title: An image encryption algorithm based on hyper-chaos and DNA sequence

abstract: A novel image encryption algorithm making using of hyper-chaos and DNA sequence is presented here. A four-dimensional hyper-chaos system is used to generate the pseudo-random sequence, which is transformed into a biologic DNA sequence to diffuse the image blocks. A circular permutation is performed on the plain-image when it is in DNA status. Together with classical structure of permutation plus diffusion, the simulation results show that the proposed image encryption algorithm has a satisfactory performance. Moreover, our method can resist the known-plaintext and chosen-plaintext attacks with four parameters r i (i?=?1,2,3,4) dependent on the plain-image. These parameters generate different key streams for different plain-image even if the initial conditions are the same.


title: New DC–DC Converter for Energy Storage System Interfacing in Fuel Cell Hybrid Electric Vehicles

abstract: The use of energy storage devices such as batteries or supercapacitors is almost mandatory in fuel cell hybrid electric vehicles, in order to guarantee load leveling, assuring braking energy recovery and good performances in transient operations. To this end, converters with bidirectional power flows are needed to connect the accumulators to the dc-link of the motor drive system. In this paper, a new conversion structure for the bidirectional interfacing of two dc voltage sources with a higher voltage dc-link is presented, where only three controllable power electronic switches are needed and where the only limit is that the sum of the two dc voltages must be lower or equal than the motor drive dc-link voltage. The analysis of the converter structure, the control and modulation systems that have been developed, and simulations and experimental results are reported


title: 2Vrms 16Ω switching headphone driver with 82% peak efficiency, 102 dB SNDR and 1.1mA/channel quiescent current

abstract: A new headphone driver IC based on a switching output stage in continuous conduction mode is described. The driver uses a sliding-mode peak-valley control scheme to regulate the inductor current. The output stage is switched between the battery voltage and the output of an integrated non-regulated inverting charge pump, and a second-order loop filter is used to regulate the output voltage. The system achieves a 2Vrms output audio signal to a 16Ω load while consuming a quiescent average current of 1.1mA/channel and 82% peak efficiency. Peak SNDR is about 102dB, A-weighted DR is 108dB, and the PSRR is more than 120dB.


title: Ethics and the Legalization of Physician-Assisted Suicide: An American College of Physicians Position Paper

abstract: Calls to legalize physician-assisted suicide have increased and public interest in the subject has grown in recent years despite ethical prohibitions. Many people have concerns about how they will die and the emphasis by medicine and society on intervention and cure has sometimes come at the expense of good end-of-life care. Some have advocated strongly, on the basis of autonomy, that physician-assisted suicide should be a legal option at the end of life. As a proponent of patient-centered care, the American College of Physicians (ACP) is attentive to all voices, including those who speak of the desire to control when and how life will end. However, the ACP believes that the ethical arguments against legalizing physician-assisted suicide remain the most compelling. On the basis of substantive ethics, clinical practice, policy, and other concerns articulated in this position paper, the ACP does not support legalization of physician-assisted suicide. It is problematic given the nature of the patient-physician relationship, affects trust in the relationship and in the profession, and fundamentally alters the medical profession's role in society. Furthermore, the principles at stake in this debate also underlie medicine's responsibilities regarding other issues and the physician's duties to provide care based on clinical judgment, evidence, and ethics. Society's focus at the end of life should be on efforts to address suffering and the needs of patients and families, including improving access to effective hospice and palliative care. The ACP remains committed to improving care for patients throughout and at the end of life.


title: Self-Assembly of Peptide Amphiphiles: From Molecules to Nanostructures to Biomaterials

abstract: Peptide amphiphiles are a class of molecules that combine the structural features of amphiphilic surfactants with the functions of bioactive peptides and are known to assemble into a variety of nanostructures. A specific type of peptide amphiphiles are known to self-assemble into one-dimensional (1D) nanostructures under physiological conditions, predominantly nanofibers with a cylindrical geometry. The resultant nanostructures could be highly bioactive and are of great interest in many biomedical applications, including tissue engineering, regenerative medicine and drug delivery. In this context, we highlight our strategies for using molecular self-assembly as a toolbox to produce peptide amphiphile nanostructures and materials and efforts to translate this technology into applications as therapeutics. We also review our recent progress in using these materials for treating spinal cord injury, inducing angiogenesis, and for hard tissue regeneration and replacement.


title: Multi-auction Approach for Solving Task Allocation Problem

abstract: Request for Proposal (RFP) problem is a type of task allocation problem where task managers need to recruit service provider agents to handle complex tasks composed of multiple sub-tasks, with the objective being to assign each sub-task to a capable agent while keeping the cost as low as possible. Most existing approaches either involve centralized algorithms or require each agent's cost for doing each sub-task to be known publicly beforehand, or attempt to force the agents to disclose such information by means of truth-telling mechanism, which is not practical in many problems where such information is sensitive and private. In this paper, we present an efficient multi-auction based mechanism that can produce near-optimal solutions without violating the privacy of the participating agents. By including an extra verification step after each bid, we can guarantee convergence to a solution while achieving optimal results in over 97% of the times in a series of experiment.


title: Cognitive load and autonomic response patterns under negative priming demand in depersonalization‐derealization disorder

abstract: Previous studies have yielded evidence for cognitive processing abnormalities and alterations of autonomic functioning in depersonalization-derealization disorder (DPRD). However, multimodal neuroimaging and psychophysiology studies have not yet been conducted to test for functional and effective connectivity under cognitive stress in patients with DPRD. DPRD and non-referred control subjects underwent a combined Stroop/negative priming task, and the neural correlates of Stroop interference effect, negative priming effect, error rates, cognitive load span and average amplitude of skin conductance responses were ascertained for both groups. Evoked haemodynamic responses for basic Stroop/negative priming activations were compared. For basic Stroop to neutral contrast, patients with DPRD differed in the location (inferior vs. superior lobule) of the parietal region involved, but showed similar activations in the left frontal region. In addition, patients with DPRD also co-activated the dorsomedial prefrontal cortex (BA9) and posterior cingulate cortex (BA31), which were also found to be the main between-group difference regions. These regions furthermore showed connectivity with frequency of depersonalization states. Evoked haemodynamic responses drawn from regions of interest indicated significant between-group differences in 30-40% of time points. Brain-behaviour correlations differed mainly in laterality, yet only slightly in regions. A reversal of autonomic patterning became evident in patients with DPRD for cognitive load spans, indicating less effective arousal suppression under cognitive stress - patients with DPRD showed positive associations of cognitive load with autonomic responses, whereas controls exhibit respective inverse association. Overall, the results of the present study show only minor executive cognitive peculiarities, but further support the notion of abnormalities in autonomic functioning in patients with DPRD.


title: Anesthetic efficacy of metomidate and comparison of plasma cortisol responses to tricaine methanesulfonate, quinaldine and clove oil anesthetized channel catfish Ictalurus punctatus

abstract: The present experiments were designed to determine the efficacy of metomidate hydrochloride as an alternative anesthetic with potential cortisol blocking properties for channel catfish Ictalurus punctatus. Channel catfish (75 g) were exposed to concentrations of metomidate ranging from 0.5 to 16 ppm for a period of 60 min. At 16-ppm metomidate, mortality occurred in 65% of the catfish. No mortalities were observed at concentrations of 8 ppm or less. The minimum concentration of metomidate producing desirable anesthetic properties was 6 ppm. At this concentration, acceptable induction and recovery times were observed in catfish ranging from 3 to 810 g average body weight. Plasma cortisol levels during metomidate anesthesia (6 ppm) were compared to fish anesthetized with tricaine methanesulfonate (100 ppm), quinaldine (30 ppm) and clove oil (100 ppm). Cortisol levels of catfish treated with metomidate and clove oil remained at baseline levels during 30 min of anesthesia (P>0.05). Plasma cortisol levels of tricaine methanesulfonate and quinaldine anesthetized catfish peaked approximately eight- and fourfold higher (P<0.05), respectively, than fish treated with metomidate. These results suggest that the physiological disturbance of channel catfish during routine-handling procedures and stress-related research could be reduced through the use of metomidate as an anesthetic.


title: Statistical relational learning for link prediction

abstract: Link prediction is a complex, inherently relational, task. Be it in the domain of scientific citations, social networks or hypertext links, the underlying data are extremely noisy and the characteristics useful for prediction are not readily available in a “flat” file format, but rather involve complex relationships among objects. In this paper, we propose the application of our methodology for Statistical Relational Learning to building link prediction models. We propose an integrated approach to building regression models from data stored in relational databases in which potential predictors are generated by structured search of the space of queries to the database, and then tested for inclusion in a logistic regression. We present experimental results for the task of predicting citations made in scientific literature using relational data taken from CiteSeer. This data includes the citation graph, authorship and publication venues of papers, as well as their word content.


title: Aligning IT, strategic orientation and organizational structure

abstract: Purpose – The purpose of the paper is to examine and analyze the alignment between (information technology) IT, strategic orientation (SO) and organizational structure (OS) and their impact on firm performance (FP).Design/methodology/approach – A theoretical framework is proposed regarding the constructs of IT, SO and OS. A model incorporating these three constructs is examined and their impact on FP is assessed using structural equation modeling (SEM). The sample data from 295 firms were obtained through structured questionnaires.Findings – The results of the SEM support the hypothesis that the alignment between IT, SO and OS significantly affects FP.Research limitations/implications – Non‐financial and intangible performance measurements are not included and the sample is not homogeneous.Practical implications – This study suggests that managers should choose the appropriate level and type of IT, depending on a firm's structure and SO, in order to benefit from the advantages of IT usage and achieve high...


