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This paper asks, "Do classics exist in megaproject management?" We identify three types of classic texts: conventional, Kuhnian, and citation classics. We find that the answer to our question depends on the definition of "classic" employed. First, "citation classics" do exist in megaproject management, and they perform remarkably well when compared to the rest of the management literature. A preliminary Top Ten of citation classics is presented. Second, there is no indication that "conventional classics" exist in megaproject management, i.e., texts recognized as definitive by a majority of experts. Third, there is also no consensus as to whether "Kuhnian classics" exist, i.e., texts with paradigmatic clout. The importance of classics seems to be accepted, however, just as work to develop, discuss, and consolidate classics is seen as essential by megaproject scholars. A set of guidelines is presented for developing classics in megaproject management research.

Classic automobiles are an important part of the automotive industry and represent the historical and technological achievements of certain eras. However, to be considered masterpieces, they must be maintained in pristine condition or restored according to strict guidelines applied by expert services. Therefore, all data about restoration processes and other relevant information about these vehicles must be rigorously documented to ensure their verifiability and immutability. Here, we report on our ongoing research to adequately provide such capabilities to the classic car ecosystem. Using a design science research approach, we have developed a blockchain-based solution using Hyperledger Fabric that facilitates the proper recording of classic car information, restoration procedures applied, and all related documentation by ensuring that this data is immutable and trustworthy while promoting collaboration between interested parties. This solution was validated and received positive feedback from various entities in the classic car sector. The enhanced and secured documentation is expected to contribute to the digital transformation of the classic car sector, promote authenticity and

Quantum machine learning applies principles such as superposition and entanglement to data processing and optimization. Variational quantum models operate on qubits in high-dimensional Hilbert spaces and provide an alternative approach to model expressivity. We compare classical models and a variational quantum classifier on the XOR problem. Logistic regression, a one-hidden-layer multilayer perceptron, and a two-qubit variational quantum classifier with circuit depths 1 and 2 are evaluated on synthetic XOR datasets with varying Gaussian noise and sample sizes using accuracy and binary cross-entropy. Performance is determined primarily by model expressivity. Logistic regression and the depth-1 quantum circuit fail to represent XOR reliably, whereas the multilayer perceptron and the depth-2 quantum circuit achieve perfect test accuracy under representative conditions. Robustness analyses across noise levels, dataset sizes, and random seeds confirm that circuit depth is decisive for quantum performance on this task. Despite matching accuracy, the multilayer perceptron achieves lower binary cross-entropy and substantially shorter training time. Hardware execution preserves the global

Since the earliest proposals for artificial neural network (ANN) models of the mind and brain, critics have pointed out key weaknesses in these models compared to human cognitive abilities. Here we review recent work that uses metalearning to overcome several classic challenges, which we characterize as addressing the Problem of Incentive and Practice -- that is, providing machines with both incentives to improve specific skills and opportunities to practice those skills. This explicit optimization contrasts with more conventional approaches that hope the desired behaviour will emerge through optimizing related but different objectives. We review applications of this principle to addressing four classic challenges for ANNs: systematic generalization, catastrophic forgetting, few-shot learning and multi-step reasoning. We also discuss how large language models incorporate key aspects of this metalearning framework (namely, sequence prediction with feedback trained on diverse data), which helps to explain some of their successes on these classic challenges. Finally, we discuss the prospects for understanding aspects of human development through this framework, and whether natural env

An interesting protocol for classical teleportation of an unknown classical state was recently suggested by Cohen, and by Gour and Meyer. In that protocol, Bob can sample from a probability distribution P that is given to Alice, even if Alice has absolutely no knowledge about P. Pursuing a similar line of thought, we suggest here a limited form of nonlocality - "classical nonlocality". Our nonlocality is the (somewhat limited) classical analogue of the Hughston-Jozsa-Wootters (HJW) quantum nonlocality. The HJW nonlocality tells us how, for a given density matrix rho, Alice can generate any rho-ensemble on the North Star. This is done using surprisingly few resources - one shared entangled state (prepared in advance), one generalized quantum measurement, and no communication. Similarly, our classical nonlocality presents how, for a given probability distribution P, Alice can generate any P-ensemble on the North Star, using only one correlated state (prepared in advance), one (generalized) classical measurement, and no communication. It is important to clarify that while the classical teleportation and the classical non-locality protocols are probably rather insignificant from a clas

