title: Minimizing Base Station Power Consumption

abstract: We propose a new radio resource management algorithm which aims at minimizing the base station supply power consumption for multi-user MIMO-OFDM. Given a base station power model that establishes a relation between the RF transmit power and the supply power consumption, the algorithm optimizes the trade-off between three basic power-saving mechanisms: antenna adaptation, power control and discontinuous transmission. The algorithm comprises two steps: a) the first step estimates sleep mode duration, resource shares and antenna configuration based on average channel conditions and b) the second step exploits instantaneous channel knowledge at the transmitter for frequency selective time-variant channels. The proposed algorithm finds the number of transmit antennas, the RF transmission power per resource unit and spatial channel, the number of discontinuous transmission time slots, and the multi-user resource allocation, such that supply power consumption is minimized. Simulation results indicate that the proposed algorithm is capable of reducing the supply power consumption by between 25% and 40%, dependend on the system load.


Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games.


title: Optimizing a Battery Energy Storage System for Primary Frequency Control

abstract: This paper presents a method for the dimensioning of a battery energy storage system (BESS) to provide a primary frequency reserve. Numerical simulations based on historic frequency measurements are used to determine the minimum possible capacity, i.e., the lowest possible cost, which fulfills the technical requirements of the grid code. We implement a novel control algorithm with adjustable state of charge limits and the application of emergency resistors. At current European market prices, an optimized lead-acid BESS can be a profitable utility solution for the primary frequency control.


GazeDirector : Fully Articulated Eye Gaze Redirection in Video


Severe Short Stature in an Adolescent Male with Prader-Willi Syndrome and Congenital Adrenal Hyperplasia: A Therapeutic Conundrum


title: A novel photometric method for real-time 3D reconstruction of fingerprint

abstract: 3D fingerprint recognition is an emerging technology in biometrics. However, current 3D fingerprint acquisition systems are usually with complex structure and high-cost and that has become the main obstacle for its popularization. In this work, we present a novel photometric method and an experimental setup for real-time 3D fingerprint reconstruction. The proposed system consists of seven LED lights that mounted around one camera. In the surface reflectance modeling of finger surface, a simplified Hanrahan-Krueger model is introduced. And a neural network approach is used to solve the model for accurate estimation of surface normals. A calibration method is also proposed to determine the lighting directions as well as the correction of the lighting fields. Moreover, to stand out the fingerprint ridge features and get better visual effects, a linear transformation is applied to the recovered normals field. Experiments on live fingerprint and the comparison with traditional photometric stereo algorithm are used to demonstrate its high performance.


title: Multi-domain spoken language understanding with transfer learning

abstract: This paper addresses the problem of multi-domain spoken language understanding (SLU) where domain detection and domain-dependent semantic tagging problems are combined. We present a transfer learning approach to the multi-domain SLU problem in which multiple domain-specific data sources can be incorporated. To implement multi-domain SLU with transfer learning, we introduce a triangular-chain structured model. This model effectively learns multiple domains in parallel, and allows use of domain-independent patterns among domains to create a better model for the target domain. We demonstrate that the proposed method outperforms baseline models on dialog data for multi-domain SLU problems.


title: Fuzzy Information Retrieval

abstract: Information retrieval used to mean looking through thousands of strings of texts to find words or symbols that matched a user's query. Today, there are many models that help index and search more effectively so retrieval takes a lot less time. Information retrieval (IR) is often seen as a subfield of computer science and shares some modeling, applications, storage applications and techniques, as do other disciplines like artificial intelligence, database management, and parallel computing. This book introduces the topic of IR and how it differs from other computer science disciplines. A discussion of the history of modern IR is briefly presented, and the notation of IR as used in this book is defined. The complex notation of relevance is discussed. Some applications of IR is noted as well since IR has many practical uses today. Using information retrieval with fuzzy logic to search for software terms can help find software components and ultimately help increase the reuse of software. This is just one practical application of IR that is covered in this book. Some of the classical models of IR is presented as a contrast to extending the Boolean model. This includes a brief mention of the source of weights for the various models. In a typical retrieval environment, answers are either yes or no, i.e., on or off. On the other hand, fuzzy logic can bring in a "degree of" match, vs. a crisp, i.e., strict match. This, too, is looked at and explored in much detail, showing how it can be applied to information retrieval. Fuzzy logic is often times considered a soft computing application and this book explores how IR with fuzzy logic and its membership functions as weights can help indexing, querying, and matching. Since fuzzy set theory and logic is explored in IR systems, the explanation of where the fuzz is ensues. The concept of relevance feedback, including pseudorelevance feedback is explored for the various models of IR. For the extended Boolean model, the use of genetic algorithms for relevance feedback is delved into. The concept of query expansion is explored using rough set theory. Various term relationships is modeled and presented, and the model extended for fuzzy retrieval. An example using the UMLS terms is also presented. The model is also extended for term relationships beyond synonyms. Finally, this book looks at clustering, both crisp and fuzzy, to see how that can improve retrieval performance. An example is presented to illustrate the concepts.


title: From junior to senior Pinocchio: A cross-sectional lifespan investigation of deception

abstract: article i nfo We present the first study to map deception across the entire lifespan. Specifically, we investigated age-related difference in lying proficiency and lying frequency. A large community sample (n = 1005) aged between 6 and 77 were surveyed on their lying frequency, and performed a reaction-time (RT) based deception task to as- sess their lying proficiency. Consistent with the inverted U-shaped pattern of age-related changes in inhibitory control that we observed in a stop signal task, we found that lying proficiency improved during childhood (in ac- curacy,not RTs), excelled inyoung adulthood (in accuracyand RTs),and worsenedthroughout adulthood(in ac- curacy and RTs). Likewise, lying frequency increased in childhood, peaked in adolescence, and decreased during adulthood. In sum, we observed important age-related difference in deception that generally fit with the U- shaped pattern of age-related changes observed in inhibitory control. Theoretical and practical implications are discussed from a cognitive view of deception.


title: Smart Irrigation Decision Support Based on Fuzzy Logic Using Wireless Sensor Network

abstract: Smart Irrigation Decision Support (SIDS) system based on fuzzy logic using Wireless Sensor Network (WSN) is considered for precision water irrigation. WSN consists of limited-energy sensor nodes which are equipped with sensing, wireless communication and processing capabilities. SIDS aims to measure the agricultural parameters including the soil temperature and soil moisture using the sensor nodes. The rate of soil moisture reduction is calculated from the current soil moisture reading and the previous one. These soil temperate and the rate of soil moisture reduction are employed as input parameters for fuzzy logic controller to produce the amount of irrigation time as output parameter. The fuzzy logic rules and linguistic values for the input and output parameters of fuzzy logic are carefully selected with the guide of agricultural experts including the farmers. The experimental results show that by using the SIDS system, the amount of irrigation time is precisely calculated based on the measured agricultural parameters. In addition, the water irrigation utilization is improved.


title: Major Depression Detection from EEG Signals Using Kernel Eigen-Filter-Bank Common Spatial Patterns

abstract: Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e., <7 trials) are used. These findings therefore suggest that the proposed method has a great potential for developing an efficient (required only a few 6-s EEG signals from the 8 electrodes over the temporal) and effective (~80% classification accuracy) EEG-based brain-computer interface (BCI) system which may, in the future, help psychiatrists provide individualized and effective treatments for MDD patients.


title: Ischiofemoral Impingement

abstract: Ischiofemoral impingement is a syndrome defined by hip pain associated with narrowing of the space between ischial tuberosity and lesser trochanter. This phenomenon leads to abnormalities of the quadratus femoris muscle, ranging from deformity and edema to tears and atrophy. This review article presents an up-to-date discussion and imaging findings of ischiofemoral impingement, along with its related anatomy, clinical presentation, differential diagnosis and treatment.


title: Radiation-pattern improvement of patch antennas using a compact soft/hard surface (SHS) structure on LTCC multilayer technology

abstract: A compact soft/hard surface (SHS) structure consisting of a square ring of short-circuited metal strips is employed to surround the patch antenna for blocking the surface-wave propagation, thus alleviating the effect of the edge diffraction and hence improving the radiation pattern. The operating frequency of the SHS is determined by the width of the metal strip, therefore it is suitable for a substrate with arbitrary thickness and dielectric constant. The compact SHS structure is realized on a low temperature co-fired ceramics (LTCC) technology. It is shown that the gain of a patch antenna can be increased to near 9 dBi through the use of the proposed compact SHS structure.


title: Design and Dynamics of a Novel Solar Tracker With Parallel Mechanism

abstract: This paper proposes a two-axis-decoupled solar tracker based on parallel mechanism. Utilizing Grassmann line geometry, the type design of the two-axis solar tracker is investigated. Then, singularity is studied to obtain the workspace without singularities. By using the virtual work principle, the inverse dynamics is derived to find out the driving torque. Taking Beijing as a sample city where the solar tracker is placed, the motion trajectory of the tracker is planned to collect the maximum solar energy. The position of the mass center of the solar mirror on the platform is optimized to minimize the driving torque. The driving torque of the proposed tracker is compared with that of a conventional serial tracker, which shows that the proposed tracker can greatly reduce the driving torque and the reducers with large reduction ratio are not necessary. Thus, the complexity and power dissipation of the system can be reduced.


title: Layout engineering to suppress hysteresis of negative capacitance FinFET

abstract: Negative capacitance (NC), which arises from two energy minima of ferroelectric material, is proposed as one of the solutions for the next generation CMOS technology. However, the side-effect (i.e., hysteresis in current-vs.-voltage characteristic) of NC FinFET should be minimized, especially for being adopted as CMOS logic devices. If the capacitance matching between the ferroelectric capacitor and the dielectric capacitor in NC FinFET is satisfied, hysteresis-free and steep switching features can be obtained. In this work, the hysteresis in current-vs.-voltage characteristic is suppressed by lowering the capacitance value of FinFET (i.e., using the layout engineering for FinFET): While the hysteresis of NC FinFET was decreased, the performance degradation was negligible.


