Miracle-dz/UniFAR-FTUs huggingface dataset:
Shape, Motion, and Parameter Estimation of Flexible Space Structures using Laser Rangefinders

Future space missions are expected to use robotic systems to assemble, inspect, and maintain large space structures in orbit. For effective planning and control, robots must know the deformations and motions of the structures with which they interact. This paper presents a method for estimating the shape, motion, and dynamic model parameters of a vibrating space structure using asynchronous raster-scanning range imagers. The method assumes that the mode shapes are approximately known a priori. A Kalman filter exploits a mechanics-based dynamic model to extract the modal frequencies and damping as well as the modal coefficients and their time rate of change. Theoretical development and experimental results using emulated space hardware are presented. Index Terms – space structure, laser rangefinder, shape estimation, motion estimation, cooperative sensing.
The Topoisomerase-related Function Gene TRF4 Affects Cellular Sensitivity to the Antitumor Agent Camptothecin*

Camptothecin is an antitumor agent that kills cells by converting DNA topoisomerase I into a DNA-damaging poison. Although camptothecin derivatives are now being used to treat tumors in a variety of clinical protocols, the cellular factors that influence sensitivity to the drug are only beginning to be understood. We report here that two genes required for sister chromatid cohesion,TRF4 and MCD1/SCC1, are also required to repair camptothecin-mediated damage to DNA. The hypersensitivity to camptothecin in the trf4 mutant does not result from elevated expression of DNA topoisomerase I. We show that Trf4 is a nuclear protein whose expression is cell cycle-regulated at a post-transcriptional level. Suppression of camptothecin hypersensitivity in the trf4 mutant by gene overexpression resulted in the isolation of three genes: another member of theTRF4 gene family, TRF5, and two genes that may influence higher order chromosome structure, ZDS1 andZDS2. We have isolated and sequenced two humanTRF4 family members, hTRF4-1 andhTRF4-2. The hTRF4-1gene maps to chromosome 5p15, a region of frequent copy number alteration in several tumor types. The evolutionary conservation ofTRF4 suggests that it may also influence mammalian cell sensitivity to camptothecin.
Low-Rank Multi-View Learning in Matrix Completion for Multi-Label Image Classification

Multi-label image classification is of significant interest due to its major role in real-world web image analysis applications such as large-scale image retrieval and browsing. Recently, matrix completion (MC) has been developed to deal with multi-label classification tasks. MC has distinct advantages, such as robustness to missing entries in the feature and label spaces and a natural ability to handle multi-label problems. However, current MC-based multi-label image classification methods only consider data represented by a single-view feature, therefore, do not precisely characterize images that contain several semantic concepts. An intuitive way to utilize multiple features taken from different views is to concatenate the different features into a long vector; however, this concatenation is prone to over-fitting and leads to high time complexity in MC-based image classification. Therefore, we present a novel multi-view learning model for MC-based image classification, called low-rank multi-view matrix completion (lrMMC), which first seeks a low-dimensional common representation of all views by utilizing the proposed low-rank multi-view learning (lrMVL) algorithm. In lrMVL, the common subspace is constrained to be low rank so that it is suitable for MC. In addition, combination weights are learned to explore complementarity between different views. An efficient solver based on fixed-point continuation (FPC) is developed for optimization, and the learned low-rank representation is then incorporated into MC-based image classification. Extensive experimentation on the challenging PASCAL VOC' 07 dataset demonstrates the superiority of lrMMC compared to other multi-label image classification approaches.
Global Optimization of Finite Mixture Models

