An Analysis of the Elastic Net Approach to the Traveling Salesman Problem. This paper analyzes the elastic net approach (Durbin and Willshaw 1987) to the traveling salesman problem of finding the shortest path through a set of cities. The elastic net approach jointly minimizes the length of an arbitrary path in the plane and the distance between the path points and the cities. The tradeoff between these two requirements is controlled by a scale parameter K. A global minimum is found for large K, and is then tracked to a small value. In this paper, we show that (1) in the small K limit the elastic path passes arbitrarily close to all the cities, but that only one path point is attracted to each city, (2) in the large K limit the net lies at the center of the set of cities, and (3) at a critical value of K the energy function bifurcates. We also show that this method can be interpreted in terms of extremizing a probability distribution controlled by K. The minimum at a given K corresponds to the maximum a posteriori (MAP) Bayesian estimate of the tour under a natural statistical interpretation. The analysis presented in this paper gives us a better understanding of the behavior of the elastic net, allows us to better choose the parameters for the optimization, and suggests how to extend the underlying ideas to other domains.

Question: What is the elastic net?


MilkyWay-2 supercomputer: system and application. On June 17, 2013, MilkyWay-2 (Tianhe-2) supercomputer was crowned as the fastest supercomputer in the world on the 41th TOP500 list. This paper provides an overview of the MilkyWay-2 project and describes the design of hardware and software systems. The key architecture features of MilkyWay-2 are highlighted, including neo-heterogeneous compute nodes integrating commodity-off-the-shelf processors and accelerators that share similar instruction set architecture, powerful networks that employ proprietary interconnection chips to support the massively parallel message-passing communications, proprietary 16-core processor designed for scientific computing, efficient software stacks that provide high performance file system, emerging programming model for heterogeneous systems, and intelligent system administration. We perform extensive evaluation with wide-ranging applications from LINPACK and Graph500 benchmarks to massively parallel software deployed in the system.

Question: What is the fastest supercomputer in the world?


Long-term electroencephalogram measurement using polymer-based dry microneedle electrode. This paper reports a successful electroencephalogram (EEG) measurement for hours using polymer-based microneedle electrodes. Needle electrodes can penetrate through the stratum corneum and therefore, do not require any skin treatment for high-quality EEG measurement. The tested needles consist of SU-8 needles, a silver film, and a nanoporous parylene protective film. In prior work, fabrication processes of polymer-based microneedles, which are considered to be more robust than silicon microneedles was developed. In this work, the electrical impedance was measured at the forehead and was verified to maintain 6 kΩ for 3 h without any skin treatment, which was low enough for EEG measurement. A headset was designed to keep the contact between the needles and skin and with its help, EEG was successfully measured from the frontal poles. The acquired signals were found to be as high quality as the standard wet electrode that required skin treatment and uncomfortable pasting of conductive gel. The developed electrodes are readily applicable to record brain activities for hours while applying little mental and physical stress to the users.

Question: How does the polymer-based dry microneedle electrode work?


Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification. Despite the steady progress in video analysis led by the adoption of convolutional neural networks (CNNs), the relative improvement has been less drastic as that in 2D static image classification. Three main challenges exist including spatial (image) feature representation, temporal information representation, and model/computation complexity. It was recently shown by Carreira and Zisserman that 3D CNNs, inflated from 2D networks and pretrained on ImageNet, could be a promising way for spatial and temporal representation learning. However, as for model/computation complexity, 3D CNNs are much more expensive than 2D CNNs and prone to overfit. We seek a balance between speed and accuracy by building an effective and efficient video classification system through systematic exploration of critical network design choices. In particular, we show that it is possible to replace many of the 3D convolutions by low-cost 2D convolutions. Rather surprisingly, best result (in both speed and accuracy) is achieved when replacing the 3D convolutions at the bottom of the network, suggesting that temporal representation learning on high-level "semantic" features is more useful. Our conclusion generalizes to datasets with very different properties. When combined with several other cost-effective designs including separable spatial/temporal convolution and feature gating, our system results in an effective video classification system that that produces very competitive results on several action classification benchmarks (Kinetics, Something-something, UCF101 and HMDB), as well as two action detection (localization) benchmarks (JHMDB and UCF101-24).

Question: What is the best way to represent temporal information?


Data-Driven Vehicle Trajectory Prediction. Vehicle trajectory or route prediction is useful in online, data-driven transportation simulation to predict future traffic patterns and congestion, among other uses. The various approaches to route prediction have varying degrees of data required to predict future vehicle trajectories. Three approaches to vehicle trajectory prediction, along with extensions, are examined to assess their accuracy on an urban road network. These include an approach based on the intuition that drivers attempt to reduce their travel time, an approach based on neural networks, and an approach based on Markov models. The T-Drive trajectory data set consisting of GPS trajectories of over ten thousand taxicabs and including 15 million data points in Beijing, China is used for this evaluation. These comparisons illustrate that using trajectory data from other vehicles can substantially improve the accuracy of forward trajectory prediction in the T-Drive data set. These results highlight the benefit of exploiting dynamic data to improve the accuracy of transportation simulation predictions.

Question: What is the difference between neural networks and Markov models?


Andromeda: Performance, Isolation, and Velocity at Scale in Cloud Network Virtualization. This paper presents our design and experience with Andromeda, Google Cloud Platform's network virtualization stack. Our production deployment poses several challenging requirements, including performance isolation among customer virtual networks, scalability, rapid provisioning of large numbers of virtual hosts, bandwidth and latency largely indistinguishable from the underlying hardware, and high feature velocity combined with high availability. Andromeda is designed around a flexible hierarchy of flow processing paths. Flows are mapped to a programming path dynamically based on feature and performance requirements. We introduce the Hoverboard programming model, which uses gateways for the long tail of low bandwidth flows, and enables the control plane to program network connectivity for tens of thousands of VMs in seconds. The on-host dataplane is based around a highperformance OS bypass software packet processing path. CPU-intensive per packet operations with higher latency targets are executed on coprocessor threads. This architecture allows Andromeda to decouple feature growth from fast path performance, as many features can be implemented solely on the coprocessor path. We demonstrate that the Andromeda datapath achieves performance that is competitive with hardware while maintaining the flexibility and velocity of a software-based architecture.

Question: What is the performance of the network virtualization stack?


ROS play a critical role in the differentiation of alternatively activated macrophages and the occurrence of tumor-associated macrophages. Differentiation to different types of macrophages determines their distinct functions. Tumor-associated macrophages (TAMs) promote tumorigenesis owing to their proangiogenic and immune-suppressive functions similar to those of alternatively activated (M2) macrophages. We report that reactive oxygen species (ROS) production is critical for macrophage differentiation and that inhibition of superoxide (O2−) production specifically blocks the differentiation of M2 macrophages. We found that when monocytes are triggered to differentiate, O2− is generated and is needed for the biphasic ERK activation, which is critical for macrophage differentiation. We demonstrated that ROS elimination by butylated hydroxyanisole (BHA) and other ROS inhibitors blocks macrophage differentiation. However, the inhibitory effect of ROS elimination on macrophage differentiation is overcome when cells are polarized to classically activated (M1), but not M2, macrophages. More importantly, the continuous administration of the ROS inhibitor BHA efficiently blocked the occurrence of TAMs and markedly suppressed tumorigenesis in mouse cancer models. Targeting TAMs by blocking ROS can be a potentially effective method for cancer treatment.

Question: What is the role of ROS in macrophage differentiation?


Differentiating optic disc edema from optic nerve head drusen on optical coherence tomography.. OBJECTIVE To assess optical coherence tomography in differentiating optic disc edema (ODE) due to papilledema and other optic neuropathies from optic nerve head drusen (ONHD).   METHODS Optical coherence tomographic images from 60 subjects (20 with ODE, 20 with ONHD, and 20 control subjects) were assessed qualitatively and quantitatively. Qualitative criteria for ODE included an elevated optic nerve head with smooth internal contour and subretinal hyporeflective space (SHYPS) with recumbent "lazy V" pattern. Optic nerve head drusen displayed a "lumpy-bumpy" internal optic nerve contour and a rapid decline in SHYPS thickness. Quantitative comparisons included retinal nerve fiber layer and SHYPS thickness.   RESULTS Optical coherence tomography differentiated ODE from ONHD qualitatively (sensitivity, 63%; specificity, 63%) and quantitatively (sensitivity, 80%; specificity, 90%). Respective differences in mean retinal nerve fiber layer thickness between ODE and ONHD were significant (P < .002) superiorly (206.8 vs 121.7 microm), nasally (176.3 vs 78.6 microm), inferiorly (247.2 vs 153.8 microm), and temporally (180.0 vs 85.5 microm). Respective differences in mean SHYPS thickness between ODE and ONHD were significant (P < .001) at radii of 0.75 mm (512.1 vs 274.4 microm), 1.5 mm (291.4 vs 103.0 microm), and 2.0 mm (145.5 vs 60.7 microm).   CONCLUSION Optical coherence tomography can differentiate ODE from ONHD, particularly when the nasal retinal nerve fiber layer and SHYPS thickness at the 2.0-mm radius are greater than 86 microm and 127 microm, respectively.

Question: How do you differentiate optic disc edema from optic nerve head drusen?


