Miracle-dz/UniFAR-FTUs huggingface dataset:
Symmetry as an Organizational Principle in Cognitive Sensor Networks

Cognitive sensor networks are able to perceive, learn, reason and act by means of a dis­ tributed, sensor/actuator, computation and communication system. In animals, cognitive capabilities do not arise from a tabula rasa, but are due in large part to the intrinsic archi­ tecture (genetics) of the animal which has been evolved over a long period of time and depends on a combination of constraints: e.g., ingest nutrients, avoid toxins, etc. We have previously shown how organism morphology arises from genetic algorithms responding to such constraints [61. Recently, it has been suggested that abstract theories relevant to spe­ cific cognitive domains are likewise genetically coded in humans (e.g., language, physics of motion, logic, etc,); thus, these theories and models are abstracted from experience over time. We call this the Domain Theory Hypothesis, and other proponents include Chom­ sky [21 and Pinker [111 (universal language), Sloman [16, 171 (artificial intelligence), and Rosenberg [131 (cooperative behavior). Some advantages of such embedded theories are that they (1) make learning more efficient, (2) allow generalization across models, and (3) allow determination of true statements about the world beyond those available from direct experience. We have shown in previous work how theories of symmetry can dramatically improve representational efficiency and aid reinforcement learning on various problems [14]. However, it remains to be shown sensory data can be organized into appropriate el­ ements so as to produce a model of a given theory. We address this here by showing how symmetric elements can be perceived by a sensor network and the role this plays in a cog­ nitive system’s ability to discover knowledge about its own structure as well as about the surrounding physical world. Our view is that cognitive sensor networks which can learn these things will not need to be pre-programmed in detail for specific tasks.
Real time discrete techniques for distortion and microstructure of long product sections

Abstract A combined suite of in house finite element (FE) and finite prism (FP) models has been developed to address real time predictions of temperature, distortion and structure during cooling of long product sections. The present paper also describes various applications of cooling, and compares the developed approaches with conventional three-dimensional and generalised plane strain formulation elements and experimental results. These models have also been used to simulate the effectiveness of alternative cooling strategies (e.g. spray cooling) in reducing levels of thermal distortion.
Durability of stabilized base courses subjected to wet–dry cycles

A laboratory study was undertaken to evaluate the durability of cementitiously stabilized aggregate specimens subjected to wet–dry (W-D) cycles, representing a base course in a pavement structure. Specifically, cylindrical specimens of aggregates stabilized with 15% cement kiln dust (CKD), 10% class C fly ash, or 10% fluidized bed ash were prepared, cured for 28 days, and then subjected to W-D cycles prior to testing for resilient modulus (M r). Four aggregates were selected in this experimental study: (1) Meridian, (2) Richard Spur, (3) Sawyer and (4) Hanson. Results showed that the M r values decreased as W-D cycles increased up to 30, except for Sawyer specimens stabilized with 15% CKD. These specimens exhibited an increase in M r values with W-D cycles up to 8, beyond which a reduction occurred. The performance of the stabilized specimens is believed to depend upon SAF (Silica, Alumina and Ferric Oxide compounds) and free lime of the stabilizing agents. Also, the optimum moisture content (OMC) and maximum dry density (MDD), were found to be a good indicator of performance pertaining to stabilization and W-D action. A regression model correlating M r with W-D cycles, SAF, free lime, OMC and MDD offered statistically promising results.
Geometric Nonlinear Analysis of Curved Box Continuous Girders Considering Shear Lag Effect

The analytical theory and computational method for thin-walled curved box girders in terms of the shear lag effect and geometric nonlinearity are presented. Based on the potential variational principle and the theory of thin-walled box girders, the geometry nonlinear governing differential equations of thin-walled curved box girder considering the influence of the shear lag effect of flange’s stress and the large deflection is established. The equation is solved by means of Newton-Raphon iteration method. The results from the present method are in good agreement with those of the test and the segment method. The numerical examples are conducted to verify the accuracy and reliability of the present theories. It is shown that the proposed formulae and method could be referenced to the design for the thin-walled curved box girders considering shear lag effect.
Porous TiN Ceramics Fabricated by Carbothermal Reduction Method

Porous TiN ceramics with high porosity and uniform pore structure was prepared following the carbothermal reduction reaction between TiO2 and carbon. Influences of sintering additives and particle size of TiO2 on the microstructure and mechanical properties of porous TiN ceramics were investigated. Microstructure and mechanical properties of porous TiN ceramics were studied by XRD, SEM and three-point bending measurement. XRD analysis proved that the TiN phase has completely formed after reaction. In addition, SEM analysis showed that the resultant porous TiN ceramics were composed of fine grains with uniform pore structure. The addition of La2O3 not only accelerated the densification of porous TiN ceramics, but also decreased the porosity and increased the flexural strength. With a decrease in TiO2 particle size, the linear shrinkage increased and the porosity decreased accordingly.
Upregulation of adenosine kinase in astrocytes in experimental and human temporal lobe epilepsy