Title of dissertation: GLOBAL OPTIMIZATION OF FINITE MIXTURE MODELS Jeffrey W. Heath Doctor of Philosophy, 2007 Dissertation directed by: Professor Michael Fu Robert H. Smith School of Business & Professor Wolfgang Jank Robert H. Smith School of Business The Expectation-Maximization (EM) algorithm is a popular and convenient tool for the estimation of Gaussian mixture models and its natural extension, modelbased clustering. However, while the algorithm is convenient to implement and numerically very stable, it only produces solutions that are locally optimal. Thus, EM may not achieve the globally optimal solution in Gaussian mixture analysis problems, which can have a large number of local optima. This dissertation introduces several new algorithms designed to produce globally optimal solutions for Gaussian mixture models. The building blocks for these algorithms are methods from the operations research literature, namely the Cross-Entropy (CE) method and Model Reference Adaptive Search (MRAS). The new algorithms we propose must efficiently simulate positive definite covariance matrices of the Gaussian mixture components. We propose several new solutions to this problem. One solution is to blend the updating procedure of CE and MRAS with the principles of Expectation-Maximization updating for the covariance matrices, leading to two new algorithms, CE-EM and MRAS-EM. We also propose two additional algorithms, CE-CD and MRAS-CD, which rely on the Cholesky decomposition to construct the random covariance matrices. Numerical experiments illustrate the effectiveness of the proposed algorithms in finding global optima where the classical EM fails to do so. We find that although a single run of the new algorithms may be slower than EM, they have the potential of producing significantly better global solutions to the model-based clustering problem. We also show that the global optimum matters in the sense that it significantly improves the clustering task. Furthermore, we provide a a theoretical proof of global convergence to the optimal solution of the likelihood function of Gaussian mixtures for one of the algorithms, namely MRAS-CD. This offers support that the algorithm is not merely an ad-hoc heuristic, but is systematically designed to produce global solutions to Gaussian mixture models. Finally, we investigate the fitness landscape of Gaussian mixture models and give evidence for why this is a difficult global optimization problem. We discuss different metrics that can be used to evaluate the difficulty of global optimization problems, and then apply them to the context of Gaussian mixture models. GLOBAL OPTIMIZATION OF FINITE MIXTURE MODELS
Molecular Cytogenetic Mapping of Chromosomal Fragments and Immunostaining of Kinetochore Proteins in Beta

By comparative multicolor FISH, we have physically mapped small chromosome fragments in the sugar beet addition lines PRO1 and PAT2 and analyzed the distribution of repetitive DNA families in species of the section Procumbentes of the genus Beta. Six repetitive probes were applied, including genotype-specific probes—satellites pTS4.1, pTS5, and pRp34 and a dispersed repeat pAp4, the telomere (TTTAGGG)n, and the conserved 18S-5.8S-25S rRNA genes. Pachytene-FISH analysis of the native centromere organization allowed proposing the origin of PRO1 and PAT2 fragments. Comparative analysis of the repetitive DNA distribution and organization in the wild beet and in the addition lines allowed the development of a physical model of the chromosomal fragments. Immunostaining revealed that the PRO1 chromosome fragment binds α-tubulin and the serine 10-phosphorylated histone H3 specific for the active centromere. This is the first experimental detection of the kinetochore proteins in Beta showing their active involvement in chromosome segregation in mitosis.
Sinusoidal movement of a grating across the monkey's fingerpad: effect of contact angle and force of the grating on afferent fiber responses

Responses were recorded from cutaneous afferents innervating mechanoreceptors in the monkey's fingerpad. When gratings of alternating grooves and ridges were moved sinusoidally back and forth across the receptive field, the responses of the afferent were often not equal for the 2 directions of movement. To investigate this phenomenon, the position of the center of the afferent's receptive field, relative to the contact area between the grating and the finger, was varied systematically. For some afferents, regardless of these relative positions, the response was always greater for a particular direction of movement. For other afferents, regardless of these relative positions, the responses for the 2 directions of movement were always equal. For a minority of afferents, the response was greater for movement in one particular direction for some relative positions and greater for movement in the opposite direction for other relative positions. Slowly adapting afferents (SAs), rapidly adapting afferents (RAs), and Pacinian afferents (PCs) exhibited all 3 types of response patterns. We could not relate these patterns to the afferent type or to the positions, in the fingerpad, of the receptive field center. The contact force between the grating and the finger was varied by varying the contact displacement (indentation). Two grating spatial periods were used. For SAs and PCs the response increased with increasing indentation for both gratings. For RAs the response to the finer grating increased with increasing indentation, but the response to the coarser grating did not.
A Note on Even-Sized Clustering Based on Optimization

Some clustering methods to classify a dataset into some clusters of which the size is more than a constant K have been proposed until now. The methods are called K-member clustering and very useful for many applications. It is natural to consider clustering methods to classify a dataset into even-sized clusters, and actually, such methods have been also proposed. However, they often output inadequate results. It is considered that the reason is that they are not based on optimization. Therefore, in the previous study, we proposed Even-sized Clustering Based on Optimization (ECBO). We improved the clustering results by the simplex method to calculate the membership grade. In this study, we propose some types of extended ECBO by introducing some concept of medoid and kernel to improve ECBO.
Dynamic Optimization of Area Revenue Insurance and Consumption Smoothing