Tracking-assisted Weakly Supervised Online Visual Object Segmentation in Unconstrained Videos. This paper tackles the task of online video object segmentation with weak supervision, i.e., labeling the target object and background with pixel-level accuracy in unconstrained videos, given only one bounding box information in the first frame. We present a novel tracking-assisted visual object segmentation framework to achieve this. On the one hand, initialized with a given bounding box in the first frame, the auxiliary object tracking module guides the segmentation module frame by frame by providing motion and region information, which is usually missing in semi-supervised methods. Moreover, compared with the unsupervised approach, our approach with such minimum supervision can focus on the target object without bringing unrelated objects into the final results. On the other hand, the video object segmentation module also improves the robustness of the visual object tracking module by pixel-level localization and objectness information. Thus, segmentation and tracking in our framework can mutually help each other in an online manner. To verify the generality and effectiveness of the proposed framework, we evaluate our weakly supervised method on two cross-domain datasets, i.e., the DAVIS and VOT2016 datasets, with the same configuration and parameter setting. Experimental results show the top performance of our method, which is even better than the leading semi-supervised methods. Furthermore, we conduct the extensive ablation study on our approach to investigate the influence of each component and main parameters.

Question: What is the name of the paper?


B-ROC Curves for the Assessment of Classifiers over Imbalanced Data Sets. The class imbalance problem appears to be ubiquitous to a large portion of the machine learning and data mining communities. One of the key questions in this setting is how to evaluate the learning algorithms in the case of class imbalances. In this paper we introduce the Bayesian Receiver Operating Characteristic (B-ROC) curves, as a set of tradeoff curves that combine in an intuitive way, the variables that are more relevant to the evaluation of classifiers over imbalanced data sets. This presentation is based on section 4 of (Cárdenas, Baras, & Seamon 2006). Introduction The term class imbalance refers to the case when in a classification task, there are many more instances of some classes than others. The problem is that under this setting, classifiers in general perform poorly because they tend to concentrate on the large classes and disregard the ones with few examples. Given that this problem is prevalent in a wide range of practical classification problems, there has been recent interest in trying to design and evaluate classifiers faced with imbalanced data sets (Japkowicz 2000; Chawla, Japkowicz, & Kołcz 2003; Chawla, Japkowicz, & Kołz 2004). A number of approaches on how to address these issues have been proposed in the literature. Ideas such as data sampling methods, one-class learning (i.e. recognitionbased learning), and feature selection algorithms, appear to be the most active research directions for learning classifiers. On the other hand the issue of how to evaluate binary classifiers in the case of class imbalances appears to be dominated by the use of ROC curves (Ferri et al. 2004; 2005) (and to a lesser extent, by error curves (Drummond & Holte 2001)). The class imbalance problem is of particular importance in intrusion detection systems (IDSs). In this paper we present and expand some of the ideas introduced in our research for the evaluation of IDSs (Cárdenas, Baras, & S

Question: How to evaluate binary classifiers in the case of class imbalances?


Knowledge management infrastructure and processes on effectiveness of nursing care. The purpose of this study is to investigate the influence of knowledge management infrastructure and process on nursing care effectiveness in selected teaching hospitals in Southwest Nigeria. The organization of nursing knowledge resources is critical to health care organizations for providing safe and high quality care for patients. Therefore, it is necessary to study the role of knowledge management in clinical nursing practice and the outcomes on the effectiveness of nursing care. Despite the critical role of nursing care in defining high-performing health care delivery, knowledge management in this area is still at an early stage of development. Based on organizational capability theory and employing concurrent mixed-method research design, this study seeks to identify the components of knowledge infrastructure and processes that influence the efficiency and effectiveness of nursing care in clinical floor nursing of selected teaching hospitals in Nigeria, a developing country in Africa. The output from the research is expected to contribute to the domain of literature/knowledge; provide awareness of the knowledge management practices in nursing care in Nigeria; assist nursing administrators, health policy makers and hospital management to exploit and make effective use of knowledge-based resources to enhance nursing care which can improve the productivity of health care organizations.

Question: What is the role of knowledge management in nursing care?


Running on four legs as though they were one. that run on one leg. The generalization uf these one-leg algorithms for control of machines with several legs is explored. The generalization is quite simple when multilegged systems run with gaits that use the support legs one at a time. For these gaits the ~ n e-l e g algorithms can be used b o control multilegged running. The cortcept of a virtual leg is introduced to further extend the approach to gaits that use the legs in pairs, such as the trot, the pace, and the bound. These quadruped running gaits map into gaits that use one virtual leg for support at a rime, for which the one-leg algorithms can provide control. This approach was used in laboratory experiments to control a quadruped machine that runs with a trotting gait. 1. ~N1RUI)IICTION UNNING is a form of legged locomotion characterized by travel at high speed and by periods of ballistic flight during which all feet leave the ground. The basic control task is to establish a pattern of leg and body motions that stabilizes the attitude and altitude of the body while propelling the body in the desired direction at the desired speed 121. The characteristics of high speed and ballistic flight suggest that dynamics and active stability are important to accomplishing this control task, and to getting a better general understanding of running. In recent work the aulhors used dynamic techniques to control an experimental one-legged machine that balances itself as it runs by hopping [ 3 , 41. The goal of these experiments was to focus on the dynamic aspects of locomo-tion, while avoiding the difficult task of coordinating the behavior of several legs. This paper addresses the task of leg coordination by starting with the algorithms that were used to control the one-legged hopping machine, and extending them to control a quadruped running machine. The general goal is to develop a set of locomotion principles that applies to the dynamic behavior of diverse legged system

Question: What is the general goal of this paper?


Big data analytics in healthcare: promise and potential. OBJECTIVE To describe the promise and potential of big data analytics in healthcare.   METHODS The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions.   RESULTS The paper provides a broad overview of big data analytics for healthcare researchers and practitioners.   CONCLUSIONS Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.

Question: What is big data analytics in healthcare?


Improving Distant Supervision with Maxpooled Attention and Sentence-Level Supervision. We propose an effective multitask learning setup for reducing distant supervision noise by leveraging sentence-level supervision. We show how sentence-level supervision can be used to improve the encoding of individual sentences, and to learn which input sentences are more likely to express the relationship between a pair of entities. We also introduce a novel neural architecture for collecting signals from multiple input sentences, which combines the benefits of attention and maxpooling. The proposed method increases AUC by 10% (from 0.261 to 0.284), and outperforms recently published results on the FBNYT dataset.

Question: How can we improve distant supervision?


Full-Bridge+SRT Hybrid DC/DC Converter for a 6.6-kW EV On-Board Charger. This paper suggests a hybrid pulsewidth-modulation (PWM) dc/dc converter for an electric vehicle (EV) on-board charger (OBC). It is composed of a full-bridge (FB) converter and a resonant converter with secondary resonance tank (SRT). They are coupled in a single transformer so that any power distribution problem that causes a size increase in the transformer can be solved. Dual power flow of the proposed charger provides an increase in charger gain and a reduction of turn-off voltage spikes of the rectifying diodes caused by transformer leakage inductance and diode junction capacitance. These characteristics help reduce diode loss and remove dissipative snubber circuitry. In addition, this structure has the additional advantage of output inductor size due to the reduction of voltage variation applied to the inductor. The feasibility of the proposed charger is verified with a 6.6-kW prototype implemented by a design procedure.

Question: How does this charger work?


Bridge Resonant Inverter For Induction Heating Applications. Induction heating is a well-known technique to produce very high temperature for applications. A large number of topologies have been developed in this area such as voltage and current source inverter. Recent developments in switching schemes and control methods have made the voltage-source resonant inverters widely used in several applications that require output power control. The series-resonant inverter needs an output transformer for matching the output power to the load but it carry high current as a result additional real power loss is occur and overall efficiency also is reduced. This project proposes a high efficiency LLC resonant inverter for induction heating applications by using asymmetrical voltage cancellation control .The proposed control method is implemented in a full-bridge topology for induction heating application. The output power is controlled using the asymmetrical voltage cancellation technique. The LLC resonant tank is designed without the use of output transformer. This results in an increase of the net efficiency of the induction heating system. The circuit is simulated using MATLAB .The circuit is implemented using PIC controller. Both simulation and hardware results are compared.

Question: How to design a resonant inverter for induction heating applications?


ShapeNet: An Information-Rich 3D Model Repository. We present ShapeNet: a richly-annotated, large-scale repository of shapes represented by 3D CAD models of objects. ShapeNet contains 3D models from a multitude of semantic categories and organizes them under the WordNet taxonomy. It is a collection of datasets providing many semantic annotations for each 3D model such as consistent rigid alignments, parts and bilateral symmetry planes, physical sizes, keywords, as well as other planned annotations. Annotations are made available through a public web-based interface to enable data visualization of object attributes, promote data-driven geometric analysis, and provide a large-scale quantitative benchmark for research in computer graphics and vision. At the time of this technical report, ShapeNet has indexed more than 3,000,000 models, 220,000 models out of which are classified into 3,135 categories (WordNet synsets). In this report we describe the ShapeNet effort as a whole, provide details for all currently available datasets, and summarize future plans.

Question: What is the shape of a banana?


Neural Multi-task Learning in Automated Assessment. Grammatical error detection and automated essay scoring are two tasks in the area of automated assessment. Traditionally these tasks have been treated independently with different machine learning models and features used for each task. In this paper, we develop a multi-task neural network model that jointly optimises for both tasks, and in particular we show that neural automated essay scoring can be significantly improved. We show that while the essay score provides little evidence to inform grammatical error detection, the essay score is highly influenced by error detection.

Question: How can neural multi-task learning be used to improve automated assessment?