Purpose: Adenosine kinase (ADK) represents the key metabolic enzyme for the regulation of extracellular adenosine levels in the brain. In adult brain, ADK is primarily present in astrocytes. Several lines of experimental evidence support a critical role of ADK in different types of brain injury associated with astrogliosis, which is also a prominent morphologic feature of temporal lobe epilepsy (TLE). We hypothesized that dysregulation of ADK is an ubiquitous pathologic hallmark of TLE.
Passive Detection of Correlated Subspace Signals in Two MIMO Channels

In this paper, we consider a two-channel multiple-input multiple-output passive detection problem, in which there is a surveillance array and a reference array. The reference array is known to carry a linear combination of broadband noise and a subspace signal of known dimension, but unknown basis. The question is whether the surveillance channel carries a linear combination of broadband noise and a subspace signal of the same dimension, but unknown basis, <italic>which is correlated with the subspace signal in the reference channel</italic>. We consider a second-order detection problem where these subspace signals are structured by an unknown, but common, <inline-formula> <tex-math notation="LaTeX">$p$</tex-math></inline-formula>-dimensional random vector of symbols transmitted from sources of opportunity, and then received through unknown <inline-formula><tex-math notation="LaTeX">$M\times p$ </tex-math></inline-formula> matrices at each of the <inline-formula><tex-math notation="LaTeX">$M$</tex-math> </inline-formula>-element arrays. The noises in each channel have spatial correlation models ranging from arbitrarily correlated to independent with identical variances. We provide a unified framework to derive the generalized likelihood ratio test for these different noise models. In the most general case of arbitrary noise covariance matrices, the test statistic is a monotone function of canonical correlations between the reference and surveillance channels.
Groupware for developing critical discussion skills

Increasingly, social and collaborative processes are seen as central to the development of thinking skills, including the skills of scientific thinking. With colleagues we have developed a software system (Belvedere) to support high-school science students in engaging in collaborative reasoning and argumentation. We discuss how Belvedere is designed to support students' collaborative processes, describe our formative evaluation studies, and provide examples of student sessions. Reflecting on students' inter-actions with Belvedere and each other, we then discuss the competencies and limitations of students in developing scientific argumentation and reasoning, as distinguished from and supported by their practice of everyday argumentation.
Filling characteristics and dosing accuracy of volumetric dosing devices in vertical form—fill—seal machines

A Study has been made of the physical characteristics of the participate products to be filled in vertical form-fill-seal machines and the configuration and dimensions of the filling and dosing devices commonly employed. Filling from cups with different opening mechanisms and filling systems using an auger have been examined, and the dosing accuracy found in all instances to be more dependent on variations in the bulk density, general particle shape and size etc. of the product being filled than on the dimensions etc of the filling device. Results are reported on many of the possible characteristics in an attempt to find the relevant one(s) but it is concluded that our knowledge of product characteristics is insufficient and that more research is required.
On the consistency of Multithreshold Entropy Linear Classifier

Multithreshold Entropy Linear Classifier (MELC) is a recent classifier idea which employs information theoretic concept in order to create a multithreshold maximum margin model. In this paper we analyze its consistency over multithreshold linear models and show that its objective function upper bounds the amount of misclassified points in a similar manner like hinge loss does in support vector machines. For further confirmation we also conduct some numerical experiments on five datasets.
Authormagic: an approach to author disambiguation in large-scale digital libraries

A collaboration of leading research centers in the field of High Energy Physics (HEP) has built INSPIRE, a novel information infrastructure, which comprises the entire corpus of about one million documents produced within the discipline, including a rich set of metadata, citation information and half a million full-text documents, and offers a unique opportunity for author disambiguation strategies. The presented approach features extended metadata comparison metrics and a three-step unsupervised graph clustering technique. The algorithm aided in identifying 200'000 individuals from 6'500'000 author signatures. Preliminary tests based on knowledge of external experts and a pilot of a crowd-sourcing system show a success rate of more than 96% within the selected test cases. The obtained author clusters serve as a recommendation for INSPIRE users to further clean the publication list in a crowd-sourced approach.
Iterative MAP and ML Estimations for Image Segmentation

Image segmentation plays an important role in computer vision and image analysis. In this paper, the segmentation problem is formulated as a labeling problem under a probability maximization framework. To estimate the label configuration, an iterative optimization scheme is proposed to alternately carry out the maximum a posteriori (MAP) estimation and the maximum-likelihood (ML) estimation. The MAP estimation problem is modeled with Markov random fields (MRFs). A graph-cut algorithm is used to find the solution to the MAP-MRF estimation. The ML estimation is achieved by finding the means of region features. Our algorithm can automatically segment an image into regions with relevant textures or colors without the need to know the number of regions in advance. In addition, under the same framework, it can be extended to another algorithm that extracts objects of a particular class from a group of images. Extensive experiments have shown the effectiveness of our approach.
Subpallial Dlx2-Expressing Cells Give Rise to Astrocytes and Oligodendrocytes in the Cerebral Cortex and White Matter

