The manipulator task verification facility (MTVF) system is a ground testing system for verifying space robot capturing operations. In this paper, a numerical simulation of the MTVF system is ...established to ensure the security of the devices in physical contact and to verify the control algorithm in the MTVF system. The MTVF simulation consists of four main modeling parts: the dynamics model of a space robot, the dynamics model of a target object, the dynamics models of the emulation robots and contact dynamics models. In order to be close to the real contact process, the nonlinear damping contact model and the 3-dimensional bristle friction force model are employed in the contact dynamics models. The MTVF simulation considering the approaching phase is simulated to show the performance of the MTVF system. Two grasping processes with different grasping velocities are conducted in the MTVF simulation to analyze and to ensure the safety of the MTVF system during the contact process. Simulation results demonstrate the necessity of the MTVF simulation.
For many machine learning problem settings, particularly with structured inputs such as sequences or sets of objects, a distance measure between inputs can be specified more naturally than a feature ...representation. However, most standard machine models are designed for inputs with a vector feature representation. In this work, we consider the estimation of a function \(f:\mathcal{X} \rightarrow \R\) based solely on a dissimilarity measure \(d:\mathcal{X}\times\mathcal{X} \rightarrow \R\) between inputs. In particular, we propose a general framework to derive a family of \emph{positive definite kernels} from a given dissimilarity measure, which subsumes the widely-used \emph{representative-set method} as a special case, and relates to the well-known \emph{distance substitution kernel} in a limiting case. We show that functions in the corresponding Reproducing Kernel Hilbert Space (RKHS) are Lipschitz-continuous w.r.t. the given distance metric. We provide a tractable algorithm to estimate a function from this RKHS, and show that it enjoys better generalizability than Nearest-Neighbor estimates. Our approach draws from the literature of Random Features, but instead of deriving feature maps from an existing kernel, we construct novel kernels from a random feature map, that we specify given the distance measure. We conduct classification experiments with such disparate domains as strings, time series, and sets of vectors, where our proposed framework compares favorably to existing distance-based learning methods such as \(k\)-nearest-neighbors, distance-substitution kernels, pseudo-Euclidean embedding, and the representative-set method.
Spectral clustering is one of the most effective clustering approaches that capture hidden cluster structures in the data. However, it does not scale well to large-scale problems due to its quadratic ...complexity in constructing similarity graphs and computing subsequent eigendecomposition. Although a number of methods have been proposed to accelerate spectral clustering, most of them compromise considerable information loss in the original data for reducing computational bottlenecks. In this paper, we present a novel scalable spectral clustering method using Random Binning features (RB) to simultaneously accelerate both similarity graph construction and the eigendecomposition. Specifically, we implicitly approximate the graph similarity (kernel) matrix by the inner product of a large sparse feature matrix generated by RB. Then we introduce a state-of-the-art SVD solver to effectively compute eigenvectors of this large matrix for spectral clustering. Using these two building blocks, we reduce the computational cost from quadratic to linear in the number of data points while achieving similar accuracy. Our theoretical analysis shows that spectral clustering via RB converges faster to the exact spectral clustering than the standard Random Feature approximation. Extensive experiments on 8 benchmarks show that the proposed method either outperforms or matches the state-of-the-art methods in both accuracy and runtime. Moreover, our method exhibits linear scalability in both the number of data samples and the number of RB features.
While the celebrated Word2Vec technique yields semantically rich
representations for individual words, there has been relatively less success in
extending to generate unsupervised sentences or ...documents embeddings. Recent
work has demonstrated that a distance measure between documents called
\emph{Word Mover's Distance} (WMD) that aligns semantically similar words,
yields unprecedented KNN classification accuracy. However, WMD is expensive to
compute, and it is hard to extend its use beyond a KNN classifier. In this
paper, we propose the \emph{Word Mover's Embedding } (WME), a novel approach to
building an unsupervised document (sentence) embedding from pre-trained word
embeddings. In our experiments on 9 benchmark text classification datasets and
22 textual similarity tasks, the proposed technique consistently matches or
outperforms state-of-the-art techniques, with significantly higher accuracy on
problems of short length.
實驗室檢查一例血小板低下症新生兒，發現無法解釋的結果，促使我們應用DNA指紋做親子鑑定。同位素32P-dATP標定的寡核苷酸（GTG）5作探針，與限制酶Hinf I ...分解後的雙親和病童DNA雜交。結果顯示沒有任何一條雜交帶是由父親傳給病童，同時病童有三條雜交帶，在雙親的相對位置上均沒發現。所以DNA指紋認定非親生父親。對本個案進行親子鑑定，不僅可以解決我們臨床上疑慮，同時也提供我們對下次懷孕是否須進行特殊週產期照顧的參考，每一個人都有自己特定的DNA指紋，臨床上應用於個體的鑑定，可以提供十分完的訊息，遠較傳統性的方式為佳，親子鑑定自然也不例外。
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