Anti-falling tree climbing mechanism optimization. This paper presents an improved anti-falling tree climbing mechanism. A study and comparison was made on an original tree climbing robotic system published in reference. This optimized robotic system only relies on one stepper to achieve the anti-falling and anti-jamming functionality under either static or dynamic situations. This improved design feature reduces the total robot weight and enhances the control system robustness. Moreover, to achieve the robot spiral climbing morphology and decrease the servomotor rotational frictional forces, a special servomotor module and a bearing supporting mechanism are designed and implemented. The optimized mechanism design of the climbing robot is modelled in SolidWorks and the climbing process is simulated using SimMechanics. The simulation outcome of the initial and improved design shows that the new design has higher climbing speed and lighter weight. Physical prototype experiment matches with the simulation outcome and verifies the feasibility and effectiveness of this redesigned tree climbing mechanism.

Question: What is the anti-falling tree climbing mechanism?


Low self-esteem during adolescence predicts poor health, criminal behavior, and limited economic prospects during adulthood.. Using prospective data from the Dunedin Multidisciplinary Health and Development Study birth cohort, the authors found that adolescents with low self-esteem had poorer mental and physical health, worse economic prospects, and higher levels of criminal behavior during adulthood, compared with adolescents with high self-esteem. The long-term consequences of self-esteem could not be explained by adolescent depression, gender, or socioeconomic status. Moreover, the findings held when the outcome variables were assessed using objective measures and informant reports; therefore, the findings cannot be explained by shared method variance in self-report data. The findings suggest that low self-esteem during adolescence predicts negative real-world consequences during adulthood.

Question: What is the long-term consequences of self-esteem?


Cultural diversity among nursing students: reanalysis of the cultural awareness scale.. Nurses are educated to provide culturally competent care. Cultural competence begins with cultural awareness, a concept previously measured with the Cultural Awareness Scale (CAS). The purpose of this study was to reanalyze the CAS to determine construct validity and differences in cultural awareness among students of varying educational levels and experiences. The sample consisted of 150 nursing students (92% female, 33.6% racial minorities). Confirmatory factor analysis yielded three factors (CFI = 0.868, TLI = 0.854, RMSEA = 0.065, and SRMR = 0.086). Cronbach's alpha ranged from 0.70 to 0.89. There were significant differences among educational levels, with lower division BSN students generally scoring higher than upper division and master's of science in nursing students. Students who had taken courses on cultural diversity or global health generally outscored those who had not taken such courses. Findings support the validity of the CAS and its applicability to research studies of cultural awareness in nursing.

Question: What is the difference between cultural awareness and cultural competence?


Hierarchical Committee of Deep CNNs with Exponentially-Weighted Decision Fusion for Static Facial Expression Recognition. We present a pattern recognition framework to improve committee machines of deep convolutional neural networks (deep CNNs) and its application to static facial expression recognition in the wild (SFEW). In order to generate enough diversity of decisions, we trained multiple deep CNNs by varying network architectures, input normalization, and weight initialization as well as by adopting several learning strategies to use large external databases. Moreover, with these deep models, we formed hierarchical committees using the validation-accuracy-based exponentially-weighted average (VA-Expo-WA) rule. Through extensive experiments, the great strengths of our committee machines were demonstrated in both structural and decisional ways. On the SFEW2.0 dataset released for the 3rd Emotion Recognition in the Wild (EmotiW) sub-challenge, a test accuracy of 57.3% was obtained from the best single deep CNN, while the single-level committees yielded 58.3% and 60.5% with the simple average rule and with the VA-Expo-WA rule, respectively. Our final submission based on the 3-level hierarchy using the VA-Expo-WA achieved 61.6%, significantly higher than the SFEW baseline of 39.1%.

Question: How many deep CNNs are there?


A recommender system based on historical usage data for web service discovery. The tremendous growth in the amount of available web services impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, these text-based recommendation approaches depend mainly on user's perspective, languages, and notations, which easily decrease recommendation's efficiency. In this paper, we present an approach in which we take into account historical usage data instead of the text-based analysis. We apply collaborative filtering technique on user's interactions. We propose and implement four algorithms to validate our approach. We also provide evaluation methods based on the precision and recall in order to assert the efficiency of our algorithms.

Question: What is the recommender system based on historical usage data?


Aperture scanning Fourier ptychographic microscopy. Fourier ptychographic microscopy (FPM) is implemented through aperture scanning by an LCOS spatial light modulator at the back focal plane of the objective lens. This FPM configuration enables the capturing of the complex scattered field for a 3D sample both in the transmissive mode and the reflective mode. We further show that by combining with the compressive sensing theory, the reconstructed 2D complex scattered field can be used to recover the 3D sample scattering density. This implementation expands the scope of application for FPM and can be beneficial for areas such as tissue imaging and wafer inspection. c © 2016 Optical Society of America OCIS codes: (180.0180) Microscopy; (090.1995) Digital holography; (110.6880) Three-dimensional image acquisition. References and links 1. G. Zheng, R. Horstmeyer, and C. Yang, "Wide-field, high-resolution Fourier ptychographic microscopy," Nat. Photonics 7, 739–745 (2013). 2. R. Horstmeyer and C. Yang, "A phase space model of Fourier ptychographic microscopy," Opt. Express 22, 338–358 (2014). 3. X. Ou, R. Horstmeyer, G. Zheng, and C. Yang, "High numerical aperture Fourier ptychography: principle, implementation and characterization," Opt. Express 23, 3472–3491 (2015). 4. X. Ou, R. Horstmeyer, C. Yang, and G. Zheng, "Quantitative phase imaging via Fourier ptychographic microscopy," Opt. Lett. 38, 4845–4848 (2013). 5. L. Tian and L. Waller, "3D intensity and phase imaging from light field measurements in an LED array microscope," Optica 2, 104–111 (2015). 6. R. Horstmeyer, J. Chung, X. Ou, G. Zheng, and C. Yang" "Diffraction tomography with Fourier ptychography," Optica 3, 827–835 (2016). 7. S. Dong, R. Horstmeyer, R. Shiradkar, K. Guo, X. Ou, Z. Bian, H. Xin, and G. Zheng, "Aperture-scanning Fourier ptychography for 3D refocusing and super-resolution macroscopic imaging," Opt. Express 22, 13586–13599 (2014). 8. R. Horstmeyer, X. Ou, J. Chung, G. Zheng, and C. Yang, "Overla

Question: What is the difference between aperture scanning and non-aperture scanning?


A Survey of Non-conventional Techniques for Low-voltage Low-power Analog Circuit Design. Designing integrated circuits able to work under low-voltage (LV) low-power (LP) condition is currently undergoing a very considerable boom. Reducing voltage supply and power consumption of integrated circuits is crucial factor since in general it ensures the device reliability, prevents overheating of the circuits and in particular prolongs the operation period for battery powered devices. Recently, non-conventional techniques i.e. bulkdriven (BD), floating-gate (FG) and quasi-floating-gate (QFG) techniques have been proposed as powerful ways to reduce the design complexity and push the voltage supply towards threshold voltage of the MOS transistors (MOST). Therefore, this paper presents the operation principle, the advantages and disadvantages of each of these techniques, enabling circuit designers to choose the proper design technique based on application requirements. As an example of application three operational transconductance amplifiers (OTA) based on these non-conventional techniques are presented, the voltage supply is only ±0.4 V and the power consumption is 23.5 μW. PSpice simulation results using the 0.18 μm CMOS technology from TSMC are included to verify the design functionality and correspondence with theory.

Question: What is the difference between BD, FG and QFG techniques?


EEG correlates of task engagement and mental workload in vigilance, learning, and memory tasks.. INTRODUCTION The ability to continuously and unobtrusively monitor levels of task engagement and mental workload in an operational environment could be useful in identifying more accurate and efficient methods for humans to interact with technology. This information could also be used to optimize the design of safer, more efficient work environments that increase motivation and productivity.   METHODS The present study explored the feasibility of monitoring electroencephalo-graphic (EEG) indices of engagement and workload acquired unobtrusively and quantified during performance of cognitive tests. EEG was acquired from 80 healthy participants with a wireless sensor headset (F3-F4,C3-C4,Cz-POz,F3-Cz,Fz-C3,Fz-POz) during tasks including: multi-level forward/backward-digit-span, grid-recall, trails, mental-addition, 20-min 3-Choice Vigilance, and image-learning and memory tests. EEG metrics for engagement and workload were calculated for each 1 -s of EEG.   RESULTS Across participants, engagement but not workload decreased over the 20-min vigilance test. Engagement and workload were significantly increased during the encoding period of verbal and image-learning and memory tests when compared with the recognition/ recall period. Workload but not engagement increased linearly as level of difficulty increased in forward and backward-digit-span, grid-recall, and mental-addition tests. EEG measures correlated with both subjective and objective performance metrics.   DISCUSSION These data in combination with previous studies suggest that EEG engagement reflects information-gathering, visual processing, and allocation of attention. EEG workload increases with increasing working memory load and during problem solving, integration of information, analytical reasoning, and may be more reflective of executive functions. Inspection of EEG on a second-by-second timescale revealed associ

Question: How to monitor the brain activity of a person?