title: Combining Bagging and Boosting

abstract: Bagging and boosting are among the most popular re- sampling ensemble methods that generate and combine a diversity of classifiers using the same learning algorithm for the base-classifiers. Boosting algorithms are considered stronger than bagging on noise- free data. However, there are strong empirical indications that bagging is much more robust than boosting in noisy settings. For this reason, in this work we built an ensemble using a voting methodology of bagging and boosting ensembles with 10 sub- classifiers in each one. We performed a comparison with simple bagging and boosting ensembles with 25 sub-classifiers, as well as other well known combining methods, on standard benchmark datasets and the proposed technique was the most accurate.


title: Breast cancer in China

abstract: Summary The health burden of cancer is increasing in China, with more than 1·6 million people being diagnosed and 1·2 million people dying of the disease each year. As in most other countries, breast cancer is now the most common cancer in Chinese women; cases in China account for 12·2% of all newly diagnosed breast cancers and 9·6% of all deaths from breast cancer worldwide. China's proportional contribution to global rates is increasing rapidly because of the population's rising socioeconomic status and unique reproductive patterns. In this Review we present an overview of present control measures for breast cancer across China, and emphasise epidemiological and socioeconomic diversities and disparities in access to care for various subpopulations. We describe demographic differences between China and high-income countries, and also within geographical and socioeconomic regions of China. These disparities between China and high-income countries include younger age at onset of breast cancer; the unique one-child policy; lower rates of provision and uptake for screening for breast cancer; delays in diagnosis that result in more advanced stage of disease at presentation; inadequate resources; and a lack of awareness about breast cancer in the Chinese population. Finally, we recommend key measures that could contribute to improved health outcomes for patients with breast cancer in China.


title: Runtime Verification of k-Safety Hyperproperties in HyperLTL

abstract: This paper introduces a novel runtime verification technique for a rich sub-class of Clarkson and Schneider's hyperproperties. The primary application of such properties is in expressing security policies (e.g., information flow) that cannot be expressed in trace-based specification languages (e.g., LTL). First, to incorporate syntactic means, we draw connections between safety and co-safety hyperproperties and the temporal logic HYPERLTL, which allows explicit quantification over multiple executions. We also define the notion of monitorability in HYPERLTL and identify classes of monitorable HYPERLTL formulas. Then, we introduce an algorithm for monitoring k-safety and co-k-safety hyperproperties expressed in HYPERLTL. Our technique is based on runtime formula progression as well as on-the-fly monitor synthesis across multiple executions. We analyze different performance aspects of our technique by conducting thorough experiments on monitoring security policies for information flow and observational determinism on a real-world location-based service dataset as well as synthetic trace sets.


title: Echo-state conditional variational autoencoder for anomaly detection

abstract: Anomaly detection involves identifying the events which do not conform to an expected pattern in data. A common approach to anomaly detection is to identify outliers in a latent space learned from data. For instance, PCA has been successfully used for anomaly detection. Variational autoencoder (VAE) is a recently-developed deep generative model which has established itself as a powerful method for learning representation from data in a nonlinear way. However, the VAE does not take the temporal dependence in data into account, so it limits its applicability to time series. In this paper we combine the echo-state network, which is a simple training method for recurrent networks, with the VAE, in order to learn representation from multivariate time series data. We present an echo-state conditional variational autoencoder (ES-CVAE) and demonstrate its useful behavior in the task of anomaly detection in multivariate time series data.


title: Toward QoI and Energy-Efficiency in Internet-of-Things Sensory Environments

abstract: Considering physical sensors with certain sensing capabilities in an Internet-of-Things (IoTs) sensory environment, in this paper, we propose an efficient energy management framework to control the duty cycles of these sensors under quality-of-information (QoI) expectations in a multitask-oriented environment. Contrary to past research efforts, our proposal is transparent and compatible both with the underlying low-layer protocols and diverse applications, and preserving energy-efficiency in the long run without sacrificing the QoI levels attained. In particular, we first introduce the novel concept of QoI-aware sensor-to-task relevancy to explicitly consider the sensing capabilities offered by a sensor to the IoT sensory environments, and QoI requirements required by a task. Second, we propose a novel concept of the critical covering set of any given task in selecting the sensors to service a task over time. Third, energy management decision is made dynamically at runtime, to reach the optimum for long-term application arrivals and departures under the constraint of their service delay. We show a case study to utilize sensors to perform environmental monitoring with a complete set of performance analysis. We further consider the signal propagation and processing latency into the proposal, and provide a thorough analysis on its impact on average measured delay probability.


title: Synthesis of azabicyclo[4.2.0]octa-1,3,5-trien-8-one analogues of 1H-imidazo[4,5-c]quinoline and evaluation of their antimicrobial and anticancer activities

abstract: In search of new and efficient antimicrobial and anticancer agents based on the imidazoquinoline structural framework, a series of novel 7-(1-isobutyl-1H-imidazo[4,5-c]quinolin-4-yl)-7-azabicyclo[4.2.0]octa-1,3,5-trien-8-ones (8a–f) were synthesized from the corresponding 2,4-dihydroxoquinoline derivative through multistep reactions. The structures of these compounds were established by IR, 1H NMR, 13C NMR and mass spectral studies. The 7-(1-isobutyl-1H-imidazo[4,5-c]quinolin-4-yl)-7-azabicyclo[4.2.0]octa-1,3,5-trien-8-one (8a–f) analogues were evaluated for their in vitro antimicrobial activity by serial dilution method minimum inhibitory concentration (MIC). The derivatives 8c, 8e and 8f exhibited excellent antibacterial activity comparable to the parent drug ampicillin with MIC value. Compounds 7a–f and 8a–f were also assessed for their cytotoxic activity (IC50) against HeLa cells using the Trypan blue exclusion assay method. The compounds 7c and 8b displayed potential anticancer activity. In a molecular docking study, these compounds showed minimum binding energy and good affinity towards the active pocket. They are believed to be good inhibitors of β-tubulin. The results of these studies provided evidence that 7-(1-isobutyl-1H-imidazo[4,5-c]quinolin-4-yl)-7-azabicyclo[4.2.0]octa-1,3,5-trien-8-one (8a–f) derivatives are a promising class of antibacterial and anticancer agents.


title: Recognition memory for emotionally negative and neutral words: an ERP study

abstract: Abstract Scalp recorded event-related potentials were used to investigate the neural activity elicited by emotionally negative and emotionally neutral words during the performance of a recognition memory task. Behaviourally, the principal difference between the two word classes was that the false alarm rate for negative items was approximately double that for the neutral words. Correct recognition of neutral words was associated with three topographically distinct ERP memory ‘old/new’ effects: an early, bilateral, frontal effect which is hypothesised to reflect familiarity-driven recognition memory; a subsequent left parietally distributed effect thought to reflect recollection of the prior study episode; and a late onsetting, right-frontally distributed effect held to be a reflection of post-retrieval monitoring. The old/new effects elicited by negative words were qualitatively indistinguishable from those elicited by neutral items and, in the case of the early frontal effect, of equivalent magnitude also. However, the left parietal effect for negative words was smaller in magnitude and shorter in duration than that elicited by neutral words, whereas the right frontal effect was not evident in the ERPs to negative items. These differences between neutral and negative words in the magnitude of the left parietal and right frontal effects were largely attributable to the increased positivity of the ERPs elicited by new negative items relative to the new neutral items. Together, the behavioural and ERP findings add weight to the view that emotionally valenced words influence recognition memory primarily by virtue of their high levels of ‘semantic cohesion’, which leads to a tendency for ‘false recollection’ of unstudied items.


title: Cooperating with Machines

abstract: Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human–machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human–machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.Artificial intelligence is now superior to humans in many fully competitive games, such as Chess, Go, and Poker. Here the authors develop a machine-learning algorithm that can cooperate effectively with humans when cooperation is beneficial but nontrivial, something humans are remarkably good at.


title: Bugram: bug detection with n-gram language models

abstract: To improve software reliability, many rule-based techniques have been proposed to infer programming rules and detect violations of these rules as bugs. These rule-based approaches often rely on the highly frequent appearances of certain patterns in a project to infer rules. It is known that if a pattern does not appear frequently enough, rules are not learned, thus missing many bugs. In this paper, we propose a new approach—Bugram—that leverages n-gram language models instead of rules to detect bugs. Bugram models program tokens sequentially, using the n-gram language model. Token sequences from the program are then assessed according to their probability in the learned model, and low probability sequences are marked as potential bugs. The assumption is that low probability token sequences in a program are unusual, which may indicate bugs, bad practices, or unusual/special uses of code of which developers may want to be aware. We evaluate Bugram in two ways. First, we apply Bugram on the latest versions of 16 open source Java projects. Results show that Bugram detects 59 bugs, 42 of which are manually verified as correct, 25 of which are true bugs and 17 are code snippets that should be refactored. Among the 25 true bugs, 23 cannot be detected by PR-Miner. We have reported these bugs to developers, 7 of which have already been confirmed by developers (4 of them have already been fixed), while the rest await confirmation. Second, we further compare Bugram with three additional graph- and rule-based bug detection tools, i.e., JADET, Tikanga, and GrouMiner. We apply Bugram on 14 Java projects evaluated in these three studies. Bugram detects 21 true bugs, at least 10 of which cannot be detected by these three tools. Our results suggest that Bugram is complementary to existing rule-based bug detection approaches.