Neural Enquirer: Learning to Query Tables


title: Design and Control of Concentric-Tube Robots

abstract: A novel approach toward construction of robots is based on a concentric combination of precurved elastic tubes. By rotation and extension of the tubes with respect to each other, their curvatures interact elastically to position and orient the robot's tip, as well as to control the robot's shape along its length. In this approach, the flexible tubes comprise both the links and the joints of the robot. Since the actuators attach to the tubes at their proximal ends, the robot itself forms a slender curve that is well suited for minimally invasive medical procedures. This paper demonstrates the potential of this technology. Design principles are presented and a general kinematic model incorporating tube bending and torsion is derived. Experimental demonstration of real-time position control using this model is also described.


title: Safety Controller Synthesis Using Human Generated Trajectories

abstract: This paper focuses on the task of safety controller synthesis, that is, designing a controller that will take a system from any point within a compact set of initial states to a point inside a set of acceptable goal states, while never entering any state that is deemed unsafe. To do this we use a human generated trajectory-based approach. We introduce the control autobisimulation function, which is the analog of the control Lyapunov function for approximate bisimulation. We consider a class of hybrid systems and use this function to determine a set of admissible feedback control laws that guarantee trajectory robustness for underlying dynamics that are linear affine, feedback linearizable, and differentially flat. This property ensures that any trajectory of the closed-loop system that is initialized within some neighborhood of a nominal trajectory will stay within some tube of the nominal trajectory when given the same input. This feedback control and input can be used as the controller for some subset of the initial states. We demonstrate how to combine multiple trajectories into a synthesized controller that satisfies the safety problem.


A Direct Step Down and Step Up ACAC Converter with Inverting and Non-Inverting Operations


title: An Adaptation of the Fast Fourier Transform for Parallel Processing

abstract: A modified version of the Fast Fourier Transform is developed and described. This version is well adapted for use in a special-purpose computer designed for the purpose. It is shown that only three operators are needed. One operator replaces successive pairs of data points by their sums and differences. The second operator performs a fixed permutation which is an ideal shuffle of the data. The third operator permits the multiplication of a selected subset of the data by a common complex multiplier. If, as seems reasonable, the slowest operation is the complex multiplications required, then, for reasonably sized date sets—e.g. 512 complex numbers—parallelization by the method developed should allow an increase of speed over the serial use of the Fast Fourier Transform by about two orders of magnitude. It is suggested that a machine to realize the speed improvement indicated is quite feasible. The analysis is based on the use of the Kronecker product of matrices. It is suggested that this form is of general use in the development and classification of various modifications and extensions of the algorithm.


title: Requirement templates for distributed 3D graphics

abstract: Requirements are the basis upon which software architecture lies. As a consequence they should be expressed as precisely as possible in order to propose the best compromise between stakeholder needs and engineering constraints. While some measurements such as frame rate or latency are a widely known mean of expressing requirements in the 3D community, they often are loosely defined. This leads to software engineering decisions which exclude some of the most promising options. This paper proposes to adapt a non-functional requirements expression template used in general software architecture to the specific case of 3D based systems engineering. It shows that in the process some interesting proposals appear as a straightforward consequence of the better definition of the system to be built.


title: Characterization of airborne microbial communities at a high-elevation site and their potential to act as atmospheric ice nuclei

abstract: Bacteria and fungi are ubiquitous in the atmosphere. The diversity and abundance of airborne microbes may be strongly influenced by atmospheric conditions or even influence atmospheric conditions themselves by acting as ice nucleators. However, few comprehensive studies have described the diversity and dynamics of airborne bacteria and fungi based on culture-independent techniques. We document atmospheric microbial abundance, community composition, and ice nucleation at a high-elevation site in northwestern Colorado. We used a standard small-subunit rRNA gene Sanger sequencing approach for total microbial community analysis and a bacteria-specific 16S rRNA bar-coded pyrosequencing approach (4,864 sequences total). During the 2-week collection period, total microbial abundances were relatively constant, ranging from 9.6 × 105 to 6.6 × 106 cells m−3 of air, and the diversity and composition of the airborne microbial communities were also relatively static. Bacteria and fungi were nearly equivalent, and members of the proteobacterial groups Burkholderiales and Moraxellaceae (particularly the genus Psychrobacter) were dominant. These taxa were not always the most abundant in freshly fallen snow samples collected at this site. Although there was minimal variability in microbial abundances and composition within the atmosphere, the number of biological ice nuclei increased significantly during periods of high relative humidity. However, these changes in ice nuclei numbers were not associated with changes in the relative abundances of the most commonly studied ice-nucleating bacteria.


title: Automated Verification of Electrum Wallet

abstract: We introduce a formal modeling in ASLan++ of the two-factor authentication protocol used by the Electrum Bitcoin wallet. This allows us to perform an automatic analysis of the wallet and show that it is secure for standard scenarios in Dolev Yao model [Dolev 1981]. The result could be derived thanks to some advanced features of the protocol analyzer such as the possibility to specify (i) new intruder deduction rules with clauses and (ii) non-deducibility constraints.


title: Improving augmented reality applications with optical flow

abstract: This paper presents an augmented reality application framework which does not require specialized hardware or pre-calibration. Features extracted, using SURF, are matched between consecutive frames in order to determine the motion of the detected known object with respect to the camera. Next, a bi-directional optical flow algorithm is used to maintain the performance of the system to real-time. The system has been tested on two case studies, a children's book and advertisement, and the results are reported.


Eigenstructure Assignment of LQR via Homotopy Method


title: A Review of Watt-Level CMOS RF Power Amplifiers

abstract: This paper reviews the design of watt-level integrated CMOS RF power amplifiers (PAs) and state-of-the-art results in the literature. To reach watt-level output power from a single-chip CMOS PA, two main strategies can be identified: use of high supply voltage and use of matching and power combination. High supply voltage limits are closely related to device design in the fabrication process. However, the maximum operating voltage can be improved by amplifier class selection, circuit solutions, and process modifications or mask changes. High output power can also be reached by the use of on-chip matching and power combination, commonly using on-chip transformers. Reliability often sets the limits for the PA design, and PA degradation mechanisms are reviewed. A compilation of state-of-the-art published results for linear and switched watt-level PAs, as well as a few fully integrated CMOS PAs, is presented and discussed.


title: High bandwidth low power operational amplifier design and compensation techniques

abstract: The need for high bandwidth operational amplifiers (op amp) exists for numerous applications. This need requires research in the area of Op Amp bandwidth extension. The exploited method in this thesis uses a class of compensation called Indirect Feedback Frequency Compensation in which the compensation current is fed back indirectly from the output to an internal high impedance node, to extend the bandwidth of an Op Amp. Among various compensation methods for operational amplifiers, indirect compensation offers potentially large benefits in regards to power to speed trade-off. The indirect compensated Op Amps can exhibit significant improvements in speed over traditional Miller compensated Op Amps and result in much smaller layout size and lower power consumption. However the technique has not been widely used in practice due to a lack of clear design procedure. This thesis develops an analytical description of how indirect compensation works and derives key trade off equations among various specifications. These results provide the insight needed for practically designing operational amplifiers with this technique. Based on the results, a step-by-step design procedure is proposed for an operational amplifier using indirect compensation. To demonstrate the proposed design procedure, a two stage Op Amp is designed. The Op Amp achieved a 2 MHz gain-bandwidth product (GBW) driving a large capacitive load (100 pF). The GBW of the Op Amp was improved by a factor of 10 times compared to the miller compensation scheme. The amplifier documented in this thesis achieved a higher simulated figures-of-merit (FoMs) compared to the state-of-art and can be directly used in integrated systems to achieve higher performance.


title: emuARM: A tool for teaching the ARM assembly language

abstract: Technology has always enhanced learning as well as the overall teaching experience. With proper tools and resources in hand, we can easily integrate educational and information technologies into the academic environment. In this paper, we present a software tool to enhance the learning of microprocessors and computer architecture for students. We have developed an ARM instruction set emulator, emuARM, which is a Java based software tool for duplicating the functions of an ARMv5 microprocessor. Here, we present the internal design and features of emuARM. We present a comparison of the features of emuARM with other present ARM emulators in the market. At the end, we present the results of a survey that attests the pedagogical value of our tool.


title: Bi-objective model for test-suite reduction based on modified condition/decision coverage

abstract: It is evidence that modified condition/decision coverage (MC/DC) is an effective verification method and can help to detect safety faults despite of its expensive cost. In regression testing, it is quite costly to rerun all of test cases in test suite because new test cases are added to test suite as the software evolves. Therefore, it is necessary to reduce the test suite to improve test efficiency and save test cost. Many existing test-suite reduction techniques are not effective to reduce MC/DC test suite. This paper proposes a new test-suite reduction technique for MC/DC: a bi-objective model that considers both the coverage degree of test case for test requirements and the capability of test cases to reveal error. Our experiment results show that the technique both reduces the size of test suite and better ensures the effectiveness of test suite to reveal error.