This study examines the interaction between insurance, credit and liquidity constraints using a stochastic dynamic model. A risk averse farmer whose objective is to manage both production and market risk is assumed to maximize the expected utility of life-time consumption by using both area revenue (AR) insurance and consumption smoothing subject to a credit constraint. The dynamic programming is coded in MATLAB using DDPSOLVE algorithm (Appendix a). DSSAT crop simulation model is used to determine optimal irrigation strategy and to simulate farm level yield. A Bayesian Model is used to estimate expected county-level revenue. The approach and results are illustrated via a numerical example using county level data from Mitchell County in Georgia. Results support the hypothesis that liquidity constraints can have a large impact on optimal insurance decisions, and that different levels of premium load, risk aversion level, and time-preferences can also affect optimal insurance decisions under liquidity constraints.
An introduction to 3D gestural interfaces

classroom use is granted without fee provided that copies are not made or distributed for commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author. 1 Course Introduction 3D gestural interfaces [Bowman et al. 2004] provide a powerful and natural way to interact with computers using the hands and body for a variety of different applications including video games, training and simulation, mobile computing, and medicine. In addition, with advancements in commodity motion sensors and user tracking technology, this interaction paradigm is becoming more commonplace in everyday computing activities [LaViola and Marks 2010; LaViola and Keefe 2011]. However, designing and accurately recognizing 3D gestures so they can be used reliably in these applications pose many different challenges. In this course, we will demystify the design and development of 3D gestural interfaces by describing the 3D gestural user interface development pipeline [LaViola 2013]. First, the course will present the latest strategies on how to collect raw 3D gesture data from the user including both passive and active tracking schemes for fingers, hands, and the whole body. Second, the course will discuss how to accurately analyze this raw data to correctly recognize 3D gestures using both heuristic algorithms and machine learning, providing guidelines for choosing the most appropriate classification strategies. Third, the course will explore the latest in 3D gesture recognition performance in terms of accuracy and gesture set size and discuss how different applications are making use of 3D gestural interaction. Finally, the course will present a summary of important future research directions that are critical to improving 3D gestural interaction.
Multiple-parameter real discrete fractional Fourier and Hartley transforms

In this paper, two new real fractional transforms with many parameters are constructed. They are the real discrete fractional Fourier transform (RDFRFT) and the real discrete fractional Hartley transform (RDFRHT). The eigenvectors of these two new transforms are all random, and they both have only two distinct eigenvalues: 1 or -1. Real eigenvectors of both two transforms are constructed from random DFT-commuting matrices. We also propose an alternative definition of RDFRHT based on a diagonal-like matrix. All of the proposed new transforms have required good properties to be fractional transforms. Finally, since outputs of proposed new transforms are random, they can be applied in image encryptions.
Improvement on a Tetris Agent

Tetris is a popular video game developed by Alexey Pajitnov in 1984, that uses tetraminos, geometric shapes that are composed of four squares. The objective is to move each one tetraminos and rotating it by 90 degree units, to create a horizontal line of ten blocks without gaps, each created line thus increasing your score. Any filled row is cleared and the blocks above it drop into the gaps. The game is finished once the screen is filled up, and one of the tetraminos goes over the top border.
Region-based coding of motion fields for low-bitrate video compression

Low-bitrate video compression requires a compact encoding of motion fields. We encode motion fields by segmenting them into regions of homogeneous motion. Each region is formed by a collection of blocks. The segmentation is represented by the connectedness relationship between adjacent blocks. To reduce the amount of connectedness information, blocks may be of variable size, as determined by a quadtree structure. We use context-based binary arithmetic coding to further reduce the amount of bits required to code the segmentation. A segmentation algorithm based on rate-distortion optimization is presented. Simulations show that such a motion-field coding scheme achieves coding efficiency comparable to the state-of-the-art H.264 codec with modest improvements.
Throughput analysis and optimization of distributed collision detection protocols in dense wireless local area networks

The wireless carrier sense multiple access with collision detection (WCSMA/CD) and carrier sense multiple access with collision resolution (CSMA/CR) protocols are considered representative distributed collision detection protocols for fully connected dense wireless local area networks. These protocols identify collisions through additional short-sensing within a collision detection (CD) period after the start of data transmission. In this study, we analyze their throughput numerically and show that the throughput has a trade-off that accords with the length of the CD period. Consequently, we obtain the optimal length of the CD period that maximizes the throughput as a closed-form solution. Analysis and simulation results show that the throughput of distributed collision detection protocols is considerably improved when the optimal CD period is allocated according to the number of stations and the length of the transmitted packet.
Computation of free surface flows with a Taylor-Galerkin/pressure-correction algorithm

A semi-implicit Taylor-Galerkin/pressure-correction finite element scheme (STGFEM) is developed for problems that manifest free surfaces associated with the incompressible creeping flow of Newtonian fluids. Such problems include stick-slip and die-swell flows, both with and without a superimposed drag flow, and for plane, axisymmetric and annular systems. The numerical solutions are compared with available analytical and numerical solutions, both in the neighbourhood of singularities and elsewhere. Close correspondence in accuracy is extracted from the literature for both stick-slip and die-swell flows. Stick-slip flow is used as a precursor study to the more complex free surface calculations involved for die-swell in extrudate flow. Two different free surface techniques are reported and results are analysed with mesh refinement and varying structure
Optimal consumption and portfolio strategies when relative risk aversion from consumption differs from relative risk aversion from wealth

