Theoretical Footing Research Articles

A Mathematical Framework for Quantum Hamiltonian Simulation and Duality

AbstractAnalogue Hamiltonian simulation is a promising near-term application of quantum computing and has recently been put on a theoretical footing alongside experiencing wide-ranging experimental success. These ideas are closely related to the notion of duality in physics, whereby two superficially different theories are mathematically equivalent in some precise sense. However, existing characterisations of Hamiltonian simulations are not sufficiently general to extend to all dualities in physics. We give a generalised duality definition encompassing dualities transforming a strongly interacting system into a weak one and vice versa. We characterise the dual map on operators and states and prove equivalence ofduality formulated in terms of observables, partition functions and entropies. A building block is a strengthening of earlier results on entropy preserving maps—extensions of Wigner’s celebrated theorem- –to maps that are entropy preserving up to an additive constant. We show such maps decompose as a direct sum of unitary and antiunitary components conjugated by a further unitary, a result that may be of independent mathematical interest.

Assessing the Effect of International Lending Interest Rate Volatility on Government Infrastructural Expenditure

Spending on government infrastructure in Nigeria is faced with several economic challenges among which international lending rate volatility is significant. This study focuses on determining the effect of interest rate fluctuation on government infrastructural expenditure between 1993 and 2022. Its theoretical footing is the loanable fund theory, as it employs the Vector Error Correction framework to analyse the various data sourced from the World Development Indicators. The stationarity test confirmed that all the data were stationary at levels. Empirical results confirmed that international lending rate fluctuation has insignificant positive impact on public infrastructural spending (government infrastructural expenditure) in Nigeria. It was also observed that a long run relationship exists between international lending interest rate fluctuation and infrastructural development in Nigeria. It was therefore recommended that the regulatory authorities should strive to maximize this opportunity to design appropriate long term policies that can enhance infrastructural expansion through the appropriate modelling of international interest rate regime rather than putting in place short term measures that are not sustainable to infrastructural development. This paper proposes an innovative bond, the floating-interest infrastructure bond, which could attract private finance in infrastructure projects. This paper explains how private finance can be channelized into infrastructure investment by the sharing of spillover tax revenues between the government and the investors using a floating-interest-rate infrastructure bond. The private sector, such as banks and postal banks, will develop long-term deposits to provide loans for infrastructure development. Unlike the usual government bond, which provides a fixed interest rate, the proposed floating-interest-rate infrastructure bond pays a floating interest. Spillover effect depends on a number of factors, suc

Cholesterol, Eukaryotic Lipid Domains, and an Evolutionary Perspective of Transmembrane Signaling.

Transmembrane signaling is essential for complex life forms. Communication across a bilayer lipid barrier is elaborately organized to convey precision and to fine-tune strength. Looking back, the steps that it has taken to enable this seemingly mundane errand are breathtaking, and with our survivorship bias, Darwinian. While this review is to discuss eukaryotic membranes in biological functions for coherence and theoretical footing, we are obliged to follow the evolution of the biological membrane through time. Such a visit is necessary for our hypothesis that constraints posited on cellular functions are mainly via the biomembrane, and relaxation thereof in favor of a coordinating membrane environment is the molecular basis for the development of highly specialized cellular activities, among them transmembrane signaling. We discuss the obligatory paths that have led to eukaryotic membrane formation, its intrinsic ability to signal, and how it set up the platform for later integration of protein-based receptor activation.

A philosophical approach to improving empirical research on posttraumatic growth

ABSTRACT Post-traumatic growth (PTG) has been a key topic of research by psychologists over the last 25 years. But the idea that a person can benefit from adversity has been around for much longer, and is a stable in many mainstream cultures, and in theological and recent philosophical thinking. However, there has been, to date, little overlap between psychological research into PTG, and philosophical thinking about similar ideas. This is unfortunate, both because philosophers are not taking up potential sources of empirical support, and because psychological research into PTG is subject to a range of criticisms and concerns. In this paper, we aim to show how philosophical thinking can address some of these, and as a result put psychological research into PTG on a firmer theoretical footing.

All-optical switching of liquid crystals at terahertz frequencies enabled by metamaterials.

