Miracle-dz/UniFAR-FTUs huggingface dataset:
A Brief Overview of Human and Robot Motor Learning

Motor learning in humans has been studied mostly through adaptation in motor tasks (by introducing perturbation in a task). Motor adaptation tasks include arm reaching movements, finger grasps and aiming tasks [1]. Perturbation are introduced in reaching or aiming movements using either force fields or visuomotor transformations. Various computational approaches have been proposed to model control and adaptation in these motor tasks.
A Framework for Automatic and Unsupervised Detection of Multiple Changes in Multitemporal Images

The detection of multiple changes (i.e., different kinds of change) in multitemporal remote sensing images is a complex problem. When multispectral images having B spectral bands are considered, an effective solution to this problem is to exploit all available spectral channels in the framework of supervised or partially supervised approaches. However, in many real applications, it is difficult/impossible to collect ground truth information for either multitemporal or single-date images. On the opposite, unsupervised methods available in the literature are not effective in handling the full information present in multispectral and multitemporal images. They usually consider a simplified subspace of the original feature space having small dimensionality and, thus, characterized by a possible loss of change information. In this paper, we present a framework for the detection of multiple changes in bitemporal and multispectral remote sensing images that allows one to overcome the limits of standard unsupervised methods. The framework is based on the following: 1) a compressed yet efficient 2-D representation of the change information and 2) a two-step automatic decision strategy. The effectiveness of the proposed approach has been tested on two bitemporal and multispectral data sets having different properties. Results obtained on both data sets confirm the effectiveness of the proposed approach.
Labeling Student Behavior Faster and More Precisely with Text Replays

We present text replays, a method for generating labels that can be used to train classifiers of student behavior. We use this method to label data as to whether students are gaming the system, within 20 intelligent tutor units on Algebra. Text replays are 2-6 times faster per label than previous methods for generating labels, such as quantitative field observations and screen replays; however, being able to generate classifiers on retrospective data at the coder’s convenience (rather than being dependent on visits to schools) makes this method about 40 times faster than quantitative field observations. Text replays also give precise predictions of student behavior at multiple grain-sizes, allowing the use of both hierarchical classifiers such as Latent Response Models (LRMs), and non-hierarchical classifiers such as Decision Trees. Training using text replay data appears to lead to better classifiers: LRMs trained using text replay data achieve higher correlation and A' than LRMs trained using quantitative field observations; Decision Trees are more precise than LRMs at identifying exactly when the behavior occurs.
Multimodal based fuzzy vault using iris retina and fingervein

Multi biometric systems consolidate evidence from multiple sources of biometric information in order to reliably determine the identity of an individual. These systems can alleviate the limitations of unibiometric systems because the different biometric sources usually compensate for the inherent limitations of the other sources. But the major challenge to such systems is the template security and fusion complexity. In this work, fuzzy vault framework is used to secure iris, retina and fingervein templates. These traits are stable and have template longevity and their combination can be used in high security applications. The proposed multimodal fuzzy vault is thus designed with fusion of feature points extracted the above three traits. This method measures the security of the resultant vault by using min-entropy.
Example-based spoken dialogue system with online example augmentation

In this paper, we propose a new method to expand an examplebased spoken dialogue system to handle context dependent utterances. The dialogue system refers to the dialogue examples to find an example that is suitable to promote dialogue. Here, the dialogue contexts are expressed in the form of dialogue slots. By constructing dialogue examples with the text of utterances and the dialogue slots, the system handle context dependent dialogue. And we also propose a new framework of spoken dialogue, named “GROW architecture” that consists of the dialogue system and a Wizard-of-OZ (WOZ) system. By using the WOZ system to add dialogue examples via network, it becomes efficient to augment dialogue examples.
Time-Series Analyses of Transcriptomes and Proteomes Reveal Molecular Networks Underlying Oil Accumulation in Canola

Understanding the regulation of lipid metabolism is vital for genetic engineering of canola (Brassica napus L.) to increase oil yield or modify oil composition. We conducted time-series analyses of transcriptomes and proteomes to uncover the molecular networks associated with oil accumulation and dynamic changes in these networks in canola. The expression levels of genes and proteins were measured at 2, 4, 6, and 8 weeks after pollination (WAP). Our results show that the biosynthesis of fatty acids is a dominant cellular process from 2 to 6 WAP, while the degradation mainly happens after 6 WAP. We found that genes in almost every node of fatty acid synthesis pathway were significantly up-regulated during oil accumulation. Moreover, significant expression changes of two genes, acetyl-CoA carboxylase and acyl-ACP desaturase, were detected on both transcriptomic and proteomic levels. We confirmed the temporal expression patterns revealed by the transcriptomic analyses using quantitative real-time PCR experiments. The gene set association analysis show that the biosynthesis of fatty acids and unsaturated fatty acids are the most significant biological processes from 2-4 WAP and 4-6 WAP, respectively, which is consistent with the results of time-series analyses. These results not only provide insight into the mechanisms underlying lipid metabolism, but also reveal novel candidate genes that are worth further investigation for their values in the genetic engineering of canola.
Auto-adjusting positive airway pressure in children with sickle cell anemia: results of a phase I randomized controlled trial

