Conditions For Embedding Research Articles

QUASI-BOOLEAN POWER OF ALGEBRAS AND IDEMPOTENT ALGEBRAS

In this paper we provide a necessity condition for embedding of the binary algebra into the quasi-boolean power of a rectangular algebra. It is also proved that every idempotent and hyperassociative algebra via the weak bihomomorphism maps in an idempotent and commutative algebra.

Imaging near-surface sharp lateral variations with surface-wave methods — Part 1: Detection and location

Near-surface sharp lateral variations can be either a target of investigation or an issue for the reconstruction of reliable subsurface models in surface-wave (SW) prospecting. Effective and computationally fast methods are consequently required for detection and location of these shallow heterogeneities. Four SW-based techniques, chosen between available literature methods, are tested for detection and location purposes. All of the techniques are updated for multifold data and then systematically applied on new synthetic and field data. The selected methods are based on computation of the energy, energy decay exponent, attenuation coefficient, and autospectrum. The multifold upgrade is based on the stacking of the computed parameters for single-shot or single-offset records and improves readability and interpretation of the final results. Detection and location capabilities are extensively evaluated on a variety of 2D synthetic models, simulating different target geometries, embedment conditions, and impedance contrasts with respect to the background. The methods are then validated on two field cases: a shallow low-velocity body in a sedimentary sequence and a hard-rock site with two embedded subvertical open fractures. For a quantitative comparison, the horizontal gradients of the four parameters are analyzed to establish uniform criteria for location estimation. All of the methods indicate ability in detecting and locating lateral variations having lower acoustic impedance than the surrounding material, with errors generally comparable or lower than the geophone spacing. More difficulties are encountered in locating targets with higher acoustic impedance than the background, especially in the presence of weak lateral contrasts, high embedment depths, and small dimensions of the object.

Embeddings of Weighted Generalized Morrey Spaces Into Lebesgue Spaces on Fractal Sets

Abstract We study embeddings of weighted local and consequently global generalized Morrey spaces defined on a quasi-metric measure set (X, d, μ) of general nature which may be unbounded, into Lebesgue spaces Ls (X), 1 ≤ s ≤ p < ∞. The main motivation for obtaining such an embedding is to have an embedding of non-separable Morrey space into a separable space. In the general setting of quasi-metric measure spaces and arbitrary weights we give a sufficient condition for such an embedding. In the case of radial weights related to the center of local Morrey space, we obtain an effective sufficient condition in terms of (fractional in general) upper Ahlfors dimensions of the set X. In the case of radial weights we also obtain necessary conditions for such embeddings of local and global Morrey spaces, with the use of (fractional in general) lower and upper Ahlfors dimensions. In the case of power-logarithmic-type weights we obtain a criterion for such embeddings when these dimensions coincide.

Soil cover patterns in the forest-steppe and steppe zones of the East European Plain

Abstract The soil cover of the forest-steppe and steppe zones of the East European Plain is characterized by diverse soil combinations revealed during large-scale and detailed soil mapping against the background of a traditional zonal sequence of dominant automorphic soils alternating from the north to the south and clearly displayed on small-scale soil maps. The composition, configuration and functioning of particular soil cover patterns are determined by the soil forming factors acting within a given area. The elementary soil areas (detailed scale) and elementary soil cover patterns maps (large scale) of the Central Russian, Kalach, and Volga Uplands are created by both traditional and digital soil mapping methods. Low-contrasting soil combinations with the background Haplic Chernozems (Loamic or Clayic, Pachic) alternating with zooturbated Haplic Chernozems (Loamic or Clayic, Pachic) on convex elements of the microtopography and Luvic Chernozems (Loamic or Clayic, Pachic) on concave elements of the microtopography prevails under conditions of thick clay loamy parent materials and free drainage. Under conditions of shallow embedding by low-permeable clayey sediments, the soil cover includes Chernozems or Chernic Phaeozems with stagnic features in some part of the soil profile or even Mollic Stagnosols. The presence of shrink-swell clays of different ages leads to the formation of Bathyvertic Chernozems, Vertic Chernozems, Vertic Chernic Phaeozems and/or Pellic Vertisols. The presence of soluble salts in the parent material leads to the development of solonetzic soil complexes consisting of Protosodic or Sodic Chernozems and different types of Solonetzes.

