Query: Classification of sensor errors for the statistical simulation of environmental perception in automated driving systems

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Stanley: The robot that won the DARPA Grand Challenge <=SEP=> Sebastian Thrun1, Mike Montemerlo1, Hendrik Dahlkamp1, David Stavens1, Andrei Aron1, James Diebel1, Philip Fong1, John Gale1, Morgan Halpenny1, Gabriel Hoffmann1, Kenny Lau1, Celia Oakley1, Mark Palatucci1, Vaughan Pratt1, Pascal Stang1, Sven Strohband2, Cedric Dupont2, Lars-Erik Jendrossek2, Christian Koelen2, Charles Markey2, Carlo Rummel2, Joe van Niekerk2, Eric Jensen2, Philippe Alessandrini2, Gary Bradski3, Bob Davies3, Scott Ettinger3, Adrian Kaehler3, Ara Nefian3, and Pamela Mahoney4

Three Decades of Driver Assistance Systems: Review and Future Perspectives <=SEP=> This contribution provides a review of fundamental goals, development and future perspectives of driver assistance systems. Mobility is a fundamental desire of mankind. Virtually any society strives for safe and efficient mobility at low ecological and economic costs. Nevertheless, its technical implementation significantly differs among societies, depending on their culture and their degree of industrialization. A potential evolutionary roadmap for driver assistance systems is discussed. Emerging from systems based on proprioceptive sensors, such as ABS or ESC, we review the progress incented by the use of exteroceptive sensors such as radar, video, or lidar. While the ultimate goal of automated and cooperative traffic still remains a vision of the future, intermediate steps towards that aim can be realized through systems that mitigate or avoid collisions in selected driving situations. Research extends the state-of-the-art in automated driving in urban traffic and in cooperative driving, the latter addressing communication and collaboration between different vehicles, as well as cooperative vehicle operation by its driver and its machine intelligence. These steps are considered important for the interim period, until reliable unsupervised automated driving for all conceivable traffic situations becomes available. The prospective evolution of driver assistance systems will be stimulated by several technological, societal and market trends. The paper closes with a view on current research fields.

USARSim: a robot simulator for research and education <=SEP=> This paper presents USARSim, an open source high fidelity robot simulator that can be used both for research and education. USARSim offers many characteristics that differentiate it from most existing simulators. Most notably, it constitutes the simulation engine used to run the virtual robots competition within the Robocup initiative. We describe its general architecture, describe examples of utilization, and provide a comprehensive overview for those interested in robot simulations for education, research and competitions.

Noise and the Reality Gap: The Use of Simulation in Evolutionary Robotics <=SEP=> The pitfalls of naive robot simulations have been recognised for areas such as evolutionary robotics. It has been suggested that carefully validated ispell slides.tex simulations with a proper treatment of noise may overcome these problems. This paper reports the results of experiments intended to test some of these claims. A simulation was constructed of a two-wheeled Khepera robot with IR and ambient light sensors. This included detailed mathematical models of the robot-environment interaction dynamics with empirically determined parameters. Artiicial evolution was used to develop recurrent dynamical network controllers for the simulated robot, for obstacle-avoidance and light-seeking tasks, using diierent levels of noise in the simulation. The evolved controllers were down-loaded onto the real robot and the correspondence between behaviour in simulation and in reality was tested. The level of correspondence varied according to how much noise was used in the simulation, with very good results achieved when realistic quantities were applied. It has been demonstrated that it is possible to develop successful robot controllers in simulation that generate almost identical behaviours in reality, at least for a particular class of robot-environment interaction dynamics.

Making Bertha Drive—An Autonomous Journey on a Historic Route <=SEP=> 125 years after Bertha Benz completed the first overland journey in automotive history, the Mercedes Benz S-Class S 500 INTELLIGENT DRIVE followed the same route from Mannheim to Pforzheim, Germany, in fully autonomous manner. The autonomous vehicle was equipped with close-to-production sensor hardware and relied solely on vision and radar sensors in combination with accurate digital maps to obtain a comprehensive understanding of complex traffic situations. The historic Bertha Benz Memorial Route is particularly challenging for autonomous driving. The course taken by the autonomous vehicle had a length of 103 km and covered rural roads, 23 small villages and major cities (e.g. downtown Mannheim and Heidelberg). The route posed a large variety of difficult traffic scenarios including intersections with and without traffic lights, roundabouts, and narrow passages with oncoming traffic. This paper gives an overview of the autonomous vehicle and presents details on vision and radar-based perception, digital road maps and video-based self-localization, as well as motion planning in complex urban scenarios.

Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis <=SEP=> Patent maps showing competition trends in technological development can provide valuable input for decision support on research and development (R&D) strategies. By introducing semantic patent analysis with advantages in representing technological objectives and structures, this paper constructs dynamic patent maps to show technological competition trends and describes the strategic functions of the dynamic maps. The proposed maps are based on subject-action-object (SAO) structures that are syntactically ordered sentences extracted using the natural language processing of the patent text; the structures of a patent encode the key findings of the invention and expertise of its inventors. Therefore, this paper introduces a method of constructing dynamic patent maps using SAO-based content analysis of patents and presents several types of dynamic patent maps by combining patent bibliographic information and patent mapping and clustering techniques. Building on the maps, this paper provides further analyses to identify technological areas in which patents have not been granted (“patent vacuums”), areas in which many patents have actively appeared (“technological hot spots”), R&D overlap of technological competitors, and characteristics of patent clusters. The proposed analyses of dynamic patent maps are illustrated using patents related to the synthesis of carbon nanotubes. We expect that the proposed method will aid experts in understanding technological competition trends in the process of formulating R&D strategies.

Fast-and-Light Stochastic ADMM <=SEP=> The alternating direction method of multipliers (ADMM) is a powerful optimization solver in machine learning. Recently, stochastic ADMM has been integrated with variance reduction methods for stochastic gradient, leading to SAG-ADMM and SDCA-ADMM that have fast convergence rates and low iteration complexities. However, their space requirements can still be high. In this paper, we propose an integration of ADMM with the method of stochastic variance reduced gradient (SVRG). Unlike another recent integration attempt called SCAS-ADMM, the proposed algorithm retains the fast convergence benefits of SAG-ADMM and SDCA-ADMM, but is more advantageous in that its storage requirement is very low, even independent of the sample size n. Experimental results demonstrate that it is as fast as SAG-ADMM and SDCA-ADMM, much faster than SCAS-ADMM, and can be used on much bigger data sets.

Security Ontology: Simulating Threats to Corporate Assets <=SEP=> Threat analysis and mitigation, both essential for corporate security, are time consuming, complex and demand expert knowledge. We present an approach for simulating threats to corporate assets, taking the entire infrastructure into account. Using this approach effective countermeasures and their costs can be calculated quickly without expert knowledge and a subsequent security decisions will be based on objective criteria. The ontology used for the simulation is based on Landwehr’s [ALRL04] taxonomy of computer security and dependability.

Low cost 60 GHz millimeter-wave microstrip patch antenna array using low-loss planar feeding scheme <=SEP=> A novel low cost feeding mechanism based upon new synthesized planar waveguide substrate integrated waveguide (SIW), is presented for microstrip antenna arrays (MPAs) at millimeter-wave (mmW) band to avoid feeding loss, specially the conduction loss. As an example, SIW-fed two dimensional (2D) 2×2 MPA array @ 60 GHz is introduced. The presented antenna is simulated by HFSS to validate its performance for mmW applications. The simulated results show a broadside gain of 11.71 dB within an operating impedance bandwidth of 500 MHz around a center frequency of 59.95 GHz. Also, the simulated radiation efficiency of the developed antenna is better than 85%, which is higher than that of conventional mmW microstrip antenna array.

Planar Array Antennas with Travelling-Wave Excitation in Millimeter-Wave Band <=SEP=> あらまし ミリ波の周波数帯において指向性走査可能な平面アンテナが望まれている.本論文では,ミリ波の 代表的な二つの平面アンテナである導波管アンテナとマイクロストリップアンテナについて,開発した平面アン テナを紹介する.ペンシルビームが必要な機械走査型アンテナに適した二次元導波管アレーアンテナと,複数の サブアレーの配列からなる電子走査方式のサブアレー平面アンテナ用に,導波管アレーアンテナとマイクロスト リップコムラインアンテナについて開発した例を示す.これらについて,各放射素子で反射特性を低減すること が,特性の向上・設計自由度の拡大に有効であることを示す.また,これらのアンテナについて低損失に給電す る構造を示すと同時に,片端から給電するよりも,中央から給電する方が,周波数帯域幅が広くなることを示す. 更に,これらのアンテナ実現のためのキーとなる進行波励振アレー設計技術について解説する. キーワード ミリ波,アレーアンテナ,平面アンテナ,導波管アンテナ,マイクロストリップアンテナ

