Construction Of Class Research Articles

Influence of lean construction wastes on the transformation-flow-value process of construction

Construction projects result in the generation of wastes, which can be broadly classified into physical and non-physical wastes. Non-physical wastes present a different challenge than physical waste due to their intangible nature. This study focuses on understanding the importance of the classical Lean Construction (LC) wastes and their degree of influence on the Transformation-Flow-Value (TFV) aspects of construction projects using empirical data. A structured questionnaire was designed and completed by 60 participants from the public and private sectors in Nigeria and analyzed using the Relative Importance Index (RII). The statistical analysis indicated that ‘Making-Do’ is the most critical non-physical waste during the transformation process of construction, followed closely by ‘Non-Utilized Talent’. ‘Waiting’ and ‘Transportation’ are the two most essential aspects in the flow aspect of construction, while ‘Overproduction’ is the most crucial waste category affecting the value aspect of construction. The findings of this study can be used for improving the productivity of construction projects by focusing on techniques to reduce the most critical non-physical wastes.

Finite relative clauses in colloquial Armenian and the phenomenon of ‘inverse attraction’

This paper presents a discussion of the syntax and typology of finite relative clauses in Armenian, based on data from a corpus of colloquial spoken language from the Republic of Armenia. In literary Armenian, the preferred strategy involves embedded postnominal relative clauses with relative pronouns. However, the spoken corpus data show a much wider usage of adjoined constructions, including left-adjoined RCs introduced by an indeclinable complementizer, a strategy which is paralleled in several other languages of the Araxes-Iran area. We also find ‘inverse attraction’ structures, where the relativized element precedes RP in RC case. Previous syntactic analyses of these constructions have concluded that, if the relativized element has a determiner other than RP, it must be outside RC, leaving the case-marking unexplained. However, all the languages in which they occur have undergone a change from RP>indeclinable complementizer, and it is proposed that the uncertain status of this element holds the key to understanding this construction, either in terms of contamination/hypercorrection, or, possibly, an intermediate ‘agreeing complementizer’ stage in the diachronic development. This interpretation can also be applied to Classical Armenian constructions where RPs can co-occur with resumptive pronouns, violating a proposed universal of relativization.

Neural Embeddings of Financial Time-Series Data

The dominant approaches for financial portfolio construction rely on estimating sample covariance and correlations matrixes, which serve as an input into a number of classical portfolio construction techniques. These classical approaches are not forward looking, are constrained by the ability to estimate covariance and correlation matrixes, and are inflexible to incorporating additional information. The authors propose a new approach of using learned representations from deep learning networks to augment such classical techniques. This approach can incorporate learned estimates of future performance and can be customized to create tailored representations best suited to meeting varying financial objectives. This article showcases one example of such an embedding, compares and contrasts it with classical approaches to portfolio construction, and discusses additional possibilities for applying representation learning in quantitative finance. TOPICS:Big data/machine learning, performance measurement, portfolio construction, simulations, statistical methods Key Findings • The authors propose a general methodology for creating tailored embeddings of financial time-series data. • The authors demonstrate one approach for creating such embeddings, analyze the induced similarity measure, and compare and contrast it with the traditional approach of using covariance and correlation as a similarity measure for securities. • The authors discuss other applications and advantages of this approach, including accounting for probabilistic estimates of future behavior and flexibility in incorporating varying business objectives.

PROJECTIVE LOOPS GENERATE RATIONAL LOOP GROUPS

AbstractWe generalize Uhlenbeck’s generator theorem of ${\mathcal{L}}^{-}\operatorname{U}_{n}$ to the full rational loop group ${\mathcal{L}}^{-}\operatorname{GL}_{n}\mathbb{C}$ and its subgroups ${\mathcal{L}}^{-}\operatorname{GL}_{n}\mathbb{R}$, ${\mathcal{L}}^{-}\operatorname{U}_{p,q}$: they are all generated by just simple projective loops. Recall that Terng–Uhlenbeck studied the dressing actions of such projective loops as generalized Bäcklund transformations for integrable systems. Our result makes a nice supplement: any rational dressing is the composition of these Bäcklund transformations. This conclusion is surprising in the sense that Lie theory suggests the indispensable role of nilpotent loops in the case of noncompact reality conditions, and nilpotent dressings appear quite complicated and mysterious. The sacrifice is to introduce some extra fake singularities. So we also propose a set of generators if fake singularities are forbidden. A very geometric and physical construction of $\operatorname{U}_{p,q}$ is obtained as a by-product, generalizing the classical construction of unitary groups.