title: An Analytical Continuous-Curvature Path-Smoothing Algorithm

abstract: An efficient and analytical continuous-curvature path-smoothing algorithm, which fits an ordered sequence of waypoints generated by an obstacle-avoidance path planner, is proposed. The algorithm is based upon parametric cubic Bezier curves; thus, it is inherently closed-form in its expression, and the algorithm only requires the maximum curvature to be defined. The algorithm is, thus, computational efficient and easy to implement. Results show the effectiveness of the analytical algorithm in generating a continuous-curvature path, which satisfies an upper bound-curvature constraint, and that the path generated requires less control effort to track and minimizes control-input variability.


title: Swoogle: Searching for Knowledge on the Semantic Web

abstract: Swoogle is an implemented system that discovers, analyzes and indexes knowledge encoded in semantic web documents on the Web. Swoogle reasons about these documents and their constituent parts (e.g., terms and triples) and records meaningful metadata about them. Swoogle provides webscale semantic web data access service, which helps human users and software systems to find relevant documents, terms and triples, via its search and navigation services. Swoogle also provides a customizable algorithm inspired by Google's PageRank algorithm but adapted to the semantics and use patterns found in semantic web documents.


title: Developmental Effects of the Pesticide Imidacloprid on Zebrafish Body Length and Mortality

abstract: The pesticide imidacloprid, a Neonicotinoid, is widely used and commercially available. Neonicotinoids are antagonists at nicotinic acetylcholine receptors, which can cause neurobehavioral effects in developing organisms. While the effects of other insecticides such as Malathion and Fipronil on zebrafish Danio rerio development have been studied, few studies describe the morphological effects of imidacloprid during zebrafish development. To test the hypothesis that imidacloprid concentration positively correlates to increased mortality and decreased body length, we exposed zebrafish to imidacloprid for five days post fertilization. Body length and embryo mortality were recorded at 1, 2, 3, 4, and 5 days post fertilization in 0 ug/L, 100 ug/L, 1,000 ug/L, and 10,000 ug/L imidacloprid concentrations. These concentrations were chosen to mimic levels that could be reached in the environment especially soon after application. Our results demonstrate statistically increased embryo mortality, with a f crit value of 3.193 and a f value of 3.098 from an ANOVA test, and impaired body length with a dose dependent correlation to the concentration of imidacloprid and a P value of less than 0.01 from a T test. Given the pesticide’s high prevalence, future studies should be considered to determine if this effect may impact livestock and human development.


title: Simple Online and Realtime Tracking

abstract: This paper explores a pragmatic approach to multiple object tracking where the main focus is to associate objects efficiently for online and realtime applications. To this end, detection quality is identified as a key factor influencing tracking performance, where changing the detector can improve tracking by up to 18.9%. Despite only using a rudimentary combination of familiar techniques such as the Kalman Filter and Hungarian algorithm for the tracking components, this approach achieves an accuracy comparable to state-of-the-art online trackers. Furthermore, due to the simplicity of our tracking method, the tracker updates at a rate of 260 Hz which is over 20x faster than other state-of-the-art trackers.


title: Breast cancer risk and environmental exposures.

abstract: Although environmental contaminants have potential to affect breast cancer risk, explicit environmental links to this disease are limited. The most well-defined environmental risk factors are radia...


title: 2002 Aoristic Signatures and the Spatio-Temporal Analysis of High Volume Crime Patterns

abstract: The spatial analysis of crime and the current focus on hotspots has pushed the area of crime mapping to the fore, especially in regard to high volume offenses such as vehicle theft and burglary. Hotspots also have a temporal component, yet police recorded crime databases rarely record the actual time of offense as this is seldom known. Police crime data tends, more often than not, to reflect the routine activities of the victims rather than the offense patterns of the offenders. This paper demonstrates a technique that uses police START and END crime times to generate a crime occurrence probability at any given time that can be mapped or visualized graphically. A study in the eastern suburbs of Sydney, Australia, demonstrates that crime hotspots with a geographical proximity can have distinctly different temporal patterns.


title: Cheaper and Better: Selecting Good Workers for Crowdsourcing

abstract: Crowdsourcing provides a popular paradigm for data collection at scale. We study the problem of selecting subsets of workers from a given worker pool to maximize the accuracy under a budget constraint. One natural question is whether we should hire as many workers as the budget allows, or restrict on a small number of top-quality workers. By theoretically analyzing the error rate of a typical setting in crowdsourcing, we frame the worker selection problem into a combinatorial optimization problem and propose an algorithm to solve it efficiently. Empirical results on both simulated and real-world datasets show that our algorithm is able to select a small number of high-quality workers, and performs as good as, sometimes even better than, the much larger crowds as the budget allows.


title: A practical framework for 802.11 MIMO rate adaptation

abstract: The emergence of MIMO antennas and channel bonding in 802.11n wireless networks has resulted in a huge leap in capacity compared with legacy 802.11 systems. This leap, however, adds complexity to optimizing transmission. Not only does the appropriate data rate need to be selected, but also the MIMO transmission technique (e.g., Spatial Diversity or Spatial Multiplexing), the number of streams, and the channel width. Incorporating these features into a rate adaptation (RA) solution requires a new set of rules to accurately evaluate channel conditions and select the appropriate transmission setting with minimal overhead. To address these challenges, our contributions in this work are two-fold. First, we propose a practical link metric that accurately captures channel conditions in MIMO 802.11n environments, and we call this metric diffSNR. Using diffSNR captured from real testbed environments, we build performance models that accuractely predict link quality in 95.5% of test cases. Practicality and deployability are guaranteed with diffSNR as it can be measured on all off-the-shelf MIMO WiFi chipsets. Second, we propose ARAMIS (Agile Rate Adaptation for MIMO Systems), a standard-compliant, closed-loop RA solution that jointly adapts rate and bandwidth, and we utilize the diffSNR-based 802.11n performance models within ARAMIS's framework. ARAMIS adapts transmission rates on a per-packet basis; we believe it is the first closed-loop, 802.11 RA algorithm that simultaneously adapts rate and channel width. We have implemented ARAMIS with diffSNR on Atheros-based devices and deployed it on our 15-node testbed. Our experiments show that ARAMIS accurately adapts to a wide variety of channel conditions with negligible overhead. Furthermore, ARAMIS outperforms existing RA algorithms in 802.11n environments with up to a 10-fold increase in throughput.


title: Design and Implementation of Firing Circuit for Single- Phase Converter

abstract:  Abstract—The theme of the paper is to design and implement the firing circuit for a converter. The necessity of getting synchronized firing pulses for the gate of the thyristor is discussed. Out of many variety of firing circuits available, the ideas behind are the two most popularly used control circuits that are namely using ramp signal and using cosine signal. It shows how a cosine controls scheme work. Detail description and functioning of each block is explained along with the waveforms at the output of the blocks. Experimental results obtained from oscillographic displays at important points of the circuits are included. In this paper, we fabricate a hardware circuit which implements the cosine control technique, test the circuit and also check that desired gate pulses for the thyristors.


title: Parameter-free auto-weighted multiple graph learning: a framework for multiview clustering and semi-supervised classification

abstract: Graph-based approaches have been successful in unsupervised and semi-supervised learning. In this paper, we focus on the real-world applications where the same instance can be represented by multiple heterogeneous features. The key point of utilizing the graph-based knowledge to deal with this kind of data is to reasonably integrate the different representations and obtain the most consistent manifold with the real data distributions. In this paper, we propose a novel framework via the reformulation of the standard spectral learning model, which can be used for multiview clustering and semisupervised tasks. Unlike other methods in the literature, the proposed methods can learn an optimal weight for each graph automatically without introducing an additive parameter as previous methods do. Furthermore, our objective under semisupervised learning is convex and the global optimal result will be obtained. Extensive empirical results on different real-world data sets demonstrate that the proposed methods achieve comparable performance with the state-of-the-art approaches and can be used more practically.


Domestic Violence Treatment for Abusive Women: A Treatment Manual


title: Cyber Migration: An Empirical Investigation on Factors that Affect Users' Switch Intentions in Social Networking Sites

abstract: In recent years, Social Networking Sites (SNSs) have increasingly gained popularity. With the existence of hundreds of such sites, it is not uncommon to have users switching between them. Thus retaining existing users and attracting new users is crucial to the success of an SNS. This paper frames the described phenomenon as a "cyber migration". We enlist and adapt the push-pull-mooring framework developed in migration literature to enhance the understanding of factors influencing the switching intentions of SNS users. A survey study was conducted which manifested dissatisfaction with member policy and peer influence as two central factors, alongside other findings. We recommend SNS practitioners to be more sensitive to users' reactions to the change in member policies. Meanwhile, how to utilize peer influences to maintain and develop membership deserves closer attention. The findings are believed to increase our understanding of an interesting Internet-enabled phenomenon, as well as making contributions to both research and practice.


title: The Whirlpool Secure Hash Function

abstract: ABSTRACT In this paper, we describe Whirlpool, which is a block-cipher-based secure hash function. Whirlpool produces a hash code of 512 bits for an input message of maximum length less than 2256 bits. The underlying block cipher, based on the Advanced Encryption Standard (AES), takes a 512-bit key and operates on 512-bit blocks of plaintext. Whirlpool has been endorsed by NESSIE (New European Schemes for Signatures, Integrity, and Encryption), which is a European Union-sponsored effort to put forward a portfolio of strong cryptographic primitives of various types.


title: Bursty feature representation for clustering text streams

abstract: Text representation plays a crucial role in classical text mining, where the primary focus was on static text. Nevertheless, well-studied static text representations including TFIDF are not optimized for non-stationary streams of information such as news, discussion board messages, and blogs. We therefore introduce a new temporal representation for text streams based on bursty features. Our bursty text representation differs significantly from traditional schemes in that it 1) dynamically represents documents over time, 2) amplifies a feature in proportional to its burstiness at any point in time, and 3) is topic independent. Our bursty text representation model was evaluated against a classical bagof-words text representation on the task of clustering TDT3 topical text streams. It was shown to consistently yield more cohesive clusters in terms of cluster purity and cluster/class entropies. This new temporal bursty text representation can be extended to most text mining tasks involving a temporal dimension, such as modeling of online blog pages.


