADEPTH

abstract: Meta-modelling is at the core of Model-Driven Engineering, where it is used for language engineering and domain modelling. The OMG's Meta-Object Facility is the standard framework for building and instantiating meta-models. However, in the last few years, several researchers have identified limitations and rigidities in such a scheme, most notably concerning the consideration of only two meta-modelling levels at the same time. ::: ::: In this paper we present METADEPTH, a novel framework that supports a dual linguistic/ontological instantiation and permits building systems with an arbitrary number of meta-levels through deep metamodelling. The framework implements advanced modelling concepts allowing the specification and evaluation of derived attributes and constraints across multiple meta-levels, linguistic extensions of ontological instance models, transactions, and hosting different constraint and action languages.


title: Inventory Pooling Under Heavy-Tailed Demand

abstract: Risk pooling has been studied extensively in the operations management literature as the basic driver behind strategies such as transshipment, manufacturing flexibility, component commonality, and drop shipping. This paper explores the benefit of risk pooling in the context of inventory management using the canonical model first studied in Eppen [Eppen GD (1979) Effects of centralization on expected costs in a multi-location newsboy problem. Management Sci. 25(5):498–501]. Specifically, we consider a single-period, multilocation newsvendor model, where n different locations face independent and identically distributed demands and linear holding and backorder costs. We show that Eppen’s celebrated result, i.e., that the expected cost savings from centralized inventory management scale with the square root of the number of locations, depends critically on the “light-tailed” nature of the demand uncertainty. In particular, we establish that the benefit from pooling relative to the decentralized case, in term...


title: ARMin II - 7 DoF rehabilitation robot: mechanics and kinematics

abstract: Task-oriented repetitive movements can improve motor recovery in patients with neurological or orthopaedic lesions. The application of robotics can serve to assist, enhance, evaluate, and document neurological and orthopaedic rehabilitation. ARMin II is the second prototype of a robot for arm therapy applicable to the training of activities of daily living. ARMin II has a semi-exoskeletal structure with seven active degrees of freedom (two of them coupled), five adjustable segments to fit in with different patient sizes, and is equipped with position and force sensors. The mechanical structure, the actuators and the sensors of the robot are optimized for patient-cooperative control strategies based on impedance and admittance architectures. This paper describes the mechanical structure and kinematics of ARMin II.


title: Incremental Unsupervised Name Disambiguation in Cleaned Digital Libraries

abstract: Name ambiguity in the context of bibliographic citations is one of the hardest problems currently faced by the Digital Library (DL) community. Here we deal with the problem of disambiguating new citations records inserted into a cleaned DL, without the need to process the whole collection, which is usually necessary for unsupervised methods. Although supervised solutions can deal with this situation, there is the costly burden of generating training data besides the fact that these methods cannot handle well the insertion of records of new authors not already existent in the repository. In this article, we propose a new unsupervised method that identifies the correct authors of the new citation records to be inserted in a DL. The method is based on heuristics that are also used to identify whether the new records belong to authors already in the digital library or not, correctly identifying new authors in most cases. Our experimental evaluation, using synthetic and real datasets, shows gains of up to 19\% when compared to a state-of-the-art method without the cost of having to disambiguate the whole DL at each new load (as done by unsupervised methods) or the need for any training (as done by supervised methods).


title: SOUL: An Edge-Cloud System for Mobile Applications in a Sensor-Rich World

abstract: With the Internet of Things, sensors are becoming ever more ubiquitous, but interacting with them continues to present numerous challenges, particularly for applications running on resource-constrained devices like smartphones. The SOUL abstractions in this paper address two issues faced by such applications: (1) access to sensors with the levels of convenience needed for their ubiquitous, dynamic use, and only by parties authorized to do so, and (2) scalability in sensor access, given today's multitude of sensors. Toward this end, SOUL, first, introduces a new abstraction for the applications to transparently and uniformly access both on-device and ambient sensors with associated actuators. Second, potentially expensive sensor-related processing needs not just occur on smartphones, but can also leverage edge-and remote-cloud resources. Finally, SOUL provides access control methods that permit users to easily define access permissions for sensors, which leverages users' social ties and captures the context in which access requests are made. SOUL demonstrates that the applications on Android platforms can scale the use of 100s of sensors with performance and energy efficiency.