2n Bordered constructions of self-dual codes from group rings

Self-dual codes, which are codes that are equal to their orthogonal, are a widely studied family of codes. Various techniques involving circulant matrices and matrices from group rings have been used to construct such codes. Moreover, families of rings have been used, together with a Gray map, to construct binary self-dual codes. In this paper, we introduce a new bordered construction over group rings for self-dual codes by combining many of the previously used techniques. The purpose of this is to construct self-dual codes that were missed using classical construction techniques by constructing self-dual codes with different automorphism groups. We apply the technique to codes over finite commutative Frobenius rings of characteristic 2 and several group rings and use these to construct interesting binary self-dual codes. In particular, we construct some extremal self-dual codes of length 64 and 68, constructing 30 new extremal self-dual codes of length 68.

Topology‐Aware Surface Reconstruction for Point Clouds

AbstractWe present an approach to incorporate topological priors in the reconstruction of a surface from a point scan. We base the reconstruction on basis functions which are optimized to provide a good fit to the point scan while satisfying predefined topological constraints. We optimize the parameters of a model to obtain a likelihood function over the reconstruction domain. The topological constraints are captured by persistence diagrams which are incorporated within the optimization algorithm to promote the correct topology. The result is a novel topology‐aware technique which can (i) weed out topological noise from point scans, and (ii) capture certain nuanced properties of the underlying shape which could otherwise be lost while performing surface reconstruction. We show results reconstructing shapes with multiple potential topologies, compare to other classical surface construction techniques, and show the completion of real scan data.

Constructing Connectome Atlas by Graph Laplacian Learning.

The recent development of neuroimaging technology and network theory allows us to visualize and characterize the whole-brain functional connectivity in vivo. The importance of conventional structural image atlas widely used in population-based neuroimaging studies has been well verified. Similarly, a "common" brain connectivity map (also called connectome atlas) across individuals can open a new pathway to interpreting disorder-related brain cognition and behaviors. However, the main obstacle of applying the classic image atlas construction approaches to the connectome data is that a regular data structure (such as a grid) in such methods breaks down the intrinsic geometry of the network connectivity derived from the irregular data domain (in the setting of a graph). To tackle this hurdle, we first embed the brain network into a set of graph signals in the Euclidean space via the diffusion mapping technique. Furthermore, we cast the problem of connectome atlas construction into a novel learning-based graph inference model. It can be constructed by iterating the following processes: (1) align all individual brain networks to a common space spanned by the graph spectrum bases of the latent common network, and (2) learn graph Laplacian of the common network that is in consensus with all aligned brain networks. We have evaluated our novel method for connectome atlas construction in comparison with non-learning-based counterparts. Based on experiments using network connectivity data from populations with neurodegenerative and neuropediatric disorders, our approach has demonstrated statistically meaningful improvement over existing methods.

Vectorial bent functions in odd characteristic and their components

Bent functions in odd characteristic can be either (weakly) regular or non-weakly regular. Furthermore one can distinguish between dual-bent functions, which are bent functions for which the dual is bent as well, and non-dual bent functions. Whereas a weakly regular bent function always has a bent dual, a non-weakly regular bent function can be either dual-bent or non-dual-bent. The classical constructions (like quadratic bent functions, Maiorana-McFarland or partial spread) yield weakly regular bent functions, but meanwhile one knows constructions of infinite classes of non-weakly regular bent functions of both types, dual-bent and non-dual-bent. In this article we focus on vectorial bent functions in odd characteristic. We first show that most p-ary bent monomials and binomials are actually vectorial constructions. In the second part we give a positive answer to the question if non-weakly regular bent functions can be components of a vectorial bent function. We present the first construction of vectorial bent functions of which the components are non-weakly regular but dual-bent, and the first construction of vectorial bent functions with non-dual-bent components.

An explicit solution to the Skorokhod embedding problem for double exponential increments

Strong approximations of uniform transport processes to the standard Brownian motion rely on the Skorokhod embedding of random walk with centered double exponential increments. In this note we make such an embedding explicit by means of a Poissonian scheme, which both simplifies classic constructions of strong approximations of uniform transport processes (Griego, 1971) and improves their rate of strong convergence (Gorostiza and Griego, 1980). We finalize by providing an extension regarding the embedding of a random walk with asymmetric double exponential increments.

Orlicz spaces associated to a quasi-Banach function space: applications to vector measures and interpolation

The Orlicz spaces $$X^{\varPhi }$$ associated to a quasi-Banach function space X are defined by replacing the role of the space $$L^1$$ by X in the classical construction of Orlicz spaces. Given a vector measure m, we can apply this construction to the spaces $$L^1_w(m),$$ $$L^1(m)$$ and $$L^1(\Vert m\Vert )$$ of integrable functions (in the weak, strong and Choquet sense, respectively) in order to obtain the known Orlicz spaces $$L^{\varPhi }_w(m)$$ and $$L^{\varPhi }(m)$$ and the new ones $$L^{\varPhi }(\Vert m\Vert ).$$ Therefore, we are providing a framework where dealing with different kind of Orlicz spaces in a unified way. Some applications to complex interpolation are also given.