Discriminative Embeddings of Latent Variable Models for Structured Data. Kernel classifiers and regressors designed for structured data, such as sequences, trees and graphs, have significantly advanced a number of interdisciplinary areas such as computational biology and drug design. Typically, kernels are designed beforehand for a data type which either exploit statistics of the structures or make use of probabilistic generative models, and then a discriminative classifier is learned based on the kernels via convex optimization. However, such an elegant two-stage approach also limited kernel methods from scaling up to millions of data points, and exploiting discriminative information to learn feature representations. We propose, structure2vec, an effective and scalable approach for structured data representation based on the idea of embedding latent variable models into feature spaces, and learning such feature spaces using discriminative information. Interestingly, structure2vec extracts features by performing a sequence of function mappings in a way similar to graphical model inference procedures, such as mean field and belief propagation. In applications involving millions of data points, we showed that structure2vec runs 2 times faster, produces models which are 10, 000 times smaller, while at the same time achieving the state-of-the-art predictive performance.

Question: What is the state-of-the-art predictive performance?


Analysis of LCLC resonant converters for high-voltage high-frequency applications. This paper proposes a novel LCLC full-bridge resonant converter with ZCS and ZVS for high-voltage high-frequency applications. In this operating model, both of ZCS and ZVS can be achieved and the switching losses can be reduced to a minimum. The proposed converter adopts the parasitic parameters of the step-up voltage transformer and one other serial capacitor is needed to form the resonant tank. Compared with other resonant converters, where only ZCS or ZVS can be achieved, the proposed working model can increase conversion efficiency with soft-switching features. Finally, A 40 V input, 4800 V output and 300 W prototype with 500 kHz switching frequency was built to verify the benefits of the proposed converter. The achieved efficiency of the converter peaked at 96.7%.

Question: What is the purpose of the paper?


Combining virtual reality enabled simulation with 3D scanning technologies towards smart manufacturing. Recent introduction of low-cost 3D sensing and affordable immersive virtual reality have lowered the barriers for creating and maintaining 3D virtual worlds. In this paper, we propose a way to combine these technologies with discrete-event simulation to improve the use of simulation in decision making in manufacturing. This work will describe how feedback is possible from real world systems directly into a simulation model to guide smart behaviors. Technologies included in the research include feedback from RGBD images of shop floor motion and human interaction within full immersive virtual reality that includes the latest headset technologies.

Question: How can we combine virtual reality with simulation?


NFC Antenna With Nonuniform Meandering Line and Partial Coverage Ferrite Sheet for Metal Cover Smartphone Applications. This paper proposes a near-field communication (NFC) antenna solution for metal-cover smartphone applications. In this NFC antenna solution, a narrow slot is initially loaded into the metal cover, and the position of this slot can be altered (with flexibility) according to the design of the smartphone's external appearance. Next, an unconventional six-turn coil (with a six-sided irregular hexagonal shape) is designed that has a nonuniform linewidth and a nonuniform line gap between two lines, and it is partially loaded with a rectangular ferrite composite. In this design, an enhanced magnetic line of forces can be realized in certain specific locations, and an excellent inductively coupled near-field receiver is achieved. Notably, this proposed NFC antenna can pass the tests required by NFC forum certification, and its performances are comparable with the traditional NFC antenna that has nonmetallic cover.

Question: What is the NFC antenna solution for metal-cover smartphone applications?


Mining Frequent Patterns in Data Streams at Multiple Time Granularities. Although frequent-pattern mining has been widely studied and used, it is challenging to extend it to data streams. Compared to mining from a static transaction data set, the streaming case has far more information to track and far greater complexity to manage. Infrequent items can become frequent later on and hence cannot be ignored. The storage structure needs to be dynamically adjusted to reflect the evolution of itemset frequencies over time. In this paper, we propose computing and maintaining all the frequent patterns (which is usually more stable and smaller than the streaming data) and dynamically updating them with the incoming data streams. We extended the framework to mine time-sensitive patterns with approximate support guarantee. We incrementally maintain tilted-time windows for each pattern at multiple time granularities. Interesting

Question: What is the time granularity of the data?


Improving Restaurants by Extracting Subtopics from Yelp Reviews. In this paper, we describe latent subtopics discovered from Yelp restaurant reviews by running an online Latent Dirichlet Allocation (LDA) algorithm. The goal is to point out demand of customers from a large amount of reviews, with high dimensionality. These topics can provide meaningful insights to restaurants about what customers care about in order to increase their Yelp ratings, which directly affects their revenue. We used the open dataset from the Yelp Dataset Challenge with over 158,000 restaurant reviews. To find latent subtopics from reviews, we adopted Online LDA, a generative probabilistic model for collections of discrete data such as text corpora. We present the breakdown of hidden topics over all reviews, predict stars per hidden topics discovered, and extend our findings to that of temporal information regarding restaurants peak hours. Overall, we have found several interesting insights and a method which could definitely prove useful to restaurant owners.

Question: What is the breakdown of hidden topics over all reviews?


Sentiment analysis on Twitter posts: An analysis of positive or negative opinion on GoJek. Online transportation, such as GoJek, is preferred by many users especially in areas where public transport is difficult to access or when there is a traffic jam. Twitter is a popular social networking site in Indonesia that can generate information from users' tweets. In this study, we proposed a system that detect public sentiments based on Twitter post about online transportation services especially GoJek. The system will collect tweets, analyze the tweets sentiments using SVM, and group them into positive and negative sentiment.

Question: What is sentiment analysis?


Stability analysis of negative impedance converter. Negative Impedance Converter (NIC) have received much attention to improve the restriction of antenna gain-bandwidth tradeoff relationship. However, a significant problem with NIC is the potential instability of unwanted oscillation due to the presence of positive feedback. For solving this problem, we propose a NIC circuit technique which is stable and has wideband characteristics of negative impedance.

Question: What is a negative impedance converter?


DTC-SVM Scheme for Induction Motors Fedwith a Three-level Inverter. Direct Torque Control is a control technique in AC drive systems to obtain high performance torque control. The conventional DTC drive contains a pair of hysteresis comparators. DTC drives utilizing hysteresis comparators suffer from high torque ripple and variable switching frequency. The most common solution to those problems is to use the space vector depends on the reference torque and flux. In this Paper The space vector modulation technique (SVPWM) is applied to 2 level inverter control in the proposed DTC-based induction motor drive system, thereby dramatically reducing the torque ripple. Then the controller based on space vector modulation is designed to be applied in the control of Induction Motor (IM) with a three-level Inverter. This type of Inverter has several advantages over the standard two-level VSI, such as a greater number of levels in the output voltage waveforms, Lower dV/dt, less harmonic distortion in voltage and current waveforms and lower switching frequencies. This paper proposes a general SVPWM algorithm for three-level based on standard two-level SVPWM. The proposed scheme is described clearly and simulation results are reported to demonstrate its effectiveness. The entire control scheme is implemented with Matlab/Simulink. Keywords—Direct torque control, space vector Pulsewidth modulation(SVPWM), neutral point clamped(NPC), two-level inverter.

Question: What is the difference between a two-level and three-level inverter?


Proxy Cryptography Revisited. In this work we revisit and formally study the notion of proxy cryptography. Intuitively, various proxy functions allow two cooperating parties F (the "FBI") and P (the "proxy") to duplicate the functionality available to t he third party U (the "user"), without being able to perform this functionality on their own (without cooperation). The concept is closely related to the notion of threshold cryptography, except we deal with only two parties P andF , and place very strict restrictions on the way the operations are performed (which is done for the sake of efficiency, usability and scalability). For example, for decryption (res p. signature)P (F ) sends a single message to F (P ), after which the latter can decrypt (sign) the message. Our formal modeling of proxy cryptography significantly generalizes, simplifies and simultaneously clarifies the model of "atomic proxy" suggested by Blaze and Strauss [4]. In particular, we define bidirectional and unidirectional var iants of our model 1, and show extremely simple generic solutions for proxy signature and encryption in these models. We also give more efficient solutions for several specific schemes. We conclude that proxy cryptography is a relatively simple concept to satisfy when looked from the correct and formal standpoint.

Question: What is proxy cryptography?


A modular architecture for building automation systems. The deployment of building automation systems (BAS) allows to increase comfort, safety and security and to reduce operational cost. Today such systems typically follow a two-layered hierarchical approach. While control networks interconnect distributed sensors, actuators and controllers, a backbone provides the necessary infrastructure for management tasks hosted by configuration and management devices. In addition, devices interconnecting the control network with the backbone and the backbone with further networks (e.g., the Internet) play a strategic role. All BAS devices contributing to a particular functionality differ in their requirements for hardware. This paper discusses requirements for devices used in the building automation domain and presents our work in progress to assemble platforms with different purposes relying on a modular architecture.

Question: What is a modular architecture for building automation systems?


Weighted cluster ensembles: Methods and analysis. Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. Cluster ensembles can provide robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out emergent spurious structures that arise due to the various biases to which each participating algorithm is tuned. In this article, we address the problem of combining multiple weighted clusters that belong to different subspaces of the input space. We leverage the diversity of the input clusterings in order to generate a consensus partition that is superior to the participating ones. Since we are dealing with weighted clusters, our consensus functions make use of the weight vectors associated with the clusters. We demonstrate the effectiveness of our techniques by running experiments with several real datasets, including high-dimensional text data. Furthermore, we investigate in depth the issue of diversity and accuracy for our ensemble methods. Our analysis and experimental results show that the proposed techniques are capable of producing a partition that is as good as or better than the best individual clustering.

Question: Weighted cluster ensembles: Methods and analysis.