Classification of Arrhythmia Using Conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria <=SEP=> This paper proposes a classification technique using conjunction of Machine Learning Algorithms and ECG Diagnostic Criteria which improves the accuracy of detecting Arrhythmia using Electrocardiogram (ECG) data. ECG is the most widely used first line clinical instrument to record the electrical activities of the heart. The data-set from UC Irvine (UCI) Machine Learning Repository was used to implement a multi-class classification for different types of heart abnormalities. After implementing rigorous data preprocessing and feature selection techniques,different machine learning algorithms such as Neural Networks, Decision trees, Random Forest, Gradient Boosting and Support Vector Machines were used. Maximum experimental accuracy of 84.82% was obtained via the conjunction of SVM and Gradient Boosting. A further improvement in accuracy was obtained by validating the factors which were important for doctors to decide between normal and abnormal heart conditions.The performance of classification is evaluated using measures such as confusion matrix, kappa-score, confidence interval, Area under curve (AUC) and overall-accuracy. Key–Words: Machine Learning, Arrhythmia Classification, ECG, Neural Networks, SVM, Gradient Boosting

Document clustering using nonnegative matrix factorization <=SEP=> A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in other techniques such as principal component analysis for semantic feature abstraction. Existing techniques for nonnegative matrix factorization are reviewed and a new hybrid technique for nonnegative matrix factorization is proposed. Performance evaluations of the proposed method are conducted on a few benchmark text collections used in standard topic detection studies.

A 107 GHz 55 dB-Ohm InP Broadband Transimpedance Amplifier IC for High-Speed Optical Communication Links <=SEP=> We report a 107 GHz baseband differential transimpedance amplifier IC for high speed optical communication links. The amplifier, comprised of two Darlington resistive feedback stages, was implemented in a 500 nm InP HBT process and demonstrates 55 dBΩ differential transimpedance gain, 30 ps group delay, P1dB = 1 dBm, and is powered by a 5.2 V supply. Differential input and output impedances are 50Ω. The IC interfaces to -2V DC at the input for connections to high-speed photodiodes and -450 mV DC at the output for interfaces to Gilbert-cell mixers and to ECL logic.

Gravity-independent mobility and drilling on natural rock using microspines <=SEP=> To grip rocks on the surfaces of asteroids and comets, and to grip the cliff faces and lava tubes of Mars, a 250 mm diameter omni-directional anchor is presented that utilizes a hierarchical array of claws with suspension flexures, called microspines, to create fast, strong attachment. Prototypes have been demonstrated on vesicular basalt and a'a lava rock supporting forces in all directions away from the rock. Each anchor can support >;160 N tangent, >;150 N at 45°, and >;180 N normal to the surface of the rock. A two-actuator selectively-compliant ankle interfaces these anchors to the Lemur IIB robot for climbing trials. A rotary percussive drill was also integrated into the anchor, demonstrating self-contained rock coring regardless of gravitational orientation. As a harder-than-zero-g proof of concept, 20mm diameter boreholes were drilled 83 mm deep in vesicular basalt samples, retaining a 12 mm diameter rock core in 3-6 pieces while in an inverted configuration, literally drilling into the ceiling.

Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning <=SEP=> The extreme learning machine (ELM), which was originally proposed for “generalized” single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.

Learning Environmental Sounds with Multi-scale Convolutional Neural Network <=SEP=> Deep learning has dramatically improved the performance of sounds recognition. However, learning acoustic models directly from the raw waveform is still challenging. Current waveform-based models generally use time-domain convolutional layers to extract features. The features extracted by single size filters are insufficient for building discriminative representation of audios. In this paper, we propose multi-scale convolution operation, which can get better audio representation by improving the frequency resolution and learning filters cross all frequency area. For leveraging the waveform-based features and spectrogram-based features in a single model, we introduce twophase method to fuse the different features. Finally, we propose a novel end-to-end network called WaveMsNet based on the multi-scale convolution operation and two-phase method. On the environmental sounds classification datasets ESC-10 and ESC-50, the classification accuracies of our WaveMsNet achieve 93.75% and 79.10% respectively, which improve significantly from the previous methods.

