Miracle-dz/UniFAR-FTUs huggingface dataset:
The words and worlds of literary narrative: the trade-off between verbal presence and direct presence in the activity of reading

This chapter disputes the notion, endorsed by much of narrative theory, that the reading of literary narrative is functionally analogous to an act of communication, where communication stands for the transfer of thought and conceptual information. The paper offers a basic typology of the sensorimotor effects of reading, which fall outside such a narrowly communication-based model of literary narrative. A main typological distinction is drawn between those sensorimotor effects pertaining to the narrative qua verbal utterance (verbal presence) and those sensorimotor effects pertaining to the imaginary physical world(s) of the story (direct presence). While verbal presence refers to the reader's vicarious perception of the voices of narrators and characters, direct presence refers to the emulated sensorimotor experience of the imaginary worlds that the narrators' and characters' utterances refer to. The paper further elaborates on how, by which kinds of narrative content and structure, direct presence may be prompted. The final section addresses some of the observational and historical caveats that must be attached to any theoretical inquiry made into the sensorimotor effects of reading. As a preliminary for further research, a few ideas about the model's potential for empirical validation are put forward. A brief, tentative history of the sensorimotor benefits of literary narrative reading is then outlined.
SimWalk: Learning network latent representations with social relation similarity

In this paper, we present a novel method, namely SimWalk, to learn latent representations of networks. SimWalk maps nodes to a continuous vector space which maximizes the likelihood of node sequences. We design a probability-guided random walk procedure based on relation similarity, which encourages node sequences to preserve context-related neighborhoods. Different with previous work which generates rigid node sequences, we believe that relations in social net­works, especially similarity, can guide the walk to generate a more linguistic sequence. In this perspective, our model learns more meaningful representations. We demonstrate SimWalk on several multi-label real-world network classification tasks over state-of-the-art methods. Our results show that SimWalk outperforms the popular methods in complex networks.
Secondary Cell-Wall-Specific Glycoprotein(s) from French Bean Hypocotyls

Specific labeling of secondary cell walls of tracheary elements and of xylary and phloem fibers has been observed when wheat germ agglutinin (WGA) and anti-WGA antibodies were used during ultrastructural studies of French bean (Phaseolus vulgaris L.) hypocotyls. In this report we demonstrate that at least part of this labeling is due to the presence of secondary cell-wall-specific glycoproteins. Three major novel glycoproteins with relative molecular weights of 55,000, 86,000, and 90,000, purified by means of WGA-Sepharose affinity chromatography, have been characterized. Their amino acid composition indicates that they are not the members of known classes of structural cell-wall proteins, since they contain no hydroxyproline, a lower level of glycine than seen in glycine-rich proteins, and very little proline. N-terminal sequences of all three proteins show no significant homology with other proteins. Antibodies were raised against electrophoretically pure 90-kD glycoprotein. These were used to localize this protein in secondary cell walls of xylem tracheary elements and in xylary and phloem fibers, i.e. in the same compartments where labeling with WGA has been observed. To our knowledge this is one of the first biochemical and ultrastructural demonstrations of secondary cell-wall-specific glycoproteins.
Air-filled substrate integrated waveguide: A low cost self-packadged technological platform for high performance millimeterwave circuits

This paper presents the recently proposed air-filled substrate integrated waveguide (AFSIW) which offers a high performance technological platform for the design of self-packaged millimeterwave components and systems. AFSIW is based on low-cost multilayer printed circuit board (PCB) manufacturing process. Its performances surpasses its dielectric-filled SIW counterpart in term of transmission loss and power handling. Numerous millimeterwave passive devices operating up to 60 GHz have been studied and experimentally demonstrated. This technology is expected to contribute to the democratization of future millimeterwave extremely high data rate communications and highly sensitive sensors.
WSBen: A Web Services Discovery and Composition Benchmark Toolkit1

In this article, a novel benchmark toolkit, WSBen, for testing web services discovery and composition algorithms is presented. The WSBen includes: (1) a collection of synthetically generated web services files in WSDL format with diverse data and model characteristics; (2) queries for testing discovery and composition algorithms; (3) auxiliary files to do statistical analysis on the WSDL test sets; (4) converted WSDL test sets that conventional AI planners can read; and (5) a graphical interface to control all these behaviors. Users can fine-tune the generated WSDL test files by varying underlying network models. To illustrate the application of the WSBen, in addition, we present case studies from three domains: (1) web service composition; (2) AI planning; and (3) the laws of networks in Physics community. It is our hope that WSBen will provide useful insights in evaluating the performance of web services discovery and composition algorithms. The WSBen toolkit is available at: http://pike.psu. edu/sw/wsben/.
An improvement to the qualitative interpolative reasoning in sparse rule base