Rational subgroups and invariants of F4

It is known that simple algebraic groups of type F4, defined over a field k, are precisely the full automorphism groups of Albert algebras over k. For an Albert division algebra A over a field k of characteristic different from 2 and 3 and G = Aut(A), the full group of algebra automorphisms of A, it is known that any connected, simple k-subgroup of G is of type A2 or D4. In this paper, we classify all k-embeddings of connected, simple groups of type A2 and D4 in G and give Galois cohomological conditions for such embeddings to exist, along with some applications.

C0-characterization of symplectic and contact embeddings and Lagrangian rigidity

We present a novel [Formula: see text]-characterization of symplectic embeddings and diffeomorphisms in terms of Lagrangian embeddings. Our approach is based on the shape invariant, which was discovered by Sikorav and Eliashberg, intersection theory and the displacement energy of Lagrangian submanifolds, and the fact that non-Lagrangian submanifolds can be displaced immediately. This characterization gives rise to a new proof of [Formula: see text]-rigidity of symplectic embeddings and diffeomorphisms. The various manifestations of Lagrangian rigidity that are used in our arguments come from [Formula: see text]-holomorphic curve methods. An advantage of our techniques is that they can be adapted to a [Formula: see text]-characterization of contact embeddings and diffeomorphisms in terms of coisotropic (or pre-Lagrangian) embeddings, which in turn leads to a proof of [Formula: see text]-rigidity of contact embeddings and diffeomorphisms. We give a detailed treatment of the shape invariants of symplectic and contact manifolds, and demonstrate that shape is often a natural language in symplectic and contact topology. We consider homeomorphisms that preserve shape, and propose a hierarchy of notions of Lagrangian topological submanifold. Moreover, we discuss shape-related necessary and sufficient conditions for symplectic and contact embeddings, and define a symplectic capacity from the shape.

Characterizations of Some Properties on Weighted Modulation and Wiener Amalgam Spaces

In this paper, we characterize some properties on weighted modulation and Wiener amalgam spaces by the corresponding properties on weighted Lebesgue spaces. As applications, we obtain sharp conditions for product inequalities, convolution inequalities, and embedding on weighted modulation and Wiener amalgam spaces. By a unified approach different from others we give a complete answer to the question of finding sharp conditions of certain relations on weighted modulation and Wiener amalgam spaces.

An Efficient Compressive Convolutional Network for Unified Object Detection and Image Compression

This paper addresses the challenge of designing efficient framework for real-time object detection and image compression. The proposed Compressive Convolutional Network (CCN) is basically a compressive-sensing-enabled convolutional neural network. Instead of designing different components for compressive sensing and object detection, the CCN optimizes and reuses the convolution operation for recoverable data embedding and image compression. Technically, the incoherence condition, which is the sufficient condition for recoverable data embedding, is incorporated in the first convolutional layer of the CCN model as regularization; Therefore, the CCN convolution kernels learned by training over the VOC and COCO image set can be used for data embedding and image compression. By reusing the convolution operation, no extra computational overhead is required for image compression. As a result, the CCN is 3.1 to 5.0 fold more efficient than the conventional approaches. In our experiments, the CCN achieved 78.1 mAP for object detection and 3.0 dB to 5.2 dB higher PSNR for image compression than the examined compressive sensing approaches.