Effects of users' envy and shame on social comparison that occurs on social network services <=SEP=> In the context of the social network service environment, we explore how discrete emotions—envy and shame, in particular—may mediate the effects of social comparison on behavior intention and psychological responses. Based on the survey responses of 446 university students, the results suggest that social comparison to media figures correlates with a range of emotional responses as well as with behavioral intention and psychological responses. Envy maintained a significantly greater association with switch intention as a behavioral intention compared to shame. Conversely, shame was significantly related to burnout as a psychological response. Further, mediational analyses were consistent with the argument that envy and shame mediate the social comparison–outcome variables relationship. This research helps to illuminate the driving mechanism for the emotional effect that social comparison on social network service could elicit from a user. This predicts the nature of the behavioral and psychological outcome associated with the comparison and has implications for an enhanced understanding of the way in which the unique social network service communication environment may stimulate this process. 2015 Elsevier Ltd. All rights reserved.

Emotion Detection From Text Documents <=SEP=> Emotion Detection is one of the most emerging issues in human computer interaction. A sufficient amount of work has been done by researchers to detect emotions from facial and audio information whereas recognizing emotions from textual data is still a fresh and hot research area. This paper presented a knowledge based survey on emotion detection based on textual data and the methods used for this purpose. At the next step paper also proposed a new architecture for recognizing emotions from text document. Proposed architecture is composed of two main parts, emotion ontology and emotion detector algorithm. Proposed emotion detector system takes a text document and the emotion ontology as inputs and produces one of the six emotion classes (i.e. love, joy, anger, sadness, fear and surprise) as the output.

Pre-clinical antigenicity studies of an innovative multivalent vaccine for human visceral leishmaniasis <=SEP=> The notion that previous infection by Leishmania spp. in endemic areas leads to robust anti-Leishmania immunity, supports vaccination as a potentially effective approach to prevent disease development. Nevertheless, to date there is no vaccine available for human leishmaniasis. We optimized and assessed in vivo the safety and immunogenicity of an innovative vaccine candidate against human visceral leishmaniasis (VL), consisting of Virus-Like Particles (VLP) loaded with three different recombinant proteins (LJL143 from Lutzomyia longipalpis saliva as the vector-derived (VD) component, and KMP11 and LeishF3+, as parasite-derived (PD) antigens) and adjuvanted with GLA-SE, a TLR4 agonist. No apparent adverse reactions were observed during the experimental time-frame, which together with the normal hematological parameters detected seems to point to the safety of the formulation. Furthermore, measurements of antigen-specific cellular and humoral responses, generally higher in immunized versus control groups, confirmed the immunogenicity of the vaccine formulation. Interestingly, the immune responses against the VD protein were reproducibly more robust than those elicited against leishmanial antigens, and were apparently not caused by immunodominance of the VD antigen. Remarkably, priming with the VD protein alone and boosting with the complete vaccine candidate contributed towards an increase of the immune responses to the PD antigens, assessed in the form of increased ex vivo CD4+ and CD8+ T cell proliferation against both the PD antigens and total Leishmania antigen (TLA). Overall, our immunogenicity data indicate that this innovative vaccine formulation represents a promising anti-Leishmania vaccine whose efficacy deserves to be tested in the context of the "natural infection".

Optimization of photovoltaic farm under partial shading effects using artificial intelligent based matrix switch controller <=SEP=> Partial shading on photovoltaic array due to weather conditions reduces the energy generated and worsening the entire photovoltaic farm efficiency. The purpose of this research was to electric physically reconfigurate PV string through switch matrix by control of smart controller. It is great to find current and voltage at TCT point approaching work of range from MPPT, despite being awarded shading effect. The research method was reflected on the algorithms design. The aim of algorithms was to clustering in two steps. The first step was clustering one or more photovoltaic string based on parallelly current. Result of first clustering continued of second step clustering, clustering the first step result in series. The algorithm work smartly by current input from current sensors. Input processing from sensor by smart controller was signal control which for switch on/off the switches function in switch matrix circuit. Switch matrix output will be forwarded to MPPT circuit. This mechanism being novelty of this research. Based on the testing, it's mechanism able to protect the current at 18A level for all cases with each case voltage consecutively 3060V, 1360V, 2040V and 1020 V. By this mechanism, can be optimality power system increased up to 9.S % .similarly be affected or not partial shading effect.