Interpolative reasoning in sparse rule base has been an important research topic in the field of artificial intelligence. To solve effectively the problem of reasoning in multivariable sparse rule base whose resulting consequences are restricted in a finite set, this paper developed a new interpolative reasoning approach and offered its algorithm. The approach deduced consequent results by converting domains of antecedent and consequent variables into ternary qualitative spaces and building ternary qualitative function among such spaces as model of system for calculation. By applying this approach to an example, the paper illustrated that the new approach is more accurate and simple than the existing interpolative reasoning methods for such problem.
CLARO : Modeling and Processing of High-Volume Uncertain Data Streams

Uncertain data streams, where data is incomplete, imprecise, and even misleading, have been observed in a variety of environments. Feeding uncertain data streams to existing stream systems can produce results of unknown quality, which is of paramount concern to monitoring applications. In this paper, we present the Claro system that supports uncertain data stream processing for data that is naturally captured using continuous random variables. The Claro system employs a unique data model that is flexible and allows efficient computation. Built on this model, we develop evaluation techniques for complex relational operators, including aggregates and joins, by exploring advanced statistical theory and approximation techniques. Our evaluation results show that our techniques can achieve high performance in stream processing while satisfying accuracy requirements, and these techniques significantly outperform a state-of-the-art sampling-based method. Furthermore, initial results of a case study show that our modeling and aggregation techniques can allow a tornado detection system to produce better quality results yet with lower execution time.
Recognizing and Discovering Human Actions from On-Body Sensor Data

We describe our initial efforts to learn high-level human behaviors from low-level gestures observed using on-body sensors. Such an activity discovery system could be used to index captured journals of a person's life automatically. In a medical context, an annotated journal could assist therapists in helping to describe and treat symptoms characteristic to behavioral syndromes such as autism. We review our current work on user-independent activity recognition from continuous data where we identify "interesting" user gestures through a combination of acceleration and audio sensors placed on the user's wrists and elbows. We examine an algorithm that can take advantage of such a sensor framework to automatically discover and label recurring behaviors, and we suggest future work where correlations of these low-level gestures may indicate higher-level activities
A methodology for tuning two-level cache hierarchy considering energy and performance

In this paper we propose a hybrid methodology for tuning both instruction and data cache configurations in a two-level memory hierarchy. The method aims to minimize energy consumption without compromise the performance. It combines two optimization mechanisms in order improve energy and performance results without increasing the configuration space. Experiments based on simulations were performed for 12 applications from the Mibench suite benchmark and the proposed methodology achieved better efficiency in 60% of the evaluated cases compared with existing heuristics.
Nonlocal pseudopotentials and diffusion Monte Carlo

We have applied the technique of evaluating a nonlocal pseudopotential with a trial function to give an approximate, local many‐body pseudopotential which was used in a valence‐only diffusion Monte Carlo (DMC) calculation. The pair and triple correlation terms in the trial function have been carefully optimized to minimize the effect of the locality approximation. We discuss the accuracy and computational demands of the nonlocal pseudopotential evaluation for the DMC method. Calculations of Si, Sc, and Cu ionic and atomic states and the Si2 dimer are reported. In most cases ∼90% of the correlation energy was recovered at the variational level and excellent estimations of the ground state energies were obtained by the DMC simulations. The small statistical error allowed us to determine the quality of the assumed pseudopotentials by comparison of the DMC results with experimental values.
Energy Efficient Design of SC-FDMA Based Uplink under QoS Constraints

We investigate Quality-of-Service (QoS) driven energy efficient design of SC-FDMA uplink. The resource allocation problem is formulated as a maximization of effective capacity based bits-per-joule capacity under statistical QoS provisioning, which is complicated due to specific constraints of SC-FDMA. We solve the problem using Canonical duality theory. Numerical results show that the proposed energy efficient design significantly enhances the energy efficiency while satisfying the QoS requirements of different users.
Mid-infrared ISO spectroscopy of NGC 4945

We have observed the central region of the nearby starburst galaxy NGC 4945 with the mid-infrared spectrom- eters SWS and PHT-S aboard ISO. We do not find any evidence for the existence of the powerful AGN, inferred from hard X-ray observations. The upper limits on our AGN tracers (Nev)14.32 m&24.3m and (Nevi) 7.65 m im- ply an AV >160 towards the NLR, assuming the NLR to be of equal strength as in the Circinus galaxy. Other pos- sibilities are discussed. The starburst excitation indicators (Neiii)15.56 m/(Neii)12.81 m and Lbol/Llyc suggest that the starburst in the central region is at least 5 10 6 yrs old, and that it accounts for at least half of the nuclear bolometric luminosity. The starburst might well power the entire bolometric luminos- ity, but the available constraints are also consistent with an up to 50% contribution of the embedded AGN. With PHT-S, at a resolution of 90, we detect strong absorption features of water ice, and, for the first time in an external galaxy, of CO 2 and CO. The same PHT-S spectrum also reveals strong emission from the family of PAH features. Finally, we have observed and de- tected several pure rotational and ro-vibrational H2 lines, two of which, the (0-0) S(0) & S(1) lines, allow us to determine the ex- citation temperature (160 K) and warm H2 mass (2.410 7 M). The low excitation temperature shows Orion-like shocks not to be representative for the entire emission of the central region of the galaxy and fairly normal PDRs to be perhaps more typical.
A nonlinear, control-oriented model for ionic polymer–metal composite actuators

