Miracle-dz/UniFAR-FTUs huggingface dataset:
The effects of particulate filters on the strain energy function and crack growth in rubbers.

The thesis presents a wide range of studies on carbon black and silica particulate reinforced rubbers. These include stress-strain, strain energy function, static and cyclic stress relaxation (stress softening), trouser test piece tearing and cyclic crack growth studies. The novel features of the work include the development of a simple strain energy function which is shown to represent the stress-strain behaviour of carbon black and silica filled rubbers up to strains of 100%. The numerical values of the constants in this function are shown to vary in a meaningful and systematic manner with the fraction of reinforcing filler and with the crosslink density. The cyclic stress relaxation studies are the first of their kind and demonstrate a significantly increased relaxation rate resulting from cycling in filled rubbers. The trouser tearing studies give some insight as to the materials and experimental variables that determine the type of tear growth and regime of tearing. The process of stress whitening around the tear tip during steady tearing in silica filled compounds provide the first opportunity to quantitatively relate the tearing energy to the hysteresis energy loss in a known volume of rubber at the tear tip. The cyclic crack growth studies show for the first time a systematic decrease in crack growth per cycle (dc/dn) at a given tearing energy as the carbon black filler content is systematically increased and as the crosslink density is decreased. A novel feature of the work is the demonstration of the effect of pre-strain in one direction on the cyclic growth rate of a crack in this direction when cyclically strained in a direction at right angles. The very large increase in dc/dn with increasing pre-strain is discussed in terms of pre-orientation of the rubber/carbon black structure.
Visualizing dynamic networks with matrix cubes

Designing visualizations of dynamic networks is challenging, both because the data sets tend to be complex and because the tasks associated with them are often cognitively demand- ing. We introduce the Matrix Cube, a novel visual representation and navigation model for dynamic networks, inspired by the way people comprehend and manipulate physical cubes. Users can change their perspective on the data by rotating or decomposing the 3D cube. These manipulations can produce a range of different 2D visualizations that emphasize specific aspects of the dynamic network suited to particular analysis tasks. We describe Matrix Cubes and the interactions that can be performed on them in the Cubix system. We then show how two domain experts, an astronomer and a neurologist, used Cubix to explore and report on their own network data.
Whole genome siRNA cell-based screen links mitochondria to Akt signaling network through uncoupling of electron transport chain

Akt activation sequesters FOXO1a away from its target genes and serves as an endpoint of a complex signaling network. A cell-based RNAi screen reveals an extensive network of genes, including UCP5, which directs nuclear localization of FOXO1a. Silencing of UCP5 disrupts the mitochondria and induces JNK1, creating a link to the Akt signaling network.
The Dorsal Visual System Predicts Future and Remembers Past Eye Position

Eye movements are essential to primate vision but introduce potentially disruptive displacements of the retinal image. To maintain stable vision, the brain is thought to rely on neurons that carry both visual signals and information about the current direction of gaze in their firing rates. We have shown previously that these neurons provide an accurate representation of eye position during fixation, but whether they are updated fast enough during saccadic eye movements to support real-time vision remains controversial. Here we show that not only do these neurons carry a fast and accurate eye-position signal, but also that they support in parallel a range of time-lagged variants, including predictive and post dictive signals. We recorded extracellular activity in four areas of the macaque dorsal visual cortex during a saccade task, including the lateral and ventral intraparietal areas (LIP, VIP), and the middle temporal (MT) and medial superior temporal (MST) areas. As reported previously, neurons showed tonic eye-position-related activity during fixation. In addition, they showed a variety of transient changes in activity around the time of saccades, including relative suppression, enhancement, and pre-saccadic bursts for one saccade direction over another. We show that a hypothetical neuron that pools this rich population activity through a weighted sum can produce an output that mimics the true spatiotemporal dynamics of the eye. Further, with different pooling weights, this downstream eye position signal (EPS) could be updated long before (<100 ms) or after (<200 ms) an eye movement. The results suggest a flexible coding scheme in which downstream computations have access to past, current, and future eye positions simultaneously, providing a basis for visual stability and delay-free visually-guided behavior.
Testing the Agreement/Tense Omission Model using an elicited imitation paradigm