Nematic liquid crystals integrated with metallic resonators (metamaterials) are intriguing hybrid systems, which not only offer added optical functionalities, but also promote strong light-matter interactions. In this report, we show with an analytical model that the electric field generated by a conventional oscillator-based terahertz time domain spectrometer is strong enough to induce partial, all-optical switching of nematic liquid crystals in such hybrid systems. Our analysis provides a robust theoretical footing for the mechanism of all-optical nonlinearity of liquid crystals, which was recently hypothesised to explain an anomalous resonance frequency shift in liquid crystal-loaded terahertz metamaterials. The integration of metallic resonators with nematic liquid crystals offers a robust approach to explore optical nonlinearity within such hybrid material systems in the terahertz range; paves the way towards increased efficiency of existing devices; and broadens the range of applications of liquid crystals in the terahertz frequency range.

Understanding regional innovation cultures: Narratives, directionality, and conservative innovation in Bavaria

In recent years, innovation policy has increasingly embraced “situated” perspectives to better account for both how culture matters for innovation in diverse settings and how to deal with growing socio-economic inequalities and conflicts in the global innovation landscape. In this paper, we develop the framework of “regional innovation cultures” to put these perspectives on more solid theoretical footing. Regional innovation cultures refer to unique ways in which regional innovation initiatives and technology developments (their goals, meanings, material organization, and actor constellations) are being brought into alignment with local identities, socio-economic legacies, and unique political cultures. Our framework foregrounds five analytic dimensions: imagined social orders, representations, political cultures, relationship to national and global initiatives, and local controversies. We apply this framework to one in-depth regional case study of the German state of Bavaria, focusing on innovation policy in three sectors: space, agriculture, and automotive. We show how Bavaria enacts what we call a “conservative innovation culture”, where innovation is framed as a way to preserve socio-economic orders rather than disrupt them, and where political and economic incumbents forcefully shape and absorb emerging niche activities. Innovation in Bavaria is performed through a political culture of small state-like corporatism that emphasizes strong coordination of all major actors, grants state bureaucracies a key role in defining directions for innovation in the form of a quasi-technonationalism, and tends to sideline alternative visions and voices for innovation. Across all three sectors, culture serves as a framing device to legitimize innovation primarily as an extension of existing social orders; conversely, innovation serves as a vehicle to reinforce dominant ideas of the public good. Through the lens of culture, innovation ceases to be primarily a source of social change and disruption, but can be understood as a mode of socio-cultural reproduction subject to tentative, constrained experimentation. Innovation initiatives cannot break too radically with existing social norms and orders without risking public support or legitimacy, thus creating a trade-off between proposing novelty and ensuring continuity. Our analysis lends further support for constructivist approaches to innovation studies, emphasizing plural and socially grounded perspectives on the rationalization, implementation, and evaluation of innovation.

Superradiance in stars:non-equilibrium approach to damping of fields in stellar media

Superradiance in black holes is well-understood but a general treatment for superradiance in stars has until now been lacking. This is surprising given the ease with which we can observe isolated neutron stars and the array of signatures which would result from stellar superradiance. In this work, we present the first systematic pipeline for computing superradiance rates in rotating stars. Our method can be used with any Lagrangian describing the interaction between the superradiant field and the constituents of the star. Our scheme falls into two parts: firstly we show how field theory at finite density can be used to express the absorption of long wavelength modes into the star in terms of microphsyical scattering processes. This allows us to derive a damped equation of motion for the bosonic field. We then feed this into an effective theory for long wavelengths (the so-called worldline formalism) to describe the amplification of superradiant modes of arbitrary multipole moment for a rapidly rotating star. Our method places stellar superradiance on a firm theoretical footing and allows the calculation of the superradiance rate arising from any interaction between a bosonic field and stellar matter.

XNAS: A Regressive/Progressive NAS for Deep Learning

Deep learning has achieved great and broad breakthroughs in many real-world applications. In particular, the task of training the network parameters has been masterly handled by back-propagation learning. However, the pursuit on optimal network structures remains largely an art of trial and error. This prompts some urgency to explore an architecture engineering process, collectively known as Neural Architecture Search (NAS). In general, NAS is a design software system for automating the search of effective neural architecture. This article proposes an X-learning NAS (XNAS) to automatically train a network’s structure and parameters. Our theoretical footing is built upon the subspace and correlation analyses between the input layer, hidden layer, and output layer. The design strategy hinges upon the underlying principle that the network should be coerced to learn how to structurally improvethe input/output correlation successively (i.e., layer by layer). It embraces both Progressive NAS (PNAS) and Regressive NAS (RNAS). For unsupervised RNAS, Principal Component Analysis (PCA) is a classic tool for subspace analyses. By further incorporating teacher’s guidance, PCA can be extended to Regression Component Analysis (RCA) to facilitate supervised NAS design. This allows the machine to extract components most critical to the targeted learning objective. We shall further extend the subspace analysis from multi-layer perceptrons to convolutional neural networks, via introduction of Convolutional-PCA (CPCA) or, more simply, Deep-PCA (DPCA). The supervised variant of DPCA will be named Deep-RCA (DRCA). The subspace analyses allow us to compute optimal eigenvectors (respectively, eigen-filters) and principal components (respectively, eigen-channels) for optimal NAS design of multi-layer perceptrons (respectively, convolutional neural networks). Based on the theoretical analysis, an X-learning paradigm is developed to jointly learn the structure and parameters of learning models. The objective is to reduce the network complexity while retaining (and sometimes improving) the performance. With carefully pre-selected baseline models, X-learning has shown great successes in numerous classification-type and/or regression-type applications. We have applied X-learning to the ImageNet datasets for classification and DIV2K for image enhancements. By applying X-learning to two types of baseline models, MobileNet and ResNet, both the low-power and high-performance application categories can be supported. Our simulations confirm that X-learning is by and large very competitive relative to the state-of-the-art approaches.