Parallel Multilevel Constructions for Constant Dimension Codes

Constant dimension codes (CDCs), as special subspace codes, have received a lot of attention due to their application in random network coding. This paper introduces a family of new codes, called rank metric codes with given ranks (GRMCs), to generalize the parallel construction in [Xu and Chen, IEEE Trans. Inf. Theory, 64 (2018), 6315–6319] and the classic multilevel construction. A Singleton-like upper bound and a lower bound for GRMCs derived from Gabidulin codes are given. Via GRMCs, two effective constructions for CDCs are presented by combining the parallel construction and the multilevel construction. Many CDCs with larger size than the previously best known codes are given. The ratio between the new lower bound and the known upper bound for $(4\delta,2\delta,2\delta)_{q}$ -CDCs is calculated. It is greater than 0.99926 for any prime power $q$ and any $\delta \geq 3$ .

Lightweight AEAD and Hashing using the Sparkle Permutation Family

We introduce the Sparkle family of permutations operating on 256, 384 and 512 bits. These are combined with the Beetle mode to construct a family of authenticated ciphers, Schwaemm, with security levels ranging from 120 to 250 bits. We also use them to build new sponge-based hash functions, Esch256 and Esch384. Our permutations are among those with the lowest footprint in software, without sacrificing throughput. These properties are allowed by our use of an ARX component (the Alzette S-box) as well as a carefully chosen number of rounds. The corresponding analysis is enabled by the long trail strategy which gives us the tools we need to efficiently bound the probability of all the differential and linear trails for an arbitrary number of rounds. We also present a new application of this approach where the only trails considered are those mapping the rate to the outer part of the internal state, such trails being the only relevant trails for instance in a differential collision attack. To further decrease the number of rounds without compromising security, we modify the message injection in the classical sponge construction to break the alignment between the rate and our S-box layer.

Lightweight AEAD and Hashing using the Sparkle Permutation Family

We introduce the Sparkle family of permutations operating on 256, 384 and 512 bits. These are combined with the Beetle mode to construct a family of authenticated ciphers, Schwaemm, with security levels ranging from 120 to 250 bits. We also use them to build new sponge-based hash functions, Esch256 and Esch384. Our permutations are among those with the lowest footprint in software, without sacrificing throughput. These properties are allowed by our use of an ARX component (the Alzette S-box) as well as a carefully chosen number of rounds. The corresponding analysis is enabled by the long trail strategy which gives us the tools we need to efficiently bound the probability of all the differential and linear trails for an arbitrary number of rounds. We also present a new application of this approach where the only trails considered are those mapping the rate to the outer part of the internal state, such trails being the only relevant trails for instance in a differential collision attack. To further decrease the number of rounds without compromising security, we modify the message injection in the classical sponge construction to break the alignment between the rate and our S-box layer.

The Trace Field of Hyperbolic Gluings

AbstractWe determine the adjoint trace field of gluings of general hyperbolic manifolds. This provides a new method to prove the nonarithmeticity of gluings, which can be applied to the classical construction of Gromov and Piatetski-Shapiro (and generalizations) as well as certain gluings of pieces of commensurable arithmetic manifolds. As an application, we give many new examples of nonarithmetic gluings and prove that the unique nonarithmetic Coxeter 5-simplex is not commensurable to any gluing of arithmetic pieces.

A survey on deep learning methods for scene flow estimation

Recently, computer vision has achieved remarkable accomplishments in many domains under the thriving of deep learning. Scene flow estimation turns from the classical manual feature construction to the deep convolutional neural network (DCNN) approaches. In this paper, we review recent works about scene flow, mainly focusing on DCNN methods. We present some milestones of scene flow in recent years, and categorize these methods into supervised and unsupervised based methods. Meanwhile, we also review some multi-task methods related to scene flow. At last, we present a performance comparison among different methods.

A conceptual approach to defining a structure for spatially distributed network-centric system for controlling the tier of air defense missile cover

In the context of the Joint Forces operation in eastern Ukraine and the new challenges of the hybrid war on the part of the Russian Federation, there is an urgent need for finding possible ways of building an effective control system for a spatially distributed network-centered air defense missile cover system. The article presents a conceptual approach to the design of the structure that would be effective for controlling such a system, which is based on both the classical construction principles of control systems and the novel network-centric approach to design of control systems. The conceptual approach takes into account the peculiarities of building a spatially distributed tier of air defense missile cover.