1 A Survey of Mixed Data Clustering Algorithms


title: Cancer Chemoprevention by Phytochemicals: Nature’s Healing Touch

abstract: Phytochemicals are an important part of traditional medicine and have been investigated in detail for possible inclusion in modern medicine as well. These compounds often serve as the backbone for the synthesis of novel therapeutic agents. For many years, phytochemicals have demonstrated encouraging activity against various human cancer models in pre-clinical assays. Here, we discuss select phytochemicals-curcumin, epigallocatechin-3-gallate (EGCG), resveratrol, plumbagin and honokiol-in the context of their reported effects on the processes of inflammation and oxidative stress, which play a key role in tumorigenesis. We also discuss the emerging evidence on modulation of tumor microenvironment by these phytochemicals which can possibly define their cancer-specific action. Finally, we provide recent updates on how low bioavailability, a major concern with phytochemicals, is being circumvented and the general efficacy being improved, by synthesis of novel chemical analogs and nanoformulations.


title: On the Relation between External Software Quality and Static Code Analysis

abstract: Only a few studies exist that try to investigate whether there is a significant correlation between external software quality and the data provided by static code analysis tools. A clarification on this issue could pave the way for more precise prediction models on the probability of defects based on the violation of programming rules. We therefore initiated a study where the defect data of selected versions of the open source development environment ldquoEclipse SDKrdquo is correlated with the data provided by the static code analysis tools PMD and FindBugs applied the source code of Eclipse. The results from this study are promising as especially some PMD rulesets show a good correlation with the defect data and could therefore serve as basis for measurement, control and prediction of software quality.


title: Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach

abstract: Although field-collected recordings typically contain multiple simultaneously vocalizing birds of different species, acoustic species classification in this setting has received little study so far. This work formulates the problem of classifying the set of species present in an audio recording using the multi-instance multi-label (MIML) framework for machine learning, and proposes a MIML bag generator for audio, i.e., an algorithm which transforms an input audio signal into a bag-of-instances representation suitable for use with MIML classifiers. The proposed representation uses a 2D time-frequency segmentation of the audio signal, which can separate bird sounds that overlap in time. Experiments using audio data containing 13 species collected with unattended omnidirectional microphones in the H. J. Andrews Experimental Forest demonstrate that the proposed methods achieve high accuracy (96.1% true positives/negatives). Automated detection of bird species occurrence using MIML has many potential applications, particularly in long-term monitoring of remote sites, species distribution modeling, and conservation planning.


title: Does gamification increase engagement with online programs? A systematic review

abstract: Background ::: Engagement in online programs is difficult to maintain. Gamification is the recent trend that offers to increase engagement through the inclusion of game-like features like points and badges, in non-game contexts. This review will answer the following question, ‘Are gamification strategies effective in increasing engagement in online programs?’ ::: ::: ::: Method ::: Eight databases (Web of Science, PsycINFO, Medline, INSPEC, ERIC, Cochrane Library, Business Source Complete and ACM Digital Library) were searched from 2010 to the 28th of October 2015 using a comprehensive search strategy. Eligibility criteria was based on the PICOS format, where “population” included adults, “intervention” involved an online program or smart phone application that included at least one gamification feature. “Comparator” was a control group, “outcomes” included engagement and “downstream” outcomes which occurred as a result of engagement; and “study design” included experimental studies from peer-reviewed sources. Effect sizes (Cohens d and 95% confidence intervals) were also calculated. ::: ::: ::: Results ::: 1017 studies were identified from database searches following the removal of duplicates, of which 15 met the inclusion criteria. The studies involved a total of 10,499 participants, and were commonly undertaken in tertiary education contexts. Engagement metrics included time spent (n = 5), volume of contributions (n = 11) and occasions visited to the software (n = 4); as well as downstream behaviours such as performance (n = 4) and healthy behaviours (n = 1). Effect sizes typically ranged from medium to large in direct engagement and downstream behaviours, with 12 out of 15 studies finding positive significant effects in favour of gamification. ::: ::: ::: Conclusion ::: Gamification is effective in increasing engagement in online programs. Key recommendations for future research into gamification are provided. In particular, rigorous study designs are required to fully examine gamification’s effects and determine how to best achieve sustained engagement.


title: Object Tracking Benchmark

abstract: Object tracking has been one of the most important and active research areas in the field of computer vision. A large number of tracking algorithms have been proposed in recent years with demonstrated success. However, the set of sequences used for evaluation is often not sufficient or is sometimes biased for certain types of algorithms. Many datasets do not have common ground-truth object positions or extents, and this makes comparisons among the reported quantitative results difficult. In addition, the initial conditions or parameters of the evaluated tracking algorithms are not the same, and thus, the quantitative results reported in literature are incomparable or sometimes contradictory. To address these issues, we carry out an extensive evaluation of the state-of-the-art online object-tracking algorithms with various evaluation criteria to understand how these methods perform within the same framework. In this work, we first construct a large dataset with ground-truth object positions and extents for tracking and introduce the sequence attributes for the performance analysis. Second, we integrate most of the publicly available trackers into one code library with uniform input and output formats to facilitate large-scale performance evaluation. Third, we extensively evaluate the performance of 31 algorithms on 100 sequences with different initialization settings. By analyzing the quantitative results, we identify effective approaches for robust tracking and provide potential future research directions in this field.


title: Similarity estimation techniques from rounding algorithms

abstract: (MATH) A locality sensitive hashing scheme is a distribution on a family $\F$ of hash functions operating on a collection of objects, such that for two objects x,y, PrheF[h(x) = h(y)] = sim(x,y), where sim(x,y) e [0,1] is some similarity function defined on the collection of objects. Such a scheme leads to a compact representation of objects so that similarity of objects can be estimated from their compact sketches, and also leads to efficient algorithms for approximate nearest neighbor search and clustering. Min-wise independent permutations provide an elegant construction of such a locality sensitive hashing scheme for a collection of subsets with the set similarity measure sim(A,B) = \frac{|A P B|}{|A P Ehe\F [d(h(P),h(Q))] x O(log n log log n). EMD(P, Q). .


title: Low Self-Esteem Is Related to Aggression, Antisocial Behavior, and Delinquency

abstract: The present research explored the controversial link between global self-esteem and externalizing problems such as aggression, antisocial behavior, and delinquency. In three studies, we found a robust relation between low self-esteem and externalizing problems. This relation held for measures of self-esteem and externalizing problems based on self-report, teachers' ratings, and parents' ratings, and for participants from different nationalities (United States and New Zealand) and age groups (adolescents and college students). Moreover, this relation held both cross-sectionally and longitudinally and after controlling for potential confounding variables such as supportive parenting, parent-child and peer relationships, achievement-test scores, socioeconomic status, and IQ. In addition, the effect of self-esteem on aggression was independent of narcissism, an important finding given recent claims that individuals who are narcissistic, not low in self-esteem, are aggressive. Discussion focuses on clarifying ...


title: Conformal k-NN Anomaly Detector for Univariate Data Streams

abstract: Anomalies in time-series data give essential and often actionable information in many applications. In this paper we consider a model-free anomaly detection method for univariate time-series which adapts to non-stationarity in the data stream and provides probabilistic abnormality scores based on the conformal prediction paradigm. Despite its simplicity the method performs on par with complex prediction-based models on the Numenta Anomaly Detection benchmark and the Yahoo! S5 dataset.


title: Permanent magnet air-cored tubular linear generator for marine energy converters.

abstract: In this paper the authors outline the performance and modelling of a prototype linear tubular permanent magnet machine with an air cored stator. The main application of the machine is in direct drive marine energy conversion systems, where speeds are typically in the region of 0.5 m/s. A simple analytical method was derived to provide an initial design of a prototype machine, which was then modelled using 2D axisymmetric finite element analysis. Experimental results are presented to verify the design, and to investigate the performance of the machine as a generator.


title: C-Phrase: A system for building robust natural language interfaces to databases

abstract: This article presents C-Phrase, a natural language interface system that can be configured by normal, non-specialized, web-based technical teams. C-Phrase models queries in an extended version of Codd's tuple calculus and uses synchronous context-free grammars with lambda-expressions to represent semantic grammars. Given an arbitrary relational database, authors rapidly build an NLI using what we term the name-tailor-define protocol. We present a small study demonstrating the effectiveness of this approach for the GEO corpus and we introduce the evaluation metric of willingness that complements the standard metrics of precision and recall. However our true evaluation comes as we open-source C-Phrase.


title: Light Field Scale-Depth Space Transform for Dense Depth Estimation

abstract: Recent development of hand-held plenoptic cameras has brought light field acquisition into many practical and low-cost imaging applications. We address a crucial challenge in light field data processing: dense depth estimation of 3D scenes captured by camera arrays or plenoptic cameras. We first propose a method for construction of light field scale-depth spaces, by convolving a given light field with a special kernel adapted to the light field structure. We detect local extrema in such scale-depth spaces, which indicate the regions of constant depth, and convert them to dense depth maps after solving occlusion conflicts in a consistent way across all views. Due to the multi-scale characterization of objects in proposed representations, our method provides depth estimates for both uniform and textured regions, where uniform regions with large spatial extent are captured at coarser scales and textured regions are found at finer scales. Experimental results on the HCI (Heidelberg Collaboratory for Image Processing) light field benchmark show that our method gives state of the art depth accuracy. We also show results on plenoptic images from the RAYTRIX camera and our plenoptic camera prototype.