Collaborative Quantization for Cross-Modal Similarity Search <=SEP=> Cross-modal similarity search is a problem about designing a search system supporting querying across content modalities, e.g., using an image to search for texts or using a text to search for images. This paper presents a compact coding solution for efficient search, with a focus on the quantization approach which has already shown the superior performance over the hashing solutions in the single-modal similarity search. We propose a cross-modal quantization approach, which is among the early attempts to introduce quantization into cross-modal search. The major contribution lies in jointly learning the quantizers for both modalities through aligning the quantized representations for each pair of image and text belonging to a document. In addition, our approach simultaneously learns the common space for both modalities in which quantization is conducted to enable efficient and effective search using the Euclidean distance computed in the common space with fast distance table lookup. Experimental results compared with several competitive algorithms over three benchmark datasets demonstrate that the proposed approach achieves the state-of-the-art performance.

High-Efficiency Single-Input Multiple-Output DC–DC Converter <=SEP=> The aim of this study is to develop a high-efficiency single-input multiple-output (SIMO) dc-dc converter. The proposed converter can boost the voltage of a low-voltage input power source to a controllable high-voltage dc bus and middle-voltage output terminals. The high-voltage dc bus can take as the main power for a high-voltage dc load or the front terminal of a dc-ac inverter. Moreover, middle-voltage output terminals can supply powers for individual middle-voltage dc loads or for charging auxiliary power sources (e.g., battery modules). In this study, a coupled-inductor-based dc-dc converter scheme utilizes only one power switch with the properties of voltage clamping and soft switching, and the corresponding device specifications are adequately designed. As a result, the objectives of high-efficiency power conversion, high step-up ratio, and various output voltages with different levels can be obtained. Some experimental results via a kilowatt-level prototype are given to verify the effectiveness of the proposed SIMO dc-dc converter in practical applications.

Phishing Detection: A Literature Survey <=SEP=> This article surveys the literature on the detection of phishing attacks. Phishing attacks target vulnerabilities that exist in systems due to the human factor. Many cyber attacks are spread via mechanisms that exploit weaknesses found in end-users, which makes users the weakest element in the security chain. The phishing problem is broad and no single silver-bullet solution exists to mitigate all the vulnerabilities effectively, thus multiple techniques are often implemented to mitigate specific attacks. This paper aims at surveying many of the recently proposed phishing mitigation techniques. A high-level overview of various categories of phishing mitigation techniques is also presented, such as: detection, offensive defense, correction, and prevention, which we belief is critical to present where the phishing detection techniques fit in the overall mitigation process.

A packing algorithm for non-Manhattan hexagon/triangle placement design by using an adaptive O-tree representation <=SEP=> A non-Manhattan Hexagon/Triangle Placement (HTP for short) paradigm is proposed in the present paper. Main feature of this paradigm lies in adapting to the Y- architecture which is one of the promising non-Manhattan VLSI circuit layout architectures. Aim of the HTP is to place a set of equilateral triangles with given size onto a hexagonal chip with maximal chip area usage. Based on the O-tree representation, some adaptive packing rules are adopted to develop an effective placement algorithm for solving the HTP problem in BBL mode. Two examples with benchmark data transformed from the Manhattan BBL mode placement (ami33/49) are presented to justify the feasibility and effectiveness of our algorithms. Experiment results demonstrate that the chip area usage of 94% is achieved through simulated annealing optimization.

Computer animation of knowledge-based human grasping <=SEP=> The synthesis of human hand motion and grasping of arbitrary shaped objects is a very complex problem. Therefore high-level control is needed to perform these actions. In order to satisfy the kinematic and physical constraints associated with the human hand and to reduce the enormous search space associated with the problem of grasping objects, a knowledge based approach is used. A three-phased scheme is presented which incorporates the role of the hand, the object, the environment and the animator. The implementation of a hand simulation system HANDS is discussed.

MIDeA: a multi-parallel intrusion detection architecture <=SEP=> Network intrusion detection systems are faced with the challenge of identifying diverse attacks, in extremely high speed networks. For this reason, they must operate at multi-Gigabit speeds, while performing highly-complex per-packet and per-flow data processing. In this paper, we present a multi-parallel intrusion detection architecture tailored for high speed networks. To cope with the increased processing throughput requirements, our system parallelizes network traffic processing and analysis at three levels, using multi-queue NICs, multiple CPUs, and multiple GPUs. The proposed design avoids locking, optimizes data transfers between the different processing units, and speeds up data processing by mapping different operations to the processing units where they are best suited. Our experimental evaluation shows that our prototype implementation based on commodity off-the-shelf equipment can reach processing speeds of up to 5.2 Gbit/s with zero packet loss when analyzing traffic in a real network, whereas the pattern matching engine alone reaches speeds of up to 70 Gbit/s, which is an almost four times improvement over prior solutions that use specialized hardware.