Vertical vibration of piles in viscoelastic non-uniform soil overlying a rigid base

This paper presents an analytical methodology that provides the dynamic response of an elastic pile embedded in viscoelastic non-uniform soil overlying a rigid base, when subjected to a harmonic vertical load. The non-uniform soil comprises two parts, featuring independent properties: the substratum soil, below the pile toe, is represented by a viscoelastic layer of finite thickness resting on rigid bedrock, while the soil surrounding the pile shaft is modelled as a series of infinitesimally thin independent viscoelastic layers. Following validation of the methodology, where we show that the presented solution degenerates to published solutions for simpler pile embedment conditions, we use results of a parametric investigation to discuss the mechanisms governing the dynamic response of the non-uniform soil–pile system. The presented solution allows modelling realistically the contribution of end-bearing resistance to vertical vibrations of piles in cases of practical interest when non-linearity effects can be effectively ignored.

The causal value of 'cómo': factivity, mirativity and illocutionary forces

The interrogative element cómo ‘how’ of Spanish has a double meaning: that of manner and that causal. With the former, it behaves like a typical interrogative element, but, with the latter, its behavior manifests the specificities of por qué ‘why’. Two analyses are proposed, always in cartographic terms: one for sentences with cómo with a causal value and another one for sentences in which cómo with this value appears in front of es que ‘is that’ (cómo es que). Indeed, the conditions for embedding for these two types are different (much more restricted for the first one than for the second one). It is proposed that sentences with cómo without es que have an exclamative illocutionary force with an interrogative component, while the force of sentences with cómo es que would be a variant of the interrogative one (interro-exclamative force). In both cases, their factual and mirative nature is syntactically relevant. The general analysis proposal is finally applied to a poorly studied element, que cosa que (Ripacurtian Catalan and Aragonese).

Reversible Data Hiding Based on Multi-Dimension Difference-Histogram and Bilinear Interpolation

A new difference-histogram modification Reversible Data Hiding (RDH) scheme by using bilinear interpolation prediction is proposed in this paper. First, the proposed method considers each neighboring pixels x and y to predict z by bilinear interpolation on four pixels which are near to pixel-pairs x and y. Then, according to d1 = x−y,d2 = y−zy, and d3 = z−zx, a new coordinate system (d1,d2,d3) is generated. In this three-dimensional coordinate, the proposed method uses six quadrants to embed secret information: points on the specified surface are used for embedding and the extending space surrounded the embedded surfaces are used for shifting. Finally, a new multi-dimension difference-pair-mapping is used to implement the reversible data embedding. Therefore, more embedding conditions are obtained, thereby increase the number of embedded pixels and get more embedding capacity.

Mechanical Safety of Embedded Electronics for In-body Wearables: A Smart Mouthguard Study

The growing popularity of contact sports drives the requirement for better design of protective equipment, such as mouthguards. Smart mouthguards with embedded electronics provide a multitude of new ways to provide increased safety and protection to users. Characterisation of how electronic components embedded in typical mouthguard material, ethylene vinyl acetate (EVA), behave under typical sports impacts is crucial for future designs. A novel pendulum impact rig using a hockey ball disc impactor was developed to investigate impact forces and component failure. Two sets of dental models (aluminium and plastic padding chemical metal) were used to manufacture post-thermoformed mouthguards. Seven embedding conditions with varying thickness of EVA (1.5 and 3 mm) and locations of electrical components were tested. Component failures were observed in four out of seven test conditions, and the experimental failure forces at which the electrical component had a 50% chance of failure were reported for those cases. The experimental results showed that an EVA thickness of 3 mm surrounding the electrical component gives the most comprehensive protection even under extreme surface conformity. Computational models on surface conformity of EVA showed that a block of EVA with a minimum thickness of 1.5 mm was better at reducing stress concentration than a shell with an overall thickness of 1.5 mm. This study demonstrated that the thickness of a mouthguard is important when protecting electrical components from extreme dental surface conformity, furthermore the surface geometry should not be overlooked when considering electrical component safety for in-body wearables that are impact prone.

