Query: Neural Variational Inference For Embedding Knowledge Graphs

Relevant passages:
Adam: A Method for Stochastic Optimization <=SEP=> We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. The method is straightforward to implement, is computationally efficient, has little memory requirements, is invariant to diagonal rescaling of the gradients, and is well suited for problems that are large in terms of data and/or parameters. The method is also appropriate for non-stationary objectives and problems with very noisy and/or sparse gradients. The hyper-parameters have intuitive interpretations and typically require little tuning. Some connections to related algorithms, on which Adam was inspired, are discussed. We also analyze the theoretical convergence properties of the algorithm and provide a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Empirical results demonstrate that Adam works well in practice and compares favorably to other stochastic optimization methods. Finally, we discuss AdaMax, a variant of Adam based on the infinity norm.

Probabilistic Principal Component Analysis <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Stochastic Variational Inference <=SEP=> We develop stochastic variational inference, a scalable algorithm for approximating posterior distributions. We develop this technique for a large class of probabilistic models and we demonstrate it with two probabilistic topic models, latent Dirichlet allocation and the hierarchical Dirichlet process topic model. Using stochastic variational inference, we analyze several large collections of documents: 300K articles from Nature, 1.8M articles from The New York Times, and 3.8M articles from Wikipedia. Stochastic inference can easily handle data sets of this size and outperforms traditional variational inference, which can only handle a smaller subset. (We also show that the Bayesian nonparametric topic model outperforms its parametric counterpart.) Stochastic variational inference lets us apply complex Bayesian models to massive data sets.

PILCO: A Model-Based and Data-Efficient Approach to Policy Search <=SEP=> In this paper, we introduce pilco, a practical, data-efficient model-based policy search method. Pilco reduces model bias, one of the key problems of model-based reinforcement learning, in a principled way. By learning a probabilistic dynamics model and explicitly incorporating model uncertainty into long-term planning, pilco can cope with very little data and facilitates learning from scratch in only a few trials. Policy evaluation is performed in closed form using state-ofthe-art approximate inference. Furthermore, policy gradients are computed analytically for policy improvement. We report unprecedented learning efficiency on challenging and high-dimensional control tasks.

Adaptive Subgradient Methods for Online Learning and Stochastic Optimization <=SEP=> We present a new family of subgradient methods that dynamica lly incorporate knowledge of the geometry of the data observed in earlier iterations to perfo rm more informative gradient-based learning. Metaphorically, the adaptation allows us to find n eedles in haystacks in the form of very predictive but rarely seen features. Our paradigm stems fro m recent advances in stochastic optimization and online learning which employ proximal funct ions to control the gradient steps of the algorithm. We describe and analyze an apparatus for adap tively modifying the proximal function, which significantly simplifies setting a learning rate nd results in regret guarantees that are provably as good as the best proximal function that can be cho sen in hindsight. We give several efficient algorithms for empirical risk minimization probl ems with common and important regularization functions and domain constraints. We experimen tally study our theoretical analysis and show that adaptive subgradient methods outperform state-o f-the-art, yet non-adaptive, subgradient algorithms.

A 14-mW 6.25-Gb/s Transceiver in 90-nm CMOS <=SEP=> This paper describes a 6.25-Gb/s 14-mW transceiver in 90-nm CMOS for chip-to-chip applications. The transceiver employs a number of features for reducing power consumption, including a shared LC-PLL clock multiplier, an inductor-loaded resonant clock distribution network, a low- and programmable-swing voltage-mode transmitter, software-controlled clock and data recovery (CDR) and adaptive equalization within the receiver, and a novel PLL-based phase rotator for the CDR. The design can operate with channel attenuation of -15 dB or greater at a bit-error rate of 10-15 or less, while consuming less than 2.25 mW/Gb/s per transceiver.

Limbic-cortical dysregulation: a proposed model of depression. <=SEP=> A working model of depression implicating failure of the coordinated interactions of a distributed network of limbic-cortical pathways is proposed. Resting state patterns of regional glucose metabolism in idiopathic depressed patients, changes in metabolism with antidepressant treatment, and blood flow changes with induced sadness in healthy subjects were used to test and refine this hypothesis. Dorsal neocortical decreases and ventral paralimbic increases characterize both healthy sadness and depressive illness; concurrent inhibition of overactive paralimbic regions and normalization of hypofunctioning dorsal cortical sites characterize disease remission. Normal functioning of the rostral anterior cingulate, with its direct connections to these dorsal and ventral areas, is postulated to be additionally required for the observed reciprocal compensatory changes, since pretreatment metabolism in this region uniquely predicts antidepressant treatment response. This model is offered as an adaptable framework to facilitate continued integration of clinical imaging findings with complementary neuroanatomical, neurochemical, and electrophysiological studies in the investigation of the pathogenesis of affective disorders.