title: Design and implementation of a low-cost dual-axes autonomous solar tracker

abstract: The use of fossil fuels and the greenhouse gases emission is a growing concern of the international community. For this reason, the research and development of alternative sources are forcing down the costs associated with renewable energy sources. Photovoltaic energy production is the best example of these exponential growing rates at the last years. However, the output power provided through the photovoltaic conversion process depends on solar irradiation, and the daily and seasonal movements directly affect the intensity of radiation received in the solar collectors. In order to optimize the energy production, this paper describes the development of a low-cost, dual axes solar tracker (DAST) with low power consumption. The work encompasses the design, construction, assembly of the entire mechanical structure, electrical systems and devices and the elaboration of the control logic responsible for all the movement of the module to search the position of maximum solar irradiation. The tracking is designed through the use of LDR sensors responsible for providing the input signal to a microcontroller. The PV panel rotates automatically based on the sun irradiance during the day while at night, the panel remains in a horizontal position to protect the mechanical structure against inclement weather. The proposed system also has a development of a 12V battery charging system used for module movement and as a power source for electronic devices. Results show that a system with the designed DAST can reduce in 8% the payback of a single PV installation when compared with a fixed structure, taking in account the solar irradiance available in the region of Serra Gaucha in South Brazil.


title: Warm pleasant feelings in the brain

abstract: Warm and cold stimuli have affective components such as feeling pleasant or unpleasant, and these components may have survival value, for approach to warmth and avoidance of cold may be reinforcers or goals for action built into us during evolution to direct our behaviour to stimuli that are appropriate for survival. Understanding the brain processing that underlies these prototypical reinforcers provides a direct approach to understanding the brain mechanisms of emotion. In an fMRI investigation in humans, we showed that the mid-orbitofrontal and pregenual cingulate cortex and the ventral striatum have activations that are correlated with the subjective pleasantness ratings made to warm (41 °C) and cold (12 °C) stimuli, and combinations of warm and cold stimuli, applied to the hand. Activations in the lateral and some more anterior parts of the orbitofrontal cortex were correlated with the unpleasantness of the stimuli. In contrast, activations in the somatosensory cortex and ventral posterior insula were correlated with the intensity but not the pleasantness of the thermal stimuli. A principle thus appears to be that processing related to the affective value and associated subjective emotional experience of thermal stimuli that are important for survival is performed in different brain areas to those where activations are related to sensory properties of the stimuli such as their intensity. This conclusion appears to be the case for processing in a number of sensory modalities, and the finding with such prototypical stimuli as warm and cold provides strong support for this principle.


Generating Informative Responses with Controlled Sentence Function


title: Logical Cryptanalysis as a SAT Problem ? Encoding and Analysis of the U . S . Data Encryption Standard

abstract: Cryptographic algorithms play a key role in computer security and the formal analysis of their robustness is of utmost importance. Yet, logic and automated reasoning tools are seldom used in the analysis of a cipher, and thus one cannot often get the desired formal assurance that the cipher is free from unwanted properties that may weaken its strength. ::: ::: In this paper, we claim that one can feasibly encode the low-level properties of state-of-the-art cryptographic algorithms as SAT problems and then use efficient automated theorem-proving systems and SAT-solvers for reasoning about them. We call this approach i>logical cryptanalysis. ::: ::: In this framework, for instance, finding a model for a formula encoding an algorithm is equivalent to finding a key with a cryptanalytic attack. Other important properties, such as cipher integrity or algebraic closure, can also be captured as SAT problems or as quantified boolean formulae. SAT benchmarks based on the encoding of cryptographic algorithms can be used to effectively combine features of “real-world” problems and randomly generated problems. ::: ::: Here we present a case study on the U.S. Data Encryption Standard (DES) and show how to obtain a manageable encoding of its properties. ::: ::: We have also tested three SAT provers, TABLEAU by Crawford and Auton, SATO by Zhang, and rel-SAT by Bayardo and Schrag, on the encoding of DES, and we discuss the reasons behind their different performance. ::: ::: A discussion of open problems and future research concludes the paper.