Biodegradation of Modified Poly ( ε-caprolactone ) in Different Environments


title: Automatic Coregistration Algorithm to Remove Canopy Shaded Pixels in UAV-Borne Thermal Images to Improve the Estimation of Crop Water Stress Index of a Drip-Irrigated Cabernet Sauvignon Vineyard

abstract: Water stress caused by water scarcity has a negative impact on the wine industry. Several strategies have been implemented for optimizing water application in vineyards. In this regard, midday stem water potential (SWP) and thermal infrared (TIR) imaging for crop water stress index (CWSI) have been used to assess plant water stress on a vine-by-vine basis without considering the spatial variability. Unmanned Aerial Vehicle (UAV)-borne TIR images are used to assess the canopy temperature variability within vineyards that can be related to the vine water status. Nevertheless, when aerial TIR images are captured over canopy, internal shadow canopy pixels cannot be detected, leading to mixed information that negatively impacts the relationship between CWSI and SWP. This study proposes a methodology for automatic coregistration of thermal and multispectral images (ranging between 490 and 900 nm) obtained from a UAV to remove shadow canopy pixels using a modified scale invariant feature transformation (SIFT) computer vision algorithm and Kmeans++ clustering. Our results indicate that our proposed methodology improves the relationship between CWSI and SWP when shadow canopy pixels are removed from a drip-irrigated Cabernet Sauvignon vineyard. In particular, the coefficient of determination (R²) increased from 0.64 to 0.77. In addition, values of the root mean square error (RMSE) and standard error (SE) decreased from 0.2 to 0.1 MPa and 0.24 to 0.16 MPa, respectively. Finally, this study shows that the negative effect of shadow canopy pixels was higher in those vines with water stress compared with well-watered vines.


title: Comparing the effects of different individualized music interventions for elderly individuals with severe dementia

abstract: Background: Individuals with dementia often experience poor quality of life (QOL) due to behavioral and psychological symptoms of dementia (BPSD). Music therapy can reduce BPSD, but most studies have focused on patients with mild to moderate dementia. We hypothesized that music intervention would have beneficial effects compared with a no-music control condition, and that interactive music intervention would have stronger effects than passive music intervention. Methods: Thirty-nine individuals with severe Alzheimer's disease were randomly and blindly assigned to two music intervention groups (passive or interactive) and a no-music Control group. Music intervention involved individualized music. Short-term effects were evaluated via emotional response and stress levels measured with the autonomic nerve index and the Faces Scale. Long-term effects were evaluated by BPSD changes using the Behavioral Pathology in Alzheimer's Disease (BEHAVE-AD) Rating Scale . Results: Passive and interactive music interventions caused short-term parasympathetic dominance. Interactive intervention caused the greatest improvement in emotional state. Greater long-term reduction in BPSD was observed following interactive intervention, compared with passive music intervention and a no-music control condition. Conclusion: Music intervention can reduce stress in individuals with severe dementia, with interactive interventions exhibiting the strongest beneficial effects. Since interactive music intervention can restore residual cognitive and emotional function, this approach may be useful for aiding severe dementia patients' relationships with others and improving QOL. The registration number of the trial and the name of the trial registry are UMIN000008801 and "Examination of Effective Nursing Intervention for Music Therapy for Severe Dementia Elderly Person" respectively.


title: Active Learning in Multi-objective Evolutionary Algorithms for Sustainable Building Design

abstract: Residential and commercial buildings are responsible for about 40% of primary energy consumption in the US. The design of a building has tremendous effect on its energy profile, and recently there has been an increased interest in developing optimization methods that support the design of high performance buildings. Previous approaches are either based on simulation optimization or on training an accurate predictive model to replace expensive energy simulations during the optimization. We propose a method, suitable for expensive multiobjective optimization in very large search spaces. In particular, we use a Gaussian Process (GP) model for the prediction and devise an active learning scheme in a multi-objective genetic algorithm to preferentially simulate only solutions that are very informative to the model's predictions for the current generation. We develop a comprehensive and publicly available benchmark for building design optimization. We show that the GP model is highly competitive as a surrogate for building energy simulations, in addition to being well-suited for the active learning setting. Our results show that our approach clearly outperforms surrogate-based optimization, and produces solutions close in hypervolume to simulation optimization, while using only a fraction of the simulations and time.


title: Vectorization of Line Drawings via Polyvector Fields

abstract: Image tracing is a foundational component of the workflow in graphic design, engineering, and computer animation, linking hand-drawn concept images to collections of smooth curves needed for geometry processing and editing. Even for clean line drawings, modern algorithms often fail to faithfully vectorize junctions, or points at which curves meet; this produces vector drawings with incorrect connectivity. This subtle issue undermines the practical application of vectorization tools and accounts for hesitance among artists and engineers to use automatic vectorization software. To address this issue, we propose a novel image vectorization method based on state-of-the-art mathematical algorithms for frame field processing. Our algorithm is tailored specifically to disambiguate junctions without sacrificing quality.


title: Joint precision optimization and high level synthesis for approximate computing

abstract: Approximate computing has been recognized as an effective low power technique for applications with intrinsic error tolerance, such as image processing and machine learning. Existing efforts on this front are mostly focused on approximate circuit design, approximate logic synthesis or processor architecture approximation techniques. This work aims at how to make good use of approximate circuits at system and block level. In particular, approximation aware scheduling, functional unit allocation and binding algorithms are developed for data intensive applications. Simple yet credible error models, which are essential for precision control in the optimizations, are investigated. The algorithms are further extended to include bitwidth optimization in fixed point computations. Experimental results, including those from Verilog simulations, indicate that the proposed techniques facilitate desired energy savings under latency and accuracy constraints.


title: Impedance control stability properties in common implementations

abstract: Various implementations of impedance control have been suggested, usually based on idealized models of the physical system. This work considers the nonideal, practical effects of computation and/or communication delays and manipulator dynamics on the behavior of two primary approaches to impedance control. The results are cast in the form of stability boundaries, i.e. the relationships between desired impedance parameters which cause marginally stable behavior in the overall system. These stability boundaries are compared for the two primary implementations, and relative benefits of each approach are discussed. These comparisons provide the basis for quantitative tradeoffs, allowing selection of control implementation approaches suited for particular manipulators or allowing quantitative decisions to be made in manipulator system design. >


title: A 2D Human Body Model Dressed in Eigen Clothing

abstract: Detection, tracking, segmentation and pose estimation of people in monocular images are widely studied. Two-dimensional models of the human body are extensively used, however, they are typically fairly crude, representing the body either as a rough outline or in terms of articulated geometric primitives. We describe a new 2D model of the human body contour that combines an underlying naked body with a low-dimensional clothing model. The naked body is represented as a Contour Person that can take on a wide variety of poses and body shapes. Clothing is represented as a deformation from the underlying body contour. This deformation is learned from training examples using principal component analysis to produce eigen clothing. We find that the statistics of clothing deformations are skewed and we model the a priori probability of these deformations using a Beta distribution. The resulting generative model captures realistic human forms in monocular images and is used to infer 2D body shape and pose under clothing. We also use the coefficients of the eigen clothing to recognize different categories of clothing on dressed people. The method is evaluated quantitatively on synthetic and real images and achieves better accuracy than previous methods for estimating body shape under clothing.


title: Mobile Health (mHealth) Approaches and Lessons for Increased Performance and Retention of Community Health Workers in Low- and Middle-Income Countries: A Review

abstract: Background: Mobile health (mHealth) describes the use of portable electronic devices with software applications to provide health services and manage patient information. With approximately 5 billion mobile phone users globally, opportunities for mobile technologies to play a formal role in health services, particularly in low- and middle-income countries, are increasingly being recognized. mHealth can also support the performance of health care workers by the dissemination of clinical updates, learning materials, and reminders, particularly in underserved rural locations in low- and middle-income countries where community health workers deliver integrated community case management to children sick with diarrhea, pneumonia, and malaria. Objective: Our aim was to conduct a thematic review of how mHealth projects have approached the intersection of cellular technology and public health in low- and middle-income countries and identify the promising practices and experiences learned, as well as novel and innovative approaches of how mHealth can support community health workers. Methods: In this review, 6 themes of mHealth initiatives were examined using information from peer-reviewed journals, websites, and key reports. Primary mHealth technologies reviewed included mobile phones, personal digital assistants (PDAs) and smartphones, patient monitoring devices, and mobile telemedicine devices. We examined how these tools could be used for education and awareness, data access, and for strengthening health information systems. We also considered how mHealth may support patient monitoring, clinical decision making, and tracking of drugs and supplies. Lessons from mHealth trials and studies were summarized, focusing on low- and middle-income countries and community health workers. Results: The review revealed that there are very few formal outcome evaluations of mHealth in low-income countries. Although there is vast documentation of project process evaluations, there are few studies demonstrating an impact on clinical outcomes. There is also a lack of mHealth applications and services operating at scale in low- and middle-income countries. The most commonly documented use of mHealth was 1-way text-message and phone reminders to encourage follow-up appointments, healthy behaviors, and data gathering. Innovative mHealth applications for community health workers include the use of mobile phones as job aides, clinical decision support tools, and for data submission and instant feedback on performance. Conclusions: With partnerships forming between governments, technologists, non-governmental organizations, academia, and industry, there is great potential to improve health services delivery by using mHealth in low- and middle-income countries. As with many other health improvement projects, a key challenge is moving mHealth approaches from pilot projects to national scalable programs while properly engaging health workers and communities in the process. By harnessing the increasing presence of mobile phones among diverse populations, there is promising evidence to suggest that mHealth can be used to deliver increased and enhanced health care services to individuals and communities, while helping to strengthen health systems. [J Med Internet Res 2013;15(1):e17]