The present study used an elicited imitation paradigm to test the prediction of Schutze & Wexler's (1996) AGREEMENT/TENSE OMISSION MODEL (ATOM) that the rate of non-nominative subjects with agreement-marked verb forms will be sufficiently low that such errors can reasonably be disregarded as noise in the data. A screening procedure identified five children who produced non-nominative subject errors (all her for she) who were then asked to repeat 24 sentences with 3sg feminine pronoun subjects (she) and agreeing main verbs or auxiliaries. All five children produced at least one non-nominative subject (her) with an agreement-marked verb form, and for none of these five children was the non-NOM+AGR rate significantly different to the rate that would be expected by chance, given the independent frequencies of non-nominative subjects and agreement-marked verb forms in their data. The three children for whom this expected (by chance) error rate was significantly greater than 10% (representing an acceptable level of noise in the data) produced non-NOM+AGR errors at a rate significantly greater than 10%, counter to the prediction of the ATOM. These results replicate and extend the naturalistic-data findings of Pine et al. using a different method. They also provide support for the use of elicited imitation as a methodology for assessing children's early grammatical knowledge.
A self-adaptive service provisioning framework for 3G+/4G mobile applications

In light of the increasing acceptance of the notion of next generation networks (NGN), which results from the convergence of the fixed and mobile telecommunications, Internet, and entertainment sectors, there is a need for the realization of next-generation service delivery platforms to allow seamless and adaptive provisioning of multimedia information and communication services to mobile users. This means that in such an NGN environment different end systems, access networks, and service platforms have to be integrated. For this highly heterogeneous communications and services environment, we propose a self-adaptive service provisioning middleware framework (ASPF), which, by interoperating with existing wireless and wireline service delivery platforms, aims to enable seamless omnipresent service provisioning to mobile users anywhere, anytime, and in any context. The ASPF is intended to "liberate" applications from space and time limitations, networks, platforms, and device dependences, minimize time-to-market constraints, and eliminate major hurdles that hinder the rapid deployment of new mobile services and applications. Key to this goal is interworking/integration with current SDPs, such as IN/CAMEL, OSA/Parlay, and the emerging IMS.
Learning a Named Entity Tagger from Gazetteers with the Partial Perceptron

While gazetteers can be used to perform named entity recognition through lookup-based methods, ambiguity and incomplete gazetteers lead to relatively low recall. A sequence model which uses more general features can achieve higher recall while maintaining reasonable precision, but typically requires expensive annotated training data. To circumvent the need for such training data, we bootstrap the learning of a sequence model with a gazetteer-driven labeling algorithm which only labels tokens in unlabeled data that it can label confidently. We present an algorithm, called the Partial Perceptron, for discriminatively learning the parameters of a sequence model from such partially labeled data. The algorithm is easy to implement and trains much more quickly than a state-of-the-art algorithm based on Conditional Random Fields with equivalent performance. Experimental results show that the learned model yields a substantial relative improvement in recall (77.3%) with some loss in precision (a 28.7% relative decrease) when compared to the gazetteer-driven method.
Damage assessment of composite laminate by using fiber optic AE sensor based on fused-tapered coupler

Damage inspection is very important for safe and reliable use of composite materials. Some non-destructive evaluation methods have been used to monitor the damage of composite. It is necessary to develop a novel fiber optic sensor and use it to monitor the damage of composite materials. This paper proposes to make an optic fiber acoustic emission (AE) sensor base on fused-taper fiber optic coupler with low price and high sensitivity, and to monitor the damage of composite materials. To prove the sensitivity of this sensor, Pencil-break test was done. To investigate the failure mode of composite materials, tensile experiment of composite laminate was also carried out. Experiment got lots of acoustic emission data under different tensile stage, Relationship of AE waveform and failure mode was established. It was found that fiber optic AE sensor with high sensitivity was successfully developed, and failure mode of composite materials was identified. This kind of research encourages the belief that the sensor will be used to structural health monitoring of larger composite structures.
Multirate distributed model predictive control of nonlinear systems

In this work, we consider the design of a distributed model predictive control scheme using multirate sampling for large-scale nonlinear systems composed of several coupled subsystems. Specifically, we assume that the states of each local subsystem can be divided into fast sampled states (which are available every sampling time) and slowly sampled states (which are available every several sampling times). The distributed model predictive controllers are connected through a shared communication network and cooperate in an iterative fashion, at time instants in which full system state measurements (both fast and slow) are available and the network closes, to guarantee closed-loop stability. When the communication network is open, the distributed controllers operate in a decentralized fashion based only on local subsystem fast sampled state information to improve closed-loop performance. In the proposed design, the controllers are designed via Lyapunov-based model predictive control. Sufficient conditions under which the state of the closed-loop system is ultimately bounded in an invariant region containing the origin are derived. The theoretical results are demonstrated through a nonlinear chemical process example.
Analysis of Cortical Arrays from Tradescantia virginiana at High Resolution Reveals Discrete Microtubule Subpopulations and Demonstrates That Confocal Images of Arrays Can Be Misleading[W]