The precise origins of postnatal subventricular zone (SVZ) cells are not known. Furthermore, the gliogenic potential of progenitors expressing Dlx genes that migrate ventrodorsally from the ganglionic eminences has not been explored in vivo. Here, we identify the embryonic origins of two distinct populations of postnatal SVZ cells: SVZ border cells, which express Zebrin II, and migratory cells in the central SVZ, which are generally devoid of Zebrin II expression (Staugaitis et al., 2001). Zebrin II is expressed by all cells of the telencephalic primordium, with its expression becoming restricted to astrocytes in the mature telencephalon. As the neuroepithelium folds during corticostriatal sulcus formation (embryonic day 13–15), a wedge of Zebrin II+ cells is created at the presumptive site of the dorsolateral SVZ. At this time, Dlx2-expressing cells and their progeny begin to migrate ventrodorsally along a medial path from the ganglionic eminences. These migratory subpallial cells invade the wedge of Zebrin II+ cells to form the central region of the SVZ. We used aDlx2/tauLacZ knock-in to perform a short-term lineage analysis of Dlx2-expressing cells throughout SVZ formation and the postnatal peak of gliogenesis.Dlx2/tauLacZ [β-galactosidase (β-gal)]-expressing cells populate the central SVZ, whereas Zebrin II-expressing cells form its borders. Furthermore, β-gal expression demonstrates a lineage relationship between Dlx2-expressing cells and glia residing in the dorsal telencephalon. We propose a model for the formation of the postnatal SVZ and demonstrate that subpallium-derivedDlx2-expressing cells give rise to astrocytes and oligodendrocytes in the white matter and cerebral cortex.
The Homogeneous Ensemble Methods for MLknn Algorithm

The multi-label classification is one of the current research orientation since it solves many real problems where each object can have several semantics. One of the categories dedicated for learning from such data is the adaptation methods. In this paper, we propose to improve the performance of the Multi-label K Nearest Neighbors (MLknn) using the ensemble methods (Bagging and Boosting), it adapts the K Nearest Neighbors algorithm to Multi-label data. The experimental results on five small to larger multi-label datasets from different domains, shows the effectiveness of the ensemble methods to improve the original algorithm.
Adaptation to New Microphones Using Artificial Neural Networks With Trainable Activation Functions

Model adaptation is a key technique that enables a modern automatic speech recognition (ASR) system to adjust its parameters, using a small amount of enrolment data, to the nuances in the speech spectrum due to microphone mismatch in the training and test data. In this brief, we investigate four different adaptation schemes for connectionist (also known as hybrid) ASR systems that learn microphone-specific hidden unit contributions, given some adaptation material. This solution is made possible adopting one of the following schemes: 1) the use of Hermite activation functions; 2) the introduction of bias and slope parameters in the sigmoid activation functions; 3) the injection of an amplitude parameter specific for each sigmoid unit; or 4) the combination of 2) and 3). Such a simple yet effective solution allows the adapted model to be stored in a small-sized storage space, a highly desirable property of adaptation algorithms for deep neural networks that are suitable for large-scale online deployment. Experimental results indicate that the investigated approaches reduce word error rates on the standard Spoke 6 task of the Wall Street Journal corpus compared with unadapted ASR systems. Moreover, the proposed adaptation schemes all perform better than simple multicondition training and comparable favorably against conventional linear regression-based approaches while using up to 15 orders of magnitude fewer parameters. The proposed adaptation strategies are also effective when a single adaptation sentence is available.
Eocene Plant Diversity at Laguna del Hunco and Río Pichileufú, Patagonia, Argentina