Relative risk aversion (RRA) of consumption (RRAC) differs from RRA of wealth (RRAW) is an empirical fact explained in the study of Meyer and Meyer (2005). However dynamic consumption/ investment problems are only solved in the finance literature when both RRA equal (RRAC = RRAW). Following the martingale route, we derive optimal consumption and investment solutions for a (CRRA) investor when both RRA differ. While our framework preserve the usual tractability of the RRAC = RRAW case, it gives the optimal consumption and portfolio problems new and essential features. As far as asset allocation is concerned, the traditional decomposition into a tangent portfolio and two portfolios that replicates the risk of pseudo bonds holds. However, the weights invested in these portfolios are all stochastic in our case but only in so far they depend on a quantity that measures how much importance investors give to satisfaction from future consumptions as opposed to that from terminal wealth. Moreover, RRA, defined in our more general case as the risk aversion from the variation of total utility (the value function), as well as elasticity of optimal wealth to consumption, both known to be constant in the RRAC = RRAW case, are now also random variables only because they depend on this satisfaction measurement quantity. Finally, this satisfaction measurement quantity, which is then the right representative of market conditions, depends also drastically on investors’ initial wealth. The latter, which plays virtually no role in the RRAC = RRAW case, is then a variable of crucial importance. JEL Classification: D81, G11, G12. 1 PRISM, University of Paris 1-Sorbonne, 17, place de la Sorbonne, 75231 Paris Cedex 05, France. E-mail address: six@univ-paris1.fr. Tel: 0140463170. Fax: 0140463177. I am highly indebted to Professor C. Mellios and Professor P. Poncet for discussions on asset allocation and encouraging me writing this article. All remaining errors are mine.
Reversed planning graphs for relevance heuristics in AI planning

Most AI planning heuristics are reachability heuristics, in the sense that they estimate the minimum plan length from the initial state to a search state. Such heuristics are best suited for use in regression state-space planners, since a progression planner would have to reconstruct the heuristic function at each new search state. However, some domains (or problem instances within a certain domain) are better suited for progression search, motivating the need for relevance heuristics that estimate the distance from a search state to the goal state. In this paper we show how to construct reversed planning graphs that can be used for computing new relevance heuristics, based on the work on extracting reachability heuristics from planning graphs, and a general framework for reversing planning domains. (Less)
Common Software-Aging-Related Faults in Fault-Tolerant Systems

In recent years, remarkable attention has been focused on software aging phenomena, in which the performance of software systems degrades with time. Fault-tolerant software systems which provide high assurance may suffer from such phenomena. Based on the common software-aging-related faults in fault-tolerant systems, a behavior model of a double-version fault-tolerant software system is established using Markov reward model. The performance of the system such as expected service rate in steady state is evaluated and the sensitivity analysis of some parameters is performed.
Decomposition of Boolean Function Sets for Boolean Neural Networks

This paper presents a new type of neuron, called Boolean neuron. We suggest algorithms for decomposition of Boolean functions sets based on Boolean neural networks that include only Boolean neurons. The advantages of these neural networks consist in the reduction of memory space and computation time in comparison to the representation of Boolean functions by usual neural networks. The Boolean neural network can be mapped to a FPGA so that our new approach substitutes classical design methods of these circuits. We show as example the AND-decomposition of a Boolean function set into unique basic functions and their mapping to the general Boolean neural network.
CONSERVED QUANTITIES ALONG THE PONTRYAGIN EXTREMALS OF QUASI-INVARIANT OPTIMAL CONTROL PROBLEMS

We study in optimal control the important relation between invariance of the problem under a family of transformations, and the existence of preserved quantities along the Pontryagin extremals. Several extensions of Noether theorem are provided, in the direction which enlarges the scope of its application. We formulate a more general version of Noether’s theorem for optimal control problems, which incorporates the possibility to consider a family of transformations depending on several parameters and, what is more important, to deal with quasi-invariant and not necessarily invariant optimal control problems. We trust that this latter extension provides new possibilities and we illustrate it with several examples, not covered by the previous known optimal control versions of Noether’s theorem.
Generative CCG Parsing with OOV Prediction

This paper presents our system for the CIPS-SIGHAN-2014 bakeoff task of Simplified Chinese Parsing (Task 3). The system adopts a generative model with OOV prediction model. The former has a PCFG form while the latter uses a three-layer hierarchical Bayesian model. The final performance on the test corpus is reported together with the performance of the OOV model.