title: Multi-objective optimization and comparative evaluation of Si soft-switched and SiC hard-switched automotive DC-DC converters

abstract: The application of soft-switching concepts or Silicon Carbide (SiC) devices are two enabling technologies to further push the efficiency, power density or specific weight of power electronics converters. For an automotive application, such as a dc-dc converter that interconnects the high voltage battery or ultra-capacitor in a hybrid electrical vehicle (HEV) or a fuel cell vehicle (FCV) to the dc-link, costs and failure rate are likewise of importance. Due to increasing requirements on multiple of these converter characteristics the comprehensive multi-objective optimization of the entire converter system gains importance. Thereto, this paper proposes an optimization method to explore the limits of power density as a function of the switching frequency and the number of phases of non-isolated bi-directional multi-phase dc-dc converters operated with soft-switching and Silicon devices and hard-switched converters that take advantage of SiC devices. In addition, the optimization includes an algorithm to determine the chip size required for the semiconductor devices under consideration of the thermal characteristics and efficiency requirements. Based on detailed analytical volume, loss and cost models of the converter components as well as on measurements demonstrative results on the optimum converter designs are provided and evaluated comparatively for the different converter concepts.


title: Do Normalization Layers in a Deep ConvNet Really Need to Be Distinct?

abstract: Yes, they do. This work investigates a perspective for deep learning: whether different normalization layers in a ConvNet require different normalizers. This is the first step towards understanding this phenomenon. We allow each convolutional layer to be stacked before a switchable normalization (SN) that learns to choose a normalizer from a pool of normalization methods. Through systematic experiments in ImageNet, COCO, Cityscapes, and ADE20K, we answer three questions: (a) Is it useful to allow each normalization layer to select its own normalizer? (b) What impacts the choices of normalizers? (c) Do different tasks and datasets prefer different normalizers? Our results suggest that (1) using distinct normalizers improves both learning and generalization of a ConvNet; (2) the choices of normalizers are more related to depth and batch size, but less relevant to parameter initialization, learning rate decay, and solver; (3) different tasks and datasets have different behaviors when learning to select normalizers.


title: Jointly Deep Multi-View Learning for Clustering Analysis

abstract: In this paper, we propose a novel Joint framework for Deep Multi-view Clustering (DMJC), where multiple deep embedded features, multi-view fusion mechanism and clustering assignments can be learned simultaneously. Our key idea is that the joint learning strategy can sufficiently exploit clustering-friendly multi-view features and useful multi-view complementary information to improve the clustering performance. How to realize the multi-view fusion in such a joint framework is the primary challenge. To do so, we design two ingenious variants of deep multi-view joint clustering models under the proposed framework, where multi-view fusion is implemented by two different schemes. The first model, called DMJC-S, performs multi-view fusion in an implicit way via a novel multi-view soft assignment distribution. The second model, termed DMJC-T, defines a novel multi-view auxiliary target distribution to conduct the multi-view fusion explicitly. Both DMJC-S and DMJC-T are optimized under a KL divergence like clustering objective. Experiments on six challenging image datasets demonstrate the superiority of both DMJC-S and DMJC-T over single/multi-view baselines and the state-of-the-art multiview clustering methods, which proves the effectiveness of the proposed DMJC framework. To our best knowledge, this is the first work to model the multi-view clustering in a deep joint framework, which will provide a meaningful thinking in unsupervised multi-view learning.


Spontaneous Affect Recognition from Audio-visual Cues using Multi-resolution Analysis


title: Small Magnetic Sensors for Space Applications

abstract: Small magnetic sensors are widely used integrated in vehicles, mobile phones, medical devices, etc for navigation, speed, position and angular sensing. These magnetic sensors are potential candidates for space sector applications in which mass, volume and power savings are important issues. This work covers the magnetic technologies available in the marketplace and the steps towards their implementation in space applications, the actual trend of miniaturization the front-end technologies, and the convergence of the mature and miniaturized magnetic sensor to the space sector through the small satellite concept.


title: A New Collision Resolution Mechanism to Enhance the Performance of IEEE 802.11 DCF

abstract: The medium-access control (MAC) protocol is one of the key components in wireless local area networks (WLANs). The main features of a MAC protocol are high throughput, good fairness, energy efficiency, and support priority guarantees, especially under distributed contention-based environment. Based on the current standardized IEEE 802.11 distributed coordination function (DCF) protocol, this paper proposes a new efficient collision resolution mechanism, called GDCF. Our main motivation is based on the observation that 802.11 DCF decreases the contention window to the initial value after each success transmission, which essentially assumes that each successful transmission is an indication that the system is under low traffic loading. GDCF takes a more conservative measure by halving the contention window size after c consecutive successful transmissions. This "gentle" decrease can reduce the collision probability, especially when the number of competing nodes is large. We compute the optimal value for c and the numerical results from both analysis and simulation demonstrate that GDCF significantly improve the performance of 802.11 DCF, including throughput, fairness, and energy efficiency. In addition, GDCF is flexible for supporting priority access by selecting different values of c for different traffic types and is very easy to implement it, as it does not requires any changes in control message structure and access procedures in DCF.


title: Genetic insights into the neurodevelopmental origins of schizophrenia

abstract: A causal explanation for the pathogenesis of schizophrenia is lacking. In this Review, Birnbaum and Weinberger explore recent genetic studies of schizophrenia and discuss how genetic and epigenetic risk of the disorder may converge to perturb key neurodevelopmental trajectories.


title: A fast nearest neighbor search algorithm by nonlinear embedding

abstract: We propose an efficient algorithm to find the exact nearest neighbor based on the Euclidean distance for large-scale computer vision problems. We embed data points nonlinearly onto a low-dimensional space by simple computations and prove that the distance between two points in the embedded space is bounded by the distance in the original space. Instead of computing the distances in the high-dimensional original space to find the nearest neighbor, a lot of candidates are to be rejected based on the distances in the low-dimensional embedded space; due to this property, our algorithm is well-suited for high-dimensional and large-scale problems. We also show that our algorithm is improved further by partitioning input vectors recursively. Contrary to most of existing fast nearest neighbor search algorithms, our technique reports the exact nearest neighbor — not an approximate one — and requires a very simple preprocessing with no sophisticated data structures. We provide the theoretical analysis of our algorithm and evaluate its performance in synthetic and real data.


title: A 2×20W 0.0013% THD+N Class-D audio amplifier with consistent performance up to maximum power level

abstract: Conventional Class-D amplifiers, although more power efficient than Class-AB amplifiers, typically do not deliver the same audio quality. The non-ideal switching behavior of the output power stages can degrade the linearity, noise and power-supply-rejection-ratio (PSRR) performance of Class-D amplifiers if employed in open-loop configurations [1]. Closed-loop Class-D amplifiers shape the non-idealities of the power amplifiers (PAs) and provide improved performance [2]. Conventional analog feedback amplifiers (AFAs) sense the PA output and feed it back to compare with the audio input signal in the analog domain. Compared with AFAs, digital feedback amplifiers (DFAs) have emerged with benefits of improved control of the loop filter and pulse-width modulation (PWM) in the digital domain. However, the DFA architecture usually demands a high-performance analog-to-digital converter (ADC) to digitize the PA output in the feedback path; This ADC's non-idealities may become the bottleneck of the system [3]. In this paper, a 2-channel Class-D digital error-feedback amplifier (DEFA) with a peak THD+N of 0.0013% is presented. A series of proposed techniques enable the DEFA to maintain its performance up to the maximum power level available.


title: LoOP: local outlier probabilities

abstract: Many outlier detection methods do not merely provide the decision for a single data object being or not being an outlier but give also an outlier score or "outlier factor" signaling "how much" the respective data object is an outlier. A major problem for any user not very acquainted with the outlier detection method in question is how to interpret this "factor" in order to decide for the numeric score again whether or not the data object indeed is an outlier. Here, we formulate a local density based outlier detection method providing an outlier "score" in the range of [0, 1] that is directly interpretable as a probability of a data object for being an outlier.


title: LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition

abstract: Facial micro-expression recognition is an upcoming area in computer vision research. Up until the recent emergence of the extensive CASMEII spontaneous micro-expression database, there were numerous obstacles faced in the elicitation and labeling of data involving facial micro-expressions. In this paper, we propose the Local Binary Patterns with Six Intersection Points (LBP-SIP) volumetric descriptor based on the three intersecting lines crossing over the center point. The proposed LBP-SIP reduces the redundancy in LBP-TOP patterns, providing a more compact and lightweight representation; leading to more efficient computational complexity. Furthermore, we also incorporated a Gaussian multi-resolution pyramid to our proposed approach by concatenating the patterns across all pyramid levels. Using an SVM classifier with leave-one-sample-out cross validation, we achieve the best recognition accuracy of 67.21 %, surpassing the baseline performance with further computational efficiency.