Image Steganography Using LSB and Edge – Detection Technique <=SEP=> 217  Abstract-Steganography is the technique of hiding the fact that communication is taking place, by hiding data in other data. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exist a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points. Steganalysis, the detection of this hidden information, is an inherently difficult problem and requires a thorough investigation so we are using " Edge detection Filter ". In this paper search how the edges of the images can be used to hiding text message in Steganography .It give the depth view of image steganography and Edge detection Filter techniques. Method: In this paper search how the edges of the images can be used to hiding text message in Steganography. For that gray image has been presented. In this method tried to find binary value of each character of text message and then in the next stage, tried to find dark places of gray image (black) by converting the original image to binary image for labeling each object of image by considering on 8 pixel connectivity. Then these images have been converted to RGB image in order to find dark places. Because in this way each sequence of gray color turns into RGB color and dark level of grey image is found by this way if the Gary image is very light the histogram must be changed manually to find just dark places. In the final stage each 8 pixels of dark places has been considered as a byte and binary value of each character has been put in low bit of each byte that was created manually by dark places pixels for increasing security of the main way of least Significant bit steganography. Steganalysis then used to evaluate the hiding process to ensure the data can be hidden in best possible way.

Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. <=SEP=> We describe here a multiplexed protein quantitation strategy that provides relative and absolute measurements of proteins in complex mixtures. At the core of this methodology is a multiplexed set of isobaric reagents that yield amine-derivatized peptides. The derivatized peptides are indistinguishable in MS, but exhibit intense low-mass MS/MS signature ions that support quantitation. In this study, we have examined the global protein expression of a wild-type yeast strain and the isogenic upf1Delta and xrn1Delta mutant strains that are defective in the nonsense-mediated mRNA decay and the general 5' to 3' decay pathways, respectively. We also demonstrate the use of 4-fold multiplexing to enable relative protein measurements simultaneously with determination of absolute levels of a target protein using synthetic isobaric peptide standards. We find that inactivation of Upf1p and Xrn1p causes common as well as unique effects on protein expression.

An Efficient Certificateless Encryption for Secure Data Sharing in Public Clouds <=SEP=> We propose a mediated certificateless encryption scheme without pairing operations for securely sharing sensitive information in public clouds. Mediated certificateless public key encryption (mCL-PKE) solves the key escrow problem in identity based encryption and certificate revocation problem in public key cryptography. However, existing mCL-PKE schemes are either inefficient because of the use of expensive pairing operations or vulnerable against partial decryption attacks. In order to address the performance and security issues, in this paper, we first propose a mCL-PKE scheme without using pairing operations. We apply our mCL-PKE scheme to construct a practical solution to the problem of sharing sensitive information in public clouds. The cloud is employed as a secure storage as well as a key generation center. In our system, the data owner encrypts the sensitive data using the cloud generated users' public keys based on its access control policies and uploads the encrypted data to the cloud. Upon successful authorization, the cloud partially decrypts the encrypted data for the users. The users subsequently fully decrypt the partially decrypted data using their private keys. The confidentiality of the content and the keys is preserved with respect to the cloud, because the cloud cannot fully decrypt the information. We also propose an extension to the above approach to improve the efficiency of encryption at the data owner. We implement our mCL-PKE scheme and the overall cloud based system, and evaluate its security and performance. Our results show that our schemes are efficient and practical.

Clustal W and Clustal X version 2.0 <=SEP=> SUMMARY
The Clustal W and Clustal X multiple sequence alignment programs have been completely rewritten in C++. This will facilitate the further development of the alignment algorithms in the future and has allowed proper porting of the programs to the latest versions of Linux, Macintosh and Windows operating systems.