This work introduces a new music generation system, called AffectMachine-Classical, that is capable of generating affective Classic music in real-time. AffectMachine was designed to be incorporated into biofeedback systems (such as brain-computer-interfaces) to help users become aware of, and ultimately mediate, their own dynamic affective states. That is, this system was developed for music-based MedTech to support real-time emotion self-regulation in users. We provide an overview of the rule-based, probabilistic system architecture, describing the main aspects of the system and how they are novel. We then present the results of a listener study that was conducted to validate the ability of the system to reliably convey target emotions to listeners. The findings indicate that AffectMachine-Classical is very effective in communicating various levels of Arousal ($R^2 = .96$) to listeners, and is also quite convincing in terms of Valence (R^2 = .90). Future work will embed AffectMachine-Classical into biofeedback systems, to leverage the efficacy of the affective music for emotional well-being in listeners.

We propose a semantic representation of the standard quantum logic QL within a classical, normal modal logic, and this via a lattice-embedding of orthomodular lattices into Boolean algebras with one modal operator. Thus our classical logic is a completion of the quantum logic QL. In other words, we refute Birkhoff and von Neumann's classic thesis that the logic (the formal character) of Quantum Mechanics would be non-classical as well as Putnam's thesis that quantum logic (of his kind) would be the correct logic for propositional inference in general. The propositional logic of Quantum Mechanics is modal but classical, and the correct logic for propositional inference need not have an extroverted quantum character. One normal necessity modality suffices to capture the subjectivity of observation in quantum experiments, and this thanks to its failure to distribute over classical disjunction. The key to our result is the translation of quantum negation as classical negation of observability.

Classic machine learning algorithms have been reviewed and studied mathematically on its performance and properties in detail. This paper intends to review the empirical functioning of widely used classical supervised learning algorithms such as Decision Trees, Boosting, Support Vector Machines, k-nearest Neighbors and a shallow Artificial Neural Network. The paper evaluates these algorithms on a sparse tabular data for classification task and observes the effect on specific hyperparameters on these algorithms when the data is synthetically modified for higher noise. These perturbations were introduced to observe these algorithms on their efficiency in generalizing for sparse data and their utility of different parameters to improve classification accuracy. The paper intends to show that these classic algorithms are fair learners even for such limited data due to their inherent properties even for noisy and sparse datasets.

We show that theories that exhibit classicalization phenomenon cease to do so as soon as they are endowed a Wilsonian weakly-coupled UV-completion that restores perturbative unitarity, despite the fact that such UV-completion does not change the leading structure of the effective low-energy theory. For example, a Chiral Lagrangian of Nambu-Goldstone bosons (pions), with or without the Higgs (QCD) UV-completion looks the same in zero momentum limit, but the latter classicalizes in high energy scattering, whereas the former does not. Thus, theory must make a definite choice, either accept a weakly-coupled UV-completion or be classicalized. The UV-awareness that determines the choice is encoded in sub-leading structure of effective low-energy action. This peculiarity has to do with the fundamental fact that in classicalizing theories high energies correspond to large distances, due to existence of the extended classical configurations sourced by energy. UV-fate of the theory can be parameterized by introducing a concept of a new quantum length-scale, de-classicalization radius. Classicalization is abolished when this radius is a dominant length. We then observe a possibility of a qual