title: Towards Time-Dependant Recommendation based on Implicit Feedback

abstract: Context-aware recommender systems (CARS) aim at improving users’ satisfaction by tailoring recommendations to each particular context. In this work we propose a contextual pre-ltering technique based on implicit user feedback. We introduce a new context-aware recommendation approach called user micro-proling . We split each single user prole into several possibly overlapping sub-proles, each representing users in particular contexts. The predictions are done using these micro-proles instead of a single user model. The users’ taste can depend on the exact partition of the contextual variable. The identication of a meaningful partition of the users’ prole and its evaluation is a non-trivial task, especially when using implicit feedback and a continuous contextual domain. We propose an o-line evaluation procedure for CARS in these conditions and evaluate our approach on a time-aware music recommendation sytem.


title: A cost driven 24GHz Doppler radar sensor development for automotive applications

abstract: This paper deals with a low-cost 24 GHz Doppler radar sensor for traffic surveillance. The basic building blocks of the transmit/receive chain, namely the antennas, the balanced power amplifier (PA), the dielectric resonator oscillator (DRO), the low noise amplifier (LNA) and the down conversion diode mixer will be presented underlining the key technologies and manufacturing approaches by means the required performances can be attained while keeping industrial costs extremely low


title: Application of Metamorphic Testing to Supervised Classifiers

abstract: Many applications in the field of scientific computing - such as computational biology, computational linguistics, and others - depend on Machine Learning algorithms to provide important core functionality to support solutions in the particular problem domains. However, it is difficult to test such applications because often there is no "test oracle" to indicate what the correct output should be for arbitrary input. To help address the quality of such software, in this paper we present a technique for testing the implementations of supervised machine learning classification algorithms on which such scientific computing software depends. Our technique is based on an approach called "metamorphic testing", which has been shown to be effective in such cases. More importantly, we demonstrate that our technique not only serves the purpose of verification, but also can be applied in validation. In addition to presenting our technique, we describe a case study we performed on a real-world machine learning application framework, and discuss how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also discuss how our findings can be of use to other areas outside scientific computing, as well.


title: Detection, Recognition and Tracking of Moving Objects from Real-time Video via SP Theory of Intelligence and Species Inspired PSO

abstract: In this paper, we address the basic problem of recognizing moving objects in video images using SP Theory of Intelligence. The concept of SP Theory of Intelligence which is a framework of artificial intelligence, was first introduced by Gerard J Wolff, where S stands for Simplicity and P stands for Power. Using the concept of multiple alignment, we detect and recognize object of our interest in video frames with multilevel hierarchical parts and subparts, based on polythetic categories. We track the recognized objects using the species based Particle Swarm Optimization (PSO). First, we extract the multiple alignment of our object of interest from training images. In order to recognize accurately and handle occlusion, we use the polythetic concepts on raw data line to omit the redundant noise via searching for best alignment representing the features from the extracted alignments. We recognize the domain of interest from the video scenes in form of wide variety of multiple alignments to handle scene variability. Unsupervised learning is done in the SP model following the DONSVIC principle and natural structures are discovered via information compression and pattern analysis. After successful recognition of objects, we use species based PSO algorithm as the alignments of our object of interest is analogues to observation likelihood and fitness ability of species. Subsequently, we analyze the competition and repulsion among species with annealed Gaussian based PSO. We have tested our algorithms on David, Walking2, FaceOcc1, Jogging and Dudek, obtaining very satisfactory and competitive results.


Anxiety Induction in Virtual Environments: An Experimental Comparison of Three General Techniques


title: PHAT: a transmembrane-specific substitution matrix. Predicted hydrophobic and transmembrane.

abstract: MOTIVATION ::: Database searching algorithms for proteins use scoring matrices based on average protein properties, and thus are dominated by globular proteins. However, since transmembrane regions of a protein are in a distinctly different environment than globular proteins, one would expect generalized substitution matrices to be inappropriate for transmembrane regions. ::: ::: ::: RESULTS ::: We present the PHAT (predicted hydrophobic and transmembrane) matrix, which significantly outperforms generalized matrices and a previously published transmembrane matrix in searches with transmembrane queries. We conclude that a better matrix can be constructed by using background frequencies characteristic of the twilight zone, where low-scoring true positives have scores indistinguishable from high-scoring false positives, rather than the amino acid frequencies of the database. The PHAT matrix may help improve the accuracy of sequence alignments and evolutionary trees of membrane proteins.


title: A MXene based all-solid-state microsupercapacitor with 3D interdigital electrode

abstract: Here we report a high capacitance all-solid-state micro supercapacitor (MSC) based on thick interdigital electrodes of 2D Transition Metal Carbides (MXene). Ti 3 C 2 T x , one type of MXene, features high pseudocapacity, great conductivity and cyclability. By MEMS technologies, the 300 μm thick interdigital fingers composed of Ti 3 C 2 T x and polymer electrolyte (PE) acts as the electrodes of a MSC, which can provide a large mass loading. The high performance electrode material and thick electrode structure contribute to the MSC with the high specific capacitance of 276 mF/cm2 and 95% capacitance retention after 1000 cycles. Moreover, the fabrication is compatible with microfabrication technologies and the devices have the potential for the integration with other electronic devices on a chip.


title: Unsupervised learning with contrastive latent variable models

abstract: In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to frame the problem as looking for patterns that are enriched in one dataset relative to another. These pairs of datasets occur commonly, for instance a population of interest vs. control or signal vs. signal free recordings. However, there are few methods that work on sets of data as opposed to data points or sequences. Here, we present a probabilistic model for dimensionality reduction to discover signal that is enriched in the target dataset relative to the background dataset. The data in these sets do not need to be paired or grouped beyond set membership. By using a probabilistic model where some structure is shared amongst the two datasets and some is unique to the target dataset, we are able to recover interesting structure in the latent space of the target dataset. The method also has the advantages of a probabilistic model, namely that it allows for the incorporation of prior information, handles missing data, and can be generalized to different distributional assumptions. We describe several possible variations of the model and demonstrate the application of the technique to de-noising, feature selection, and subgroup discovery settings.


title: Automatic Categorization of Privacy Policies: A Pilot Study

abstract: Privacy policies are a nearly ubiquitous feature of websites and online services, and the contents of such policies are legally binding for users. However, the obtuse language and sheer length of most privacy policies tend to discourage users from reading them. We describe a pilot experiment to use automatic text categorization to answer simple categorical questions about privacy policies, as a first step toward developing automated or semi-automated methods to retrieve salient features from these policies. Our results tentatively demonstrate the feasibility of this approach for answering selected questions about privacy policies, suggesting that further work toward user-oriented analysis of these policies could be fruitful.


title: BLCUNLP: Corpus Pattern Analysis for Verbs Based on Dependency Chain

abstract: We implemented a syntactic and semantic tagging system for SemEval 2015 Task 15: Corpus Pattern Analysis. For syntactic tagging, we present a Dependency Chain Search Algorithm that is found to be effective at identifying structurally distant subjects and objects. Other syntactic labels are identified using rules defined over dependency parse structures and the output of a verb classification module. Semantic tagging is performed using a simple lexical mapping table combined with postprocessing rules written over phrase structure constituent types and named entity information. The final score of our system is 0.530 F1, ranking second in this task.


title: Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation

abstract: In this paper we propose an approach to holistic scene understanding that reasons jointly about regions, location, class and spatial extent of objects, presence of a class in the image, as well as the scene type. Learning and inference in our model are efficient as we reason at the segment level, and introduce auxiliary variables that allow us to decompose the inherent high-order potentials into pairwise potentials between a few variables with small number of states (at most the number of classes). Inference is done via a convergent message-passing algorithm, which, unlike graph-cuts inference, has no submodularity restrictions and does not require potential specific moves. We believe this is very important, as it allows us to encode our ideas and prior knowledge about the problem without the need to change the inference engine every time we introduce a new potential. Our approach outperforms the state-of-the-art on the MSRC-21 benchmark, while being much faster. Importantly, our holistic model is able to improve performance in all tasks.


title: Framework for the statistical shape analysis of brain structures using spharm-pdm

abstract: Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology. ::: ::: The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T2 two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps. Additional visualization of the group tests are provided via mean difference magnitude and vector maps, as well as maps of the group covariance information. ::: ::: The correction for multiple comparisons is performed via two separate methods that each have a distinct view of the problem. The first one aims to control the family-wise error rate (FWER) or false-positives via the extrema histogram of non-parametric permutations. The second method controls the false discovery rate and results in a less conservative estimate of the false-negatives.


title: Third-person perceptions and online games: A comparison of perceived antisocial and prosocial game effects

abstract: The popularity of online games has inspired controversial discussion on the effects of game-play in both mass media and academia. However, we know little about ordinary people's opinions about the effects of game-play. The current study applies the theory of the third-person effect (TPE) to examine people's perceptions of game effects on self and on others, and detects significant third-person perceptual discrepancies. The results show that the magnitude of third-person perceptions is influenced by the social desirability of the message, the social distance between self and others, and by differential exposure to online games. The data supports the hypotheses that Internet efficacy and interdependent self-construal are significant antecedents of third-person perceptions, and partially supports the interaction effect of self-construal with Internet-efficacy and the interaction effect of self-construal with media social desirability.


