Miracle-dz/UniFAR-FTUs huggingface dataset:
Signal-to-noise, Crosstalk, and Long Range Problem Difficulty in Genetic Algorithms

This paper presents a signal-to-noise perspective of the search bias introduced by genetic algorithms. A decision theoretic signal-to-noise framework is used to show that there are two fundamental modes of introducing diiculty through search bias: 1) sending a wrong signal and 2) increasing or decreasing noise depending on the direction of signal. The main purpose of this paper is to identify crosstalk as another possible source of problems , caused by search bias in genetic algorithms. I show that a small modiication of the one-max problem can convert it to dii-cult to solve because of crosstalk. This paper also studies the royal road functions (Forrest and Mitchell, 1994) and demonstrates that crosstalk plays a major role in making R2 harder than R1 to solve. This clearly shows that increasing signal in the right direction alone does not necessarily make a problem easy for GA, unless the noise is also reduced.
Altered Mitochondrial Dynamics Contributes to Endothelial Dysfunction in Diabetes Mellitus

Background— Endothelial dysfunction contributes to the development of atherosclerosis in patients with diabetes mellitus, but the mechanisms of endothelial dysfunction in this setting are incompletely understood. Recent studies have shown altered mitochondrial dynamics in diabetes mellitus with increased mitochondrial fission and production of reactive oxygen species. We investigated the contribution of altered dynamics to endothelial dysfunction in diabetes mellitus. Methods and Results— We observed mitochondrial fragmentation (P=0.002) and increased expression of fission-1 protein (Fis1; P<0.0001) in venous endothelial cells freshly isolated from patients with diabetes mellitus (n=10) compared with healthy control subjects (n=9). In cultured human aortic endothelial cells exposed to 30 mmol/L glucose, we observed a similar loss of mitochondrial networks and increased expression of Fis1 and dynamin-related protein-1 (Drp1), proteins required for mitochondrial fission. Altered mitochondrial dynamics was associated with increased mitochondrial reactive oxygen species production and a marked impairment of agonist-stimulated activation of endothelial nitric oxide synthase and cGMP production. Silencing Fis1 or Drp1 expression with siRNA blunted high glucose–induced alterations in mitochondrial networks, reactive oxygen species production, endothelial nitric oxide synthase activation, and cGMP production. An intracellular reactive oxygen species scavenger provided no additional benefit, suggesting that increased mitochondrial fission may impair endothelial function via increased reactive oxygen species. Conclusion— These findings implicate increased mitochondrial fission as a contributing mechanism for endothelial dysfunction in diabetic states.
Cultural attributes and risk perception: the moderating role of different types of research and development

Abstract Drawing on the grid–group culture theory, this study examines hypotheses to explain how the cultural attributes of an organization influence professionals’ perceptions of risk in the context of research and development (R&D) activities. Specifically, we explore whether two dimensions of cultural attributes – the grid dimension and the group dimension – affect organizational commitment and risk perception. We also investigate whether the impact of the cultural attributes on risk perception is mediated by organizational commitment and whether different types of R&D activities – applied research and developmental research – serve as a moderating variable on the relationships among cultural attributes, organizational commitment, and risk perception. The partial least squares method was used to analyze data collected from full-time senior researchers with over five years of experience at a large technology institute. Our findings indicate that cultural attributes influence the risk perception of R&D professionals through the mediating function of organizational commitment. Further, we found that different types of R&D activities have moderating effects on the relationship between organizational commitment and risk perception.
Representations and cohomology of finite group schemes

The article covers developments in the representation theory of finite group schemes over the last fifteen years. We start with the finite generation of cohomology of a finite group scheme and proceed to discuss various consequences and theories that ultimately grew out of that result. This includes the theory of one-parameter subgroups and rank varieties for infinitesimal group schemes; the $\pi$-points and $\Pi$-support spaces for finite group schemes, modules of constant rank and constant Jordan type, and construction of bundles on projective varieties associated with cohomology ring of an infinitesimal group scheme $G$. In the last section we discuss varieties of elementary subalgebras of modular Lie algebras, generalizations of modules of constant Jordan type, and new constructions of bundles on projective varieties associated to a modular Lie algebra.
Initial Version of State Transition Algorithm

In terms of the concepts of state and state transition, a new algorithm-State Transition Algorithm (STA) is proposed in order to probe into classical and intelLigent optimization algorithms. On the basis of state and state transition, it becomes much simpler and easier to understand. As for continuous function optimization problems, three special operators named rotation, translation and expansion are presented. While for discrete function optimization problems, an operator called general elementary transformation is introduced. Finally, with 4 common benchmark continuous functions and a discrete problem used to test the performance of STA, the experiment shows that STA is a promising algorithm due to its good search capabiLity.
Adaptive Vehicle Navigation With En Route Stochastic Traffic Information