AVAILABILITY
The programs can be run on-line from the EBI web server: http://www.ebi.ac.uk/tools/clustalw2. The source code and executables for Windows, Linux and Macintosh computers are available from the EBI ftp site ftp://ftp.ebi.ac.uk/pub/software/clustalw2/

Evaluating three treatments for borderline personality disorder: a multiwave study. <=SEP=> OBJECTIVE
The authors examined three yearlong outpatient treatments for borderline personality disorder: dialectical behavior therapy, transference-focused psychotherapy, and a dynamic supportive treatment.


METHOD
Ninety patients who were diagnosed with borderline personality disorder were randomly assigned to transference-focused psychotherapy, dialectical behavior therapy, or supportive treatment and received medication when indicated. Prior to treatment and at 4-month intervals during a 1-year period, blind raters assessed the domains of suicidal behavior, aggression, impulsivity, anxiety, depression, and social adjustment in a multiwave study design.


RESULTS
Individual growth curve analysis revealed that patients in all three treatment groups showed significant positive change in depression, anxiety, global functioning, and social adjustment across 1 year of treatment. Both transference-focused psychotherapy and dialectical behavior therapy were significantly associated with improvement in suicidality. Only transference-focused psychotherapy and supportive treatment were associated with improvement in anger. Transference-focused psychotherapy and supportive treatment were each associated with improvement in facets of impulsivity. Only transference-focused psychotherapy was significantly predictive of change in irritability and verbal and direct assault.


CONCLUSIONS
Patients with borderline personality disorder respond to structured treatments in an outpatient setting with change in multiple domains of outcome. A structured dynamic treatment, transference-focused psychotherapy was associated with change in multiple constructs across six domains; dialectical behavior therapy and supportive treatment were associated with fewer changes. Future research is needed to examine the specific mechanisms of change in these treatments beyond common structures.

A Corpus and Model Integrating Multiword Expressions and Supersenses <=SEP=> This paper introduces a task of identifying and semantically classifying lexical expressions in running text. We investigate the online reviews genre, adding semantic supersense annotations to a 55,000 word English corpus that was previously annotated for multiword expressions. The noun and verb supersenses apply to full lexical expressions, whether singleor multiword. We then present a sequence tagging model that jointly infers lexical expressions and their supersenses. Results show that even with our relatively small training corpus in a noisy domain, the joint task can be performed to attain 70% class labeling F1.

The neural basis of rationalization: cognitive dissonance reduction during decision-making. <=SEP=> People rationalize the choices they make when confronted with difficult decisions by claiming they never wanted the option they did not choose. Behavioral studies on cognitive dissonance provide evidence for decision-induced attitude change, but these studies cannot fully uncover the mechanisms driving the attitude change because only pre- and post-decision attitudes are measured, rather than the process of change itself. In the first fMRI study to examine the decision phase in a decision-based cognitive dissonance paradigm, we observed that increased activity in right-inferior frontal gyrus, medial fronto-parietal regions and ventral striatum, and decreased activity in anterior insula were associated with subsequent decision-related attitude change. These findings suggest the characteristic rationalization processes that are associated with decision-making may be engaged very quickly at the moment of the decision, without extended deliberation and may involve reappraisal-like emotion regulation processes.

Sparse Non-rigid Registration of 3D Shapes <=SEP=> Non-rigid registration of 3D shapes is an essential task of increasing importance as commodity depth sensors become more widely available for scanning dynamic scenes. Non-rigid registration is much more challenging than rigid registration as it estimates a set of local transformations instead of a single global transformation, and hence is prone to the overfitting issue due to underdetermination. The common wisdom in previous methods is to impose an ℓ2-norm regularization on the local transformation differences. However, the ℓ2-norm regularization tends to bias the solution towards outliers and noise with heavy-tailed distribution, which is verified by the poor goodness-of-fit of the Gaussian distribution over transformation differences. On the contrary, Laplacian distribution fits well with the transformation differences, suggesting the use of a sparsity prior. We propose a sparse non-rigid registration (SNR) method with an ℓ1-norm regularized model for transformation estimation, which is effectively solved by an alternate direction method (ADM) under the augmented Lagrangian framework. We also devise a multi-resolution scheme for robust and progressive registration. Results on both public datasets and our scanned datasets show the superiority of our method, particularly in handling large-scale deformations as well as outliers and noise.