title: Use of a Novel Robotic Interface to Study Finger Motor Control

abstract: Stroke is the leading cause of permanent adult disability in the U.S., frequently resulting in chronic motor impairments. Rehabilitation of the upper limb, particularly the hand, is especially important as arm and hand deficits post-stroke limit the performance of activities of daily living and, subsequently, functional independence. Hand rehabilitation is challenging due to the complexity of motor control of the hand. New instrumentation is needed to facilitate examination of the hand. Thus, a novel actuated exoskeleton for the index finger, the FingerBot, was developed to permit the study of finger kinetics and kinematics under a variety of conditions. Two such novel environments, one applying a spring-like extension torque proportional to angular displacement at each finger joint and another applying a constant extension torque at each joint, were compared in 10 stroke survivors with the FingerBot. Subjects attempted to reach targets located throughout the finger workspace. The constant extension torque assistance resulted in a greater workspace area (p < 0.02) and a larger active range of motion for the metacarpophalangeal joint (p < 0.01) than the spring-like assistance. Additionally, accuracy in terms of reaching the target was greater with the constant extension assistance as compared to no assistance. The FingerBot can be a valuable tool in assessing various hand rehabilitation paradigms following stroke.


title: Expert Cloud: A Cloud-based framework to share the knowledge and skills of human resources

abstract: Introducing and implementing the Expert Cloud to find, share, exploit, manage and employ the HR.Presenting EaaS as a new service in Cloud computing.Virtualizing the HR as well as sharing his/her skill, knowledge and experiences. Human resources (HRs) as the most important asset of any organizations play a significant role in their success; but, HR virtualization and sharing her/his knowledge and skills was not studied in detail. In this paper, we present a new framework named Expert Cloud to enable the Cloud users for requesting the human expertise and skills without any knowledge of their location and to share the skill, knowledge and experiences of HRs. Also, we analyze, design and implement the Expert Cloud employing Internet infrastructures and Cloud computing concepts. The results show that the Expert Cloud improves the HR utilization, decreases customer response time and task completion time in order to achieve high level of customers' satisfaction and better performance of HR in organization. We conclude the paper with some suggestions for future researches and practices.


title: An efficient approximation for the Generalized Assignment Problem

abstract: We present a simple family of algorithms for solving the Generalized Assignment Problem (GAP). Our technique is based on a novel combinatorial translation of any algorithm for the knapsack problem into an approximation algorithm for GAP. If the approximation ratio of the knapsack algorithm is α and its running time is O(f(N)), our algorithm guarantees a (1 + α)-approximation ratio, and it runs in O(M ċ f(N) + M ċ N), where N is the number of items and M is the number of bins. Not only does our technique comprise a general interesting framework for the GAP problem; it also matches the best combinatorial approximation for this problem, with a much simpler algorithm and a better running time.


Abstract The Design Space of Register Renaming Techniques in Superscalar Processors


title: Robust and fast similarity search for moving object trajectories

abstract: An important consideration in similarity-based retrieval of moving object trajectories is the definition of a distance function. The existing distance functions are usually sensitive to noise, shifts and scaling of data that commonly occur due to sensor failures, errors in detection techniques, disturbance signals, and different sampling rates. Cleaning data to eliminate these is not always possible. In this paper, we introduce a novel distance function, Edit Distance on Real sequence (EDR) which is robust against these data imperfections. Analysis and comparison of EDR with other popular distance functions, such as Euclidean distance, Dynamic Time Warping (DTW), Edit distance with Real Penalty (ERP), and Longest Common Subsequences (LCSS), indicate that EDR is more robust than Euclidean distance, DTW and ERP, and it is on average 50% more accurate than LCSS. We also develop three pruning techniques to improve the retrieval efficiency of EDR and show that these techniques can be combined effectively in a search, increasing the pruning power significantly. The experimental results confirm the superior efficiency of the combined methods.


title: High performance lithographic hotspot detection using hierarchically refined machine learning

abstract: Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address these issues, we propose a high performance lithographic hotspot detection flow with ultra-fast speed and high fidelity. It consists of a novel set of hotspot signature definitions and a hierarchically refined detection flow with powerful machine learning kernels, ANN (artificial neural network) and SVM (support vector machine). We have implemented our algorithm with industry-strength engine under real manufacturing conditions in 45nm process, and showed that it significantly out-performs previous state-of-the-art algorithms in hotspot detection false alarm rate (2.4X to 2300X reduction) and simulation run-time (5X to 237X reduction), meanwhile archiving similar or slightly better hotspot detection accuracies. Such high performance lithographic hotspot detection under real manufacturing conditions is especially suitable for guiding lithography friendly physical design.


A COMPARATIVE ANALYSIS OF DECISION MAKING METHODS FOR THE SEISMIC RETROFIT OF RC BUILDINGS


title: Fixed-frequency beam steering from a stub-loaded microstrip leaky-wave antenna

abstract: A microstrip leaky-wave antenna (MLWA) is designed for fixed-frequency beam steering. The main beam direction of this antenna is controlled by changing the periodic reactive loading of a microstrip line. This reactive loading is provided by a set of periodic patches closely coupled to the stubs in the microstrip line. These patches can be selectively connected to the ground using PIN diodes. The proposed reconfigurable antenna can steer main beam from 40° to 64° in discrete steps at 6.2 GHz.


title: Cost effective management frameworks for intrusion detection systems

abstract: This paper discusses the financial benefit of intrusion detection systems (IDS) deployment techniques and addresses the problems of bridging the gap between technical security solutions and the business need for it. This is an area of interest to both the research and the business community; most IDSes balance host and network monitoring, but the decision about how to adjust usage of each technique tends to be made in a rather ad-hoc way, or based upon effectiveness of detection only without regard to cost of technique. In practice, selections based on how well a strategy helps a company to perform are preferable and methodologies supporting a selection process of this type will assist an Information Technology officer to explain security mechanism selections more effectively to CEOs. In this context, the approach we propose could be applied when choosing one intrusion detection system over another based on which has a better or higher return on investment for the company.Through a case study, we illustrate the benefits of a better IDS management that leads to a positive Return on Investment (ROI) for IDS deployment. We conceive strategies and approaches to support effective decision-making about which techniques are appropriate for the cost effective management of the IDS in a given environment. It is our intent that this research will serve as a foundation for the formal description of cost structures, analysis, and selection of effective implementation approaches to support the management of IDS deployments.


title: HEAL-health monitoring in emergency vehicles with their authentication by RFID and location tracking by GPS

abstract: Cloud Computing is a fast technology, which is based on virtualization technique. It uses pay per use model that provides on-demand service for the cloud users. Radio Frequency Identification (RFID) is another technology which is used in tracking objects. Integrating the RFID technology with the massive cloud storage, we propose a system in which emergency vehicles such as ambulance, fire engine are made traffic free by blocking the other vehicles. RFID tag is fixed on the emergency vehicles which are scanned by the RFID reader placed at the traffic signal. GPS is used to find the current location of the emergency vehicle and to track the vehicles. We also focus on the patient's health condition mainly the heart beat rate, pulse and oxygen saturation content using heart beat rate sensor and pulse oximeter sensor and the details are instantly updated to the hospital database. Whenever the emergency vehicle is identified, the traffic is cleared so that it is provided with the way much faster to reach the destination.


Soft Robots Using Compliant Tensegrity Structures and Soft Sensors


title: Privacy Tipping Points in Smartphones Privacy Preferences

abstract: The aim of this research was to understand what affects people's privacy preferences in smartphone apps. We ran a four-week study in the wild with 34 participants. Participants were asked to answer questions, which were used to gather their personal context and to measure their privacy preferences by varying app name and purpose of data collection. Our results show that participants shared the most when no information about data access or purpose was given, and shared the least when both of these details were specified. When just one of either purpose or the requesting app was shown, participants shared less when just the purpose was specified than when just the app name was given. We found that the purpose for data access was the predominant factor affecting users' choices. In our study the purpose condition vary from being not specified, to vague to be very specific. Participants were more willing to disclose data when no purpose was specified. When a vague purpose was shown, participants became more privacy-aware and were less willing to disclose their information. When specific purposes were shown participants were more willing to disclose when the purpose for requesting the information appeared to be beneficial to them, and shared the least when the purpose for data access was solely beneficial to developers.


Investigating the Relationship between Literary Genres and Emotional Plot Development


title: Spatial deformation transfer

abstract: Much effort is invested in generating natural deformations of three-dimensional shapes. Deformation transfer simplifies this process by allowing to infer deformations of a new shape from existing deformations of a similar shape. Current deformation transfer methods can be applied only to shapes which are represented as a single component manifold mesh, hence their applicability to real-life 3D models is somewhat limited. We propose a novel deformation transfer method, which can be applied to a variety of shape representations -- tet-meshes, polygon soups and multiple-component meshes. Our key technique is deformation of the space in which the shape is embedded. We approximate the given source deformation by a harmonic map using a set of harmonic basis functions. Then, given a sparse set of user-selected correspondence points between the source and target shapes, we generate a deformation of the target shape which has differential properties similar to those of the source deformation. Our method requires only the solution of linear systems of equations, and hence is very robust and efficient. We demonstrate its applicability on a wide range of deformations, for different shape representations.