Sleep related breathing disorders and especially low nocturnal oxygen saturation may favor complications of sickle cell disease (SCD) In this phase I trial, autoadjusting continuous positive airway pressure (CPAP) was shown to be a feasible and safe therapy for SCD children. Sleep breathing disorders were improved, and there was evidence of a trend towards reduction of diurnal pain. Low nocturnal oxygen saturation (SpO2) is implicated in complications of Sickle Cell Anemia (SCA). Twenty-four children with SCA were randomized to receive overnight auto-adjusting continuous positive airway pressure (auto-CPAP) with supplemental oxygen, if required, to maintain SpO2 ≥94% or as controls. We assessed adherence, safety, sleep parameters, cognition and pain. Twelve participants randomized to auto-CPAP (3 with oxygen) showed improvement in Apnea/Hypopnea Index (p<0.001), average desaturation events >3%/hour (p=0.02), mean nocturnal SpO2 (p=0.02) and cognition. Primary efficacy endpoint (Processing Speed Index) showed no group differences (p=0.67), but a second measure of processing speed and attention (Cancellation) improved in those receiving treatment (p=0.01). No bone marrow suppression, rebound pain or serious adverse event resulting from auto-CPAP use was observed. Six weeks of auto-CPAP therapy is feasible and safe in children with SCA, significantly improving sleep-related breathing disorders and at least one aspect of cognition.
Extremely high-Q factor dielectric resonators for millimeter-wave applications

It has been proven, based on a rigorous electromagnetic analysis, that spherical TE10p-mode Bragg-reflection resonators exhibit many times higher Q factors than corresponding cylindrical TE01delta-mode dielectric resonators, dielectric whispering-gallery-mode resonators, or empty spherical TE10p-mode cavities. Rigorous equations have been derived that allow optimally designed Q-factor and "quarter-wavelength" reflector-multilayered-spherical Bragg-reflection resonators. Experiments have been performed on three-layer spherical resonators made of single-crystal YAG and single-crystal quartz. The unloaded Q factor for the TE012 mode in these resonators was 1.04times105 at 26.26 GHz for YAG and 6.4times104 at 27.63 GHz for quartz
Lipid-modified, cysteinyl-containing peptides of diverse structures are efficiently S-acylated at the plasma membrane of mammalian cells

A variety of cysteine-containing, lipid-modified peptides are found to be S-acylated by cultured mammalian cells. The acylation reaction is highly specific for cysteinyl over serinyl residues and for lipid- modified peptides over hydrophilic peptides. The S-acylation process appears by various criteria to be enzymatic and resembles the S- acylation of plasma membrane-associated proteins in various characteristics, including inhibition by tunicamycin. The substrate range of the S-acylation reaction encompasses, but is not limited to, lipopeptides incorporating the motifs myristoylGC- and -CXC(farnesyl)- OCH3, which are reversibly S-acylated in various intracellular proteins. Mass-spectrometric analysis indicates that palmitoyl residues constitute the predominant but not the only type of S-acyl group coupled to a lipopeptide carrying the myristoylGC- motif, with smaller amounts of S-stearoyl and S-oleoyl substituents also detectable. Fluorescence microscopy using NBD-labeled cysteinyl lipopeptides reveals that the products of lipopeptide S-acylation, which cannot diffuse between membranes, are in almost all cases localized preferentially to the plasma membrane. This preferential localization is found even at reduced temperatures where vesicular transport from the Golgi complex to the plasma membrane is suppressed, strongly suggesting that the plasma membrane itself is the preferred site of S- acylation of these species. Uniquely among the lipopeptides studied, species incorporating an unphysiological N-myristoylcysteinyl- motif also show substantial formation of S-acylated products in a second, intracellular compartment identified as the Golgi complex by its labeling with a fluorescent ceramide. Our results suggest that distinct S-acyltransferases exist in the Golgi complex and plasma membrane compartments and that S-acylation of motifs such as myristoylGC- occurs specifically at the plasma membrane, affording efficient targeting of cellular proteins bearing such motifs to this membrane compartment.
Neural reinforcement learning for an obstacle avoidance behavior

Reinforcement learning (RL) offers a set of various algorithms for in-situation behavior synthesis for robots. The Q-learning technique is certainly the most used of the RL methods. Multilayer perceptron implementations of the Q-learning have been proposed, due to the interest of the restricted memory need and the generalization capability. Self-organizing map implementation of the Q-learning followed. We propose to study the use and discuss the interest of this implementation comparing to a multilayer perceptron implementation or more classical ones. Experiments are performed in the real world with the miniature robot Khepera. (3 pages)
Justifying the small‐world phenomenon via random recursive trees