Referral Practices of School Counselors.


title: Liquid: A Detection-Resistant Covert Timing Channel Based on IPD Shaping

abstract: Abstract Covert timing channels provide a way to surreptitiously leak information from an entity in a higher-security level to an entity in a lower level. The difficulty of detecting or eliminating such channels makes them a desirable choice for adversaries that value stealth over throughput. When one considers the possibility of such channels transmitting information across network boundaries, the threat becomes even more acute. A promising technique for detecting covert timing channels focuses on using entropy-based tests. This method is able to reliably detect known covert timing channels by using a combination of entropy and conditional entropy to detect anomalies in shape and regularity, respectively. This dual approach is intended to make entropy-based detection robust against both current and future channels. In this work, we show that entropy-based detection can be defeated by a channel that intelligently and adaptively manipulates the metrics used for detection. Specifically, we propose a new passive covert channel that uses a portion of the inter-packet delays in a compromised stream to smooth out the shape distortions detected by the entropy test. As a passive channel, it is not as prone to regularity-based detection as previously proposed active channels. We introduce a model for analyzing the effect of our techniques on the entropy of the channel and empirically investigate the accuracy of the model. In network experiments and simulation, we validate this model and demonstrate that the proposed channel successfully evades entropy-based detection and other known tests while maintaining reasonable throughput.


title: Fault tolerant control of a quadrotor UAV using sliding mode control

abstract: In this paper, the sliding mode approach is used to control of a quadrotor unmanned aerial vehicle (UAV) in the presence of external disturbance and actuator fault. Fault detection unit can detect the actuator fault using a state estimator. Then it reconfigures the structure of controller such that some control performance is achieved. The proposed control structure has the advantage of disturbance rejection in the fault-free condition. Moreover it can recover some of control performances when a fault occurs. Different simulations have been carried out to show the performance and effectiveness of the proposed method.


title: An adaptive spatial fuzzy clustering algorithm for 3-D MR image segmentation

abstract: An adaptive spatial fuzzy c-means clustering algorithm is presented in this paper for the segmentation of three-dimensional (3-D) magnetic resonance (MR) images. The input images may be corrupted by noise and intensity nonuniformity (INU) artifact. The proposed algorithm takes into account the spatial continuity constraints by using a dissimilarity index that allows spatial interactions between image voxels. The local spatial continuity constraint reduces the noise effect and the classification ambiguity. The INU artifact is formulated as a multiplicative bias field affecting the true MR imaging signal. By modeling the log bias field as a stack of smoothing B-spline surfaces, with continuity enforced across slices, the computation of the 3-D bias field reduces to that of finding the B-spline coefficients, which can be obtained using a computationally efficient two-stage algorithm. The efficacy of the proposed algorithm is demonstrated by extensive segmentation experiments using both simulated and real MR images and by comparison with other published algorithms.


title: Malware in the Future? Forecasting Analyst Detection of Cyber Events

abstract: There have been extensive efforts in government, academia, and industry to anticipate, forecast, and mitigate cyber attacks. A common approach is time-series forecasting of cyber attacks based on data from network telescopes, honeypots, and automated intrusion detection/prevention systems. This research has uncovered key insights such as systematicity in cyber attacks. Here, we propose an alternate perspective of this problem by performing forecasting of attacks that are analyst-detected and -verified occurrences of malware. We call these instances of malware cyber event data. Specifically, our dataset was analyst-detected incidents from a large operational Computer Security Service Provider (CSSP) for the U.S. Department of Defense, which rarely relies only on automated systems. Our data set consists of weekly counts of cyber events over approximately seven years. Since all cyber events were validated by analysts, our dataset is unlikely to have false positives which are often endemic in other sources of data. Further, the higher-quality data could be used for a number for resource allocation, estimation of security resources, and the development of effective risk-management strategies. We used a Bayesian State Space Model for forecasting and found that events one week ahead could be predicted. To quantify bursts, we used a Markov model. Our findings of systematicity in analyst-detected cyber attacks are consistent with previous work using other sources. The advanced information provided by a forecast may help with threat awareness by providing a probable value and range for future cyber events one week ahead. Other potential applications for cyber event forecasting include proactive allocation of resources and capabilities for cyber defense (e.g., analyst staffing and sensor configuration) in CSSPs. Enhanced threat awareness may improve cybersecurity.


title: Stagewise Lasso

abstract: Many statistical machine learning algorithms minimize either an empirical loss function as in AdaBoost, or a penalized empirical loss as in Lasso or SVM. A single regularization tuning parameter controls the trade-off between fidelity to the data and generalizability, or equivalently between bias and variance. When this tuning parameter changes, a regularization "path" of solutions to the minimization problem is generated, and the whole path is needed to select a tuning parameter to optimize the prediction or interpretation performance. Algorithms such as homotopy-Lasso or LARS-Lasso and Forward Stagewise Fitting (FSF) (aka e-Boosting) are of great interest because of their resulted sparse models for interpretation in addition to prediction. ::: ::: In this paper, we propose the BLasso algorithm that ties the FSF (e-Boosting) algorithm with the Lasso method that minimizes the L1 penalized L2 loss. BLasso is derived as a coordinate descent method with a fixed stepsize applied to the general Lasso loss function (L1 penalized convex loss). It consists of both a forward step and a backward step. The forward step is similar to e-Boosting or FSF, but the backward step is new and revises the FSF (or e-Boosting) path to approximate the Lasso path. In the cases of a finite number of base learners and a bounded Hessian of the loss function, the BLasso path is shown to converge to the Lasso path when the stepsize goes to zero. For cases with a larger number of base learners than the sample size and when the true model is sparse, our simulations indicate that the BLasso model estimates are sparser than those from FSF with comparable or slightly better prediction performance, and that the the discrete stepsize of BLasso and FSF has an additional regularization effect in terms of prediction and sparsity. Moreover, we introduce the Generalized BLasso algorithm to minimize a general convex loss penalized by a general convex function. Since the (Generalized) BLasso relies only on differences not derivatives, we conclude that it provides a class of simple and easy-to-implement algorithms for tracing the regularization or solution paths of penalized minimization problems.


title: Annual Killifish Transcriptomics and Candidate Genes for Metazoan Diapause.

abstract: Dormancy has evolved in all major metazoan lineages. It is critical for survival when environmental stresses are not conducive to growth, maturation, or reproduction. Embryonic diapause is a form of dormancy where development is reversibly delayed and metabolism is depressed. We report the diapause transcriptome of the annual killifish Nematolebias whitei, and compare gene expression between diapause embryos and free-living larvae to identify a candidate set of 945 differentially expressed "diapause" genes for this species. Similarity of transcriptional patterns among N. whitei and other diapausing animals is striking for a small set of genes associated with stress resistance, circadian rhythm, and metabolism, while other genes show discordant patterns. Although convergent evolution of diapause may require shared molecular mechanisms for fundamental processes, similar physiological phenotypes also may arise through modification of alternative pathways. Annual killifishes are a tractable model system for comparative transcriptomic studies on the evolution of diapause.


title: How to add new knowledge to already trained deep learning models applied to semantic localization

abstract: The capacity of a robot to automatically adapt to new environments is crucial, especially in social robotics. Often, when these robots are deployed in home or office environments, they tend to fail because they lack the ability to adapt to new and continuously changing scenarios. In order to accomplish this task, robots must obtain new information from the environment, and then add it to their already learned knowledge. Deep learning techniques are often used to tackle this problem successfully. However, these approaches, complete retraining of the models, which is highly time-consuming. In this work, several strategies are tested to find the best way to include new knowledge in an already learned model in a deep learning pipeline, putting the spotlight on the time spent for this training. We tackle the localization problem in the long term with a deep learning approach and testing several retraining strategies. The results of the experiments are discussed and, finally, the best approach is deployed on a Pepper robot.


title: Impact of rank optimization on downlink non-orthogonal multiple access (NOMA) with SU-MIMO

abstract: Non-orthogonal multiple access (NOMA) is a promising radio access technique for further cellular enhancements toward the 5th generation (5G) mobile communication systems. Single-user multiple-input multiple-output (SU-MIMO) is one of the key technologies in long term evolution (LTE) /LTE-Advanced systems. It is proved that NOMA combined with SU-MIMO techniques can achieve further system performance improvement. In this paper, we focus on the impact of rank optimization on the performance of NOMA with SU-MIMO in downlink. Firstly, a geometry based rank adjustment method is studied. Secondly, an enhanced feedback method for rank adjustment is discussed. The simulation results show that the performance gain of NOMA improves with the proposed two rank adjustment methods. Compared to orthogonal access system, large performance gains can be achieved for NOMA, which are about 23% for cell average throughput and 33% for cell-edge user throughput.