title: Information Fusion in Biometrics

abstract: User verification systems that use a single biometric indicator often have to contend with noisy sensor data, restricted degrees of freedom and unacceptable error rates. Attempting to improve the performance of individual matchers in such situations may not prove to be effective because of these inherent problems. Multimodal biometric systems seek to alleviate some of these drawbacks by providing multiple evidences of the same identity. These systems also help achieve an increase in performance that may not be possible by using a single biometric indicator. This paper addresses the problem of information fusion in verification systems. Experimental results on combining three biometric modalities (face, fingerprint and hand geometry) are also presented.


title: Shame, self-criticism, self-stigma, and compassion in Acceptance and Commitment Therapy

abstract: Within the past decade, empirical evidence has emerged supporting the use of Acceptance and Commitment Therapy (ACT) targeting shame and self-stigma. Little is known about the role of self-compassion in ACT, but evidence from other approaches indicates that self-compassion is a promising means of reducing shame and self-criticism. The ACT processes of defusion, acceptance, present moment, values, committed action, and self-as-context are to some degree inherently self-compassionate. However, it is not yet known whether the self-compassion inherent in the ACT approach explains ACT's effectiveness in reducing shame and stigma, and/or whether focused self-compassion work may improve ACT outcomes for highly self-critical, shame-prone people. We discuss how ACT for shame and stigma may be enhanced by existing approaches specifically targeting self-compassion.


title: Bandwidth Extension for Hierarchical Speech and Audio Coding in ITU-T Rec. G.729.1

abstract: Recommendation G.729.1 is a new ITU-T standard which was approved in May 2006. This recommendation describes a hierarchical speech and audio coding algorithm built on top of a narrowband core codec. One challenge in the codec design is the generation of a wideband signal with a very limited additional bit rate (less than 2 kb/s). In this paper, we describe the respective codec layer, which extends the transmitted acoustic bandwidth from the narrowband frequency range (50 Hz-4 kHz) to the wideband frequency range (50 Hz-7 kHz). The underlying algorithm uses a fairly coarse parametric description of the temporal and spectral energy envelopes of the high frequency band (4-7 kHz). This parameter set is quantized with a bit rate of 1.65 kb/s. At the decoder side, the high-frequency components are regenerated by appropriately shaping a synthetically generated ldquoexcitation signal.rdquo Apart from the algorithmic description and a discussion, we state a complexity evaluation as well as some listening test results.


title: Using statistics to learn words and grammatical categories:how high frequency words assist language acquisition

abstract: Recent studies suggest that high-frequency words may benefit speech segmentation (Bortfeld, Morgan, Golinkoff, & Rathbun, 2005) and grammatical categorisation (Monaghan, Christiansen, & Chater, 2007). To date, these tasks have been examined separately, but not together. We familiarised adults with continuous speech comprising repetitions of target words, and compared learning to a language in which targets appeared alongside high-frequency marker words. Marker words reliably preceded targets, and distinguished them into two otherwise unidentifiable categories. Participants completed a 2AFC segmentation test, and a similarity judgement categorisation test. We tested transfer to a wordpicture mapping task, where words from each category were used either consistently or inconsistently to label actions/objects. Participants segmented the speech successfully, but only demonstrated effective categorisation when speech contained high-frequency marker words. The advantage of marker words extended to the early stages of the transfer task. Findings indicate the same high-frequency words may assist speech segmentation and grammatical categorisation


title: Teleo-Reactive Programs for Agent Control

abstract: A formalism is presented for computing and organizing actions for autonomous agents in dynamic environments. We introduce the notion of teleo-reactive (T-R) programs whose execution entails the construction of circuitry for the continuous computation of the parameters and conditions on which agent action is based. In addition to continuous feedback, T-R programs support parameter binding and recursion. A primary difference between T-R programs and many other circuit-based systems is that the circuitry of T-R programs is more compact; it is constructed at run time and thus does not have to anticipate all the contingencies that might arise over all possible runs. In addition, T-R programs are intuitive and easy to write and are written in a form that is compatible with automatic planning and learning methods. We briefly describe some experimental applications of T-R programs in the control of simulated and actual mobile robots.


title: Applications of text mining within systematic reviews

abstract: Systematic reviews are a widely accepted research method. However, it is increasingly difficult to conduct them to fit with policy and practice timescales, particularly in areas which do not have well indexed, comprehensive bibliographic databases. Text mining technologies offer one possible way forward in reducing the amount of time systematic reviews take to conduct. They can facilitate the identification of relevant literature, its rapid description or categorisation, and its summarisation. In this paper, we describe the application of four text mining technologies, namely, automatic term recognition, document clustering, classification and summarisation, which support the identification of relevant studies in systematic reviews. The contributions of text mining technologies to improve reviewing efficiency are considered and their strengths and weaknesses explored.We conclude that these technologies do have the potential to assist at various stages of the review process. However, they are relatively unknown in the systematic reviewing community, and substantial evaluation and methods development are required before their possible impact can be fully assessed.