Spatially Arranged Sparse Recurrent Neural Networks for Energy Efficient Associative Memory.

The development of hardware neural networks, including neuromorphic hardware, has been accelerated over the past few years. However, it is challenging to operate very large-scale neural networks with low-power hardware devices, partly due to signal transmissions through a massive number of interconnections. Our aim is to deal with the issue of communication cost from an algorithmic viewpoint and study learning algorithms for energy-efficient information processing. Here, we consider two approaches to finding spatially arranged sparse recurrent neural networks with the high cost-performance ratio for associative memory. In the first approach following classical methods, we focus on sparse modular network structures inspired by biological brain networks and examine their storage capacity under an iterative learning rule. We show that incorporating long-range intermodule connections into purely modular networks can enhance the cost-performance ratio. In the second approach, we formulate for the first time an optimization problem where the network sparsity is maximized under the constraints imposed by a pattern embedding condition. We show that there is a tradeoff between the interconnection cost and the computational performance in the optimized networks. We demonstrate that the optimized networks can achieve a better cost-performance ratio compared with those considered in the first approach. We show the effectiveness of the optimization approach mainly using binary patterns and apply it also to gray-scale image restoration. Our results suggest that the presented approaches are useful in seeking more sparse and less costly connectivity of neural networks for the enhancement of energy efficiency in hardware neural networks.

Compactness Properties for Modulation Spaces

We prove that if omega _1 and omega _2 are moderate weights and mathscr {B} is a suitable (quasi-)Banach function space, then a necessary and sufficient condition for the embedding i, :, M (omega _1,mathscr {B})rightarrow M (omega _2,mathscr {B}) between two modulation spaces to be compact is that the quotient omega _2/omega _1 vanishes at infinity. Moreover we show, that the boundedness of omega _2/omega _1 is a necessary and sufficient condition for the previous embedding to be continuous.

Optimization of Recurrence Quantification Analysis for Detecting the Presence of Multiple Sclerosis

The visual patterns in recurrence plots of time-series data can be quantified using recurrence quantification analysis (RQA), a phase-space-based method. The ability to quantitate recurrence plots affords the possibility of using them to solve central biomedical problems, for example detecting the presence of neurological diseases. Our goal was to assess this application by statistically comparing the values of plot-based quantifiers of electroencephalograms (EEGs) from patients having multiple sclerosis (MS) with values from the EEGs of control subjects. First, employing a model system consisting of the addition of known deterministic signals to the EEG of normal subjects, we empirically identified the embedding conditions that facilitated detection of the effect of the addition of the signals. Second, we used the conditions thus identified to compare EEGs from 10 patients with MS and 10 age- and gender-matched control subjects, using seven standard recurrence-plot quantifiers. We identify embedding dimension of 5 points and time delay of 5 points as conditions that maximize the ability of RQA to detect the presence of deterministic activity in EEGs time series sampled at 500 Hz. The values of the RQA quantifiers computed from the EEGs of the MS patients were significantly greater than the corresponding values from the controls, indicating that the presence of the disease was associated with detectable changes in the EEG (family-wise error < 0.05%). Recurrence plots detected the occurrence of alterations in EEGs associated with the presence of MS, indicating a decreased complexity (increased order) of brain electrical activity associated with brain disease.

An improved reversible data hiding scheme based on pixel permutation

Low payload capacity is a flaw in pixel exchange-based reversible data hiding schemes. In order to improve it, this paper proposes a triangular pixel pair structure, which requires fewer pixels than a traditional 2 × 2 pixel block. At the same time, the corresponding embedding conditions are also proposed to screen out those pixel pairs that may reduce image visual quality seriously after data embedding. Embeddable pixel pair is binarised by its feature. And, Huffman coding method is used to create free space for secret data while ensuring reversibility. Eventually, data is embedded into a cover image by switching embeddable pixel pair features. The experimental results show that the greyscale histogram of the cover image is never modified by the proposed scheme. Consequently, secret data cannot be detected by the statistical steganalysis based on the greyscale histogram. Both payload capacity and imperceptibility get enhanced obviously, and PSNR is at the same level as the schemes that do not provide robustness of the greyscale histogram.