title: Gain-Enhanced Patch Antennas With Loading of Shorting Pins

abstract: A new gain-enhanced patch antenna with loading of shorting pins is proposed in this paper. Four metallic pins are symmetrically placed in the two diagonals of a square patch resonator to electrically short the patch and ground plane. These shorting pins tremendously perturb the field distribution beneath the patch due to their shunt inductive effect. As these four pins are moved outward along the two orthogonal diagonals away from the center, their influence on the field distribution over the patch is strengthened to gradually raise its dominant mode, i.e., TM010 mode, resonant frequency as the pin-to-pin spacing is enlarged. At a fixed resonant frequency, the overall area of this proposed patch antenna with four pins results to be increased. As such, its radiation directivity or gain gets to be enhanced due to the enlarged antenna area. After extensive analysis is executed, two square patch antennas with and without loaded pins are designed and fabricated. The simulated and measured results agree with each other, and they have evidently demonstrated that the radiation directivity can be enhanced up to 11.0 dBi, or about 2.9 dB increment, by virtue of the proposed approach.


title: Applying mass spectrometry-based proteomics to genetics, genomics and network biology

abstract: The systematic and quantitative molecular analysis of mutant organisms that has been pioneered by studies on mutant metabolomes and transcriptomes holds great promise for improving our understanding of how phenotypes emerge. Unfortunately, owing to the limitations of classical biochemical analysis, proteins have previously been excluded from such studies. Here we review how technical advances in mass spectrometry-based proteomics can be applied to measure changes in protein abundance, posttranslational modifications and protein-protein interactions in mutants at the scale of the proteome. We finally discuss examples that integrate proteomics data with genomic and phenomic information to build network-centred models, which provide a promising route for understanding how phenotypes emerge.


title: Phenotyping and Genotyping Characterization of Proteus Vulgaris After Biofield Treatment

abstract: Proteus vulgaris (P. vulgaris) is widespread in nature, mainly found in flora of human gastrointestinal tract. The current study was attempted to investigate the effects of Mr. Trivedi’s biofield treatment on lyophilized as well as revived state of P. vulgaris for antimicrobial susceptibility pattern, biochemical characteristics, and biotype. P. vulgaris cells were procured from Micro BioLogics Inc., USA, in sealed pack bearing the American Type Culture Collection (ATCC 33420) number and stored according to the recommended storage protocol until needed for experiments. Lyophilized vial of ATCC strain of P. vulgaris were divided in two parts, Gr. I: control and Gr. II: treatment. Group II was further subdivided into two parts, Gr. IIA and Gr. IIB. Gr. IIA was analysed on day 10. Gr. IIB was stored and analysed on day 143. After retreatment on day 143, the sample was divided into three separate tubes. First, second and third tubes were analysed on day 5, 10 and 15 respectively. All experimental parameters were studied using automated Micro Scan Walk-Away ® system. The 16S rDNA sequencing of lyophilized treated sample was carried out to correlate the phylogenetic relationship of P. vulgaris with other bacterial species after treatment. The antimicrobial susceptibility and minimum inhibitory concentration showed 10.71% and 15.63% alteration respectively in treated cells of P. vulgaris as compared to control. It was observed that few biochemical reactions (6%) were altered in the treated groups with respect to control. Moreover, biotype number was substantially changed in treated cells, Gr. IIA (62060406, Proteus penneri) on day 10 as compared to control (62070406; Proteus vulgaris). 16S rDNA analysis showed that the identified sample in this experiment was Proteus vulgaris after biofield treatment. However, the nearest homolog genus-species was found to be Proteus hauseri. The results suggested that biofield treatment has impact on P. vulgaris in lyophilized as well as revived state.


title: The Importance of Being Recurrent for Modeling Hierarchical Structure

abstract: Recent work has shown that recurrent neural networks (RNNs) can implicitly capture and exploit hierarchical information when trained to solve common natural language processing tasks (Blevins et al., 2018) such as language modeling (Linzen et al., 2016; Gulordava et al., 2018) and neural machine translation (Shi et al., 2016). In contrast, the ability to model structured data with non-recurrent neural networks has received little attention despite their success in many NLP tasks (Gehring et al., 2017; Vaswani et al., 2017). In this work, we compare the two architectures—recurrent versus non-recurrent—with respect to their ability to model hierarchical structure and find that recurrency is indeed important for this purpose. The code and data used in our experiments is available at https://github.com/ ketranm/fan_vs_rnn


title: The need for supportive care among head and neck cancer patients: psychometric assessment of the Dutch version of the Supportive Care Needs Survey Short-Form (SCNS-SF34) and the newly developed head and neck cancer module (SCNS-HNC)

abstract: PURPOSE ::: The purpose of this study is to assess the psychometric properties of the Dutch version of the 34-item Short-Form Supportive Care Needs Survey (SCNS-SF34) and the newly developed module for head and neck cancer (HNC) patients (SCNS-HNC). ::: ::: ::: METHODS ::: HNC patients were included from two cross-sectional studies. Content validity of the SCNS-HNC was analysed by examining redundancy and completeness of items. Factor structure was assessed using confirmatory and exploratory factor analyses. Cronbach's alpha, Spearman's correlation, Mann-Whitney U test, Kruskall-Wallis and intraclass correlation coefficients (ICC) were used to assess internal consistency, construct validity and test-retest reliability. ::: ::: ::: RESULTS ::: Content validity of the SCNS-HNC was good, although some HNC topics were missing. For the SCNS-SF34, a four-factor structure was found, namely physical and daily living, psychological, sexuality and health system and information and patient support (alpha = .79 to .95). For the SCNS-HNC, a two-factor structure was found, namely HNC-specific functioning and lifestyle (alpha = .89 and .60). Respectively, 96 and 89 % of the hypothesised correlations between the SCNS-SF34 or SCNS-HNC and other patient-reported outcome measures were found; 57 and 67 % also showed the hypothesised magnitude of correlation. The SCNS-SF34 domains discriminated between treatment procedure (physical and daily living p = .02 and psychological p = .01) and time since treatment (health system, information and patient support p = .02). Test-retest reliability of SCNS-SF34 domains and HNC-specific functioning domain was above .70 (ICC = .74 to .83), and ICC = .67 for the lifestyle domain. Floor effects ranged from 21.1 to 70.9 %. ::: ::: ::: CONCLUSIONS ::: The SCNS-SF34 and SCNS-HNC are valid and reliable instruments to evaluate the need for supportive care among (Dutch) HNC patients.


title: A Decentralized Framework for Multi-Agent Robotic Systems

abstract: Over the past few years, decentralization of multi-agent robotic systems has become an important research area. These systems do not depend on a central control unit, which enables the control and assignment of distributed, asynchronous and robust tasks. However, in some cases, the network communication process between robotic agents is overlooked, and this creates a dependency for each agent to maintain a permanent link with nearby units to be able to fulfill its goals. This article describes a communication framework, where each agent in the system can leave the network or accept new connections, sending its information based on the transfer history of all nodes in the network. To this end, each agent needs to comply with four processes to participate in the system, plus a fifth process for data transfer to the nearest nodes that is based on Received Signal Strength Indicator (RSSI) and data history. To validate this framework, we use differential robotic agents and a monitoring agent to generate a topological map of an environment with the presence of obstacles.


title: ENSEMBLE DECISION TREE CLASSIFIER FOR BREAST CANCER DATA

abstract: Data mining is the process of analyzing large quantities of data and summarizing it into useful information. In medical diagnoses the role of data mining approaches increasing rapidly. Particularly Classification algorithms are very helpful in classifying the data, which is important in decision making process for medical practitioners. Further to enhance the classifier accuracy various pre-processing techniques and ensemble techniques were developed. In this study a hybrid approach, CART classifier with feature selection and bagging technique has been considered to evaluate the performance in terms of accuracy and time for classification of various breast cancer datasets.


title: IFCaaS: Information Flow Control as a Service for Cloud Security

abstract: With the maturity of service-oriented architecture (SOA) and Web technologies, web services have become critical components of Software as a Service (SaaS) applications in cloud ecosystem environments. Most SaaS applications leverage multi-tenant data stores as a back end to keep and process data with high agility. Although these technologies promise impressive benefits, they put SaaS applications at risk against novel as well as prevalent attack vectors. This security risk is further magnified by the loss of control and lack of security enforcement over sensitive data manipulated by SaaS applications. An effective solution is needed to fulfill several requirements originating in the dynamic and complex nature of such applications. Inspired by the rise of Security as a Service (SecaaS) model, this paper introduces "Information Flow Control as a Service (IFCaaS)". IFCaaS lays the foundation of cloud-delivered IFC-based security analysis and monitoring services. As an example of the adoption of the IFCaaS, this paper presents a novel framework that addresses the detection of information flow vulnerabilities in SaaS applications. Our initial experiments show that the framework is a viable solution to protect against data integrity and confidentiality violations leading to information leakage.


title: An all-digital bang-bang PLL using two-point modulation and background gain calibration for spread spectrum clock generation

abstract: An all-digital spread spectrum clock generator (SSCG) using two-point modulation is presented. To calibrate the gain mismatch between two modulation paths, a background gain calibration method is proposed. To reduce power consumption and design complexity, the bang-bang phase-frequency detector (BBPFD) is used instead of the time-to-digital converter (TDC). The prototype chip has been fabricated in a 65-nm CMOS process and it consumes 6 mW at 2.5 GHz. The measured minimum rms jitter is 1.58 ps.