Parametric derivatives in inverse conductivity problems with total variation regularization

The focus of this paper is an improved differentiability result for the forward map in inverse problems involving elliptic partial differential equations, and examination of its significance in the context of the electrical impedance tomography (EIT) problem with total variation (TV) regularization. We base our analysis on the Fréchet derivative of the mapping which takes a given conductivity function (spatially varying) in an electrostatic model to a corresponding elliptic PDE solution, and we develop the implications of a certain compactness property of the parameter space. By following this approach, we show Fréchet differentiability with a weaker norm (the L1 norm) for the parameter space than is usually used (the L∞ norm), thus improving the Fréchet differentiability result. The EIT problem with TV regularization is well studied in the literature, and several authors have addressed the Fréchet differentiability question. However, to the best of our knowledge and as we argue, our result is the strongest analytical result in this context. Many derivative-based methods such as Gauss–Newton and Levenburg-Marquardt lie at the heart of many proposed methods for EIT, and the results described herein for these derivative calculations provide a firm theoretical footing for them.

Big data in action: An overview of big data studies in tourism and hospitality literature

Tourism research has marched into the big data arena and brought remarkable developments. Despite the promising role of big data and increasing volume of research, it is worth noting that current big data research in the tourism and hospitality field is rather vague or insufficient, particularly from the perspectives of its philosophical grounding, methodological approaches, and implications. This article aims to provide a comprehensive review of big data research from the tourism and hospitality literature. A content analysis of 146 big-data-related articles identifies the research and methodological trends in these fields. The findings reveal that big data have expanded the scope of tourism research. It offers useful and practical knowledge for destination, hotel, revenue, and reputation management. However, big data research is rather limited in methodologies, and there is a need for more solid theoretical and philosophical footings for significant knowledge generation.

Perspectives on Service Quality Dimensions and Customer Satisfaction in the Nigerian Banking Industry

Customer satisfaction is a critical success factor in the service industry. It is an essential marketing concept that is critical in different sectors in the manufacturing and service industries. Customer satisfaction is a measure of how products offered by a company meet or exceed customer expectations. Focusing on and satisfying the needs of customers are very important for the survival and continued existence of service firms especially banking services. The purpose of this study is to examine the extant literature on the relationship between service quality dimensions and customer satisfaction as it applies to the Nigerian banking industry. In order to provide a theoretical footing, this study adopted the Expectation-confirmation theory (ECT). The basis for this adoption is that the Expectation-confirmation theory (ECT) explains post-purchase or post-adoption satisfaction as a function of expectations, perceived performance, and disconfirmation of beliefs. The study clearly shows that service quality dimensions play an important role as a driver for higher customer satisfaction level. Therefore, managers in the banking industry are advised to give more attention to the five (5) service quality dimensions namely: tangibility, reliability, responsiveness, assurance and empathy. These five service quality dimensions can be major influencers of customer satisfaction in the banking industry.

Thermal relic of self-interacting dark matter with retarded decay of mediator

The existence of a light mediator is beneficial to some phenomena in astroparticle physics, such as the core-cusp problem and diversity problem. It can decouple from Standard Model to avoid direct detection constraints, generally realized by retard decay of the mediator. Their out-of-equilibrium decay process changes the dark matter (DM) freeze-out via temperature discrepancy. This type of hidden sector (HS) typically requires a precision calculation of the freeze-out process considering HS temperature evolution and the thermal average of the cross-section. If the mediator is light sufficiently, we can not ignore the s-wave radiative bound state formation process from the perspective of CMB ionization and Sommerfeld enhancement. We put large mass splitting between DM and mediator, different temperature evolution on the same theoretical footing, discussing the implication for DM relic density in this HS. We study this model and illustrate its property by considering the general Higgs-portal dark matter scenario, which includes all the relevant constraints and signals. It shows that the combination of BBN and CMB constraint favors the not-too-hot HS, rinf< 102, for the positive cubic interaction of mediator scenario. On the other hand, the negative cubic interaction is ruled out except for our proposed blind spot scenario.