Telerobotics, automation, and human supervisory control [Review]. In Telerobotics, Automation, and Human Supervisory Control, Thomas B. Sheridan shows that progress in robotics depends not only on change in technology, but also on advances in humans' relationships to machines. Sheridan, Professor of Engineering and Applied Psychology at M.I.T., has spent decades studying human-machine relations. His idea of "human supervisory control" (just "supervisory control" for short) has the potential to bring robotics out of the laboratory and into the difficult and messy world. Traditionally, the public and researchers alike have focused on automation which seeks to build into a machine all necessary capabilities for achieving a particular task, including sensors, actuators, servos, and algorithms. In contrast to automatic control, supervisory control puts a human back "in the loop," usually at the highest levels of abstraction, while maintaining lower-level functions within the machine. In a supervisory control system human and machine work together. The operator may intervene and operate manually in a particular situation, or she (Sheridan refers to the generic operator as female) might turn over control to an automatic controller for a time, acting as monitor of the task at hand. Typically the "system" (including the human) will operate somewhere on the spectrum between manual and automatic, dividing the task in different degrees depending on the situation. When the operator and the machine are not collocated but are connected by some communication channel, the system is said to be telerobotic. Determining the proper balance of man and machine, manual and automatic control, or providing tools to do so, is the goal of Sheridan's book. An important modern example of a supervisory control system (although not a telerobotic one) is a modem aircraft. The pilot can command the flight surfaces directly and fly manually, or she can program an autopilot to fly a complete route, in

Question: What is the difference between telerobotics and automation?


A brief survey of blackhole detection and avoidance for ZRP protocol in MANETs. Within Mobile Ad-Hoc Network(MANETs) unusual categories of routing protocols have been determined. It is a group of wireless system. MANET is other susceptible to a variety of attack than wired system. Black hole attack is further meticulous intimidation to MANETs. The routing protocols of MANET is less protected and therefore consequenced the system with malicious node. There are various routing protocols being used for MANETs. All routing protocols have been briefly discussed, however, Zone Routing Protocol (ZRP) is discussed in detail. Black hole is a major security threat for MANETs. Hence, in this paper, the various techniques used for detection and avoidance of Black hole attack in MANETs using ZRP routing protocol have been discussed.

Question: What is a MANET?


Do citations and readership identify seminal publications?. This work presents a new approach for analysing the ability of existing research metrics to identify research which has strongly influenced future developments. More specifically, we focus on the ability of citation counts and Mendeley reader counts to distinguish between publications regarded as seminal and publications regarded as literature reviews by field experts. The main motivation behind our research is to gain a better understanding of whether and how well the existing research metrics relate to research quality. For this experiment we have created a new dataset which we call TrueImpactDataset and which contains two types of publications, seminal papers and literature reviews. Using the dataset, we conduct a set of experiments to study how citation and reader counts perform in distinguishing these publication types, following the intuition that causing a change in a field signifies research quality. Our research shows that citation counts work better than a random baseline (by a margin of 10%) in distinguishing important seminal research papers from literature reviews while Mendeley reader counts do not work better than the baseline.

Question: What is the difference between a seminal paper and a literature review?


Variational Inference for Beta-Bernoulli Dirichlet Process Mixture Models. A commonly used paradigm in diverse application areas is to assume that an observed set of individual binary features is generated from a Bernoulli distribution with probabilities varying according to a Beta distribution. In this paper, we present our nonparametric variational inference algorithm for the Beta-Bernoulli observation model. Our primary focus is clustering discrete binary data using the Dirichlet process (DP) mixture model.

Question: What is the beta-bernoulli mixture model?


Predictive reward signal of dopamine neurons.. The effects of lesions, receptor blocking, electrical self-stimulation, and drugs of abuse suggest that midbrain dopamine systems are involved in processing reward information and learning approach behavior. Most dopamine neurons show phasic activations after primary liquid and food rewards and conditioned, reward-predicting visual and auditory stimuli. They show biphasic, activation-depression responses after stimuli that resemble reward-predicting stimuli or are novel or particularly salient. However, only few phasic activations follow aversive stimuli. Thus dopamine neurons label environmental stimuli with appetitive value, predict and detect rewards and signal alerting and motivating events. By failing to discriminate between different rewards, dopamine neurons appear to emit an alerting message about the surprising presence or absence of rewards. All responses to rewards and reward-predicting stimuli depend on event predictability. Dopamine neurons are activated by rewarding events that are better than predicted, remain uninfluenced by events that are as good as predicted, and are depressed by events that are worse than predicted. By signaling rewards according to a prediction error, dopamine responses have the formal characteristics of a teaching signal postulated by reinforcement learning theories. Dopamine responses transfer during learning from primary rewards to reward-predicting stimuli. This may contribute to neuronal mechanisms underlying the retrograde action of rewards, one of the main puzzles in reinforcement learning. The impulse response releases a short pulse of dopamine onto many dendrites, thus broadcasting a rather global reinforcement signal to postsynaptic neurons. This signal may improve approach behavior by providing advance reward information before the behavior occurs, and may contribute to learning by modifying synaptic transmission. The dopamine reward signal is supplemented by activity in

Question: What is the difference between a teaching signal and a reinforcement signal?


Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA. Feature Extraction Approaches Based On Matrix Pattern: MatPCA and MatFLDA Songcan Chen 1* Yulian Zhu 1 Daoqiang Zhang 1 Jing-Yu Yang 2 Dept. of Computer Science and Engineering, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, People's Republic of China. Dept. of Computer Science, Nanjing University of Science & Technology, Nanjing 210094, People's Republic of China Abstract Principle component analysis (PCA) and Fisher linear discriminant analysis (FLDA), as two popular feature extraction approaches in Pattern recognition and data analysis, extract so-needed features directly based on vector patterns, i.e., before applying them, any non-vector pattern such as an image is first vectorized into a vector pattern by some technique like concatenation. However, such a vectorization has been proved not to be beneficial for image recognition due to consequences of both the algebraic feature extraction approach and 2DPCA. In this paper, inspired by the above two approaches, we try an opposite direction to extract features for any vector pattern by first matrixizing it into a matrix pattern and then applying the matrixized versions of PCA and FLDA, MatPCA and MatFLDA, to the pattern. MatFLDA uses, in essence, the same principle as the algebraic feature extraction approach and is constructed in terms of similar objective function to FLDA while MatPCA uses a minimization of the reconstructed error for the training samples like PCA to obtain a set of projection vectors, which is somewhat different derivation from 2DPCA despite of equivalence. Finally experiments on 10 publicly obtainable datasets show that both MatPCA and MatFLDA gain performance improvement in different degrees respectively on 7 and 5

Question: What is the difference between MatPCA and MatFLDA?


Predicting Neural Activity Patterns Associated with Sentences Using a Neurobiologically Motivated Model of Semantic Representation.. We introduce an approach that predicts neural representations of word meanings contained in sentences then superposes these to predict neural representations of new sentences. A neurobiological semantic model based on sensory, motor, social, emotional, and cognitive attributes was used as a foundation to define semantic content. Previous studies have predominantly predicted neural patterns for isolated words, using models that lack neurobiological interpretation. Fourteen participants read 240 sentences describing everyday situations while undergoing fMRI. To connect sentence-level fMRI activation patterns to the word-level semantic model, we devised methods to decompose the fMRI data into individual words. Activation patterns associated with each attribute in the model were then estimated using multiple-regression. This enabled synthesis of activation patterns for trained and new words, which were subsequently averaged to predict new sentences. Region-of-interest analyses revealed that prediction accuracy was highest using voxels in the left temporal and inferior parietal cortex, although a broad range of regions returned statistically significant results, showing that semantic information is widely distributed across the brain. The results show how a neurobiologically motivated semantic model can decompose sentence-level fMRI data into activation features for component words, which can be recombined to predict activation patterns for new sentences.

Question: How does a neurobiologically motivated semantic model predict neural activity patterns associated with sentences?


Scribbles to Vectors: Preparation of Scribble Drawings for CAD Interpretation. This paper describes the work carried out on off-line paper based scribbles such that they can be incorporated into a sketch-based interface without forcing designers to change their natural drawing habits. In this work, the scribbled drawings are converted into a vectorial format which can be recognized by a CAD system. This is achieved by using pattern analysis techniques, namely the Gabor filter to simplify the scribbled drawing. Vector line are then extracted from the resulting drawing by means of Kalman filtering.

Question: How to convert scribbles to vectors?


A Systematic Comparison of Various Statistical Alignment Models. We present and compare various methods for computing word alignments using statistical or heuristic models. We consider the five alignment models presented in Brown, Della Pietra, Della Pietra, and Mercer (1993), the hidden Markov alignment model, smoothing techniques, and refinements. These statistical models are compared with two heuristic models based on the Dice coefficient. We present different methods for combining word alignments to perform a symmetrization of directed statistical alignment models. As evaluation criterion, we use the quality of the resulting Viterbi alignment compared to a manually produced reference alignment. We evaluate the models on the German-English Verbmobil task and the French-English Hansards task. We perform a detailed analysis of various design decisions of our statistical alignment system and evaluate these on training corpora of various sizes. An important result is that refined alignment models with a first-order dependence and a fertility model yield significantly better results than simple heuristic models. In the Appendix, we present an efficient training algorithm for the alignment models presented.

Question: What is the difference between a statistical and a heuristic alignment model?


