Miracle-dz/UniFAR-FTUs huggingface dataset:
Constitutive modeling of active polymers

This thesis develops a three-dimensional constitutive model of active polymeric materials, including changes in material volume and properties due to actuation. Active polymers reversibly change shape, volume and/or material properties in response to some external stimulus. Electric fields are a common driving force for this actuation, though changes in pH, temperature, magnetic field, or visible light have been studied in their role as stimuli. The study and future use of conducting polymer actuators i important because of their potential to meet and exceed the capabilities of currently available materials in a variety of fields, most notably in the field of artificial muscles. This thesis first reviews the basic principles of continuum mechanics, followed by a review of the classical and current statistical mechanics based constitutive models for rubber-like materials. A theoretical model for reversibly swelling active polymer gels is presented. Material responses to different homogeneous deformation conditions are then studied as a function of swelling. The effect of inert rigid filler particles on the swelling behavior of an active polymer is then studied using a closed-form solution of a boundary value problem developed in the literature. The constitutive model is also used in a study of the behavior of a bimorph strip, noting that the responsive nature of active polymers is often mechanically accentuated by layering with other materials, with implications for future work on transient analysis. Finite element analysis is implemented for verification and analysis of the boundary value problem. Thesis Supervisor: Mary C. Boyce Title: Professor of Mechanical Engineering
Gene Expression Network Reconstruction by LEP Method Using Microarray Data

Gene expression network reconstruction using microarray data is widely studied aiming to investigate the behavior of a gene cluster simultaneously. Under the Gaussian assumption, the conditional dependence between genes in the network is fully described by the partial correlation coefficient matrix. Due to the high dimensionality and sparsity, we utilize the LEP method to estimate it in this paper. Compared to the existing methods, the LEP reaches the highest PPV with the sensitivity controlled at the satisfactory level. A set of gene expression data from the HapMap project is analyzed for illustration.
Advanced Control of a Steam Generator

Economic feasibility of a nuclear power plant requires smooth and uninterrupted plant operation in the face of varying electrical power demand. The feed-water system in a nuclear power plant is a major contributor to plant unavailability. The investigations about operation experiences of a power plant show that one of frequent causes of shutdowns is by violation of safety limits on the water level and is common at low operating power where the plant exhibits strong non-minimum phase characteristics. This paper presents a framework for addressing this problem based on a method of advanced control based on fuzzy model predictive control. Furthermore, fuzzy model based fault detection methods are elaborated envisaging fault tolerant control. Results from validation of the advanced control system are presented. A new concept of modular advanced control system designed for a seamless and gradual integration into the existing distributed control system is proposed
Monoclonal antibodies demonstrate the organization of axons in the leech

Monoclonal antibodies have been generated that bind to subsets of neurons within the leech central nervous system (Zipser, B., and R. McKay (1981) Nature 289: 549–554). In this report we describe the binding patterns of monoclonal antibodies to subsets of axons in the leech using HRP-immunohistochemistry. Each antibody has a characteristic staining pattern in the connective, the large bundle of axons that runs the length of the nerve cord connecting each ganglion to its rostral and caudal neighbors. These staining patterns are consistent along the rostrocaudal axis of each animal, between animals of the same species, and, in many cases, between animals of different species. These results show that axonal position, like neuron cell body position, is a consistent feature of the organization of the leech central nervous system. Two antibodies bind to all of the axons in particular fascicles that are delimited by glial cell processes; another binds to single axons in fascicles that contain other, unstained axons. The grouping of antibody-identified axons into fascicles does not correlate in a simple way with the grouping of neuron cell bodies identified with the same antibody. The presence of one of these antigens on the surface of axons suggests a possible role in axon fasciculation. This report shows that molecular heterogeneity is a property of axons as well as of neuron cell bodies and demonstrates the organization of specific antibody-identified groups of axons within the connective.
TESTING THE GAMMA-RAY BURST PULSE START CONJECTURE

We test the hypothesis that prompt gamma-ray burst (GRB) pulse emission starts simultaneously at all energies (the Pulse Start Conjecture). Our analysis, using a sample of BATSE bursts observed with four-channel, 64 ms data and performed using a pulse-fit model, generally supports this hypothesis for the Long GRB class, although a few discrepant pulses belong to bursts observed during times characterized by low signal-to-noise ratio, hidden pulses, and/or significant pulse overlap. The typical uncertainty in making this statement is <0.4 s for pulses in Long GRBs (and <0.2 s for 40% of the pulses) and perhaps <0.1 s for pulses in Short GRBs. When considered along with the Epk decline found in GRB pulse evolution, this result implies that energy is injected at the beginning of each and every GRB pulse, and the subsequent spectral evolution, including the pulse peak intensity, represents radiated energy losses from this initial injection.
A New Switching-Based Median Filtering Scheme and Algorithm for Removal of High-Density Salt and Pepper Noise in Images