Towards a science of user engagement ( Position Paper )


title: Enhancements to Bluetooth Baseband Security

abstract: Bluetooth system has been developed by Bluetooth Special Interest Group (Bluetooth SIG) as a cable replacement for short-range connectivity. In Bluetooth, special effort has been taken to develop and standardise adequate security mechanisms and procedures for protecting the wireless radio link. This set of mechanisms is defined in the Bluetooth Baseband specification [5] and is referred to as Bluetooth Baseband security. It is based on strong cryptographic algorithms and well-established security principles. Still, more work is required to integrate Bluetooth Baseband security into various applications that may have very different link layer security requirements. Bluetooth Baseband security is implemented in the Bluetooth module and is common to all Bluetooth units. Also the application specific security functionality may need to be standardised for interoperability. Recently, Jakobsson and Wetzel identified some potential trap holes in Bluetooth security in [11]. Their main concerns were certain weak options included in the Bluetooth security standard. They also criticised the way Bluetooth units make themselves discoverable by other units just by broadcasting messages that include their unique identities in clear. The purpose of this paper is to introduce some recent work in the area of Bluetooth Baseband security. Specifically, we address the problems discovered by Jakobsson and Wetzel, and develop possible counter measures. First, a brief introduction to Bluetooth Baseband security is given. Then major security shortcomings are identified. These include usage of the unit key and the short Bluetooth PIN value in the initialisation procedure, on the one hand, and the privacy problem created by location tracking, on the other hand. In section 4, we discuss passkey-based methods of exchanging the Bluetooth link key. Using public key cryptography adequate security can be provided while keeping the passkey short for the user's convenience. Furthermore, in section 5, an application to LAN access is developed enabling access point roaming. Finally, in section 6, we describe a technique that offers reasonable protection against location tracking. Identities of Bluetooth units can be efficiently hidden from unauthorised units using temporary Bluetooth device addresses.


title: Home Energy Management Systems in Future Smart Grids

abstract: We present a detailed review of various Home Energy Management Schemes (HEM,s). HEM,s will increase savings, reduce peak demand and Pto Average Ratio (PAR). Among various applications of smart grid technologies, home energy management is probably the most important one to be addressed. Various steps have been taken by utilities for efficient energy consumption.New pricing schemes like Time of Use (ToU), Real Time Pricing (RTP), Critical Peak Pricing (CPP), Inclining Block Rates (IBR) etc have been been devised for future smart grids.Home appliances and/or distributed energy resources coordination (Local Generation) along with different pricing schemes leads towards efficient energy consumption. This paper addresses various communication and optimization based residential energy management schemes and different communication and networking technologies involved in these schemes. INDEX TERMS—Smart grid, Home energy management, optimization.


Vol. 2, Issue 2,Mar-Apr 2012, pp.1288-1296 Network Intrusion Detection using SNORT


title: Sharable and Individual Multi-View Metric Learning

abstract: This paper presents a sharable and individual multi-view metric learning (MvML) approach for visual recognition. Unlike conventional metric leaning methods which learn a distance metric on either a single type of feature representation or a concatenated representation of multiple types of features, the proposed MvML jointly learns an optimal combination of multiple distance metrics on multi-view representations, where not only it learns an individual distance metric for each view to retain its specific property but also a shared representation for different views in a unified latent subspace to preserve the common properties. The objective function of the MvML is formulated in the large margin learning framework via pairwise constraints, under which the distance of each similar pair is smaller than that of each dissimilar pair by a margin. Moreover, to exploit the nonlinear structure of data points, we extend MvML to a sharable and individual multi-view deep metric learning (MvDML) method by utilizing the neural network architecture to seek multiple nonlinear transformations. Experimental results on face verification, kinship verification, and person re-identification show the effectiveness of the proposed sharable and individual multi-view metric learning methods.


title: Novel approach to separation of musical signal sources by NMF

abstract: This paper proposes a method to separate polyphonic music signal into signals of each musical instrument by NMF: Non-negative Matrix Factorization based on preservation of spectrum envelope. Sound source separation is taken as a fundamental issue in music signal processing and NMF is becoming common to solve it because of its versatility and compatibility with music signal processing. Our method bases on a common feature of harmonic signal: spectrum envelopes of musical signal in close pitches played by the harmonic music instrument would be similar. We estimate power spectrums of each instrument by NMF with restriction to synchronize spectrum envelope of bases which are allocated to all possible center frequencies of each instrument. This manipulation means separation of components which refers to tones of each instrument and realizes both of separation without pre-training and separation of signal including harmonic and non-harmonic sound. We had an experiment to decompose mixture sound signal of MIDI instruments into each instrument and evaluated the result by SNR of single MIDI instrument sound signals and separated signals. As a result, SNR of lead guitar and drums approximately marked 3.6 and 6.0 dB and showed significance of our method.


title: Multiobjective Flexible Job Shop Scheduling Using Memetic Algorithms

abstract: In this paper, we propose new memetic algorithms (MAs) for the multiobjective flexible job shop scheduling problem (MO-FJSP) with the objectives to minimize the makespan, total workload, and critical workload. The problem is addressed in a Pareto manner, which aims to search for a set of Pareto optimal solutions. First, by using well-designed chromosome encoding/decoding scheme and genetic operators, the nondominated sorting genetic algorithm II (NSGA-II) is adapted for the MO-FJSP. Then, our MAs are developed by incorporating a novel local search algorithm into the adapted NSGA-II, where some good individuals are chosen from the offspring population for local search using a selection mechanism. Furthermore, in the proposed local search, a hierarchical strategy is adopted to handle the three objectives, which mainly considers the minimization of makespan, while the concern of the other two objectives is reflected in the order of trying all the possible actions that could generate the acceptable neighbor. In the experimental studies, the influence of two alternative acceptance rules on the performance of the proposed MAs is first examined. Afterwards, the effectiveness of key components in our MAs is verified, including genetic search, local search, and the hierarchical strategy in local search. Finally, extensive comparisons are carried out with the state-of-the-art methods specially presented for the MO-FJSP on well-known benchmark instances. The results show that the proposed MAs perform much better than all the other algorithms.


title: Attacks on Fitness Trackers Revisited: A Case-Study of Unfit Firmware Security

abstract: Fitness trackers - wearables that continuously record a wearer's step count and related activity data - are quickly gaining in popularity. Apart from being useful for individuals seeking a more healthy lifestyle, their data is also being used in court and by insurance companies to adjust premiums. For these use cases, it is essential to ensure authenticity and integrity of data. Here we demonstrate a flaw in the way firmware for Withings' Activit\'e is verified, allowing an adversary to compromise the tracker itself. This type of attack has so far not been applied to fitness trackers. Vendors have started mitigating previous attacks, which manipulated data by interfering with wireless channels, or by physically moving the tracker to fool sensors. Hardware similarities amongst different trackers suggest findings can be transferred to other tracker as well.


title: Construction of simple graphs with a target joint degree matrix and beyond

abstract: In networking research, it is often desirable to generate synthetic graphs with certain properties. In this paper, we present a new algorithm, 2K_Simple, for exact construction of simple graphs with a target joint degree matrix (JDM). We prove that the algorithm constructs exactly the target JDM and that its running time is linear in the number of edges. Furthermore, we show that the algorithm poses less constraints on the graph structure than previous state-of-the-art construction algorithms. We exploit this flexibility to extend 2K_Simple and design two algorithms that achieve additional network properties on top of the exact target JDM. In particular, 2K_Simple_Clustering produces simple graphs with a target JDM and average clustering coefficient close to a target, while 2K_Simple_Attributes produces exactly simple graphs with a target JDM and joint occurrence of node attribute pairs. We exhaustively evaluate our algorithms through simulation for small graphs, and we also demonstrate their benefits in generating graphs that resemble real-world social networks in terms of accuracy and speed; we reduce the running time by orders of magnitudes compared to previous approaches that rely on Monte Carlo Markov Chains.


title: A heuristic algorithm for the job shop scheduling problem

abstract: The job shop scheduling problem that is concerned with minimizing makespan is discussed. A new heuristic algorithm that embeds an improved shifting bottleneck procedure into the Tabu Search (TS) technique is presented. This algorithm is different from the previous procedures, because the improved shifting bottleneck procedure is a new procedure for the problem, and the two remarkable strategies of intensification and diversification of TS are modified. In addition, a new kind of neighborhood structure is defined and the method for local search is different from the previous. This algorithm has been tested on many common problem benchmarks with various sizes and levels of hardness and compared with several other algorithms. Computational experiments show that this algorithm is one of the most effective and efficient algorithms for the problem. Especially, it obtains a lower upbound for an instance with size of 50 jobs and 20 machines within a short period.


title: Impacts of the Implementation of Electronic Invoicing on Buyer-Seller Relationships

abstract: In this paper, we examine the effects of the implementation of electronic invoicing on buyer-seller relationships. We apply the framework by Cannon and Perreault [1] to analyze the changes in the relationship connectors between the two parties. Drawing on qualitative interview data from a single case study, we find that the implementation of electronic invoicing has a strong impact on the information exchange component as well as on the operational linkages component. In addition, we find that both parties have made adaptations due to the implementation of electronic invoicing. It was also concluded that the switching costs have increased and the commitment improved between the seller and its customers. While the investments in electronic invoicing usually reduce costs for the buyers, the sellers benefit from the implementation of electronic invoicing in creating long-lasting relationships and even dependence between the two parties.


Abstractive Unsupervised Multi-Document Summarization using Paraphrastic Sentence Fusion


title: Effective Feature Integration for Automated Short Answer Scoring

abstract: A major opportunity for NLP to have a realworld impact is in helping educators score student writing, particularly content-based writing (i.e., the task of automated short answer scoring). A major challenge in this enterprise is that scored responses to a particular question (i.e., labeled data) are valuable for modeling but limited in quantity. Additional information from the scoring guidelines for humans, such as exemplars for each score level and descriptions of key concepts, can also be used. Here, we explore methods for integrating scoring guidelines and labeled responses, and we find that stacked generalization (Wolpert, 1992) improves performance, especially for small training sets.