Production-run software failure diagnosis via hardware performance counters. Sequential and concurrency bugs are widespread in deployed software. They cause severe failures and huge financial loss during production runs. Tools that diagnose production-run failures with low overhead are needed. The state-of-the-art diagnosis techniques use software instrumentation to sample program properties at run time and use off-line statistical analysis to identify properties most correlated with failures. Although promising, these techniques suffer from high run-time overhead, which is sometimes over 100%, for concurrency-bug failure diagnosis and hence are not suitable for production-run usage.  We present PBI, a system that uses existing hardware performance counters to diagnose production-run failures caused by sequential and concurrency bugs with low overhead. PBI is designed based on several key observations. First, a few widely supported performance counter events can reflect a wide variety of common software bugs and can be monitored by hardware with almost no overhead. Second, the counter overflow interrupt supported by existing hardware and operating systems provides a natural and effective mechanism to conduct event sampling at user level. Third, the noise and non-determinism in interrupt delivery complements well with statistical processing.  We evaluate PBI using 13 real-world concurrency and sequential bugs from representative open-source server, client, and utility programs, and 10 bugs from a widely used software-testing benchmark. Quantitatively, PBI can effectively diagnose failures caused by these bugs with a small overhead that is never higher than 10%. Qualitatively, PBI does not require any change to software and presents a novel use of existing hardware performance counters.

Question: What is the overhead of PBI?


Data Distillation: Towards Omni-Supervised Learning. We investigate omni-supervised learning, a special regime of semi-supervised learning in which the learner exploits all available labeled data plus internet-scale sources of unlabeled data. Omni-supervised learning is lower-bounded by performance on existing labeled datasets, offering the potential to surpass state-of-the-art fully supervised methods. To exploit the omni-supervised setting, we propose data distillation, a method that ensembles predictions from multiple transformations of unlabeled data, using a single model, to automatically generate new training annotations. We argue that visual recognition models have recently become accurate enough that it is now possible to apply classic ideas about self-training to challenging real-world data. Our experimental results show that in the cases of human keypoint detection and general object detection, state-of-the-art models trained with data distillation surpass the performance of using labeled data from the COCO dataset alone.

Question: What is data distillation?


Design and Modeling of a Textile Pressure Sensor for Sitting Posture Classification. A textile pressure sensor has been designed for measuring pressure distribution on the human body. Electrodes built with conductive textiles are arranged on both sides of a compressible spacer, forming a variable capacitor. The use of textiles makes unobtrusive, comfortable, lightweight, and washable sensors possible. This simplifies the goal to integrate such sensors into clothing in the future, to be simple, and fast to mount just as getting dressed. Hysteresis induced by the spacer of the sensor has been modeled with the Preisach model to reduce the measurement error from 24% to 5% on average and the maximal error from above 50% to below 10%. Standard textiles that are not specially optimized for low hysteresis can be used for designing the sensor due to the modeling. The model may also be used for other pressure or even strain sensors to reduce their hysteresis. The modeling enhances the accuracy of the textile sensor to those of commercial, nontextile pressure sensing mats. Furthermore, we used the sensor system for the classification of sitting postures in a chair. The data of nine subjects have been classified with Naive Bayes classifier, achieving an average recognition rate of 82%. We show that the textile sensor performs similarly to a commercial, nontextile pressure sensing mat for this application.

Question: How to classify sitting postures?


The dependency paradox in close relationships: accepting dependence promotes independence.. Using multiple methods, this investigation tested the hypothesis that a close relationship partner's acceptance of dependence when needed (e.g., sensitive responsiveness to distress cues) is associated with less dependence, more autonomous functioning, and more self-sufficiency (as opposed to more dependence) on the part of the supported individual. In two studies, measures of acceptance of dependency needs and independent functioning were obtained through couple member reports, by observing couple members' behaviors during laboratory interactions, by observing responses to experimentally manipulated partner assistance provided during an individual laboratory task, and by following couples over a period of 6 months to examine independent goal striving as a function of prior assessments of dependency acceptance. Results provided converging evidence in support of the proposed hypothesis. Implications of the importance of close relationships for optimal individual functioning are discussed.

Question: What is the dependency paradox in close relationships?


Chemical structure recognition and prediction: A machine learning technique. A chemical structure recognition system designed to identify and predict hand-drawn chemical diagrams is presented. Although the proposed system is primarily intended to help chemists complete their chemical structure drawings and predict and complete the chemical structure, it is also envisioned to be utilized as a learning aid in chemistry education on a student's touchscreen device or on a teacher's smart board. The proposed system continuously provides the user with real-time feedback about the most relevant combinations to complete the chemical compound. A constantly updated list of prioritized candidate structures is displayed to the user. If the user is an expert, he or she will be allowed to contribute to the learning process of the proposed system. The proposed system deploys two trainable Bayesian networks, one at its input and another one at its output. Between the two networks, the classification of hand- drawn chemical structure is done using morphological image processing, LDA and PCA, incorporating therefore both image- based and feature-based techniques. This incorporation contributes considerably to the increased performance of the proposed system by enabling it to efficiently recognize messy drawings and noisy sketches like those containing over-traced strokes. Performance comparison with existing state of the art techniques proved the system to be outperforming in terms of recognition accuracy.

Question: What is a chemical structure recognition system?


A Compact Circularly Polarized Cross-Shaped Slotted Microstrip Antenna. A compact cross-shaped slotted microstrip patch antenna is proposed for circularly polarized (CP) radiation. A symmetric, cross shaped slot is embedded along one of the diagonal axes of the square patch for CP radiation and antenna size reduction. The structure is asymmetric (unbalanced) along the diagonal axes. The overall size of the antenna with CP radiation can be reduced by increasing the perimeter of the symmetric cross-shaped slot within the first patch quadrant of the square patch. The performance of the CP radiation is also studied by varying the size and angle variation of the cross-shaped slot. A measured 3-dB axial-ratio (AR) bandwidth of around 6.0 MHz is achieved with the CP cross-shaped slotted microstrip antenna, with an 18.0 MHz 10-dB return-loss bandwidth. The measured boresight gain is more than 3.8 dBic over the operating band, while the overall antenna volume is 0.273λo × 0.273λo × 0.013λo (λο operating wavelength at 910 MHz).

Question: What is the size of the antenna?


Howard: A Dynamic Excavator for Reverse Engineering Data Structures. Even the most advanced reverse engineering techniques and products are weak in recovering data structures in stripped binaries—binaries without symbol tables. Unfortunately, forensics and reverse engineering without data structures is exceedingly hard. We present a new solution, known as Howard, to extract data structures from C binaries without any need for symbol tables. Our results are significantly more accurate than those of previous methods — sufficiently so to allow us to generate our own (partial) symbol tables without access to source code. Thus, debugging such binaries becomes feasible and reverse engineering becomes simpler. Also, we show that we can protect existing binaries from popular memory corruption attacks, without access to source code. Unlike most existing tools, our system uses dynamic analysis (on a QEMU-based emulator) and detects data structures by tracking how a program uses memory.

Question: How does Howard work?


Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. In this paper, we propose an efficient vehicle trajectory prediction framework based on recurrent neural network. Basically, the characteristic of the vehicle's trajectory is different from that of regular moving objects since it is affected by various latent factors including road structure, traffic rules, and driver's intention. Previous state of the art approaches use sophisticated vehicle behavior model describing these factors and derive the complex trajectory prediction algorithm, which requires a system designer to conduct intensive model optimization for practical use. Our approach is data-driven and simple to use in that it learns complex behavior of the vehicles from the massive amount of trajectory data through deep neural network model. The proposed trajectory prediction method employs the recurrent neural network called long short-term memory (LSTM) to analyze the temporal behavior and predict the future coordinate of the surrounding vehicles. The proposed scheme feeds the sequence of vehicles' coordinates obtained from sensor measurements to the LSTM and produces the probabilistic information on the future location of the vehicles over occupancy grid map. The experiments conducted using the data collected from highway driving show that the proposed method can produce reasonably good estimate of future trajectory.

Question: How to predict the future trajectory of a vehicle?


RFID Technology for IoT-Based Personal Healthcare in Smart Spaces. The current evolution of the traditional medical model toward the participatory medicine can be boosted by the Internet of Things (IoT) paradigm involving sensors (environmental, wearable, and implanted) spread inside domestic environments with the purpose to monitor the user's health and activate remote assistance. RF identification (RFID) technology is now mature to provide part of the IoT physical layer for the personal healthcare in smart environments through low-cost, energy-autonomous, and disposable sensors. It is here presented a survey on the state-of-the-art of RFID for application to body centric systems and for gathering information (temperature, humidity, and other gases) about the user's living environment. Many available options are described up to the application level with some examples of RFID systems able to collect and process multichannel data about the human behavior in compliance with the power exposure and sanitary regulations. Open challenges and possible new research trends are finally discussed.

Question: What is the state of the art of RFID for application to body centric systems?


Evolved Machines Shed Light on Robustness and Resilience. In biomimetic engineering, we may take inspiration from the products of biological evolution: we may instantiate biologically realistic neural architectures and algorithms in robots, or we may construct robots with morphologies that are found in nature. Alternatively, we may take inspiration from the process of evolution: we may evolve populations of robots in simulation and then manufacture physical versions of the most interesting or more capable robots that evolve. If we follow this latter approach and evolve both the neural and morphological subsystems of machines, we can perform controlled experiments that provide unique insight into how bodies and brains can work together to produce adaptive behavior, regardless of whether such bodies and brains are instantiated in a biological or technological substrate. In this paper, we review selected projects that use such methods to investigate the synergies and tradeoffs between neural architecture, morphology, action, and adaptive behavior.