Ionic polymer–metal composites (IPMCs) form an important category of electroactive polymers and have many potential applications in biomedical, robotic and micro/nanomanipulation systems. In this paper, a nonlinear, control-oriented model is proposed for IPMC actuators. A key component in the proposed model is the nonlinear capacitance of the IPMC. A nonlinear partial differential equation (PDE), which can capture the fundamental physics in the IPMC, is fully considered in the derivation of nonlinear capacitance. A systems perspective is taken to get the nonlinear mapping from the voltage to the induced charge by analytically solving the nonlinear PDE at the steady state when a step voltage is applied. The nonlinear capacitance is incorporated into a circuit model, which includes additionally the pseudocapacitance due to the electrochemical adsorption process, the ion diffusion resistance, and the nonlinear DC resistance of the polymer, to capture electrical dynamics of the IPMC. With electromechanical coupling, the curvature output is derived based on the circuit model. The proposed model is formulated in the state space, which will be the starting point for nonlinear controller design. Experimental verification shows that the proposed model can capture the major nonlinearities in the electrical response of the IPMC.
Structural insight into the binding interactions of modeled structure of Arabidopsis thaliana urease with urea: an in silico study

Urease (EC 3.5.1.5., urea amidohydrolase) catalyzes the hydrolysis of urea to ammonia and carbon dioxide. Urease is present to a greater abundance in plants and plays significant role related to nitrogen recycling from urea. But little is known about the structure and function of the urease derived from the Arabidopsis thaliana, the model system of choice for research in plant biology. In this study, a three-dimensional structural model of A. thaliana urease was constructed using computer-aided molecular modeling technique. The characteristic structural features of the modeled structure were then studied using atomistic molecular dynamics simulation. It was observed that the modeled structure was stable and regions between residues index (50–80, 500–700) to be significantly flexible. From the docking studies, we detected the possible binding interactions of modeled urease with urea. Ala399, Ile675, Thr398, and Thr679 residues of A. thaliana urease were observed to be significantly involved in binding with the substrate urea. We also compared the docking studies of ureases from other sources such as Canavalia ensiformis, Helicobacter pylori, and Bacillus pasteurii. In addition, we carried out mutation analysis to find the highly mutable amino acid residues of modeled A. thaliana urease. In this particular study, we observed Met485, Tyr510, Ser786, Val426, and Lys765 to be highly mutable amino acids. These results are significant for the mutagenesis analysis. As a whole, this study expounds the salient structural features as well the binding interactions of the modeled structure of A. thaliana urease.
Feature selection and classification using ensembles of genetic programs and within-class and between-class permutations

Many feature selection methods are based on the assumption that important features are highly correlated with their corresponding classes, but mainly uncorrelated with each other. Often, this assumption can help eliminate redundancies and produce good predictors using only a small subset of features. However, when the predictability depends on interactions between features, such methods will fail to produce satisfactory results. In this paper a method that can find important features, both independently and dependently discriminative, is introduced. This method works by performing two different types of permutation tests that classify each of the features as either irrelevant, independently predictive or dependently predictive. It was evaluated using a classifier based on an ensemble of genetic programs. The attributes chosen by the permutation tests were shown to yield classifiers at least as good as the ones obtained when all attributes were used during training - and often better. The proposed method also fared well when compared to other attribute selection methods such as RELIEFF and CFS. Furthermore, the ability to determine whether an attribute was independently or dependently predictive was confirmed using artificial datasets with known dependencies.
Assessment of energy expenditure in children using the RT3 accelerometer

Abstract The aim of this study was to evaluate the utility of the RT3 accelerometer in young children, compare its accuracy with heart rate monitoring, and develop an equation to predict energy expenditure from RT3 output. Forty-two volunteers (mean age 12.2 years, s = 1.1) exercised at two horizontal and graded walking speeds (4 and 6 km · h−1, 0% grade and 6% grade), and one horizontal running speed (8 km · h−1, 0% grade), on a treadmill. Energy expenditure and oxygen consumption ([Vdot]O2) served as the criterion measures. Comparison of RT3 estimates (counts and energy expenditure) demonstrated significant differences at 4, 6, and 8 km · h−1 on level ground (P < 0.01), while no significant differences were noted between horizontal and graded walking at 4 and 6 km · h−1. Correlation and regression analyses indicated no advantage of vector magnitude over the vertical plane (X) alone. A strong relationship between RT3 estimates and indirect calorimetry across all speeds was obtained (r = 0.633–0.850, P < 0.01). A child-specific prediction equation (adjusted R 2 = 0.753) was derived and cross-validated that offered a valid energy expenditure estimate for walking/running activities. Despite recognized limitations, the RT3 may be a useful tool for the assessment of children's physical activity during walking and running.
tinySLAM: A SLAM algorithm in less than 200 lines C-language program