This paper develops an adaptive approach for vehicle navigation in a stochastic network with real-time en route traffic information. This stochastic and adaptive approach is formulated as a probabilistic dynamic programming problem and is solved through a backward recursive procedure. The formulation, as a modeling framework, is designed to be able to incorporate various sources of information and real-time traffic states to improve routing quality. In this paper, we prove that the approach outperforms deterministic instantaneous shortest paths in a statistical sense. We also analyze the algorithm's computational efficiency. The results from numerical examples are included to illustrate the performance of the adaptive routing policy that was generated by the formulation.
Shape object classification using echoes inside a virtual environment application

This work investigates the geometric object-shape classification using the echoes generated by various kinds of obstacles in a cellular automata based virtual environment for ultra-sound propagation. The virtual environment is implemented as a JAVA platform [1] capable of emulate sound propagation in a controlled 2D environment. The echoes are preprocessed by a Feature Processor Vector Unit (FVPU) and then classified using 2 neural networks: Fast Support Vector Classifier (FSVC) and Extreme Learning Machine (ELM).
Improvement in solving balancing problems for machining transfer lines with blocks of parallel operations

In the volume 38 of IIE Transactions (2006), Dolgui et al. introduced a line balancing problem in the machining environment, characterized by a number of particularities in comparison with the well-studied assembly line balancing problem, for example, parameterized operation times, non-strict precedence constraints and simultaneous operations performing. An exact method based on MIP formulation was suggested for solving small and medium size problems. In this paper, a number of effective pre-processing procedures are derived in order to decrease the problem size and reduce the number of variables and constraints. Their application allows solving even large-size problems to optimality. Experimental results are reported.
Pitch behavior detection for automatic prominence recognition

In this paper a non-supervised approach for automatic syllable prominence recognition is presented. Previous research in this field showed that syllable nuclei energy and duration are the main cues for prominence detection. The role of the fundamental frequency has also been investigated in the past but was considered secondary or irrelevant for this task. The proposed system uses the energy and the duration of the nucleus while taking into account also the pitch behavior. The algorithm was tested by comparing its results with the annotations of two human experts and a 5.6% accuracy increase with respect to the system not using the pitch behavior was found.
VectorBase: improvements to a bioinformatics resource for invertebrate vector genomics

VectorBase (http://www.vectorbase.org) is a NIAID-supported bioinformatics resource for invertebrate vectors of human pathogens. It hosts data for nine genomes: mosquitoes (three Anopheles gambiae genomes, Aedes aegypti and Culex quinquefasciatus), tick (Ixodes scapularis), body louse (Pediculus humanus), kissing bug (Rhodnius prolixus) and tsetse fly (Glossina morsitans). Hosted data range from genomic features and expression data to population genetics and ontologies. We describe improvements and integration of new data that expand our taxonomic coverage. Releases are bi-monthly and include the delivery of preliminary data for emerging genomes. Frequent updates of the genome browser provide VectorBase users with increasing options for visualizing their own high-throughput data. One major development is a new population biology resource for storing genomic variations, insecticide resistance data and their associated metadata. It takes advantage of improved ontologies and controlled vocabularies. Combined, these new features ensure timely release of multiple types of data in the public domain while helping overcome the bottlenecks of bioinformatics and annotation by engaging with our user community.
Ameliorating buyer's remorse

Keeping in pace with the increasing importance of commerce conducted over the Web, several e-commerce websites now provide admirable facilities for helping consumers decide what product to buy and where to buy it. However, since the prices of durable and high-tech products generally fall over time, a buyer of such products is often faced with a dilemma: Should she buy the product now or wait for cheaper prices? We present the design and implementation of Prodcast, an experimental system whose goal is to help consumers decide when to buy a product. The system makes use of forecasts of future prices based on price histories of the products, incorporating features such as sales volume, seasonality, and competition in making its recommendation. We describe techniques that are well-suited for this task and present a comprehensive evaluation of their relative merits using retail sales data for electronic products. Our back-testing of the system indicates that the system is capable of helping consumers time their purchase, resulting in significant savings to them.
NetApp Autosupport Analysis

Big data has taken the tech industry by storm as storage costs go down and analytics tools improve to enable businesses to make better decisions faster. NetApp is one such company that collects customer machine configurations through NetApp Autosupport to help customers troubleshoot errors. This project leverages the Autosupport data to gain insights into the production environment as well as the QA environment in terms of their relationships to each other. Using the K-Means algorithm and direct matching method, we have identified eight common customer configuration groups, top customer configurations not tested by any QA machines, and top QA machines not testing any customer configurations. The methodology is still maturing, and requires input from both developers and subject area experts. The results we found can be used to enhance the test environment for QA, target development of features for developers, and increase confidence in product and services for customers.
(CdSe)ZnS Core-Shell Quantum Dots - Synthesis and Characterization of a Size Series of Highly Luminescent Nanocrystallites