We present a new technique for proving logarithmic upper bounds for diameters of evolving random graph models, which is based on defining a coupling between random graphs and variants of random recursive trees. The advantage of the technique is three‐fold: it is quite simple and provides short proofs, it is applicable to a broad variety of models including those incorporating preferential attachment, and it provides bounds with small constants. We illustrate this by proving, for the first time, logarithmic upper bounds for the diameters of the following well known models: the forest fire model, the copying model, the PageRank‐based selection model, the Aiello‐Chung‐Lu models, the generalized linear preference model, directed scale‐free graphs, the Cooper‐Frieze model, and random unordered increasing k‐trees. Our results shed light on why the small‐world phenomenon is observed in so many real‐world graphs. © 2016 Wiley Periodicals, Inc. Random Struct. Alg. , 50, 201–224, 2017
A robust method for aligning large-photometric-variation and noisy images

We propose a novel robust method to accurately align large-photometric-variation and noisy images emerged from high dynamic range (HDR) imaging. First, the keypoints are detected from optimal multi-binary images to eliminate the noise and photometric effects. The feature descriptor, which is translation-, rotation- and scale-invariant and robust to noise and photometric changes, is then developed, and feature matching is implemented efficiently by use of the structure information of the keypoints. Finally, mutual information (MI) is employed in the RANSAC method for homography estimation and performance assessment, which makes the results accurate and stable. Experiments carried out on synthesized images demonstrate that the proposed method is much more robust to both photometric changes and noise than the SIFT method, the state-of-the-art alignment method.
Hardware-based Parallel Computing for Real-time Simulation of Soft-object Deformation

In the last two decades there has been an increasing interest in the field of haptics science. Real-time simulation of haptic interaction with non-rigid deformable object/tissue is computationally demanding. The computational bottleneck in finiteelement (FE) modeling of deformable objects is in solving a large but sparse linear system of equations at each time step of the simulation. Depending on the mechanical properties of the object, high-fidelity stable haptic simulations require an update rate in the order of 100− 1000 Hz. Direct software-based implementations that use conventional computers are fairly limited in the size of the model that they can process at such high rates. In this thesis, a new hardware-based parallel implementation of the iterative Conjugate Gradient (CG) algorithm for solving linear systems of equations is proposed. Sparse matrix-vector multiplication (SpMxV) is the main computational kernel in iterative solution methods such as the CG algorithm. Modern microprocessors exhibit poor performance in executing memory-bound tasks such as SpMxV. In the proposed hardware architecture, a novel organization of on-chip memory resources enables concurrent utilization of a large number of fixed-point computing units on a FPGA device for performing the calculations. The result is a powerful parallel computing platform that can iteratively solve the system of iv equations arising from the FE models of object deformation within the timing constraint of real-time haptics applications. Numerical accuracy of the fixed-point implementation, the hardware architecture design, and issues pertaining to the degree of parallelism and scalability of the solution are discussed in details. The proposed computing platform in this thesis is successfully employed in a set of haptic interaction experiments using static and dynamic linear FE-based models.
The Small Volume Liquid Density Sensor Using Surface Acoustic Wave

Shear horizontal surface acoustic wave (SH-SAW) are generated and detected by interdigital transducers (IDT). When the viscoelastic liquid comes in contact with the delay path in a SH-SAW device, the viscous coupling action would effect on the wave propagation. Consequently the frequency, phase and propagating velocity of the SH-SAW are altered. In this paper the properties of the acoustic wave in a liquid overlay on a piezoelectric substrate are investigated. The theory analysis and initial experiment are presented. The result shows when signal frequency is 57.8 MHz, the SH-SAW can be excited effectively in the lithium niobate crystal. By measuring the phase change of the SH-SAW, the liquid density can be detected. Compared with traditional liquid density sensor, the SH-SAW liquid density sensor has the advantages of simple construction, high precision and little sample etc
Probabilistic forward model for electroencephalography source analysis

Source localization by electroencephalography (EEG) requires an accurate model of head geometry and tissue conductivity. The estimation of source time courses from EEG or from EEG in conjunction with magnetoencephalography (MEG) requires a forward model consistent with true activity for the best outcome. Although MRI provides an excellent description of soft tissue anatomy, a high resolution model of the skull (the dominant resistive component of the head) requires CT, which is not justified for routine physiological studies. Although a number of techniques have been employed to estimate tissue conductivity, no present techniques provide the noninvasive 3D tomographic mapping of conductivity that would be desirable. We introduce a formalism for probabilistic forward modeling that allows the propagation of uncertainties in model parameters into possible errors in source localization. We consider uncertainties in the conductivity profile of the skull, but the approach is general and can be extended to other kinds of uncertainties in the forward model. We and others have previously suggested the possibility of extracting conductivity of the skull from measured electroencephalography data by simultaneously optimizing over dipole parameters and the conductivity values required by the forward model. Using Cramer–Rao bounds, we demonstrate that this approach does not improve localization results nor does it produce reliable conductivity estimates. We conclude that the conductivity of the skull has to be either accurately measured by an independent technique, or that the uncertainties in the conductivity values should be reflected in uncertainty in the source location estimates.
Current practices and perspectives for metadata on web ontologies and rules