Spanish null complement anaphora at the grammar-discourse interface

The study of null complement anaphora (NCA) is important for the grammar-discourse interface as it seems to incarnate some of the core processes commonly found in other discourse phenomena, namely: reference, anaphora, ellipsis, inference, salience, or accommodation. Although NCA has been studied extensively, the debate whether it is an elliptical or an anaphoric process is still ongoing. This paper contributes to the view of NCA as a deep anaphor by providing evidence from Spanish discourse. I argue that NCA is an anaphor that selects the most prominent antecedent in well-defined discourse hierarchical structures. My analysis is mainly based on the comparison among Spanish neuter pronouns and NCA, and on the observed similar behavior shown by NCA and pronouns under discourse embedding conditions. Previous accounts on NCA are reviewed (Depiante 2000, 2001; Williamson 2012), and a tentative analysis of VP ellipsis based on discourse relations (Hardt &amp; Romero 2004) is applied to Spanish NCA.

Compactness of embedding of generalized higher order Lipschitz classes

In this paper sufficient conditions for continuous and compact embeddings of generalized higher order Lipschitz classes on a compact subset of n-dimensional real Euclidean spaces are obtained.

Characterization of embeddings of Sobolev-type spaces into generalized Hölder spaces defined by Lp-modulus of smoothness

We prove a sharp estimate for the k-modulus of smoothness, modelled upon a Lp-Lebesgue space, of a function f in WkLpnn+kp,p(Ω), where Ω is a domain with minimally smooth boundary and finite Lebesgue measure, k,n∈N, k<n and nn−k<p<+∞. This sharp estimate is used to establish necessary and sufficient conditions for continuous embeddings of Sobolev-type spaces into generalized Hölder spaces defined by means of the k-modulus of smoothness. General results are illustrated with examples. In particular, we obtain a generalization of the classical Jawerth embeddings.

Effect of Morphological Changes due to Increasing Carbon Nanoparticles Content on the Quasi-Static Mechanical Response of Epoxy Resin.

Mechanical failure in epoxy polymer and composites leads them to commonly be referred to as inherently brittle due to the presence of polymerization-induced microcrack and microvoids, which are barriers to high-performance applications, e.g., in aerospace structures. Numerous studies have been carried out on epoxy’s strengthening and toughening via nanomaterial reinforcement, e.g., using rubber nanoparticles in the epoxy matrix of new composite aircraft. However, extremely cautious process and functionalization steps must be taken in order to achieve high-quality dispersion and bonding, the development of which is not keeping pace with large structures applications. In this article, we report our studies on the mechanical performance of an epoxy polymer reinforced with graphite carbon nanoparticles (CNPs), and the possible effects arising from a straightforward, rapid stir-mixing technique. The CNPs were embedded in a low viscosity epoxy resin, with the CNP weight percentage (wt %) being varied between 1% and 5%. Simplified stirring embedment was selected in the interests of industrial process facilitation, and functionalization was avoided to reduce the number of parameters involved in the study. Embedment conditions and timing were held constant for all wt %. The CNP filled epoxy resin was then injected into an aluminum mold and cured under vacuum conditions at 80 °C for 12 h. A series of test specimens were then extracted from the mold, and tested under uniaxial quasi-static tension, compression, and nanoindentation. Elementary mechanical properties including failure strain, hardness, strength, and modulus were measured. The mechanical performance was improved by the incorporation of 1 and 2 wt % of CNP but was degraded by 5 wt % CNP, mainly attributed to the morphological change, including re-agglomeration, with the increasing CNP wt %. This change strongly correlated with the mechanical response in the presence of CNP, and was the major governing mechanism leading to both mechanical improvement and degradation.