Imperceptible adversarial attacks have recently attracted increasing research interests. Existing methods typically incorporate external modules or loss terms other than a simple $l_p$-norm into the attack process to achieve imperceptibility, while we argue that such additional designs may not be necessary. In this paper, we rethink the essence of imperceptible attacks and propose two simple yet effective strategies to unleash the potential of PGD, the common and classical attack, for imperceptibility from an optimization perspective. Specifically, the Dynamic Step Size is introduced to find the optimal solution with minimal attack cost towards the decision boundary of the attacked model, and the Adaptive Early Stop strategy is adopted to reduce the redundant strength of adversarial perturbations to the minimum level. The proposed PGD-Imperceptible (PGD-Imp) attack achieves state-of-the-art results in imperceptible adversarial attacks for both untargeted and targeted scenarios. When performing untargeted attacks against ResNet-50, PGD-Imp attains 100$\%$ (+0.3$\%$) ASR, 0.89 (-1.76) $l_2$ distance, and 52.93 (+9.2) PSNR with 57s (-371s) running time, significantly outperforming exi

In 1914, Ramanujan presented a collection of 17 elegant and rapidly converging formulae for $π$. Among these, one of the most celebrated is the following series: \[\frac{1}π=\frac{2\sqrt{2}}{9801}\sum_{n=0}^{\infty}\frac{26390n+1103}{\left(n!\right)^4} \frac{\left(4n\right)!}{396^{4n}}\] In this paper, we give a full proof of this classic formula using hypergeometric series and a special type of lattice sums due to Zucker and Robertson. We will also use some results by Dirichlet and Edwards in algebraic number theory.

In early May 2022, the Terra ecosystem collapsed after the algorithmic stablecoin failed to maintain its peg. Emergency measures were taken by Terraform Labs (TFL) in an attempt to protect Luna and UST, but then were abruptly abandoned by TFL for Luna 2.0 several days later. At this time, the Luna Classic blockchain has been left crippled and in limbo for the last two months. In the face of impossible odds, the Luna Classic community has self organized and rallied to build and restore the blockchain. This technical document outlines the steps we, the community, have taken towards the emergency management of the Luna Classic blockchain in the weeks after the UST depeg. We outline precisely what would be implemented on-chain to mitigate the concerns of affected stakeholders, and build trust for external partners, exchanges, and third-party developers. For the Luna Classic community, validators, and developers, this outlines concrete steps on how passed governance can and will be achieved. We openly audit our own code and welcome any feedback for improvement. Let us move forward together as the true community blockchain.

Let $ n $ be an integer and $ n\ge 2 $. A classic integral quadratic form over local fields is called classic $ n $-universal if it represents all $n$-ary classic integral quadratic forms. We determine the equivalent conditions and minimal testing sets for classic $ n $-universal quadratic forms over dyadic local fields.

Since proposed in [X. Zhang and C.-W. Shu, J. Comput. Phys., 229: 3091--3120, 2010], the Zhang--Shu framework has attracted extensive attention and motivated many bound-preserving (BP) high-order discontinuous Galerkin and finite volume schemes for various hyperbolic equations. A key ingredient in the framework is the decomposition of the cell averages of the numerical solution into a convex combination of the solution values at certain quadrature points, which helps to rewrite high-order schemes as convex combinations of formally first-order schemes. The classic convex decomposition originally proposed by Zhang and Shu has been widely used over the past decade. It was verified, only for the 1D quadratic and cubic polynomial spaces, that the classic decomposition is optimal in the sense of achieving the mildest BP CFL condition. Yet, it remained unclear whether the classic decomposition is optimal in multiple dimensions. In this paper, we find that the classic multidimensional decomposition based on the tensor product of Gauss--Lobatto and Gauss quadratures is generally not optimal, and we discover a novel alternative decomposition for the 2D and 3D polynomial spaces of total degre