AI for Dynamic Difficulty Adjustment in Games


title: NVMain 2.0: A User-Friendly Memory Simulator to Model (Non-)Volatile Memory Systems

abstract: In this letter, a flexible memory simulator - NVMain 2.0, is introduced to help the community for modeling not only commodity DRAMs but also emerging memory technologies, such as die-stacked DRAM caches, non-volatile memories (e.g., STT-RAM, PCRAM, and ReRAM) including multi-level cells (MLC), and hybrid non-volatile plus DRAM memory systems. Compared to existing memory simulators, NVMain 2.0 features a flexible user interface with compelling simulation speed and the capability of providing sub-array-level parallelism, fine-grained refresh, MLC and data encoder modeling, and distributed energy profiling.


title: Cellular traffic offloading onto network-assisted device-to-device connections

abstract: While operators have finally started to deploy fourth generation broadband technology, many believe it will still be insufficient to meet the anticipated demand in mobile traffic over the coming years. Generally, the natural way to cope with traffic acceleration is to reduce cell size, and this can be done in many ways. The most obvious method is via picocells, but this requires additional CAPEX and OPEX investment to install and manage these new base stations. Another approach, which avoids this additional CAPEX/OPEX, involves offloading cellular traffic onto direct D2D connections whenever the users involved are in proximity. Given that most client devices are capable of establishing concurrent cellular and WiFi connections today, we expect the majority of immediate gains from this approach to come from the use of the unlicensed bands.


title: Detecting friendly , flirtatious , awkward , and assertive speech in speed-dates

abstract: Automatically detecting human social intentions and attitudes from spoken conversation is an important task for speech processing and social computing. We describe a system for detecting interpersonal stance: whether a speaker is flirtatious, friendly, awkward, or assertive. We make use of a new spoken corpus of over 1000 4-min speed-dates. Participants rated themselves and their interlocutors for these interpersonal stances, allowing us to build detectors for style both as interpreted by the speaker and as perceived by the hearer. We use lexical, prosodic, and dialog features in an SVM classifier to detect very clear styles (the strongest 10% in each stance) with up to 75% accuracy on previously seen speakers (50% baseline) and up to 59% accuracy on new speakers (48% baseline). A feature analysis suggests that flirtation is marked by joint focus on the woman as a target of the conversation, awkwardness by decreased speaker involvement, and friendliness by a conversational style including other-directed laughter and appreciations. Our work has implications for our understanding of interpersonal stance, their linguistic expression, and their automatic extraction.


title: A Novel Dual-Band, Dual-Polarized, Miniaturized and Low-Profile Base Station Antenna

abstract: In this paper, a novel dual-band, dual-polarized, miniaturized and low-profile base station antenna operating in the frequency bands of 820–960 and 1710–2170 MHz is designed. Elements are arranged such that high-frequency elements are embedded in low frequency elements to reduce volume. A baffle is used to reflect the transmitted power density in the forward direction and also improve isolation between elements. Therefore, surrounding isolation baffles and rectangular baffles are appended around high-frequency elements and low-frequency elements, respectively. The diameter of the proposed antenna cover is only 200 mm, which is smaller than the existing antenna diameter of 280 mm. Compared with the other commonly used antennas, the proposed antenna also has some advantages such as concealment and low profile using a tubular form of radome, which can easily integrate the proposed antenna with the surrounding environment. The measured results verify that the proposed antenna meets the stringent design requirements: voltage standing wave ratio (VSWR) is less than 1.3, the isolation is greater than 30 dB, and the pattern parameters also meet telecommunications industry standards.


title: Differential Evolution With Two-Level Parameter Adaptation

abstract: The performance of differential evolution (DE) largely depends on its mutation strategy and control parameters. In this paper, we propose an adaptive DE (ADE) algorithm with a new mutation strategy DE/lbest/1 and a two-level adaptive parameter control scheme. The DE/lbest/1 strategy is a variant of the greedy DE/best/1 strategy. However, the population is mutated under the guide of multiple locally best individuals in DE/lbest/1 instead of one globally best individual in DE/best/1. This strategy is beneficial to the balance between fast convergence and population diversity. The two-level adaptive parameter control scheme is implemented mainly in two steps. In the first step, the population-level parameters $F_{p}$ and $CR_{p}$ for the whole population are adaptively controlled according to the optimization states, namely, the exploration state and the exploitation state in each generation. These optimization states are estimated by measuring the population distribution. Then, the individual-level parameters $F_{i}$ and $CR_{i}$ for each individual are generated by adjusting the population-level parameters. The adjustment is based on considering the individual's fitness value and its distance from the globally best individual. This way, the parameters can be adapted to not only the overall state of the population but also the characteristics of different individuals. The performance of the proposed ADE is evaluated on a suite of benchmark functions. Experimental results show that ADE generally outperforms four state-of-the-art DE variants on different kinds of optimization problems. The effects of ADE components, parameter properties of ADE, search behavior of ADE, and parameter sensitivity of ADE are also studied. Finally, we investigate the capability of ADE for solving three real-world optimization problems.


title: A phase I study of obatoclax mesylate, a Bcl-2 antagonist, plus topotecan in solid tumor malignancies

abstract: Purpose ::: To establish the safety, maximum tolerated dose (MTD), recommended phase II dose, and preliminary antitumor activity of obatoclax mesylate (GX15-070MS), a Bcl-2 antagonist, in combination with topotecan in patients with solid tumor malignancies.


title: Vulnerabilities in Biometric Encryption Systems

abstract: The goal of a biometric encryption system is to embed a secret into a biometric template in a way that can only be decrypted with a biometric image from the enroled person. This paper describes a potential vulnerability in such systems that allows a less-than-brute force regeneration of the secret and an estimate of the enrolled image. This vulnerability requires the biometric comparison to “leak” some information from which an analogue for a match score may be calculated. Using this match score value, a “hill-climbing” attack is performed against the algorithm to calculate an estimate of the enrolled image, which is then used to decrypt the code. Results are shown against a simplified implementation of the algorithm of Soutar et al. (1998).


title: A Framework for Distributed Semantic Annotation of Musical Score: "Take It to the Bridge!"

abstract: Music notation expresses performance instructions in a way commonly understood by musicians, but printed paper parts are limited to encodings of static, a priori knowledge. In this paper we present a platform for multi-way communication between collaborating musicians through the dynamic modification of digital parts: the Music Encoding and Linked Data (MELD) framework for distributed real-time annotation of digital music scores. MELD users and software agents create semantic annotations of music concepts and relationships, which are associated with musical structure specified by the Music Encoding Initiative schema (MEI). Annotations are expressed in RDF, allowing alternative music vocabularies (e.g., popular vs. classical music structures) to be applied. The same underlying framework retrieves, distributes, and processes information that addresses semantically distinguishable music elements. Further knowledge is incorporated from external sources through the use of Linked Data. The RDF is also used to match annotation types and contexts to rendering actions which display the annotations upon the digital score. Here, we present a MELD implementation and deployment which augments the digital music scores used by musicians in a group performance, collaboratively changing the sequence within and between pieces in a set list.


title: Cognitive consequences of trilingualism

abstract: Aims and objectives:The objectives of the present research were to examine the cognitive consequences of trilingualism and explain them relative to the cognitive consequences of bilingualism.Approach:A comparison of cognitive abilities in trilinguals and bilinguals was conducted. In addition, we proposed a cognitive plasticity framework to account for cognitive differences and similarities between trilinguals and bilinguals.Data and analysis:Three aspects of cognition were analyzed: (1) cognitive reserve in older adults, as measured by age of onset of Alzheimer’s disease and mild cognitive impairment; (2) inhibitory control in children and younger adults, as measured by response times on behavioral Simon and flanker tasks; and (3) memory generalization in infants and toddlers, as measured by accuracy on behavioral deferred imitation tasks. Results were considered within a framework of cognitive plasticity, which took into account several factors that may affect plasticity including the age of learning a t...


title: A comparison of methods for differential expression analysis of RNA-seq data

abstract: BackgroundFinding genes that are differentially expressed between conditions is an integral part of understanding the molecular basis of phenotypic variation. In the past decades, DNA microarrays have been used extensively to quantify the abundance of mRNA corresponding to different genes, and more recently high-throughput sequencing of cDNA (RNA-seq) has emerged as a powerful competitor. As the cost of sequencing decreases, it is conceivable that the use of RNA-seq for differential expression analysis will increase rapidly. To exploit the possibilities and address the challenges posed by this relatively new type of data, a number of software packages have been developed especially for differential expression analysis of RNA-seq data.ResultsWe conducted an extensive comparison of eleven methods for differential expression analysis of RNA-seq data. All methods are freely available within the R framework and take as input a matrix of counts, i.e. the number of reads mapping to each genomic feature of interest in each of a number of samples. We evaluate the methods based on both simulated data and real RNA-seq data.ConclusionsVery small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the ‘limma’ method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.