The origins of South America’s exceptional plant diversity are poorly known from the fossil record. We report on unbiased quantitative collections of fossil floras from Laguna del Hunco (LH) and Río Pichileufú (RP) in Patagonia, Argentina. These sites represent a frost‐free humid biome in South American middle latitudes of the globally warm Eocene. At LH, from 4,303 identified specimens, we recognize 186 species of plant organs and 152 species of leaves. Adjusted for sample size, the LH flora is more diverse than comparable Eocene floras known from other continents. The RP flora shares several taxa with LH and appears to be as rich, although sampling is preliminary. The two floras were previously considered coeval. However, 40Ar/39Ar dating of three ash‐fall tuff beds in close stratigraphic association with the RP flora indicates an age of \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage[OT2,OT1]{fontenc} \newcommand\cyr{ \renewcommand\rmdefault{wncyr} \renewcommand\sfdefault{wncyss} \renewcommand\encodingdefault{OT2} \normalfont \selectfont} \DeclareTextFontCommand{\textcyr}{\cyr} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} \landscape $$47.46\pm 0.05$$ \end{document} Ma, 4.5 million years younger than LH, for which one tuff is reanalyzed here as \documentclass{aastex} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{bm} \usepackage{mathrsfs} \usepackage{pifont} \usepackage{stmaryrd} \usepackage{textcomp} \usepackage{portland,xspace} \usepackage{amsmath,amsxtra} \usepackage[OT2,OT1]{fontenc} \newcommand\cyr{ \renewcommand\rmdefault{wncyr} \renewcommand\sfdefault{wncyss} \renewcommand\encodingdefault{OT2} \normalfont \selectfont} \DeclareTextFontCommand{\textcyr}{\cyr} \pagestyle{empty} \DeclareMathSizes{10}{9}{7}{6} \begin{document} \landscape $$51.91\pm 0.22$$ \end{document} Ma. Thus, diverse floral associations in Patagonia evolved by the Eocene, possibly in response to global warming, and were persistent and areally extensive. This suggests extraordinary richness at low latitudes via the latitudinal diversity gradient, corroborated by published palynological data from the Eocene of Colombia.
Analysis of Impact Factors of China's Carbon Dioxide Emissions

This paper estimates the quantity of CO2 emissions in 30 provinces of China covering the year from 1999 to 2010, combining static and dynamic panel data model.Meanwhile, we use instruments to control the endogeny of the two models, analyzing the impact factors of China's CO2 emissions comprehensively and objectively. The result shows that a inverted U-shaped relationship is found between per capita GDP and CO2 emissions per capita . And it means that the Environmental Kuznets Hypothesis is verified in China.And energy consumption structure, industrial structure and urbanization have a positive impact on China's CO2 emissions. The CO2 emissions of last period have a crucial impact on the emissions of current period.
Radiation‐Assisted Synthesis of Composites, Materials, Compounds, and Nanostructures

Modern techniques of radiation synthesis, applied for the synthesis of materials, are reviewed, including ionizing radiation (α-particles, β-particles/electron beam, γ- and X-rays, neutron and proton irradiation, and ion beams), as well as nonionizing laser ablation and UV irradiation. Formation and/or modification processes of a series of materials, in particular, for nanostructural composites, on the basis of elemental metals, oxides, salts, and polymers are discussed. Radiation-fabricated membranes are briefly described. Keywords: nanocomposites; radiation; synthesis; materials; membranes
Computational creativity for counterdeception in information fusion

Setting out from a discussion on why information fusion systems are vulnerable to deception, we note that exposing deception requires creativity. The burgeoning field of computational creativity is briefly reviewed and we sketch a high-level architecture for enhancing information fusion systems with computational creativity agents for the purpose of increasing robustness against deception. A more in depth exposition of how computational creativity integrates with a situation assessment sub-system based on statistical relational learning is provided and we note similarities between the different modes of this configuration and common human creativity techniques. One of these techniques is studied in a detailed example and used for revealing a suitable technical format for the creative input. Furthermore, we study how creative inputs are generated based on conceptual blending analysis applied to a library of deception stratagems.
Crevice Corrosion Behavior of 45 Molybdenum-Containing Stainless Steels in Seawater.

Abstract : Thirty-six wrought and nine cast commercial molybdenum-containing stainless steels and nickel alloys were exposed to filtered natural seawater at 30 degree C for 30 days using multiple-crevice washers to create a crevice condition. The relative crevice corrosion behavior of these alloys in seawater is analyzed, and the effect of composition discussed. (Author)
Agents' Monitoring Approach for Big Data

Data sources are becoming more prevalent as digital devices in the form of sensor networks, capture and record vast amounts of individual and environmental data. Much of this data is to some extent redundant, as it is captured or held at many locations, or is of a low priority level, so can safely be ignored. The distributed nature of such data, however, means that it is impossible to identify redundant or useless information without full scale analysis. The velocity of data arrival, the volume of data and the heterogeneous nature (variety) of the data makes this task unfeasible for any real time analysis, which is becoming more desirable in real world situations. Thus this paper is looking to utilize a multi-agent system or federation of agents to analyse data in a distributed manner. The method of setting up such an agent team is proposed so as to engender a cohesive team ethos endowing the agent federation with the power of a single agent's goal. It is then shown that this leads to a specific network topology to emerge within the agent team. Furthermore such a topology allows an acquaintance monitoring algorithm to be applied. This is shown to actively attenuate and prioritize data by suggesting only those sensor nodes that are likely to be in possession of useful and non-redundant data, using only data local to each agent team member. The results are gained, in this first instance, by a simulation.