THE STRUCTURE ( S ) OF PARTICLE VERBS <=SEP=> • complex heads are formed either in the lexicon (cf. Booij 1990 , Johnson 1991 , Koizumi 1993, Neeleman 1994, Neeleman & Weerman 1993, Stiebels 1996, Stiebels & Wunderlich 1994, Wiese 1996, Ackerman & Webelhuth 1998 ) or by some kind of overt or covert incorporation of the particle into the verb (cf. van Riemsdijk 1978, Baker 1988, Koopman 1995, Olsen 1995a, 1995b, Zeller 1997a, 1997b, 1998) • small clause structure (cf. Kayne 1985, Guéron 1987 , 1990, Hoekstra 1988, Grewendorf 1990, Bennis 1992, Mulder 1992 , den Dikken 1992, 1995 , Zwart 1997 among others) 1

State of the Art in Stereoscopic and Autostereoscopic Displays <=SEP=> Underlying principles of stereoscopic direct-view displays, binocular head-mounted displays, and autostereoscopic direct-view displays are explained and some early work as well as the state of the art in those technologies are reviewed. Stereoscopic displays require eyewear and can be categorized based on the multiplexing scheme as: 1) color multiplexed (old technology but there are some recent developments; low-quality due to color reproduction and crosstalk issues; simple and does not require additional electronics hardware); 2) polarization multiplexed (requires polarized light output and polarization-based passive eyewear; high-resolution and high-quality displays available); and 3) time multiplexed (requires faster display hardware and active glasses synchronized with the display; high-resolution commercial products available). Binocular head-mounted displays can readily provide 3-D, virtual images, immersive experience, and more possibilities for interactive displays. However, the bulk of the optics, matching of the left and right ocular images and obtaining a large field of view make the designs quite challenging. Some of the recent developments using unconventional optical relays allow for thin form factors and open up new possibilities. Autostereoscopic displays are very attractive as they do not require any eyewear. There are many possibilities in this category including: two-view (the simplest implementations are with a parallax barrier or a lenticular screen), multiview, head tracked (requires active optics to redirect the rays to a moving viewer), and super multiview (potentially can solve the accommodation-convergence mismatch problem). Earlier 3-D booms did not last long mainly due to the unavailability of enabling technologies and the content. Current developments in the hardware technologies provide a renewed interest in 3-D displays both from the consumers and the display manufacturers, which is evidenced by the recent commercial products and new research results in this field.

Importance of audio feature reduction in automatic music genre classification <=SEP=> Multimedia database retrieval is rapidly growing and its popularity in online retrieval systems is consequently increasing. Large datasets are major challenges for searching, retrieving, and organizing the music content. Therefore, a robust automatic music-genre classification method is needed for organizing this music data into different classes according to specific viable information. Two fundamental components are to be considered for genre classification: audio feature extraction and classifier design. In this paper, we propose diverse audio features to precisely characterize the music content. The feature sets belong to four groups: dynamic, rhythmic, spectral, and harmonic. From the features, five statistical parameters are considered as representatives, including the fourth-order central moments of each feature as well as covariance components. Ultimately, insignificant representative parameters are controlled by minimum redundancy and maximum relevance. This algorithm calculates the score level of all feature attributes and orders them. Only high-score audio features are considered for genre classification. Moreover, we can recognize those audio features and distinguish which of the different statistical parameters derived from them are important for genre classification. Among them, mel frequency cepstral coefficient statistical parameters, such as covariance components and variance, are more frequently selected than the feature attributes of other groups. This approach does not transform the original features as do principal component analysis and linear discriminant analysis. In addition, other feature reduction methodologies, such as locality-preserving projection and non-negative matrix factorization are considered. The performance of the proposed system is measured based on the reduced features from the feature pool using different feature reduction techniques. The results indicate that the overall classification is competitive with existing state-of-the-art frame-based methodologies.

A Dual-Band Dual-Circular-Polarization Antenna for Ka-Band Satellite Communications <=SEP=> A novel Ka-band dual-band dual-circularly-polarized antenna array is presented in this letter. A dual-band antenna with left-hand circular polarization for the Ka-band downlink frequencies and right-hand circular polarization for the Ka-band uplink frequencies is realized with compact annular ring slots. By applying the sequential rotation technique, a 2 × 2 subarray with good performance is obtained. This letter describes the design process and presents simulation and measurement results.