Robust, high-resolution, indexed 3D slowness surfaces for Rayleigh-type waves on Lithium Niobate via parallelised Newtonian flow phase tracking

Micro-Electromechanical System Surface Acoustic Wave (MEMS SAW) devices are key technological enablers in RF telecommunications filters, inertial systems and biosensors. Recent developments in Focused SAW (FSAW) device design demand high quality, high resolution dispersion data for multiple in-plane directions, typically presented as a slowness curve, to facilitate design. Very few slowness curves are available in the literature for free surfaces not aligned to the crystallographic axes except in a few special cases. The first major novelty in the present work is a bespoke computational approach to solving this problem with a distinct theoretical basis. After a brief summary of the motivation, the mathematical formulation and its theoretical footing are outlined. The most important algorithms are given as pseudocode, and source code, datasets and extended visualisation available online are referenced. The structure of the method is illustrated via application to the bulk wave problem. The model is then applied to Lithium Niobate to generate new results characterising SAW propagation velocity for all in-plane directions over one full circle of plane inclinations. These results are presented as a two-dimensional velocity surface. This new data constitutes the second area of novelty herein. Finally, the applicability of the work to FSAW design is indicated by examining the RMS variation of propagation velocity for different cut plane inclinations.

A high-fidelity approach to correlate the nucleate pool boiling data of roughened surfaces

The existing nucleate pool boiling correlations have theoretical footings and their usefulness is restricted by the failure to effectively account for the surface effect. To tackle this problem, a high-fidelity approach based on deep learning has been developed to predict the nucleate boiling surfaces subject to various surface roughness. The proposed model accounts for the effect of surface roughness, roughness fabrication method, surface material, surface inclination, saturation temperature, and pressure on the pool boiling performance of dielectric liquids, water, and refrigerants. The proposed method can accurately predict the boiling heat transfer performance of roughened surfaces by including the most influential surface characteristics, testing conditions, and liquid thermophysical properties into the architecture of the developed model. Correlation matrix identifies that heat flux, surface inclination, surface roughness, thermal conductivity of surface material, liquid saturation temperature, and pressure are the prime factors to affect the nucleate boiling heat transfer coefficient. Different neural networks (DNNs) are built and tested in order to find an optimal model based on an experimental dataset of 13000 data points. The final selected model can estimate the investigated parameter with a coefficient of determination (R2) = 0.994 and mean absolute error (MAE) = 0.65. The suggested method can be utilized to predict the boiling heat transfer performance of a variety of roughened surfaces subject to different working fluids and testing conditions.

Improving ECC Ozonesonde Data Quality: Assessment of Current Methods and Outstanding Issues

AbstractWe review the current state of knowledge of ozonesonde uncertainty and bias, with reference to recent developments in laboratory and field experiments. In the past 20 years ozonesonde precision has improved by a factor of 2, primarily through the adoption of strict standard operating procedures. The uncertainty budget for the ozone partial pressure reading has contributions from stoichiometry, cell background current, pump efficiency and temperature, sensing solution type, and volume. Corrections to historical data for known issues may reduce biases but simultaneously introduce additional uncertainties. This paper describes a systematic approach to quantifying these uncertainties by considering the physical and chemical processes involved and attempts to place our estimates on a firm theoretical or empirical footing. New equations or tables for ozone/iodine conversion efficiency, humidity and temperature corrections to pump flow rate, and altitude‐dependent pump flow corrections are presented, as well as detailed discussion of stoichiometry and conversion efficiencies. The nature of the so‐called “background current” is considered in detail. Two other factors particularly affecting past measurements, uncertainties and biases in the pressure measurement, and the comparison of sonde profiles to spectrophotometric measurements of total column ozone, are also discussed. Several quality assurance issues remain, but are tractable problems that can be addressed with further research. This will be required if the present goal of better than 5% overall uncertainty throughout the global ozonesonde network is to be achieved.