The Semantic Web contains a number of knowledge artifacts, including OWL ontologies, RIF rule sets and Resource Description Framework data sets. Effective exchange and management of these artifacts demands the use of metadata and prompt availability of accurate reference documentation. In this paper, we analyse the current practices in metadata usage for OWL ontologies and we propose a vocabulary for annotating RIF rules. We also introduce a software tool - Parrot - that exploits these annotations and produces reference documentation for combinations of ontologies and rules.
Massive Protoplanetary Disks in the Trapezium Region

We determine the disk mass distribution around 336 stars in the young (~1 Myr) Orion Nebula cluster by imaging a 2. ′5 × 2. ′5 region in 3 mm continuum emission with the Owens Valley Millimeter Array. For this sample of 336 stars, we observe 3 mm emission above the 3 σ noise level toward 10 sources, six of which have also been detected optically in silhouette against the bright nebular background. In addition, we detect 20 objects in 3 mm continuum emission that do not correspond to known near-IR cluster members. Comparisons of our measured fluxes with longer wavelength observations enable rough separation of dust emission from thermal free-free emission, and we find substantial dust emission toward most objects. For the sample of 10 objects detected at both 3 mm and near-IR wavelengths, eight exhibit substantial dust emission. Excluding the two high-mass stars (θ1 Ori A and the BN object) and assuming a gas-to-dust ratio of 100, we estimate circumstellar masses ranging from 0.13 to 0.39 M☉. For the cluster members not detected at 3 mm, images of individual objects are stacked to constrain the mean 3 mm flux of the ensemble. The average flux is detected at the 3 σ confidence level and implies an average disk mass of 0.005 M☉, comparable to the minimum-mass solar nebula. The percentage of stars in Orion surrounded by disks more massive than ~0.1 M☉ is consistent with the disk mass distribution in Taurus, and we argue that massive disks in Orion do not appear to be truncated through close encounters with high-mass stars. Comparison of the average disk mass and number of massive dusty structures in Orion with similar surveys of the NGC 2024 and IC 348 clusters is used to constrain the evolutionary timescales of massive circumstellar disks in clustered environments.
Assessing the Factors that Affect the Persistence and Graduation Rates of Native American Students in Postsecondary Education

William E. Knight, Advisor This study sought to answer two primary research questions: (a) Are there statistically significant differences between the persistence to graduation rates of Native American college students who attend tribal colleges and Native American college students who attend mainstream institutions of higher education in America; and (b) What are the factors that affect the persistence to graduation rates of Native American college students? Results of institutional-level data analysis using data obtained through the Integrated Postsecondary Education Data System (IPEDS) showed statistically significant differences in enrollment, persistence, and graduation and degree receipt based on institutional characteristics. Further analysis using student-level data obtained through license to use restricted access data sets from the National Postsecondary Student Aid Study 1995-96 and the Beginning Postsecondary Students Longitudinal Study 19962001 confirmed findings from IPEDS analysis, but advanced analysis was not fully completed because the number of cases was insufficient to maintain stability in testing. One major finding of this study was that the broad claims of tribal college success are not entirely supported within the data that were examined.
Adaptive Projection Operators in Multiresolution Scientific Visualization

Recently, multiresolution visualization methods have become an indispensable ingredient of real-time interactive postprocessing. The enormous databases, typically coming along with some hierarchical structure, are locally resolved on different levels of detail to achieve a significant savings of CPU and rendering time. In this paper, the method of adaptive projection and the corresponding operators on data functions, respectively, are introduced. They are defined and discussed as mathematically rigorous foundations for multiresolution data analysis. Keeping in mind data from efficient numerical multigrid methods, this approach applies to hierarchical nested grids consisting of elements which are any tensor product of simplices, generated recursively by an arbitrary, finite set of refinement rules from some coarse grid. The corresponding visualization algorithms, e.g. color shading on slices or isosurface rendering, are confined to an appropriate depth-first traversal of the grid hierarchy. A continuous projection of the data onto an adaptive, extracted subgrid is thereby calculated recursively. The presented concept covers different methods of local error measurement, time-dependent data which have to be interpolated from a sequence of key frames, and a tool for local data focusing. Furthermore, it allows for a continuous level of detail.