title: A framework for practical, automated black-box testing of component-based software

abstract: This paper outlines a general strategy for automated black-box testing of software components that includes: automatic generation of component test drivers, automatic generation of black-box test data, and automatic or semi-automatic generation of component wrappers that serve as test oracles. This research in progress unifies several threads of testing research, and preliminary work indicates that practical levels of testing automation are possible.


title: Graphene Based Plasmonic Tunable Low Pass Filters in the THz Band

abstract: We propose the concept, synthesis, analysis, and design of graphene-based plasmonic tunable low-pass filters operating in the THz band. The proposed structure is composed of a graphene strip transferred onto a dielectric and a set of polysilicon DC gating pads located beneath it. This structure implements a stepped impedance low-pass filter for the propagating surface plasmons by adequately controlling the guiding properties of each strip section through graphene's field effect. A synthesis procedure is presented to design filters with desired specifications in terms of cut-off frequency, in-band performance, and rejection characteristics. The electromagnetic modeling of the structure is efficiently performed by combining an electrostatic scaling law to compute the guiding features of each strip section with a transmission line and transfer-matrix framework, approach further validated via full wave simulations. The performance of the proposed filters is evaluated in practical scenarios, taking into account the presence of the gating bias and the influence of graphene's losses. These results, together with the high miniaturization associated with plasmonic propagation, are very promising for the future use and integration of the proposed filters with other graphene and silicon-based elements in innovative THz communication systems.


[Photocoagulation of cavernous hemangioma of the retina].


title: Magnetic properties of CoNRhMnanoparticles: experiment and theory

abstract: The magnetism of Co–Rh nanoparticles is investigated experimentally and theoretically. The particles (≈2 nm) have been synthesized by decomposition of organometallic precursors in mild conditions of pressure and temperature, under hydrogen atmosphere and in the presence of a polymer matrix. The magnetic properties are determined by SQUID, Mossbauer spectroscopy, and X-ray magnetic circular dichroism (XMCD). The structural and chemical properties are characterized by wide angle X-ray scattering, transmission electronic microscopy and X-ray absorption near edge spectroscopy. All the studied Co–Rh clusters are magnetic with an average spin moment per atom µ that is larger than the one of macroscopic crystals or alloys with similar concentrations. The experimental results and comparison with theory suggest that the most likely chemical arrangement is a Rh core, with a Co-rich outer shell showing significant Co–Rh mixing at the interface. Measured and calculated magnetic anisotropy energies (MAEs) are found to be higher than in pure Co clusters. Moreover, one observes that the MAEs can be tuned to some extent by varying the Rh concentration. These trends are well accounted for by theory, which in addition reveals important spin and orbital moments induced at the Rh atoms as well as significant orbital moments at the Co atoms. These play a central role in the interpretation of experimental data as a function of Co–Rh content. A more detailed analysis from a local perspective shows that the orbital and spin moments at the Co–Rh interface are largely responsible for the enhancement of the magnetic moments and magnetic anisotropy.


title: BamView: viewing mapped read alignment data in the context of the reference sequence

abstract: Summary: BamView is an interactive Java application for visualizing the large amounts of data stored for sequence reads which are aligned against a reference genome sequence. It supports the BAM (Binary Alignment/Map) format. It can be used in a number of contexts including SNP calling and structural annotation. BamView has also been integrated into Artemis so that the reads can be viewed in the context of the nucleotide sequence and genomic features. Availability: BamView and Artemis are freely available (under a GPL licence) for download (for MacOSX, UNIX and Windows) at: http://bamview.sourceforge.net/


title: RoboBrain: Large-Scale Knowledge Engine for Robots

abstract: In this paper we introduce a knowledge engine, which learns and shares knowledge representations, for robots to carry out a variety of tasks. Building such an engine brings with it the challenge of dealing with multiple data modalities including symbols, natural language, haptic senses, robot trajectories, visual features and many others. The \textit{knowledge} stored in the engine comes from multiple sources including physical interactions that robots have while performing tasks (perception, planning and control), knowledge bases from the Internet and learned representations from several robotics research groups. ::: We discuss various technical aspects and associated challenges such as modeling the correctness of knowledge, inferring latent information and formulating different robotic tasks as queries to the knowledge engine. We describe the system architecture and how it supports different mechanisms for users and robots to interact with the engine. Finally, we demonstrate its use in three important research areas: grounding natural language, perception, and planning, which are the key building blocks for many robotic tasks. This knowledge engine is a collaborative effort and we call it RoboBrain.


title: Code fragment summarization

abstract: Current research in software engineering has mostly focused on the retrieval accuracy aspect but little on the presentation aspect of code examples, e.g., how code examples are presented in a result page. We investigate the feasibility of summarizing code examples for better presenting a code example. Our algorithm based on machine learning could approximate summaries in an oracle manually generated by humans with a precision of 0.71. This result is promising as summaries with this level of precision achieved the same level of agreement as human annotators with each other.


title: Managing Congenitally Missing Lateral Incisors. Part I: Canine Substitution

abstract: UNLABELLED ::: Dentists often encounter patients with missing or malformed teeth. The maxillary lateral incisor is the second most common congenitally absent tooth. There are three treatment options that exist for replacing missing lateral incisors. They include canine substitution, a tooth-supported restoration, or a single-tooth implant. Selecting the appropriate option depends on the malocclusion, specific space requirements, tooth-size relationship, and size and shape of the canine. The ideal treatment is the most conservative option that satisfies individual esthetic and functional requirements. Often the ideal option is canine substitution. Although the orthodontist positions the canine in the most esthetic and functional location, the restorative dentist often needs to place a porcelain veneer or crown to re-create normal lateral incisor shape and color. This article closely examines patient selection and illustrates the importance of interdisciplinary treatment planning to achieve optimal esthetics. It is the first in a three-part series discussing the three treatment alternatives for replacing missing lateral incisors. ::: ::: ::: CLINICAL SIGNIFICANCE ::: Patients with congenitally missing lateral incisors often raise difficult treatment planning issues. Therefore, to produce the most predictable esthetic results, it is important to choose the treatment that will best address the initial diagnosis. This article is the first in a three-part series that describes the different treatments available for patients with congenitally missing lateral incisors. This first article focuses on canine substitution as a method of tooth replacement for these missing teeth. The general dentist will learn to evaluate specific patient selection criteria and determine whether canine substitution is an appropriate treatment alternative for replacing missing lateral incisors. The orthodontist will understand how to position the canines to satisfy functional requirements and achieve proper esthetics. Finally, the importance of interdisciplinary team treatment planning is emphasized as a requirement for achieving optimal final esthetics.


title: The ketogenic diet as a non-pharmacological treatment for HIV-associated neurocognitive disorder: A descriptive analysis

abstract: Mitochondrial dysfunction is associated with abnormal glucose metabolism, inflammation and greater oxidative stress in the brain, each of which may contribute uniquely and perhaps synergistically to HIV-Associated Neurocognitive Disorders (HAND) risk. The ketogenic (i.e., low carbohydrate) diet provides the brain with a highly efficient mitochondrial fuel and is associated with improved cognitive performance in older adults with impaired neurocognitive functioning secondary to ageing, Alzheimer's, and Parkinson's disease; however, whether these cognitive gains are generalizable to older adults with HAND is unknown. Thus, the process and cognitive outcomes of the first participant randomized to the intervention and to the control group were investigated in this case-comparison study. To our knowledge, this is first report to establish the plausibility of the ketogenic diet as a treatment for HAND.


title: Effect of inhalation aromatherapy with lavender essential oil on stress and vital signs in patients undergoing coronary artery bypass surgery: A single-blinded randomized clinical trial.

abstract: OBJECTIVES ::: At present, aromatherapy is used widely in medical research. This study aimed to investigate the effects of inhalation aromatherapy using lavender essential oil to reduce mental stress and improve the vital signs of patients after coronary artery bypass surgery (CABG). ::: ::: ::: DESIGN ::: A single-blinded randomized controlled trial was conducted with 60 patients who had undergone CABG in a 2-day intervention that targeted stress reduction. ::: ::: ::: PARTICIPANTS ::: Sixty subjects following coronary artery bypass surgery in two aromatherapy and control groups. ::: ::: ::: SETTING ::: The study was conducted in Ekbatan Therapeutic and Educational Center, Hamadan, Iran, in 2013. ::: ::: ::: INTERVENTIONS ::: On the second and third days after surgery, the aromatherapy group patients received two drops of 2% lavender essential oil for 20min and the control group received two drops of distilled water as a placebo. ::: ::: ::: MAIN OUTCOME MEASURES ::: The primary outcome was mental stress, which was measured before and after the intervention using the DASS-21 questionnaire. The secondary outcomes were vital signs, including the heart rate, respiratory rate, and systolic and diastolic blood pressure, which were measured before and after the intervention. ::: ::: ::: RESULTS ::: The individual characteristics of the aromatherapy and control groups were the same. There were no significant difference in the mean mental stress scores and vital signs of the aromatherapy and control groups on the second or third days after surgery. ::: ::: ::: CONCLUSION ::: Inhalation aromatherapy with lavender essential oil had no significant effects on mental stress and vital signs in patients following CABG, except the systolic blood pressure.