Question: What is biomimetic engineering?


aBCD18 - An advanced 0.18um BCD technology for PMIC application. We present a new advanced 0.18um BCD(Bipolar-CMOS-DMOS) technology with the key features being a 40V HV-MOS and an SSTC(Sidewall Selective Transistor Cell) type EEPROM as well as complimentary available analog devices such as a high gain BJT, 4fF/um2 MIM capacitor, and 10k Ω/sq. poly resistor. To reduce device area and enhance latch up immunity, a 15um depth deep trench isolation process has been developed, which will help to significantly reduce the chip size.

Question: What is the size of the bcd18?


Local potassium signaling couples neuronal activity to vasodilation in the brain. The mechanisms by which active neurons, via astrocytes, rapidly signal intracerebral arterioles to dilate remain obscure. Here we show that modest elevation of extracellular potassium (K+) activated inward rectifier K+ (Kir) channels and caused membrane potential hyperpolarization in smooth muscle cells (SMCs) of intracerebral arterioles and, in cortical brain slices, induced Kir-dependent vasodilation and suppression of SMC intracellular calcium (Ca2+) oscillations. Neuronal activation induced a rapid (<2 s latency) vasodilation that was greatly reduced by Kir channel blockade and completely abrogated by concurrent cyclooxygenase inhibition. Astrocytic endfeet exhibited large-conductance, Ca2+-sensitive K+ (BK) channel currents that could be activated by neuronal stimulation. Blocking BK channels or ablating the gene encoding these channels prevented neuronally induced vasodilation and suppression of arteriolar SMC Ca2+, without affecting the astrocytic Ca2+ elevation. These results support the concept of intercellular K+ channel–to–K+ channel signaling, through which neuronal activity in the form of an astrocytic Ca2+ signal is decoded by astrocytic BK channels, which locally release K+ into the perivascular space to activate SMC Kir channels and cause vasodilation.

Question: How does potassium signaling couple neuronal activity to vasodilation in the brain?


Random Projection and Hashing. Random Projection is another class of methods used for low-rank matrix approximation. A random projection algorithm projects datapoints from a high-dimensional space R n onto a lower-dimensional subspace R r (r ⌧ n) using a random matrix S 2 R r⇥n. The key idea of random mapping comes from the Johnson-Lindenstrauss lemma[7] (we will explain later in detail) that says " if points in a vector space are projected onto a randomly selected subspace of suitably high dimension, then the distances between points are approximately preserved ". Random projection methods are computationally efficient and sufficiently accurate in practice for dimen-sionality reduction of high-dimensional datasets. Moreover, since complexity of many geometric algorithms depends significantly on the dimension, applying random projection as pre-processing is a common task in many data mining applications. As opposed to column sampling methods, that need to access data for approximating the low-rank subspace, random projections are data oblivious, as their computation involves only a random matrix S. We start with the basic concepts and definitions required to understand random projections techniques. Definition 1 (Column Space). Consider a matrix A 2 R n⇥d (n > d). Notice that as one ranges over all vectors x 2 R d , Ax ranges over all linear combinations of the columns of A and therefore defines a d-dimensional subspace of R n , which we refer to as the column space of A and denote it by C(A). Definition 2 (` 2-Subspace Embedding). A matrix S 2 R r⇥n provides a subspace embedding for C(A) if kSAxk 2 2 = (1 ± ")kAxk 2 2 , 8x 2 R d. Such matrix S provides a low distortion embedding, and is called a (1 ± ") ` 2-subspace embedding. Using an`2-subspace embedding, one can work with SA 2 R r⇥d instead of A 2 R n⇥d. Typically r ⌧ n, so we are working with a smaller matrix that reduces the time/space complexity of many algorithms. However note that definitely r needs to be l

Question: What is a 2-subspace embedding?


Automated Plant Disease Analysis (APDA): Performance Comparison of Machine Learning Techniques. Plant disease analysis is one of the critical tasks in the field of agriculture. Automatic identification and classification of plant diseases can be supportive to agriculture yield maximization. In this paper we compare performance of several Machine Learning techniques for identifying and classifying plant disease patterns from leaf images. A three-phase framework has been implemented for this purpose. First, image segmentation is performed to identify the diseased regions. Then, features are extracted from segmented regions using standard feature extraction techniques. These features are then used for classification into disease type. Experimental results indicate that our proposed technique is significantly better than other techniques used for Plant Disease Identification and Support Vector Machines outperforms other techniques for classification of diseases.

Question: What is the performance of the proposed technique?


Building an Ontology of Cyber Security. Situation awareness depends on a reliable perception of the environment and comprehension of its semantic structures. In this respect, cyberspace presents a unique challenge to the situation awareness of users and analysts, since it is a unique combination of human and machine elements, whose complex interactions occur in a global communication network. Accordingly, we outline the underpinnings of an ontology of secure operations in cyberspace, presenting the ontology framework and providing two modeling examples. We make the case for adopting a rigorous semantic model of cyber security to overcome the current limits of the state of the art. Keywords— cyber security, ontology, situation awareness,

Question: What is the ontology of cyber security?


Core stability exercise principles.. Core stability is essential for proper load balance within the spine, pelvis, and kinetic chain. The so-called core is the group of trunk muscles that surround the spine and abdominal viscera. Abdominal, gluteal, hip girdle, paraspinal, and other muscles work in concert to provide spinal stability. Core stability and its motor control have been shown to be imperative for initiation of functional limb movements, as needed in athletics. Sports medicine practitioners use core strengthening techniques to improve performance and prevent injury. Core strengthening, often called lumbar stabilization, also has been used as a therapeutic exercise treatment regimen for low back pain conditions. This article summarizes the anatomy of the core, the progression of core strengthening, the available evidence for its theoretical construct, and its efficacy in musculoskeletal conditions.

Question: What is the core?


Single-Fed Low Profile Broadband Circularly Polarized Stacked Patch Antenna. A simple technique is developed in this communication to increase the axial ratio (AR) bandwidth and achieve good impedance matching of a single-fed low profile circularly polarized (CP) stacked patch antenna. The proposed antenna is composed of a driven patch layer and a parasitic patch layer. The driven patch layer consists of a truncated main patch, a parasitic patch and a probe feeding structure while the stacked patch layer is comprised of five patches. The proposed antenna combines the attractive features such as low profile, wide impedance and AR bandwidths, high gain as well as easiness of design, manufacture and integration. The antenna operating at 6 GHz band is designed and fabricated on an FR4 substrate and the overall volume is 0.8 λ0 ×0.8 λ0 ×0.09 λ0. Measured results show that the antenna achieves an impedance bandwidth of more than 30% for dB, a 3-dB AR bandwidth of about 20.7%, and a gain level of over 7.9 dBi within the 3-dB AR bandwidth.

Question: What is the volume of the antenna?


60-GHz Millimeter-Wave Life Detection System (MLDS) for Noncontact Human Vital-Signal Monitoring. A first reported experimental study of a 60 GHz millimeter-wave life detection system (MLDS) for noncontact human vital-signal monitoring is presented. This detection system is constructed by using V-band millimeter-wave waveguide components. A clutter canceller is implemented in the system with an adjustable attenuator and phase shifter. It performs clutter cancellation for the transmitting power leakage from the circulator and background reflection to enhance the detecting sensitivity of weak vital signals. The noncontact vital signal measurements have been conducted on a human subject in four different physical orientations from distances of 1 and 2 m. The time-domain and spectrum waveforms of the measured breathing and heartbeat are presented. This prototype system will be useful for the development of the 60-GHz CMOS MLDS detector chip design.

Question: What is a noncontact vital-signal monitoring system?


A keyword extraction method from twitter messages represented as graphs. Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness. A fundamental step in text mining techniques consists of building a model for text representation. The model known as vector space model, VSM, is the most well-known and used among these techniques. However, some difficulties and limitations of VSM, such as scalability and sparsity, motivate the proposal of alternative approaches. This paper proposes a keyword extraction method for tweet collections that represents texts as graphs and applies centrality measures for finding the relevant vertices (keywords). To assess the performance of the proposed approach, three different sets of experiments are performed. The first experiment applies TKG to a text from the Time magazine and compares its performance with that of the literature. The second set of experiments takes tweets from three different TV shows, applies TKG and compares it with TFIDF and KEA, having human classifications as benchmarks. Finally, these three algorithms are applied to tweets sets of increasing size and their computational running time is measured and compared. Altogether, these experiments provide a general overview of how TKG can be used in practice, its performance when compared with other standard approaches, and how it scales to larger data instances. The results show that TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such

Question: What is a vector space model?


From Non-Functional Requirements to Design through Patterns. Design patterns aid in documenting and communicating proven design solutions to recurring problems. They describe not only how to solve design problems, but also why a solution is chosen over others and what trade-offs are made. Non-functional requirements (NFRs) are pervasive in descriptions of design patterns. They play a crucial role in understanding the problem being addressed, the trade-offs discussed, and the design solution proposed. However, since design patterns are mostly expressed as informal text, the structure of the design reasoning is not systematically organised. This paper proposes a systematic treatment of NFRs in descriptions of patterns and when applying patterns during design. The approach organises, analyses and refines non-functional requirements, and provides guidance and reasoning support when applying patterns during the design of a software system. Three design patterns taken from the literature are used to illustrate this approach.

Question: What is a design pattern?