Using machine learning algorithms for housing price prediction: The case of Fairfax County, Virginia housing data <=SEP=> House sales are determined based on the Standard & Poor’s Case-Shiller home price indices and the housing price index of the Office of Federal Housing Enterprise Oversight (OFHEO). These reflect the trends of the US housing market. In addition to these housing price indices, the development of a housing price prediction model can greatly assist in the prediction of future housing prices and the establishment of real estate policies. This study uses machine learning algorithms as a research methodology to develop a housing price prediction model. To improve the accuracy of housing price prediction, this paper analyzes the housing data of 5359 townhouses in Fairfax County, Virginia, gathered by the Multiple Listing Service (MLS) of the Metropolitan Regional Information Systems (MRIS). We develop a housing price prediction model based on machine learning algorithms such as C4.5, RIPPER, Naïve Bayesian, and AdaBoost and compare their classification accuracy performance. We then propose an improved housing price prediction model to assist a house seller or a real estate agent make better informed decisions based on house price valuation. The experiments demonstrate that the RIPPER algorithm, based on accuracy, consistently outperforms the other models in the performance of housing price prediction. 2014 Elsevier Ltd. All rights reserved.

Using plane + parallax for calibrating dense camera arrays <=SEP=> A light field consists of images of a scene taken from different viewpoints. Light fields are used in computer graphics for image-based rendering and synthetic aperture photography, and in vision for recovering shape. In this paper, we describe a simple procedure to calibrate camera arrays used to capture light fields using a plane + parallax framework. Specifically, for the case when the cameras lie on a plane, we show (i) how to estimate camera positions up to an affine ambiguity, and (ii) how to reproject light field images onto a family of planes using only knowledge of planar parallax for one point in the scene. While planar parallax does not completely describe the geometry of the light field, it is adequate for the first two applications which, it turns out, do not depend on having a metric calibration of the light field. Experiments on acquired light fields indicate that our method yields better results than full metric calibration.

Performance Analysis, Mapping, and Multiobjective Optimization of a Hybrid Robotic Machine Tool <=SEP=> A serial-parallel hybrid machine tool is expected to integrate the respective features of pure serial/parallel mechanism. The traditional method of hybridization is to connect n (n ≥ 2) mechanisms bottom to head, in which at least one should be a parallel mechanism. One unique approach called Mechanism Hybridization is to embed one serial mechanism inside of a pure parallel mechanism, which greatly changes its overall performance. Based on this idea, an X-Y gantry system including a five-axis hybrid manipulator is developed, which is expected to be applied as the next generation of computer numerical control machine. The inverse kinematics and Jacobian matrix are derived. Since performance improvement is one of the most important factors that greatly affect the application potential of hybrid manipulators in different industry fields, to deeply investigate the comprehensive features, the local/global performance indexes of stiffness, dexterity, and manipulability are mathematically modeled and mapped. A discrete-boundary-searching method is developed to calculate and visualize the workspace. Pareto-based evolutionary multiobjective performance optimization is implemented to simultaneously improve the four indexes, and the representative nondominated solutions are listed. The proposed methodologies are generic and applicable for the design, modeling, and improvement of other parallel/hybrid robotic machine tools.

Simulation in software engineering training <=SEP=> Simulation is frequently used for training in many application areas like aviation and economics, but not in software engineering. We present the SESAM project which focuses on software engineering education using simulation. In the SESAM project a simulator was developed. Using this simulator, a student can take the role of a software project manager. The simulated software project can be finished within a couple of hours because it is simulated in “quick-motion” mode.
In this paper, the background and goals of the SESAM project are presented. A new simulation model, the so called QA model, is introduced. The model behavior is demonstrated by investigating and comparing different strategies for software development. The results of experiments based on the QA model are reported. Finally, conclusions are drawn from the experiments and future work is outlined.

Effects of Transformational Leadership Training on Attitudinal and Financial Outcomes : A Field Experiment <=SEP=> A pretest-posttest control-group design (N = 20) was used to assess the effects of transformational leadership training, with 9 and 11 managers assigned randomly to training and control groups, respectively. Training consisted of a 1-day group session and 4 individual booster sessions thereafter on a monthly basis. Multivariate analyses of covariance, with pretest scores as the covariate, showed that the training resulted in significant effects on subordinates' perceptions of leaders' transformational leadership, subordinates' own organizational commitment, and 2 aspects of branch-level financial performance.