Poisson-Commutative Subalgebras of 𝒮(𝔤) associated with Involutions

Abstract The symmetric algebra ${\mathcal{S}}({{\mathfrak{g}}})$ of a reductive Lie algebra ${{\mathfrak{g}}}$ is equipped with the standard Poisson structure, that is, the Lie–Poisson bracket. Poisson-commutative subalgebras of ${\mathcal{S}}({{\mathfrak{g}}})$ attract a great deal of attention because of their relationship to integrable systems and, more recently, to geometric representation theory. The transcendence degree of a Poisson-commutative subalgebra ${\mathcal C}\subset{\mathcal{S}}({{\mathfrak{g}}})$ is bounded by the “magic number” ${\boldsymbol{b}}({{\mathfrak{g}}})$ of ${{\mathfrak{g}}}$. There are two classical constructions of $\mathcal C$ with ${\textrm{tr.deg}}\,{\mathcal C}={\boldsymbol{b}}({{\mathfrak{g}}})$. The 1st one is applicable to $\mathfrak{gl}_n$ and $\mathfrak{so}_n$ and uses the Gelfand–Tsetlin chains of subalgebras. The 2nd one is known as the “argument shift method” of Mishchenko–Fomenko. We generalise the Gelfand–Tsetlin approach to chains of almost arbitrary symmetric subalgebras. Our method works for all types. Starting from a symmetric decompositions ${{\mathfrak{g}}}={{\mathfrak{g}}}_0\oplus{{\mathfrak{g}}}_1$, Poisson-commutative subalgebras ${{\mathcal{Z}}},\tilde{{\mathcal{Z}}}\subset{\mathcal{S}}({{\mathfrak{g}}})^{{{\mathfrak{g}}}_0}$ of the maximal possible transcendence degree are constructed. If the ${{\mathbb{Z}}}_2$-contraction ${{\mathfrak{g}}}_0\ltimes{{\mathfrak{g}}}_1^{\textsf{ab}}$ has a polynomial ring of symmetric invariants, then $\tilde{{\mathcal{Z}}}$ is a polynomial maximal Poisson-commutative subalgebra of ${\mathcal{S}}({{\mathfrak{g}}})^{{{\mathfrak{g}}}_0}$ and its free generators are explicitly described.

Vertical genera

The classical construction of a genus on a closed manifold is extended to the case of families of manifolds parametrised by a space X. The genera considered here are built from certain generalised Pontryagin and Chern classes as maps from bordism cohomology to the singular cohomology of X.

A thermodynamically consistent formulation for bending of thermoviscoelastic beams for small deformation, small strain based on classical continuum mechanics

The work presented in this paper extends the kinematic assumption free and thermodynamically consistent formulation for bending of thermoelastic beams presented by Surana et al. for bending of thermoviscoelastic beams with dissipation mechanism without memory. We consider small strain, small deformation physics in Lagrangian description. Conservation and balance laws of classical continuum mechanics (CCM) constitute the mathematical model for the physics considered in this paper. Constitutive variables and their argument tensors are established using conjugate pairs in the entropy inequality, additional desired physics and the principle of equipresence. Cauchy stress tensor is decomposed into equilibrium stress tensor () and deviatoric stress tensor (). Constitutive theory for is derived using Helmholtz free energy density in conjunction with incompressibility condition. The constitutive theory for is derived in by first establishing its argument tensors using conjugate pairs in the entropy inequality and other desired physics and then using the representation theorem with complete basis (integrity). The constitutive theory for is a nonlinear constitutive theory in terms of strain tensor containing up to fifth degree terms in the components of the strain tensor and an ordered rate theory in the strain rate tensors up to order n. Simplified linear ordered rate theory for is also presented. The formulation presented here for thermoviscoelastic beams is based on the conservation and balance laws of classical continuum mechanics and construction of beam finite element formulation using hpk framework with variationally consistent integral form. In this approach the mathematical model consist of true conservation and balance laws and the choice of local approximations for the beam finite elements facilitates incorporation of required kinematic description based on the application. This approach is free of the a priori assumptions of kinematic relations, computations are unconditionally stable and the local approximations can be of higher degree (p) and of higher order (k). This approach addresses slender as well as deep beam physics and can be used to measure error in the computed solution through residual functional. The rate constitutive theory is based on the second law thermodynamics consistent with physics of deformation. Mathematical details of new formulation and the model problem studies and comparisons with currently used beam models are presented for slender as well as deep beams. This approach permits consideration of reversible (thermoelastic) as well as irreversible (thermoviscoelastic) processes. It is shown that Rayleigh damping used currently to derive modal damping has no physical basis and can lead to spurious solution. The dissipation mechanism presented here has physical basis and yields non-spurious and valid solutions Model problem studies are presented for undamped natural modes of vibration, damped and undamped transient dynamic response. The results obtained from the formulation presented here are compared with published works.

Hopf algebras for matroids over hyperfields

Recently, M. Baker and N. Bowler introduced the notion of matroids over hyperfields as a unifying theory of various generalizations of matroids. In this paper we generalize the notion of minors and direct sums from ordinary matroids to matroids over hyperfields. Using this we generalize the classical construction of matroid-minor Hopf algebras to the case of matroids over hyperfields.