A new switching-based median filtering scheme for restoration of images that are highly corrupted by salt and pepper noise is proposed. An algorithm based on the scheme is developed. The new scheme introduces the concept of substitution of noisy pixels by linear prediction prior to estimation. A novel simplified linear predictor is developed for this purpose. The objective of the scheme and algorithm is the removal of high-density salt and pepper noise in images. The new algorithm shows significantly better image quality with good PSNR, reduced MSE, good edge preservation, and reduced streaking. The good performance is achieved with reduced computational complexity. A comparison of the performance is made with several existing algorithms in terms of visual and quantitative results. The performance of the proposed scheme and algorithm is demonstrated.
Generalized contextualization method for XML information retrieval

A general re-weighting method, called contextualization, for more efficient element ranking in XML retrieval is introduced. Re-weighting is based on the idea of using the ancestors of an element as a context: if the element appears in a good context -- good interpreted as probability of relevance -- its weight is increased in relevance scoring; if the element appears in a bad context, its weight is decreased. The formal presentation of contextualization is given in a general XML representation and manipulation frame, which is based on utilization of structural indices. This provides a general approach independent of weighting schemas or query languages.Contextualization is evaluated with the INEX test collection. We tested four runs: no contextualization, parent, root and tower contextualizations. The contextualization runs were significantly better than no contextualization. The root contextualization was the best among the re-weighted runs.
Afferent Input Selects NMDA Receptor Subtype to Determine the Persistency of Hippocampal LTP in Freely Behaving Mice

The glutamatergic N-methyl-D-aspartate receptor (NMDAR) is critically involved in many forms of hippocampus-dependent memory that may be enabled by synaptic plasticity. Behavioral studies with NMDAR antagonists and NMDAR subunit (GluN2) mutants revealed distinct contributions from GluN2A- and GluN2B-containing NMDARs to rapidly and slowly acquired memory performance. Furthermore, studies of synaptic plasticity, in genetically modified mice in vitro, suggest that GluN2A and GluN2B may contribute in different ways to the induction and longevity of synaptic plasticity. In contrast to the hippocampal slice preparation, in behaving mice, the afferent frequencies that induce synaptic plasticity are very restricted and specific. In fact, it is the stimulus pattern and not variations in afferent frequency that determine the longevity of long-term potentiation (LTP) in vivo. Here, we explored the contribution of GluN2A and GluN2B to LTP of differing magnitudes and persistence in freely behaving mice. We applied differing high-frequency stimulation (HFS) patterns at 100 Hz to the hippocampal CA1 region, to induce NMDAR-dependent LTP in wild-type (WT) mice, that endured for <1 h (early (E)-LTP), (LTP, 2–4 h) or >24 h (late (L)-LTP). In GluN2A-knockout (KO) mice, E-LTP (HFS, 50 pulses) was significantly reduced in magnitude and duration, whereas LTP (HFS, 2 × 50 pulses) and L-LTP (HFS, 4 × 50 pulses) were unaffected compared to responses in WT animals. By contrast, pharmacological antagonism of GluN2B in WT had no effect on E-LTP but significantly prevented LTP. E-LTP and LTP were significantly impaired by GluN2B antagonism in GluN2A-KO mice. These data indicate that the pattern of afferent stimulation is decisive for the recruitment of distinct GluN2A and GluN2B signaling pathways that in turn determine the persistency of hippocampal LTP. Whereas brief bursts of patterned stimulation preferentially recruit GluN2A and lead to weak and short-lived forms of LTP, prolonged, more intense, afferent activation recruits GluN2B and leads to robust and persistent LTP. These unique signal-response properties of GluN2A and GluN2B enable qualitative differentiation of information encoding in hippocampal synapses.
Density Estimation with Adaptive Sparse Grids for Large Data Sets

Nonparametric density estimation is a fundamental problem of statistics and data mining. Even though kernel density estimation is the most widely used method, its performance highly depends on the choice of the kernel bandwidth, and it can become computationally expensive for large data sets. We present an adaptive sparse-grid-based density estimation method which discretizes the estimated density function on basis functions centered at grid points rather than on kernels centered at the data points. Thus, the costs of evaluating the estimated density function are independent from the number of data points. We give details on how to estimate density functions on sparse grids and develop a cross validation technique for the parameter selection. We show numerical results to confirm that our sparse-grid-based method is well-suited for large data sets, and, finally, employ our method for the classification of astronomical objects to demonstrate that it is competitive to current kernel-based density estimation approaches with respect to classification accuracy and runtime.
Invariant integration theory on non-compact quantum spaces: Quantum (n,1)-matrix ball