Calorimetry with Deep Learning : Particle Classification , Energy Regression , and Simulation for High-Energy Physics


title: Real-time 3D rendering with hatching

abstract: We present an approach for real-time pen-and-ink hatching renderings on large scenes. Starting with 3D models including photorealistic textures and materials, we aim to propose a solution that produces hatched renderings. As we consider scene objects described as polygonal meshes with their own textures, we produce once hatching patterns at different tones and resolutions considering the material of each object. To achieve that, we create a flow direction map per texture pixel, using contour characteristics extracted from the original texture and then interpolated. Stroke trajectories are thus generated depending on the flow direction and using B-splines, providing tones from light-to-dark. Tones are then stored in a mutli-resolution tonal art map. Moreover, we aim to overcome the limitations of existing hatching rendering methods by introducing an illumination model, fully implemented on GPU and able to manage three shading types: regular shadow, soft/cast shadow and self-shadowing. Tones and hatching resolutions are, therefore, assigned according to local/global illumination supporting multiple light sources. Our model, both dedicated for 3D static model renderings and 3D model animation, supports model deformations and is also spatially and temporally coherent since it gives continuous hatching strokes during object animations and/or light displacements.


title: Describing Videos by Exploiting Temporal Structure

abstract: Recent progress in using recurrent neural networks (RNNs) for image description has motivated the exploration of their application for video description. However, while images are static, working with videos requires modeling their dynamic temporal structure and then properly integrating that information into a natural language description model. In this context, we propose an approach that successfully takes into account both the local and global temporal structure of videos to produce descriptions. First, our approach incorporates a spatial temporal 3-D convolutional neural network (3-D CNN) representation of the short temporal dynamics. The 3-D CNN representation is trained on video action recognition tasks, so as to produce a representation that is tuned to human motion and behavior. Second we propose a temporal attention mechanism that allows to go beyond local temporal modeling and learns to automatically select the most relevant temporal segments given the text-generating RNN. Our approach exceeds the current state-of-art for both BLEU and METEOR metrics on the Youtube2Text dataset. We also present results on a new, larger and more challenging dataset of paired video and natural language descriptions.


title: Sketch-based modeling with few strokes

abstract: We present a novel sketch-based system for the interactive modeling of a variety of free-form 3D objects using just a few strokes. Our technique is inspired by the traditional illustration strategy for depicting 3D forms where the basic geometric forms of the subjects are identified, sketched and progressively refined using few key strokes. We introduce two parametric surfaces, rotational and cross sectional blending, that are inspired by this illustration technique. We also describe orthogonal deformation and cross sectional oversketching as editing tools to complement our modeling techniques. Examples with models ranging from cartoon style to botanical illustration demonstrate the capabilities of our system.


title: www.elsevier.com/locate/jss Lessons from applying the systematic literature review process within the software engineering domain

abstract: A consequence of the growing number of empirical studies in software engineering is the need to adopt systematic approaches to assessing and aggregating research outcomes in order to provide a balanced and objective summary of research evidence for a particular topic. The paper reports experiences with applying one such approach, the practice of systematic literature review, to the published studies relevant to topics within the software engineering domain. The systematic literature review process is summarised, a number of reviews being undertaken by the authors and others are described and some lessons about the applicability of this practice to software engineering are extracted.The basic systematic literature review process seems appropriate to software engineering and the preparation and validation of a review protocol in advance of a review activity is especially valuable. The paper highlights areas where some adaptation of the process to accommodate the domain-specific characteristics of software engineering is needed as well as areas where improvements to current software engineering infrastructure and practices would enhance its applicability. In particular, infrastructure support provided by software engineering indexing databases is inadequate. Also, the quality of abstracts is poor; it is usually not possible to judge the relevance of a study from a review of the abstract alone.


