2,301 research outputs found
A novel optimal path-planning and following algorithm for wheeled robots on deformable terrains
No full textAn immense body of research has focused on path-planning and following of wheeled robots in unstructured surfaces. Nonholonomic robots traveling over deformable terrains together with complex operating conditions, however, pose further challenges in terms of a higher demand for robustness and optimality. In this paper, a Chaos-enhanced Accelerated Particle Swarm Optimization (CAPSO) algorithm is employed for planning an optimal path of a wheeled robot, so as to ensure shortest path from the starting point to the target location together with safety through guaranteed avoidance of collisions with static and dynamic obstacles. The fundamental terramechanics concepts are employed to derive essential forces and moments acting on the wheeled robot. Subsequently, a kineto-dynamic model of the robot is developed for designing a novel robust control algorithm based on an exponential-integral-sliding mode (EISMC) scheme and a RBF-NN approximator. The results revealed that the proposed algorithm is responsive and robust to withstand adverse effects of structured and unstructured uncertainties by using the designed adaptation law according to the Lyapunov stability theorem. The effectiveness of the proposed algorithm is also validated against several reported frameworks.</p
Scalable k-NN graph construction for visual descriptors
No full textThe k-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct k-NN graphs remains a challenge, especially for large-scale high-dimensional data. In this paper, we propose a new approach to construct approximate k-NN graphs with emphasis in: efficiency and accuracy. We hierarchically and randomly divide the data points into subsets and build an exact neighborhood graph over each subset, achieving a base approximate neighborhood graph; we then repeat this process for several times to generate multiple neighborhood graphs, which are combined to yield a more accurate approximate neighborhood graph. Furthermore, we propose a neighborhood propagation scheme to further enhance the accuracy. We show both theoretical and empirical accuracy and efficiency of our approach to k-NN graph construction and demonstrate significant speed-up in dealing with large scale visual data.Computer Science, Artificial IntelligenceComputer Science, Interdisciplinary ApplicationsEngineering, Electrical & ElectronicEICPCI-S(ISTP)1
Strange hadron and resonance production in Pb-Pb collisions at = 2.76 TeV with ALICE experiment at LHC
No full textThe ALICE experiment at the LHC has measured the production of strange hadrons and resonances in Pb-Pb and pp collisions at unprecedented high beam energies. The study of strange hadrons and resonances helps us to understand the properties of the medium created in the heavy-ion collisions and its evolution. We present the yields () at mid-rapidity for strange hadrons (, , , their anti-particles and ) and resonances ( and ) for several collision centrality intervals. The results from Pb-Pb collisions at = 2.76 TeV are presented and compared to corresponding results from pp collisions and lower energy measurements. Baryon to meson ratios and resonance to non-resonance particle ratios relative to pp collisions are shown as a function of collision centrality and compared with the results at lower energies.The ALICE experiment at the LHC has measured the production of strange hadrons and resonances in Pb-Pb and pp collisions at unprecedented high beam energies. The study of strange hadrons and resonances helps us to understand the properties of the medium created in the heavy-ion collisions and its evolution. We present the yields (dN/dy) at mid-rapidity for strange hadrons (@L,@X^-,@W^-, their anti-particles and K_S^0) and resonances (@f and K^@?^0) for several collision centrality intervals. The results from Pb-Pb collisions at =2.76TeV are presented and compared to corresponding results from pp collisions and lower energy measurements. Baryon to meson ratios and resonance to non-resonance particle ratios relative to pp collisions are shown as a function of collision centrality and compared with the results at lower energies.The ALICE experiment at the LHC has measured the production of strange hadrons and resonances in Pb–Pb and pp collisions at unprecedented high beam energies. The study of strange hadrons and resonances helps us to understand the properties of the medium created in the heavy-ion collisions and its evolution. We present the yields ( dN/dy ) at mid-rapidity for strange hadrons ( Λ,Ξ−,Ω− , their anti-particles and KS0 ) and resonances ( ϕ and K⁎0 ) for several collision centrality intervals. The results from Pb–Pb collisions at sNN=2.76TeV are presented and compared to corresponding results from pp collisions and lower energy measurements. Baryon to meson ratios and resonance to non-resonance particle ratios relative to pp collisions are shown as a function of collision centrality and compared with the results at lower energies.The ALICE experiment at the LHC has measured the production of strange hadrons and resonances in Pb-Pb and pp collisions at unprecedented high beam energies. The study of strange hadrons and resonances helps us to understand the properties of the medium created in the heavy-ion collisions and its evolution. We present the yields () at mid-rapidity for strange hadrons (, , , their anti-particles and ) and resonances ( and ) for several collision centrality intervals. The results from Pb-Pb collisions at = 2.76 TeV are presented and compared to corresponding results from pp collisions and lower energy measurements. Baryon to meson ratios and resonance to non-resonance particle ratios relative to pp collisions are shown as a function of collision centrality and compared with the results at lower energies
Energy flux in isotropic turbulence under large variations of external forcing