We study the power of classical and quantum algorithms equipped with nonuniform advice, in the form of a coin whose bias encodes useful information. This question takes on particular importance in the quantum case, due to a surprising result that we prove: a quantum finite automaton with just two states can be sensitive to arbitrarily small changes in a coin's bias. This contrasts with classical probabilistic finite automata, whose sensitivity to changes in a coin's bias is bounded by a classic 1970 result of Hellman and Cover. Despite this finding, we are able to bound the power of advice coins for space-bounded classical and quantum computation. We define the classes BPPSPACE/coin and BQPSPACE/coin, of languages decidable by classical and quantum polynomial-space machines with advice coins. Our main theorem is that both classes coincide with PSPACE/poly. Proving this result turns out to require substantial machinery. We use an algorithm due to Neff for finding roots of polynomials in NC; a result from algebraic geometry that lower-bounds the separation of a polynomial's roots; and a result on fixed-points of superoperators due to Aaronson and Watrous, originally proved in the con

Computer vision and image processing address many challenging applications. While the last decade has seen deep neural network architectures revolutionizing those fields, early methods relied on 'classic', i.e., non-learned approaches. In this study, we explore the differences between classic and deep learning (DL) algorithms to gain new insight regarding which is more suitable for a given application. The focus is on two challenging ill-posed problems, namely faint edge detection and multispectral image registration, studying recent state-of-the-art DL and classic solutions. While those DL algorithms outperform classic methods in terms of accuracy and development time, they tend to have higher resource requirements and are unable to perform outside their training space. Moreover, classic algorithms are more transparent, which facilitates their adoption for real-life applications. As both classes of approaches have unique strengths and limitations, the choice of a solution is clearly application dependent.

In this chapter, we present the main classic machine learning methods. A large part of the chapter is devoted to supervised learning techniques for classification and regression, including nearest-neighbor methods, linear and logistic regressions, support vector machines and tree-based algorithms. We also describe the problem of overfitting as well as strategies to overcome it. We finally provide a brief overview of unsupervised learning methods, namely for clustering and dimensionality reduction.

The March 5th, 1979 gamma-ray transient has long been thought to be fundamentally different from the classic gamma-ray bursts (GRBs). It had recurrences, pulsations, and a soft spectral component unlike classic GRBs. With the exception of the soft component reported from the Konus experiment, the unusual characteristics of March 5th were detectable main peak differs markedly from the published Konus spectrum. Rather than being dominated by a soft component similar to that observed in the soft gamma repeaters (SGRs), the ICE-PVO spectrum appears to be consistent with a classic GRB spectrum, especially above 100 keV. We believe that, given the ICE-PVO spectral observations, the March 5th transient would have been classified as a classic GRB when it was discovered. The SGRs and GRBs could be consanguineous: high-velocity neutron stars initially produce SGR events (and, occasionally a GRB like March 5th) and when they are older and in the galactic corona, they go through a GRB phase. The March 5th event demonstrates that high-velocity neutron stars at distances of tens kpc are capable of producing events like classic GRBs.

We generalize intermediate value Theorem to metric space,and make use of it to discuss existence of classic solution of the Boussinesq equation.

The purpose of this study is to give a performance comparison between several classic hand-crafted and deep key-point detector and descriptor methods. In particular, we consider the following classical algorithms: SIFT, SURF, ORB, FAST, BRISK, MSER, HARRIS, KAZE, AKAZE, AGAST, GFTT, FREAK, BRIEF and RootSIFT, where a subset of all combinations is paired into detector-descriptor pipelines. Additionally, we analyze the performance of two recent and perspective deep detector-descriptor models, LF-Net and SuperPoint. Our benchmark relies on the HPSequences dataset that provides real and diverse images under various geometric and illumination changes. We analyze the performance on three evaluation tasks: keypoint verification, image matching and keypoint retrieval. The results show that certain classic and deep approaches are still comparable, with some classic detector-descriptor combinations overperforming pretrained deep models. In terms of the execution times of tested implementations, SuperPoint model is the fastest, followed by ORB. The source code is published on \url{https://github.com/kristijanbartol/keypoint-algorithms-benchmark}.