This paper presents a Laser-SLAM algorithm which can be programmed in less than 200 lines C-language program. The first idea aimed to develop and implement a simple SLAM algorithm providing good performances without exceeding 200 lines in a C-language program. We use a robotic platform called MinesRover, a six wheels robot with several sensors. We based our work and calculations on a laser sensor and the odometry of the robot. The article presents the different capabilities of the platform and the way we use them in order to improve our programs. We also illustrates the difficulties encountered during the programming and testing of the algorithm. This work shows the possibility to perform complex tasks using simple and easily programmable algorithms.
Robust sliding mode controllers design techniques for stabilization of multivariable time-delay systems with parameter perturbations and external disturbances

In this paper, robust delay-independent stabilization of multivariable single state-delayed systems with mismatching parameter uncertainties and matching/mismatching external disturbances are considered. To achieve this goal, two types of robust sliding mode controllers design techniques are advanced. The first is an integral sliding mode controller design modification to Shyu and Yan type controller design. The mismatching sliding conditions are parametrically obtained by using the Lyapunov-Razumikhin-Hale method and formulated in terms of some matrix norm inequalities. In the second contribution, a new combined sliding mode controller design technique for the stabilization of multivariable single state-delayed systems with mismatching parameter perturbations is advanced by using the Lyapunov-Krasovskii V-functional method. The sliding, global stability and delay-dependent β-stability conditions are parametrically obtained and formulated in terms of matrix inequalities. A sliding mode controller design e.
Dual HER2/PIK3CA Targeting Overcomes Single-Agent Acquired Resistance in HER2-Amplified Uterine Serous Carcinoma Cell Lines In Vitro and In Vivo

HER2/neu gene amplification and PIK3CA driver mutations are common in uterine serous carcinoma (USC) and may represent ideal therapeutic targets against this aggressive variant of endometrial cancer. We examined the sensitivity to neratinib, taselisib, and the combination of the two compounds in in vitro and in vivo experiments using PIK3CA-mutated and PIK3CA wild-type HER2/neu–amplified USC cell lines. Cell viability and cell-cycle distribution were assessed using flow-cytometry assays. Downstream signaling was assessed by immunoblotting. Preclinical efficacy of single versus dual inhibition was evaluated in vivo using two USC xenografts. We found both single-agent neratinib and taselisib to be active but only transiently effective in controlling the in vivo growth of USC xenografts harboring HER2/neu gene amplification with or without oncogenic PIK3CA mutations. In contrast, the combination of the two inhibitors caused a stronger and long-lasting growth inhibition in both USC xenografts when compared with single-agent therapy. Combined targeting of HER2 and PIK3CA was associated with a significant and dose-dependent increase in the percentage of cells in the G0–G1 phase of the cell cycle and a dose-dependent decline in the phosphorylation of S6. Importantly, dual inhibition therapy initiated after tumor progression in single-agent–treated mice was still remarkably effective at inducing tumor regression in both large PIK3CA and pan-ErbB inhibitor–resistant USC xenografts. Dual HER2/PIK3CA blockade may represent a novel therapeutic option for USC patients harboring tumors with HER2/neu gene amplification and mutated or wild-type PIK3CA resistant to chemotherapy. Mol Cancer Ther; 14(11); 2519–26. ©2015 AACR.
Enabling Long Debug Traces of HLS Circuits Using Bandwidth-Limited Off-Chip Storage Devices

High-level synthesis (HLS) has gained considerable traction in recent years. Despite considerable strides in the development of quality HLS compilers, one area that is often cited as a barrier to HLS adoption is the difficulty in debugging HLS produced circuits. Recent academic work has presented techniques that use on-chip memories to efficiently record execution of HLS circuits, and map the captured data back to the original source code to provide the user with a software-like debug experience. However, limited on-chip memory results in very short debug traces, which may force a designer to spend multiple debug iterations to resolve complicated bugs. In this work we present techniques to enable off-chip capture of HLS debug information. While off-chip storage does not suffer from the capacity limitations of on-chip memory, its usage introduces a new challenge: limited bandwidth. In this work we show how information from within the HLS flow can be leveraged to generated a streamed debug trace within given bandwidth constraints. For a bandwidth limited interface, we show that our techniques allow the user to observe 19x more source code variables than using a basic approach.