Training Deep and Recurrent Networks with Hessian-Free Optimization. Hessian-Free optimization (HF) is an approach for unconstrained minimization of real-valued smooth objective functions. Like standard Newton's method, it uses local quadratic approximations to generate update proposals. It belongs to the broad class of approximate Newton methods that are practical for problems of very high dimensionality, such as the training objectives of large neural networks. Different algorithms that use many of the same key principles have appeared in the literatures of various communities under different names such as Newton-CG, CG-Steihaug, Newton-Lanczos, and Truncated Newton [27, 28], but applications to machine learning and especially neural networks, have been limited or non-existent until recently. With the work of Martens [22] and later Martens and Sutskever [23] it has been demonstrated that such an approach, if carefully designed and implemented, can work very well for optimizing nonconvex functions such as the training objective for deep neural networks and recurrent neural networks (RNNs), given sensible random initializations. This was significant because gradient descent methods have been observed to be very slow and sometimes completely ineffective [16, 4, 17] on these problems, unless special non-random initializations schemes like layer-wise pre-training [16, 15, 3] are used. HF, which is a general optimization-based approach, can be used in conjunction with or as an alternative to existing pre-training methods and is more widely applicable, since it relies on fewer assumptions about the specific structure of the network. In this report we will first describe the basic HF approach, and then examine well-known general purpose performance-improving techniques as well as others that are specific to HF (versus other Truncated-Newton type approaches) or to neural networks. We will also provide practical tips for creating efficient and correct implementations, and d

Question: What is the training objective for deep neural networks?


Compact slow-wave millimeter-wave bandpass filter (BPF) using open-loop resonator. A compact slow-wave millimeter-wave bandpass filter for wireless communication systems is developed and proposed in this paper. The filter is based on an open-loop resonator that is composed of a microstrip line with both ends loaded with folded open stubs. The folded arms of the open stubs are utilized to increase the loading capacitance to ground. In addition, the folded arms of the open stubs are also used to introduce cross couplings. The cross couplings can create transmission zeros at the edges of the desired passband. As a result, the filter can exhibit a passband with sharp rejection skirts. The filter is designed to have a fractional bandwidth of about 3.3% at a center frequency of 30.0 GHz. The filter design is realized using Rogers RT6002 substrate with a dielectric constant of 2.94 and a thickness of 0.127 mm. The filter design is successfully fabricated using the printed circuit board (PCB) technology. The fabricated filter is measured and an excellent agreement between the simulated and measured results is achieved. The fabricated filter has the advantages of compact size and excellent performance.

Question: What is a compact slow-wave millimeter-wave bandpass filter?


Risk Management : Coordinating Corporate Investment and Financing Policies. This paper develops a general framework for analyzing corporate risk management policies. We begin by observing that if external sources of finance are more costly to corporations than internally generated funds, there will typically be a benefit to hedging: hedging adds value to the extent that it helps ensure that a corporation has sufficient internal funds available to take advantage of attractive investment opportunities. We then argue that this simple observation has wide ranging implications for the design of risk management strategies. We delineate how these strategies should depend on such factors as shocks to investment and financing opportunities. We also discuss exchange rate hedging strategies for multinationals, as well as strategies involving "nonlinear" instruments like options. CORPORATIONS TAKE RISK MANAGEMENT very seriously—recent surveys find that risk management is ranked by financial executives as one of their most important objectives.^ Given its real-world prominence, one might guess that the topic of risk management would command a great deal of attention from researchers in finance, and that practitioners would therefore have a welldeveloped body of wisdom from which to draw in formulating hedging strategies. Such a guess would, however, be at best only partially correct. Finance theory does do a good job of instructing firms on the implementation of hedges. For example, if a refining company decides that it wants to use options to reduce its exposure to oil prices by a certain amount, a BlackScholes type model can help the company calculate the number of contracts needed. Indeed, there is an extensive literature that covers numerous practical aspects of what might be termed "hedging mechanics," from the computation of hedge ratios to the institutional peculiarities of individual contracts. Unfortunately, finance theory has had much less clear cut guidance to offer o

Question: What is the difference between a risk management and a risk management policy?


Semantic full-text search with broccoli. We combine search in triple stores with full-text search into what we call \emph{semantic full-text search}. We provide a fully functional web application that allows the incremental construction of complex queries on the English Wikipedia combined with the facts from Freebase. The user is guided by context-sensitive suggestions of matching words, instances, classes, and relations after each keystroke. We also provide a powerful API, which may be used for research tasks or as a back end, e.g., for a question answering system. Our web application and public API are available under \url{http://broccoli.cs.uni-freiburg.de}.

Question: What is the semantic full-text search with broccoli?


Inventory Constrained Maritime Routing and Scheduling for Multi-Commodity Liquid Bulk.

Question: What is the name of the document?


Making sense of social media streams through semantics: A survey. Using semantic technologies for mining and intelligent information access to social media is a challenging, emerging research area. Traditional search methods are no longer able to address the more complex information seeking behaviour in media streams, which has evolved towards sense making, learning, investigation, and social search. Unlike carefully authored news text and longer web context, social media streams pose a number of new challenges, due to their large-scale, short, noisy, contextdependent, and dynamic nature. This paper defines five key research questions in this new application area, examined through a survey of state-of-the-art approaches for mining semantics from social media streams; user, network, and behaviour modelling; and intelligent, semanticbased information access. The survey includes key methods not just from the Semantic Web research field, but also from the related areas of natural language processing and user modelling. In conclusion, key outstanding challenges are discussed and new directions for research are proposed.

Question: What is the canadian military?


Cognitive architectures: Research issues and challenges. In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. 2008 Published by Elsevier B.V.

Question: What is a cognitive architecture?


Stochastic Language Generation For Spoken Dialogue Systems. The two current approaches to language generation, Template-based and rule-based (linguistic) NLG, have limitations when applied to spoken dialogue systems, in part because they were developed for text generation. In this paper, we propose a new corpus-based approach to natural language generation, specifically designed for spoken dialogue systems.

Question: What is a corpus-based approach to language generation?


Beyond budgeting and Agile Software Development: a Conceptual Framework for the Performance Management of Agile Software Development Teams. Around the same time as the emergence of agile methods as a formalized concept, the management accounting literature introduced the concept of Beyond Budgeting as a performance management model for changing business environments. Both concepts share many similarities with both having a distinctly agile or adaptive perspective. The Beyond Budgeting model promises to enable companies to keep pace with changing business environments, quickly create and adapt strategy and empower people throughout the organization to make effective changes. This research in progress paper attempts to develop the Beyond Budgeting model within the context of agile software development teams. The twelve Beyond Budgeting principles are discussed and a research framework is presented. This framework is being used in two case studies to investigate the organizational issues and challenges that affect the performance of agile software

Question: What is the difference between agile and beyond budgeting?


Yelp Dataset Challenge: Review Rating Prediction. Review websites, such as TripAdvisor and Yelp, allow users to post online reviews for various businesses, products and services, and have been recently shown to have a significant influence on consumer shopping behaviour. An online review typically consists of free-form text and a star rating out of 5. The problem of predicting a user's star rating for a product, given the user's text review for that product, is called Review Rating Prediction and has lately become a popular, albeit hard, problem in machine learning. In this paper, we treat Review Rating Prediction as a multi-class classification problem, and build sixteen different prediction models by combining four feature extraction methods, (i) unigrams, (ii) bigrams, (iii) trigrams and (iv) Latent Semantic Indexing, with four machine learning algorithms, (i) logistic regression, (ii) Näıve Bayes classification, (iii) perceptrons, and (iv) linear Support Vector Classification. We analyse the performance of each of these sixteen models to come up with the best model for predicting the ratings from reviews. We use the dataset provided by Yelp for training and testing the models.

Question: What is the best model for predicting the ratings from reviews?


CSIFT: A SIFT Descriptor with Color Invariant Characteristics. SIFT has been proven to be the most robust local invariant feature descriptor. SIFT is designed mainly for gray images. However, color provides valuable information in object description and matching tasks. Many objects can be misclassified if their color contents are ignored. This paper addresses this problem and proposes a novel colored local invariant feature descriptor. Instead of using the gray space to represent the input image, the proposed approach builds the SIFT descriptors in a color invariant space. The built Colored SIFT (CSIFT) is more robust than the conventional SIFT with respect to color and photometrical variations. The evaluation results support the potential of the proposed approach.

Question: What is the difference between the SIFT and the CSIFT?


Noise and Bandwidth analysis for PMOS based wide swing cascode current mirror using 0.35 um CMOS-MEMS technology. In this work, we propose a PMOS based wide swing cascode current mirror (WSSCM) for 0.35 um CMOS-MEMS technology. The proposed circuit shows a high bandwidth current mirror with high output resistance capability operating at low voltage levels. Noise, output resistance and bandwidth analysis are presented. CADENCE SPECTRE circuit design tool using MIMOS 0.35 um and BSIM3 transistor models is used to carry out the simulations. The circuit achieves an amplification of 120.04 uA, the input referred noise of the circuit is 75.77 nV/√Hz with a minimum power consumption of 0.33 mW and a high output resistance of 15 MO .

Question: How to design a current mirror using 0.35 um CMOS-MEMS technology?


EFFICIENT CONSTRAINED PATH PLANNING VIA SEARCH IN STATE LATTICES. We propose a novel approach to constrained path planning that is based on a special search space which efficiently encodes feasible paths. The paths are encoded implicitly as connections between states, but only feasible and local connections are included. Once this search space is developed, we systematically generate a near-minimal set of spatially distinct path primitives. This set expresses the local connectivity of constrained motions and also eliminates redundancies. The set of primitives is used to define heuristic search, and thereby create a very efficient path planner at the chosen resolution. We also discuss a wide variety of space and terrestrial robotics applications where this motion planner can be especially useful.

Question: What is a constrained path planner?