Cortical microtubule arrays are highly organized networks involved in directing cellulose microfibril deposition within the cell wall. Their organization results from complex interactions between individual microtubules and microtubule-associated proteins. The precise details of these interactions are often not evident using optical microscopy. Using high-resolution scanning electron microscopy, we analyzed extensive regions of cortical arrays and identified two spatially discrete microtubule subpopulations that exhibited different stabilities. Microtubules that lay adjacent to the plasma membrane were often bundled and more stable than the randomly aligned, discordant microtubules that lay deeper in the cytoplasm. Immunolabeling revealed katanin at microtubule ends, on curves, or at sites along microtubules in line with neighboring microtubule ends. End binding 1 protein also localized along microtubules, at microtubule ends or junctions between microtubules, and on the plasma membrane in direct line with microtubule ends. We show fine bands in vivo that traverse and may encircle microtubules. Comparing confocal and electron microscope images of fluorescently tagged arrays, we demonstrate that optical images are misleading, highlighting the fundamental importance of studying cortical microtubule arrays at high resolution.
Interactions Between Electrical Activity and Cortical Microcirculation Revealed by Imaging Spectroscopy: Implications for Functional Brain Mapping

Modern neuroimaging techniques use signals originating from microcirculation to map brain function. In this study, activity-dependent changes in oxyhemoglobin, deoxyhemoglobin, and light scattering were characterized by an imaging spectroscopy approach that offers high spatial, temporal, and spectral resolution. Sensory stimulation of cortical columns initiates tissue hypoxia and vascular responses that occur within the first 3 seconds and are highly localized to individual cortical columns. However, the later phase of the vascular response is less localized, spreading over distances of 3 to 5 millimeters.
AIMR: An Adaptive Page Management Policy for Hybrid Memory Architecture with NVM and DRAM

The last few years have witnessed the emergence of Non-Volatile Memories (NVMs), which are actively pursued as scalable substitutes for traditional DRAM-based main memory due to higher scalability and lower leakage power. However, current NVM technologies also exhibit potential drawbacks including lower endurance, higher dynamic power, and longer write latency. Recent studies show that hybrid memory architectures involving NVM and DRAM are able to effectively utilize the merits of both memory devices. However, allocating write-intensive pages to NVM can still greatly impeding the performance of the overall memory system. In this paper, we provide an adaptive page management policy called AIMR (Adaptive page Insertion, Migration, and Replacement) for hybrid memory architecture. The main objective of our scheme is to ensure DRAM absorbs most memory writes while maintaining a high performance of the overall system. Specifically, we use both "recency" and "frequency" features to estimate the future memory access patterns and then carefully insert, migrate, and replace pages in the hybrid memory without setting any additional user-defined parameters. Experimental results show that AIMR can achieve average NVM write count, memory access latency, and memory energy consumption reduction by 19.3%, 45.8% and 27.8%, respectively compared with the existing page management policies under hybrid memory architecture.
Performance bounds for erasure, list and feedback schemes with linear block codes

A message independence property and some new performance upper bounds are reported for erasure, list and decision-feedback schemes with linear block codes transmitted over memoryless symmetric channels. Similarly to the classical work of Forney, this work is focused on the derivation of some Gallager-type bounds on the achievable tradeoffs for these coding schemes, where the main novelty is the suitability of the bounds for both random and structured linear block codes (or code ensembles). The bounds are applicable to finite-length codes and to the asymptotic case of infinite block length, and they are applied to low-density parity-check (LDPC) code ensembles.
In vitro differentiation of mouse embryonic stem cells after activation by retinoic acid

Embryonic stem (ES) cells are pluripotent cells isolated from the inner cell mass of blastocysts. ES cells are able to differentiate into the three primitive layers (endodem, mesoderm, and ectoderm) of the organism, including the germline. In recent reports mouse ES cells have been successfully applied in the treatment of spinal cord injury, hereditary myelin disorder of the central nervous system, and diabetes mellitus. In this study, we investigated the induction of mouse ES cell differentiation, using culture of embryoid bodies (EBs) into the diverse tissues. EBs were formed by culturing ES cells (129/SV strain) in DMEM supplemented with 10% FBS, in the absence of feeder cells and leukemia inhibitory factor (LIF): EBs were induced to differentiate by treatment with retinoic acid (RA). In control medium (non-RA medium) beating muscles, blood vessels, hemocytes, and cartilages were frequently observed in EBs. Moreover, when EBs were cultured in medium including RA (5 × 10−8 M, and 5 × 10−9 M), differentiation of the optic vesicle, lens, retina, and neural groove was observed. In this study we demonstrated that an efficient system for inducing the differentiation of ES cells using EBs.
Coexistence of Lateral and Co-Tuned Inhibitory Configurations in Cortical Networks