title: Adaptive Virtual Exhibitions

abstract: The paper presents a generic framework for designing and implementing multiuser virtual exhibitions that adapt visitors’ preferences and goals and foster the emergence of communities with common interests. A user-oriented platform for designing and executing virtual exhibitions has been presented in which implicit generation and adjustment of user profile allows exhibition to dynamically adapt content presentation to users’ interest and preferences. http://dx.doi.org/10.14429/djlit.33.4604


title: Exploring vibrotactile feedback on the body and foot for the purpose of pedestrian navigation

abstract: In this paper, we present an evaluation of vibrotactile onbody feedback for the purpose of pedestrian navigation. For this specific task, many researchers already provide different approaches such as vibrating belts, wristbands or shoes. Still, there are issues left that have to be considered, such as which body position is most suitable, what kind of vibration patterns are easy to interpret, and how applicable are vibrotactile feedback systems in real scenarios. To find answers, we reconstructed prototypes commonly found in literature and continued to further evaluate different foot-related designs. On the one hand, we learned that vibrotactile feedback at the foot reduces visual attention and thus also potentially reduces stress. However, on the other hand, we found that urban space can be very diverse, and ambiguous and therefore a vibrotactile system cannot completely replace common path finding systems for pedestrians. Rather, we envision such a system to be applied complementary as an assistive technology.


title: Text detection based on convolutional neural networks with spatial pyramid pooling

abstract: Text detection is a difficult task due to the significant diversity of the texts appearing in natural scene images. In this paper, we propose a novel text descriptor, SPP-net, extracted by equipping the Convolutional Neural Network (CNN) with spatial pyramid pooling. We first compute the feature maps from the original text lines without any cropping or warping, and then generate the fixed-size representations for text discrimination. Experimental results on the latest ICDAR 2011 and 2013 datasets have proven that the proposed descriptor outperforms the state-of-the-art methods by a noticeable margin on F-measure with its merit of incorporating multi-scale text information and its flexibility of describing text regions with different sizes and shapes.


title: Dependency Link Embeddings: Continuous Representations of Syntactic Substructures

abstract: We present a simple method to learn continuous representations of dependency substructures (links), with the motivation of directly working with higher-order, structured embeddings and their hidden relationships, and also to avoid the millions of sparse, template-based word-cluster features in dependency parsing. These link embeddings allow a significantly smaller and simpler set of unary features for dependency parsing, while maintaining improvements similar to state-of-the-art, n-ary word-cluster features, and also stacking over them. Moreover, these link vectors (made publicly available) are directly portable as offthe-shelf, dense, syntactic features in various NLP tasks. As one example, we incorporate them into constituent parse reranking, where their small feature set again matches the performance of standard non-local, manuallydefined features, and also stacks over them.


title: Real time patient monitoring system based on Internet of Things

abstract: The popularity of Internet of Things is increasing day by day in the area of remote monitoring system. The remote monitoring systems include, vehicle or assets monitoring, kids/pets monitoring, fleet management, parking management, water and oil leakage, energy grid monitoring etc. In this paper, we have proposed an intelligent patient monitoring system for monitoring the patients' health condition automatically through sensors based connected networks. Several sensors are used for gathering the biological behaviors of a patient. The meaningful biological information are then forwarded to the IoT cloud. The system is more intelligent that can able to detect the critical condition of a patient by processing sensors data and instantly provides push notification to doctors/nurses as well as hospital in-charge personal. The doctors and nurses get benefited from this system by observing their corresponding patients remotely without visiting in person. Patients' relatives can also get benefited from this system with limited access.


title: Fuzzy Approximation-Based Adaptive Backstepping Optimal Control for a Class of Nonlinear Discrete-Time Systems With Dead-Zone

abstract: In this paper, an adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete-time systems. The controlled systems are in a strict-feedback frame and contain unknown functions and nonsymmetric dead-zone. For this class of systems, the control objective is to design a controller, which not only guarantees the stability of the systems, but achieves the optimal control performance as well. This immediately brings about the difficulties in the controller design. To this end, the fuzzy logic systems are employed to approximate the unknown functions in the systems. Based on the utility functions and the critic designs, and by applying the backsteppping design technique, a reinforcement learning algorithm is used to develop an optimal control signal. The adaptation auxiliary signal for unknown dead-zone parameters is established to compensate for the effect of nonsymmetric dead-zone on the control performance, and the updating laws are obtained based on the gradient descent rule. The stability of the control systems can be proved based on the difference Lyapunov function method. The feasibility of the proposed control approach is further demonstrated via two simulation examples.


title: Work environment factors other than staffing associated with nurses' ratings of patient care quality.

abstract: BACKGROUND ::: The impact of registered nurse (RN) staffing on patient care quality has been extensively studied. Identifying additional modifiable work environment factors linked to patient care quality is critical as the projected shortage of approximately 250,000 RNs over the next 15 years will limit institutions' ability to rely on RN staffing alone to ensure high-quality care. ::: ::: ::: PURPOSE ::: We examined the association between RNs' ratings of patient care quality and several novel work environment factors adjusting for the effects of two staffing variables: reported patient-to-RN ratios and ratings of staffing adequacy. ::: ::: ::: METHODOLOGY ::: We used a cross-sectional, correlational design and a mailed survey to collect data in 2009 from a national sample of RNs (n = 1,439) in the United States. A multivariate logistic regression was used to analyze the data. ::: ::: ::: FINDINGS ::: Work group cohesion, nurse-physician relations, procedural justice, organizational constraints, and physical work environment were associated with RNs' ratings of quality, adjusting for staffing. Furthermore, employment in a Magnet hospital and job satisfaction were positively related to ratings of quality, whereas supervisory support was not. ::: ::: ::: PRACTICE IMPLICATIONS ::: Our evidence demonstrates the importance of considering RN work environment factors other than staffing when planning improvements in patient care quality. Health care managers can use the results of our study to strategically allocate resources toward work environment factors that have the potential to improve quality of care.


title: Thermal damage in three-dimensional vivo bio-tissues induced by moving heat sources in laser therapy

abstract: The thermal damage of a three-dimensional bio-tissue model irradiated by a movable laser beam was studied in this work. By employing the DPL biological heat conduction model and Henriques’ thermal damage assessment model, the distribution of burn damage of vivo human tissue during laser therapy was analytically obtained. The influences of laser moving velocity, laser spot size, phase lags of heat flux and temperature gradient were discussed. It was found that the laser moving speed and the laser spot size greatly influence the thermal damage degree by affecting the energy concentration degree. The increases of the laser moving speed and laser spot size can enlarge the irradiated region and reduce the burn degree. A greater phase lag of temperature gradient led to lower accumulation of thermal energy and lower burn degree. However, the increment of heat flux phase lag leads to the thermal energy accumulation and more serious burn degree in the irradiated region.


title: A review of Cloud Learning Management System (CLMS) based on Software as a Service (SaaS)

abstract: Cloud computing technology is growing rapidly, attracting great attention of educational institutions as it is believed to improved learning efficiency. There are many Cloud Learning Management System (CLMS) based on Software as a Service (SaaS) available and offered for free in the market. However, selecting the one that fit to purpose for educational institution or educators are still challenging. This paper seeks to provide a comparative guideline for educational institution and educators in selecting the best LMS's to be used based on the provided features. The study reviewed and analytically compared 6 major CLMS base on SaaS such as Collaborize Classroom, CourseSites, Ecto, Edmodo, GoConqr and Google Classroom.


title: Nonlinear Model-Based Approach for Accurate Stability Prediction of One-Bit Higher-Order Delta–Sigma Modulators

abstract: The current approaches on predicting the stability of delta-sigma (Δ-Σ) modulators are mostly confined to dc inputs. This poses limitations as practical applications of Δ-Σ modulators involve a wide range of signals other than dc. In this paper, a quasi-linear model for Δ-Σ modulators with nonlinear feedback control analysis is presented, which accurately predicts the stability of higher-order single-loop 1-bit Δ-Σ modulators for various types of input signals, such as single sinusoids, dual sinusoids, multiple sinusoids, and Gaussian. Theoretical values are shown to match closely with simulation results. The results of this paper would significantly speed up the design and evaluation of higher-order single-loop 1-bit Δ-Σ modulators for various applications, including those that may require multiple-sinusoidal inputs or any general input composed of a finite number of sinusoidal components, circumventing the need to perform detailed time-consuming simulations to quantify stability limits. By using the proposed method, the difference between the predicted and the actual stable amplitude limits results in an error of less than 1 dB in the in-band signal-to-noise ratio for the third-order and higher-order Δ-Σ modulators for single-sinusoidal inputs. For single-sinusoidal, dual-sinusoidal, multiple-sinusoidal, and Gaussian inputs, the error is less than 2 dB for the fifth-order modulator and reduces to less than 1 dB for the sixth-order and higher-order Δ-Σ modulators.


title: Automatic CPU-GPU communication management and optimization

abstract: The performance benefits of GPU parallelism can be enormous, but unlocking this performance potential is challenging. The applicability and performance of GPU parallelizations is limited by the complexities of CPU-GPU communication. To address these communications problems, this paper presents the first fully automatic system for managing and optimizing CPU-GPU communcation. This system, called the CPU-GPU Communication Manager (CGCM), consists of a run-time library and a set of compiler transformations that work together to manage and optimize CPU-GPU communication without depending on the strength of static compile-time analyses or on programmer-supplied annotations. CGCM eases manual GPU parallelizations and improves the applicability and performance of automatic GPU parallelizations. For 24 programs, CGCM-enabled automatic GPU parallelization yields a whole program geomean speedup of 5.36x over the best sequential CPU-only execution.