An operator theoretic approach to invariant integration theory on non-compact quantum spaces is introduced on the example of the quantum (n,1)-matrix ball O_q(Mat_{n,1}). In order to prove the existence of an invariant integral, operator algebras are associated to O_q(Mat_{n,1}) which allow an interpretation as ``rapidly decreasing'' functions and as functions with compact support on the quantum (n,1)-matrix ball. It is shown that the invariant integral is given by a generalization of the quantum trace. If an operator representation of a first order differential calculus over the quantum space is known, then it can be extended to the operator algebras of integrable functions. Hilbert space representations of O_q(Mat_{n,1}) are investigated and classified. Some topological aspects concerning Hilbert space representations are discussed.
Biomedical Information Extraction with Predicate-Argument Structure Patterns

Due to the ever growing amount of publications, Information Extraction (IE) from text is increasingly is recognized as one of crucial technologies in bioinformatics. However, for IE to be practically applicable, adaptability/portability of a system is crucial, considering extremely diverse demands in biomedical IE application. We should be able to construct a set of “extraction rules” adapted for a specific application at low
Modeling of Transcription and DNA Replication Using the E-CELL Simulation System

We are currently constructing a \self-replicating" hypothetical cell by integrating models of DNA replication, transcription and translation, as well as other metabolic pathways, using the E-CELL simulation system, which is a generic software package for simulation of cellular processes [1]. We discuss a problem in designing such an integrative model with both transcription and replication; that is, the problem of DNA polymerase colliding with RNA polymerase when both of them simultaneously process the same part of chromosome.
Autonomous personal mobility scooter for multi-class mobility-on-demand service

In this paper, we describe the design and development of an autonomous personal mobility scooter that was used in public trials during the 2016 MIT Open House, for the purpose of raising public awareness and interest about autonomous vehicles. The scooter is intended to work cooperatively with other classes of autonomous vehicles such as road cars and golf cars to improve the efficacy of mobility-on-demand transportation solutions. The scooter is designed to be robust, reliable, and safe, while operating under prolonged durations. The flexibility in fleet expansion is shown by replicating the system architecture and sensor package that has been previously implemented in a road car and golf cars. We show that the vehicle performed robustly with small localization variance. A survey of the users shows that the public is very receptive to the concept of the autonomous personal mobility device.
Substrate thickness effect on transformer

Due to ever-increasing demand for high-level integration, high system (mechanical) flexibility, small form factor, and/or thermal conductivity, the integrated circuits (ICs) need to be thin, preferably <200µm. One potential drawback of thin IC is that the efficiency of the RF transformer may be degraded, hence resulting in reduced overall system efficiency and increased power dissipation. In this paper, the effects of substrate thickness of the ICs on transformer performance are analyzed. A fully symmetrical interleaved transformer is designed and simulated based on CMOS 65nm process and the analyses herein are based on the 3D Lumped substrate model. We show that the efficiency of the transformer remains largely uncompromised when the substrate thickness is >50µm and reduced by ∼28.6% (from 0.7 to 0.5) at 2GHz when the substrate thickness is reduced to an ultra-thin 10µm. The results herein provide a guideline for optimization of substrate thickness without affecting the performance of transformer.
Genetic-based K-means algorithm for selection of feature variables

This paper proposes a genetic-based K-means (GK) algorithm for selection of the k value and selection of feature variables by minimizing an associated objective function. The algorithm combines the advantage of genetic algorithm (GA) and K-means to search the subspace thoroughly. Therefore, our algorithm converges globally. A weighting junction is then introduced to initialize the parameters of the algorithm. The experiments on a synthetic dataset and a real dataset shows that (i) GK outperforms K-means since GK achieves the minimal value of the objective junction and (ii) GK with the weighting function performs better than GK
Efficient surface reconstruction from contours based on two‐dimensional Delaunay triangulation

This paper introduces an efficient method for surface reconstruction from sectional contours. The surface between neighbouring sections is reconstructed based on the consistent utilization of the two‐dimensional constrained Delaunay triangulation. The triangulation is used to extract the parametric domain and to solve the problems associated with correspondence, tiling and branching in a general framework. Natural distance interpolations are performed in order to complete the mapping of the added intermediate points. Surface smoothing and remeshing are conducted to optimize the initial surface triangulations. Several examples are presented to demonstrate the effectiveness and efficiency of the proposed approach. Copyright © 2005 John Wiley & Sons, Ltd.
National saving rates and population growth: a new model and new evidence.