The responses of neurons in sensory cortex depend on the summation of excitatory and inhibitory synaptic inputs. How the excitatory and inhibitory inputs scale with stimulus depends on the network architecture, which ranges from the lateral inhibitory configuration where excitatory inputs are more narrowly tuned than inhibitory inputs, to the co-tuned configuration where both are tuned equally. The underlying circuitry that gives rise to lateral inhibition and co-tuning is yet unclear. Using large-scale network simulations with experimentally determined connectivity patterns and simulations with rate models, we show that the spatial extent of the input determined the configuration: there was a smooth transition from lateral inhibition with narrow input to co-tuning with broad input. The transition from lateral inhibition to co-tuning was accompanied by shifts in overall gain (reduced), output firing pattern (from tonic to phasic) and rate-level functions (from non-monotonic to monotonically increasing). The results suggest that a single cortical network architecture could account for the extended range of experimentally observed response types between the extremes of lateral inhibitory versus co-tuned configurations.
Determination of Phenol Compounds In Surface Water Matrices by Bar Adsorptive Microextraction-High Performance Liquid Chromatography-Diode Array Detection

Bar adsorptive microextraction combined with liquid desorption followed by high performance liquid chromatography with diode array detection (BAµE-LD/HPLC-DAD) is proposed for the determination of trace levels of five phenol compounds (3-nitrophenol, 4-nitrophenol, bisphenol-A, 4-n-octylphenol and 4-n-nonylphenol) in surface water matrices. By using a polystyrene-divinylbenzene copolymer (PS-DVB) sorbent phase, high selectivity and efficiency is achieved even against polydimethylsiloxane through stir bar sorptive extraction. Assays performed by BAµE(PS-DVB)-LD/HPLC-DAD on 25 mL water samples spiked at the 10.0 µg/L levels yielded recoveries over 88.0% ± 5.7% for all five analytes, under optimized experimental conditions. The analytical performance showed good precision (RSD < 15%), detection limits of 0.25 µg/L and linear dynamic ranges (1.0–25.0 μg/L) with determination coefficient higher than 0.9904. By using the standard addition method, the application of the present method to surface water matrices allowed very good performances at the trace level. The proposed methodology proved to be a suitable alternative to monitor phenol compounds in surface water matrices, showing to be easy to implement, reliable, sensitive and requiring a low sample volume.
Krylov Subspace Approximation for Local Community Detection

Community detection is an important information mining task in many fields including computer science, social sciences, biology and physics. For increasingly common large network data sets, global community detection is prohibitively expensive, and attention has shifted to methods that mine local communities, i.e. methods that identify all latent members of a particular community from a few labeled seed members. To address this semi-supervised mining task, we propose a local spectral subspace-based community detection method, called LOSP. A sampling technique is first applied around the seeds to significantly reduce the scale of the search. Then we define a family of local spectral subspaces based on Krylov subspaces, and seek a sparse indicator for the target community via an one norm minimization over the Krylov subspace. Variants of LOSP depend on types of random walks with different diffusion speeds, regular random walk or inverse random walk, dimension of the local spectral subspace and steps of diffusions. The effectiveness of the proposed LOSP approach is theoretically analyzed based on Rayleigh quotients, and is experimentally verified on a wide variety of real-world networks across social, production and biological domains, as well as on an extensive set of synthetic LFR benchmark datasets.
Monitoring of greater sage-grouse habitats and populations /

v Acknowledgments We appreciate the support and advice members of the Western States Sage and Columbian Sharp-tailed Grouse Technical Committee provided. Terry Crawforth encouraged our efforts and acted as a liaison among federal and state agencies that deal with sage-grouse. Earlier drafts of this manuscript were improved by reviews from Knick provided advice on landscape ecology issues and Michelle Commons-Kemner shared insights on data collection. Finally, much of this work would not have been possible were it not for the tireless efforts of numerous graduate students and field technicians. Thank you for all of those long days and longer, cold nights.
In-situ visualization for global hybrid simulations

Petascale simulations have become mission critical in diverse areas of science and engineering. Knowledge discovery from such simulations remains a major challenge and is becoming more urgent as the march towards ultra-scale computing with millions of cores continues. One major issue with the current paradigm of running the simulations and saving the data to disk for post-processing is that it is only feasible to save the data at a small number of time slices. This low temporal resolution of the saved data is a serious handicap in many studies where the time evolution of the system is of principle interest. One way to address this I/O issue is through in-situ visualization strategies. The idea is to minimize data storage by extracting important features of the data and saving them, rather than raw data, at high temporal resolution. Parallel file systems of current petascale and future exascale systems are expensive shared resources and need to be utilized effectively, and similarly archival storage can be limited and both of these will benefit from in-situ visualization as it will lead to intelligent way of utilizing storage. In this paper, we present preliminary results from our in-situ visualization for global hybrid (electron fluid, kinetic ions) simulations which are used to study the interaction of the solar wind with planetary magnetospheres such as the Earth and Mercury. In particular, we examine the overhead and effect on code performance associated with the inline computations associated with in-situ visualization.