An approach to finite-time controller design for a class of T-S fuzzy systems <=SEP=> This paper studies the finite-time stabilization problem for a class of nonlinear systems described by Takagi-Sugeno (T-S) fuzzy dynamic models with parametric uncertainties. A novel non-Lipschitz continuous state feedback control scheme with augmented dynamics is developed. It is shown that finite-time convergence of the closed loop system can be achieved, and the potential controller singularity problem can be avoided. Finally, one example is given to illustrate the effectiveness of the proposed control approach.

The Human Hippocampus and Spatial and Episodic Memory <=SEP=> Finding one's way around an environment and remembering the events that occur within it are crucial cognitive abilities that have been linked to the hippocampus and medial temporal lobes. Our review of neuropsychological, behavioral, and neuroimaging studies of human hippocampal involvement in spatial memory concentrates on three important concepts in this field: spatial frameworks, dimensionality, and orientation and self-motion. We also compare variation in hippocampal structure and function across and within species. We discuss how its spatial role relates to its accepted role in episodic memory. Five related studies use virtual reality to examine these two types of memory in ecologically valid situations. While processing of spatial scenes involves the parahippocampus, the right hippocampus appears particularly involved in memory for locations within an environment, with the left hippocampus more involved in context-dependent episodic or autobiographical memory.

3D Sampling Textures for Creative Design and Manufacturing <=SEP=> 1 Texture synthesized from 3D scans of snake-skin and mushroom laminae. ABSTRACT 3D sampling is a robust new strategy for exploring and creating designs. 3D textures are sampled and mixed almost like music to create new multifunctional surfaces and material microstructures (Figure 1). This paper presents several design cases performed using 3D sampling techniques, demonstrating how they can be used to explore and enhance product ideas, variations, and functions in subtle and responsive ways. In each case, design variations are generated and their mechanical behavior is evaluated against performance criteria using computational simulation or empirical testing. This work aims to promote creativity and discovery by introducing irregular geometric structures within trial-and-error feedback loops. Sayjel Vijay Patel SUTD DManD Centre

Large-Scale Liquid Simulation on Adaptive Hexahedral Grids <=SEP=> Regular grids are attractive for numerical fluid simulations because they give rise to efficient computational kernels. However, for simulating high resolution effects in complicated domains they are only of limited suitability due to memory constraints. In this paper we present a method for liquid simulation on an adaptive octree grid using a hexahedral finite element discretization, which reduces memory requirements by coarsening the elements in the interior of the liquid body. To impose free surface boundary conditions with second order accuracy, we incorporate a particular class of Nitsche methods enforcing the Dirichlet boundary conditions for the pressure in a variational sense. We then show how to construct a multigrid hierarchy from the adaptive octree grid, so that a time efficient geometric multigrid solver can be used. To improve solver convergence, we propose a special treatment of liquid boundaries via composite finite elements at coarser scales. We demonstrate the effectiveness of our method for liquid simulations that would require hundreds of millions of simulation elements in a non-adaptive regime.

Power optimization for FinFET-based circuits using genetic algorithms <=SEP=> Reducing power consumption is one of the important design goals for circuit designers. Power optimization techniques for bulk CMOS-based circuit designs have been extensively studied. As technology scales, FinFET has been proposed as an alternative for bulk CMOS when technology scales beyond 32 nm technology (E.J. Nowak et al., 2004). In this paper, we propose a power optimization framework for FinFET based circuit design, based on genetic algorithms. We exploit the unique feature of independent gate (IG) controls for FinFET devices to reduce the power consumption, and combine with other low power techniques such as multi-Vdd and gate sizing to achieve power optimization for FinFET-based circuits. We use 32 nm PTM FinFET device model (W. Zhao and Y. Cao, 2006) and conduct experiments on ISCAS benchmarks. The experimental results show that our methodology can achieve over 80% power reduction while satisfying the same performance constraints, comparing to the case that all the FinFET transistors are tuned to be the fastest ones.

Improving the intrinsic calibration of a Velodyne LiDAR sensor <=SEP=> LiDAR (Light detection and ranging) sensors are widely used in research and development. As such, they build the base for the evaluation of newly developed ADAS (Advanced Driver Assistance Systems) functions in the automotive field where they are used for ground truth establishment. However, the factory calibration provided for the sensors is not able to satisfy the high accuracy requirements by such applications. In this paper we propose a concept to easily improve the existing calibration of a Velodyne LiDAR sensor without the need for special calibration setups which can even be used to enhance already recorded data.