We report a synthesis of highly luminescent (CdSe)ZnS composite quantum dots with CdSe cores ranging in diameter from 23 to 55 A. The narrow photoluminescence (fwhm ≤ 40 nm) from these composite dots spans most of the visible spectrum from blue through red with quantum yields of 30−50% at room temperature. We characterize these materials using a range of optical and structural techniques. Optical absorption and photoluminescence spectroscopies probe the effect of ZnS passivation on the electronic structure of the dots. We use a combination of wavelength dispersive X-ray spectroscopy, X-ray photoelectron spectroscopy, small and wide angle X-ray scattering, and transmission electron microscopy to analyze the composite dots and determine their chemical composition, average size, size distribution, shape, and internal structure. Using a simple effective mass theory, we model the energy shift for the first excited state for (CdSe)ZnS and (CdSe)CdS dots with varying shell thickness. Finally, we characterize the.
Induction motor torque control via discrete-time sliding mode

This paper presents a discrete-time sliding mode controller combined with the block-control technique for induction motors. The sliding mode controller is designed to force the system to track a torque reference and a flux magnitude reference, on the basis of a nonlinear discrete-time model. Then, a reduced order observer is designed for rotor fluxes. Simulations illustrate robustness with respect to external load torque. The proposed control scheme is compared with a direct field oriented controller.
Antimicrobial Polymeric Materials with Quaternary Ammonium and Phosphonium Salts

Polymeric materials containing quaternary ammonium and/or phosphonium salts have been extensively studied and applied to a variety of antimicrobial-relevant areas. With various architectures, polymeric quaternary ammonium/phosphonium salts were prepared using different approaches, exhibiting different antimicrobial activities and potential applications. This review focuses on the state of the art of antimicrobial polymers with quaternary ammonium/phosphonium salts. In particular, it discusses the structure and synthesis method, mechanisms of antimicrobial action, and the comparison of antimicrobial performance between these two kinds of polymers.
Illusory Shapes via Phase Transition

We propose a new variational illusory shape (VIS) model via phase fields and phase transitions. It is inspired by the first-order variational illusory contour (VIC) model proposed by Jung and Shen (J. Visual Comm. Image Repres., 19:42-55, 2008). Under the new VIS model, illusory shapes are represented by phase values close to 1 while the rest by values close to 0. The 0-1 transition is achieved by an elliptic energy with a double-well potential, as in the theory of Γ-convergence. The VIS model is non-convex, with the zero field as its trivial global optimum. To seek visually meaningful local optima that can induce illusory shapes, an iterative algorithm is designed and its convergence behavior is closely studied. Several generic numerical examples confirm the versatility of the model and the algorithm.
Analysis of ryanodine receptor clusters in rat and human cardiac myocytes

Single rat ventricular myocytes and human ventricle tissue sections were labeled with antibodies against the ryanodine receptor (RyR) and α-actinin to examine the 3D distribution of RyRs with confocal microscopy. Image contrast was maximized by refractive index matching and deconvolution. The RyR label formed discrete puncta representing clusters of RyRs or “couplons” around the edges of the myofilaments with a nearest-neighbor spacing of 0.66 ± 0.06 μm in rat and 0.78 ± 0.07 μm in human. Each bundle of myofibrils was served by approximately six couplons, which supplied a cross-sectional area of ≈0.6 μm2 in rat and ≈0.8 μm2 in human. Although the couplons were in reasonable registration with z-lines, there were discontinuities in the longitudinal position of sarcomeres so that dislocations in the order of RyR clusters occurred. There was ≈53% longitudinal registration of RyR clusters, suggesting a nonrandom placement of couplons around the sarcomere. These data can explain the spherical propagation of Ca2+ waves and provide quantitative 3D data sets needed for accurate modeling of cardiac Ca2+-induced Ca2+ release. By quantifying labeling intensity in rat ventricular myocytes, a lower limit of 78 RyRs per cluster (on average) was obtained. By modeling the couplon as a disk wrapping around a t-tubule and fitting cluster images, 95% of couplons contained between 120 and 260 RyRs (assuming that RyRs are tight packed with a spacing of 29 nm). Assuming similar labeling efficiency in human, from the fluorescence intensity alone we estimate that human ventricular myocytes contain ≈30% fewer RyRs per couplon than rat.
Abundance of Substructure in Clusters of Galaxies