title: Collaborative Ensemble Learning: Combining Collaborative and Content-Based Information Filtering via Hierarchical Bayes

abstract: Collaborative filtering (CF) and content-based filtering (CBF) have widely been used information filtering applications, both approaches having their individual strengths and weaknesses. This paper proposes a novel probabilistic framework to unify CF and CBF, named collaborative ensemble learning. Based on content based probabilistic models for each user's preferences (the CBF idea), it combines a society of users' preferences to predict an active user's preferences (the CF idea). While retaining an intuitive explanation, the combination scheme can be interpreted as a hierarchical Bayesian approach in which a common prior distribution is learned from related experiments. It does not require a global training stage and thus can incrementally incorporate new data. We report results based on two data sets, the neuters-21578 text data set and a data base of user opionions on art images. For both data sets, collaborative ensemble achieved excellent performance in terms of recommendation accuracy. In addition to recommendation engines, collaborative ensemble learning is applicable to problems typically solved via classical hierarchical Bayes, like multisensor fusion and multitask learning.


title: Classification of three varieties of peach fruit using artificial neural network assisted with image processing techniques.

abstract: Peaches are rich in a variety of vitamins and minerals such as carbohydrates, organic acids, pigments, phenolics, vitamins, volatiles, antioxidants and little amounts of proteins and lipids. Iran was Seventh country of the peach producers in the world in 2010. Quality is one of the important factors in marketing of agricultural products. Grading machines have great importance in the quality inspection systems. Most of the current grading machines operate based on machine vision systems to detect blemishes and defects of products, where one image or more are taken for each individual object and the results of processing will decide the quality of the object. Grading and sorting of agricultural products using machine vision in conjunction with pattern recognition techniques, including neural networks, offers many advantages over the conventional optical or mechanical sorting devices. This paper aims to introduce a system that is using machine vision algorithms and Neural Network classifier to classify three varieties of peach fruit. Three cultivars, namely, Anjiri peach cultivar and Shalil Nectarine cultivar, varieties of Iran and Elberta peach cultivar variety of United states were randomly handpicked in two stage of growth, immature and mature on 30 July 2011 and 30 agues 2011 from an orchard located at the Miandoab, west Azerbaijan, Iran, and for each peach cultivar and stage of growth 45 fruits were randomly selected from picked peaches. Image processing technology in the agricultural research has made significant development. An image-capturing system was designed to provide an enclosed and uniform light illumination and to obtain standard images from the samples. The images were sent via a USB capture device to a computer provided with image acquisition and processing toolboxes of MATLAB software (Version R2011a, The Math Works Inc., MA, USA) to visualize, acquire and process the images directly from the computer. Some qualitative information is extracted from the objects to be analyzed in the images. This information was used as inputs to the algorithms for classifying the objects into different categories. In this study feature vector that consider as network input consist of 12 components of color spaces and three components of shape features. After network was trained, confusion matrices for mature and immature fruits were obtained. Total classification accuracy was 98.5% and 99.3% for mature and immature fruits respectively.


title: Three-dimensional flexible and conductive interconnected graphene networks grown by chemical vapour deposition

abstract: [Chen, Zongping; Ren, Wencai; Gao, Libo; Liu, Bilu; Pei, Songfeng; Cheng, Hui-Ming] Chinese Acad Sci, Shenyang Natl Lab Mat Sci, Inst Met Res, Shenyang 110016, Peoples R China.;Cheng, HM (reprint author), Chinese Acad Sci, Shenyang Natl Lab Mat Sci, Inst Met Res, Shenyang 110016, Peoples R China;cheng@imr.ac.cn


title: A 0.18μm CMOS fully integrated RFDAC and VGA for WCDMA transmitters

abstract: A digital IF to RF converter architecture with an RF VGA targeted for WCDMA transmitters is presented. The RFDAC consists of a 1.5-bits current steering DAC with an embedded semi-digital FIR filter and a mixer with an LC load. The VGA controls the output power in 10 dB steps. The prototype is fabricated in 0.18 mum CMOS technology and achieves 0 dBm output power at 2 GHz and 33 dB image rejection. The measured rms EVM and ACPR show the system fulfills the standard specifications for the WCDMA. The chip draws 123.9 mA from 1.8 V supply. The die area is 0.8 times 1.6 mm2.