This study presents new analysis and evidence on the link between population growth and national saving. Analysis was based on the variable rate-of-growth effect model (Mason 1981; Fry and Mason 1982) which distinguishes 2 population growth effects: the rate of growth effect and the dependency effect. The papers 1st section reviews previous research on the saving population growth link including applications of the variable rate-of-growth effect model. Its 2nd part presents an extension of the variable rate-of-growth effect model by linking factors determining the number of children reared to the budget shares devoted to childbearing and in turn to the national saving rate. Part 3 presents estimates from international cross-section data covering the 1960-80 period. The final section uses the neoclassical growth model to show circumstances under which the rate of growth effect dominates the dependency effect reversing the relationship between population growth and saving. Available evidence from the international cross-section supports the proposition that a higher dependency ratio leads to lower saving particularly among countries with moderate to high rates of income growth. At the mean rate of growth observed over the last 2 decades for the 70 countries analyzed a decline from a high to a low childbearing regime generated an increase in the net national saving rate of about 5% -- nearly a 50% increase. The results reported also addressed the magnitude and validity of the equivalent adult consumer unit. Aggregate consumption data imply an equivalent adult consumer unit of about 1/3. The analysis implies that simulation models that are based on equivalent adult consumer unit can provide useful insights about the relationship between population growth and aggregate consumption and saving rates. The analysis fails to fully resolve the issue of the relative importance of the rate of growth and dependency effects. It is based on the neoclassical growth model for which the equilibrium rate of growth of national income increases point for point with an increase in the rate of growth of population. Given the long periods required to adjust from one equilibrium to another the steady-state results of the neoclassical model may have limited relevance to the design and evaluation of development and population policy. Although the model proposed clearly delineates the link between children household saving and national saving the role ascribed to children is limited. Only that children require household resources for their support is acknowledged.
Introduction to Methods for Music Classification Based on Audio Data

The subject of music information retrieval (MIR) is to analyze and categorize music pieces. Over the last years many approaches have been designed to automatically extract music data from the digitized audio signal. This article presents a survey of the state-of-the-art algorithms on the basis of a broad literature study and a tool analysis. It should help to navigate through different MIR techniques and tools. An overview of different music features to characterize timbre, harmony, melody and rhythmic information is given. The various time scales of feature extraction to form meta-features from basic features are discussed. The task-specific pruning of features is presented to reduce the computational complexity. The article continues with a discussion of different classification techniques and how the results are evaluated. Finally the properties of four state-of-the-art MIR tools are outlined.
Evidence of Conformational Selection Driving the Formation of Ligand Binding Sites in Protein-Protein Interfaces

Many protein-protein interactions (PPIs) are compelling targets for drug discovery, and in a number of cases can be disrupted by small molecules. The main goal of this study is to examine the mechanism of binding site formation in the interface region of proteins that are PPI targets by comparing ligand-free and ligand-bound structures. To avoid any potential bias, we focus on ensembles of ligand-free protein conformations obtained by nuclear magnetic resonance (NMR) techniques and deposited in the Protein Data Bank, rather than on ensembles specifically generated for this study. The measures used for structure comparison are based on detecting binding hot spots, i.e., protein regions that are major contributors to the binding free energy. The main tool of the analysis is computational solvent mapping, which explores the surface of proteins by docking a large number of small “probe” molecules. Although we consider conformational ensembles obtained by NMR techniques, the analysis is independent of the method used for generating the structures. Finding the energetically most important regions, mapping can identify binding site residues using ligand-free models based on NMR data. In addition, the method selects conformations that are similar to some peptide-bound or ligand-bound structure in terms of the properties of the binding site. This agrees with the conformational selection model of molecular recognition, which assumes such pre-existing conformations. The analysis also shows the maximum level of similarity between unbound and bound states that is achieved without any influence from a ligand. Further shift toward the bound structure assumes protein-peptide or protein-ligand interactions, either selecting higher energy conformations that are not part of the NMR ensemble, or leading to induced fit. Thus, forming the sites in protein-protein interfaces that bind peptides and can be targeted by small ligands always includes conformational selection, although other recognition mechanisms may also be involved.
ON SUPERVISED AND SEMI-SUPERVISED k-NEAREST NEIGHBOR ALGORITHMS

The k-nearest neighbor (kNN) is one of the simplest classifl- cation methods used in machine learning. Since the main component of kNN is a distance metric, kernelization of kNN is possible. In this pa- per kNN and semi-supervised kNN algorithms are empirically compared on two data sets (the USPS data set and a subset of the Reuters-21578 text categorization corpus). We use a soft version of the kNN algorithm to handle multi-label classiflcation settings. Semi-supervision is performed by using data-dependent kernels.