No full textWe investigate the response of energy flux in isotropic turbulence to step-function like perturbation in external forcing at large length scales. From both physical experiments and direct numerical simulations, we measured the evolution of the Eulerian velocity structure functions, such as , , before and after the perturbation in forcing. In both cases, we observed the cascade of the energy excess at large scales cascade through scales to the dissipative range, which can be used to study the dynamics of the cascade, and in particular, to estimate the relevant time scales
Racism Detection by Analyzing Differential Opinions Through Sentiment Analysis of Tweets Using Stacked Ensemble GCR-NN Model
No full textWith social media's dominating role in the socio-political landscape, several existing and new forms of racism took place on social media. Racism has emerged on social media in different forms, both hidden and open, hidden with the use of memes and open as the racist remarks using fake identities to incite hatred, violence, and social instability. Although often associated with ethnicity, racism is now thriving based on color, origin, language, cultures, and most importantly religion. Social media opinions and remarks provocating racial differences have been regarded as a serious threat to social, political, and cultural stability and have threatened the peace of different countries. Consequently, social media being the leading source of racist opinions dissemination should be monitored and racism remarks should be detected and blocked timely. This study aims at detecting Tweets that contain racist text by performing the sentiment analysis of Tweets. Owing to the superior performance of deep learning, a stacked ensemble deep learning model is assembled by combining gated recurrent unit (GRU), convolutional neural networks (CNN), and recurrent neural networks RNN, called, Gated Convolutional Recurrent- Neural Networks (GCR-NN). GRU is on the top in the GCR-NN model to extract the suitable and prominent features from raw text, CNN extracts important features for RNN to make accurate predictions. Obviously, several experiments are conducted to investigate and analyze the performance of the proposed GCR-NN within the scope of machine learning and deep learning models indicating the superior performance of GCR-NN with increased 0.98 accuracy. The proposed GCR-NN model can detect 97% of the tweets that contain racist comments
Book launch and discussion
No full textBook launch event for Nick Drake: Dreaming England (Reaktion 2013) at the NN Cafe, Number 9 Guildhall Road, Northampton, NN1 1DP, Thursday 3rd October 2013.
Author Nathan Wiseman-Trowse talked about and read from his book on the musician Nick Drake. Music was provided by Gregg Cave and Ant Savage and the book's photographer Paul Hillery DJd. The event was publically promoted and around sixty attended
French Translations of Swedish NN Compounds : With Special Focus on Translation Techniques
No full textThis study examines Swedish NN compounds and their French translations in a bidirectional parallel corpus. A total of 1,741 Swedish NN compounds were attested and 81% corresponded to N or to N A or N de (Det) N constructions in French. The frequency of Swedish NN compounds differs depending on whether the text is source text or target text as well as on text genre (fiction vs. non-fiction) and the style of the author or translator. This work has also classified 1,027 French translations of Swedish NN compounds based on their translation technique. Established equivalent was by far the most frequent technique followed by Generalization. The remaining techniques, such as Modulation, Transposition,and Explicitation, were rarely used. A conclusion drawn from this work is that it is difficult to maintain a strict division between translation studies and contrastive linguistics in an analysis of word formation and translation techniques.</p
Pathogenic huntingtin inhibits fast axonal transport by activating JNK3 and phosphorylating kinesin
No full textAuthor Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Nature America for personal use, not for redistribution. The definitive version was published in Nature Neuroscience 12 (2009): 864-871, doi:10.1038/nn.2346.Selected vulnerability of neurons in Huntington’s disease (HD) suggests alterations in a cellular
process particularly critical for neuronal function. Supporting this idea, pathogenic Htt (polyQ-Htt)
inhibits fast axonal transport (FAT) in various cellular and animal HD models (mouse and squid),
but the molecular basis of this effect remains unknown. Here we show that polyQ-Htt inhibits FAT
through a mechanism involving activation of axonal JNK. Accordingly, increased activation of JNK
was observed in vivo in cellular and animal HD models. Additional experiments indicate that
polyQ-Htt effects on FAT are mediated by the neuron-specific JNK3, and not ubiquitously
expressed JNK1, providing a molecular basis for neuron-specific pathology in HD. Mass
spectrometry identified a residue in the kinesin-1 motor domain phosphorylated by JNK3, and this
modification reduces kinesin-1 binding to microtubules. These data identify JNK3 as a critical
mediator of polyQ-Htt toxicity and provides a molecular basis for polyQ-Htt-induced inhibition of
FAT.This work was supported by 2007/2008 MBL summer fellowship to GM; an HDSA
grant to GM; NIH grants MH066179 to GB; and ALSA, Muscular Dystrophy Association, and NIH
(NS23868, NS23320, NS41170) grants to STB
Identity and Intimacy in Human-Computer Improvisation
No full textArtificial intelligence invites a new approach to computing in live music performance. Computers and human performers might collaborate on an equal basis. The perceived identities of participants, both human and machine, are enriched but problematic. The conflicting relationships between these identities impact upon both performers’ and listeners’ experience. The film Orlacs Hände is a starting point for a speculative discussion about human-computer improvisation, problems of identity, the self and the Other, social intimacy and the therapeutic process
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