It is widely recognized that cold dark matter (CDM) models predict abundant dark matter substructure in halos of all sizes. Galaxy-galaxy lensing provides a unique opportunity to directly measure the presence and the mass of such substructures in clusters of galaxies. Here we present the mass function of substructures obtained from lensing in massive Hubble Space Telescope cluster lenses and compare it with that obtained from high-resolution cosmological N-body simulations. We find excellent agreement in the slope and amplitude in the mass range 1011-1012.5 M☉ probed by the observations, highlighting a significant success of the CDM theory. At lower mass scales, the simulations predict a large abundance of small substructures below our detection threshold with galaxy-galaxy lensing, with many of them being plausibly associated with faint cluster galaxies. Our results suggest that the CDM substructure abundance on the scale of clusters is in good agreement with present observational data.
Brief announcement: locally-accessible implementations for distributed shared memory multiprocessors

We consider asynchronous multiprocessors that support the distributed shared memory (DSM) model. Algorithms for such multiprocessors exploit the ability to co-locate shared objects with particular processes in order to reduce the cost of accessing shared memory. When a shared object fits inside a single memory word and operations on it are supported directly through machine instructions, it can be made local to any process simply by fixing its physical address. We show that even if the shared object is not supported in hardware directly, it can always be simulated using a software implementation that behaves as though it is local to some designated process. That is, operations applied by the designated process on the implemented object access only local base objects, which is non-trivial when processes synchronize by busy-waiting. We also discuss time complexity bounds for such locally-accessible implementations.
Geo-Social Co-location Mining

Modern technology to capture geo-spatial information produces a huge flood of geo-spatial and geo-spatio-temporal data with a new user mentality of utilizing this technology to voluntarily share information. This location information, enriched with social information, is a new source to discover new and useful knowledge. This work introduces geo-social co-location mining, the problem of finding social groups that are frequently found at the same location. This problem has applications in social sciences, allowing to research interactions between social groups and permitting social-link prediction. It can be divided into two sub-problems. The first sub-problem of finding spatial co-location instances, requires to properly address the inherent uncertainty in geo-social network data, which is a consequence of generally very sparse check-in data, and thus very sparse trajectory information. For this purpose, we propose a probabilistic model to estimate the probability of a user to be located at a given location at a given time, creating the notion of probabilistic co-locations. The second sub-problem of mining the resulting probabilistic co-location instances requires efficient methods for large databases having a high degree of uncertainty. Our approach solves this problem by extending solutions for probabilistic frequent itemset mining. Our experimental evaluation performed on real (but anonymized) geo-social network data shows the high efficiency of our approach, and its ability to find new social interactions.
A Model for the Recognition of Discourse Relations

Discourse parsing is often realized as a process of identifying elementary discourse units (EDUs) and adding rhetorical structure on top of them, by specifying which (spans of) EDUs interact with each other and what kind of rhetorical relations hold between them. In this paper we describe our model for the recognition of discourse relations and we report the results of experiments performed in this model. The experiments are run on the RST-DT corpus, using a fine-grained taxonomy of relations consisting in 167 distinct labels, compared to 41 distinct labels used in previous research. We also show how a combination of two classifiers, which discriminate between explicit and implicit relations, achieves better performance than a single feature-rich classifier.
Growth, cell and nuclear divisions in some bacteria.

SUMMARY: The timing of cell and nuclear division of certain enteric bacteria was determined under conditions of balanced growth. Organisms were grown in a high refractive index medium and photographed at frequent intervals with a phase-contrast microscope. This allowed an estimation of the time between successive divisions of nuclei and cells (interdivision times) and the growth rate of each individual. The interdivision times of cell and nuclear divisions had a similar degree of variation (coefficients of variation of about 20%). The interdivision times of sister cells and sister nuclei were positively correlated to a significant degree. The correlation between mothers and daughters was negative to a significant degree in some, but not all, experiments. The correlation between interdivision times of a cell and that of its corresponding nucleus was positive in most experiments. The rate of mass increase of individual cells was estimated by measuring the rate of elongation. Within the limitations of the method of observation, it could be concluded that cells grew exponentially between successive divisions. Different individuals grew with very nearly the same rate constant. The variation in size of cells at the time of nuclear and cell division was smaller (coefficients of variation of about 10%) than that of the interdivision times. Some observations on the morphological changes of nuclei during growth and division are presented.
Approaches for Specifying the Level-1 Error Structure When Synthesizing Single-Case Data

ABSTRACT Multilevel modeling has been utilized for combining single-case experimental design (SCED) data assuming simple level-1 error structures. The purpose of this study is to compare various multilevel analysis approaches for handling potential complexity in the level-1 error structure within SCED data, including approaches assuming simple and complex error structures (heterogeneous, autocorrelation, and both) and those using fit indices to select between alternative error structures. A Monte Carlo study was conducted to empirically validate the suggested multilevel modeling approaches. Results indicate that each approach leads to fixed effect estimates with little to no bias and that inferences for fixed effects were frequently accurate, particularly when a simple homogeneous level-1 error structure or a first-order autoregressive structure was assumed and the inferences were based on the Kenward-Roger method. Practical implications and recommendations are discussed.