A German Twitter Snapshot <=SEP=> We present a new corpus of German tweets. Due to the relatively small number of German messages on Twitter, it is possible to collect a virtually complete snapshot of German twitter messages over a period of time. In this paper, we present our collection method which produced a 24 million tweet corpus, representing a large majority of all German tweets sent in April, 2013. Further, we analyze this representative data set and characterize the German twitterverse. While German Twitter data is similar to other Twitter data in terms of its temporal distribution, German Twitter users are much more reluctant to share geolocation information with their tweets. Finally, the corpus collection method allows for a study of discourse phenomena in the Twitter data, structured into discussion threads.

Work strain, health, and absenteeism: a meta-analysis. <=SEP=> Work strain has been argued to be a significant cause of absenteeism in the popular and academic press. However, definitive evidence for associations between absenteeism and strain is currently lacking. A theory focused meta-analysis of 275 effects from 153 studies revealed positive but small associations between absenteeism and work strain, psychological illness, and physical illness. Structural equation modeling results suggested that the strain-absence connection may be mediated by psychological and physical symptoms. Little support was received for the purported volitional distinction between absence frequency and time lost absence measures on the basis of illness. Among the moderators examined, common measurement, midterm and stable sources of variance, and publication year received support.

Extreme Learning Machine for Multi-Label Classification <=SEP=> Xia Sun 1,*, Jingting Xu 1, Changmeng Jiang 1, Jun Feng 1, Su-Shing Chen 2 and Feijuan He 3 1 School of Information Science and Technology, Northwest University, Xi’an 710069, China; jingtingxu03@gmail.com (J.X.); yadxjcm@163.com (C.J.); fengjun@nwu.edu.cn (J.F.) 2 Computer Information Science and Engineering, University of Florida, Gainesville, FL 32608, USA; suchen@cise.ufl.edu 3 Department of Computer Science, Xi’an Jiaotong University City College, Xi’an 710069, China; hfj@mail.xjtu.edu.cn * Correspondence: raindy@nwu.edu.cn; Tel.: +86-29-8830-8119

Computational Thinking … and Doing <=SEP=> A t our most recent CiSE meeting, one of the board members approached me during a short break and asked: " Why do you do this? you seem to have better things to do. " seeking clarification , i came to understand that he Was Wondering Why a computer scientist such as myself would want to be on the editorial board of a publication that isn't necessarily focused on the " tradition " of computer science. then and there, i realized that a computer sci-entist's role is something worth explaining and understanding. much has been made recently of computational thinking, a term coined by Jeanette Wing at the us national science foundation. 1 While i have my doubts about the term's longevity, 2 it struck me as an excellent starting point for explaining what a computer scientist is and does, especially in the CiSE context. computer scientists are an increasingly important part of interdisciplinary—or multidisciplinary, a term i prefer—problem solving. We help others understand the merits of bringing the right methods (algorithms), architecture/design (software engineering), and programming tools (languages and systems) and techniques (visualization) to science and engineering applications. so, as i see it, computer scientists bring a panoply of options to the table that enhance multi disciplinary research. if you will, we're about computational doing—and doing it right. as the former chez thiruvathukal in scientific programming , i like to view computer science's various ideas as the masala found in my (and possibly your) favorite curries. this brings me back to why i'm here. i've always had an eye to applying computer science to other disciplines, scientific and otherwise. in the past few months, i've begun working on two new (funded) projects—one addresses healthcare (modeling health issues in social networks using agent-based simulation) and the other addresses digital humanities (developing collaborative tools for tex-tual studies). in both cases, computer science plays a critical role in that both projects have expected software and experimental outcomes. i don't always write about my other disciplinary interests in CiSE, but given my emphasis on programming and emerging technologies , it's comforting to know that i've joined a group of people committed to exploring the intersections with and showing respect for other disciplines. for the sake of scholarship and meaningful discourse, we need more places like CiSE, and we must keep the content fresh and exciting—even during these challenging times when all publications face …