Gaussian process foreground subtraction and power spectrum estimation for 21 cm cosmology

ABSTRACT One of the primary challenges in enabling the scientific potential of 21 cm intensity mapping at the epoch of reionization (EoR) is the separation of astrophysical foreground contamination. Recent works have claimed that Gaussian process regression (GPR) can robustly perform this separation, particularly at low Fourier k wavenumbers where the EoR signal reaches its peak signal-to-noise ratio. We revisit this topic by casting GPR foreground subtraction (GPR-FS) into the quadratic estimator formalism, thereby putting its statistical properties on stronger theoretical footing. We find that GPR-FS can distort the window functions at these low k modes, which, without proper decorrelation, make it difficult to probe the EoR power spectrum. Incidentally, we also show that GPR-FS is in fact closely related to the widely studied inverse covariance weighting of the optimal quadratic estimator. As a case study, we look at recent power spectrum upper limits from the Low-Frequency Array (LOFAR) that utilized GPR-FS. We pay close attention to their normalization scheme, showing that it is particularly sensitive to signal loss when the EoR covariance is misestimated. This has possible ramifications for recent astrophysical interpretations of the LOFAR limits, because many of the EoR models ruled out do not fall within the bounds of the covariance models explored by LOFAR. Being more robust to this bias, we conclude that the quadratic estimator is a more natural framework for implementing GPR-FS and computing the 21 cm power spectrum.

Translation Teaching and Cognitive Linguistics

This paper is mainly concerned with the implications of cognitive linguistics for translation teaching and pedagogy. It sets out to succinctly chart some presumed shortcomings of replacement-based pedagogical methods that have long been centred around linear mechanical substitution of linguistic signs and patterns. Replacement approach, the paper argues, falls short of reinforcing what it takes to be the conceptual competence. In this connection, we account for our main assumption that translation teaching should be based on a sound theoretical footing that takes the conceptual system and the frames, or other structuring entities, populating it on board. Experimentally focusing on the conceptual system, cognitive linguistics’ framework, we contend building on some relevant literature, provides a wide range of far reaching procedural models conductive to the innovation of translation pedagogy and practice. The examples investigated in the paper reveal that translation teaching may be more prolific if it is equally based on such models, which inform our understanding of textual lexico-semantic units in terms of their surface functioning as prompts serving for dynamically constructing semantic-conceptual equivalence.

Exciton-Scattering-Induced Dephasing in Two-Dimensional Semiconductors.

Enhanced Coulomb interactions in monolayer transition metal dichalcogenides cause tightly bound electron-hole pairs (excitons) that dominate their linear and nonlinear optical response. The latter includes bleaching, energy renormalizations, and higher-order Coulomb correlation effects like biexcitons and excitation-induced dephasing. While the first three are extensively studied, no theoretical footing for excitation-induced dephasing in exciton-dominated semiconductors is available so far. In this Letter, we present microscopic calculations based on excitonic Heisenberg equations of motion and identify the coupling of optically pumped excitons to exciton-exciton scattering continua as the leading mechanism responsible for an optical-power-dependent linewidth broadening (excitation-induced dephasing) and sideband formation. Performing time-, momentum-, and energy-resolved simulations, we quantitatively evaluate the exciton-induced dephasing for the most common monolayer transition metal dichalcogenides and find an excellent agreement with recent experiments.

Electron ionization via dark matter-electron scattering and the Migdal effect

There are currently several existing and proposed experiments designed to probe sub-GeV dark matter (DM) using electron ionization in various materials. The projected signal rates for these experiments assume that this ionization yield arises only from DM scattering directly off electron targets, ignoring secondary ionization contributions from DM scattering off nuclear targets. We investigate the validity of this assumption and show that if sub-GeV DM couples with comparable strength to both protons and electrons, as would be the case for a dark photon mediator, the ionization signal from atomic scattering via the Migdal effect scales with the atomic number $Z$ and 3-momentum transfer $\mathbf{q}$ as $Z^2 \mathbf{q}^2$. The result is that the Migdal effect is always subdominant to electron scattering when the mediator is light, but that Migdal-induced ionization can dominate over electron scattering for heavy mediators and DM masses in the hundreds of MeV range. We put these two ionization processes on identical theoretical footing, address some theoretical uncertainties in the choice of atomic wavefunctions used to compute rates, and discuss the implications for DM scenarios where the Migdal process dominates, including for XENON10, XENON100, and the recent XENON1T results on light DM scattering.

Courts Beyond Judging

Across all fifty states, a woefully understudied institution of government is responsible for a broad range of administrative, legislative, law enforcement, and judicial functions. That important institution is the state courts. While the literature has examined the federal courts and federal judges from innumerable angles, study of the state courts as institutions of state government — and not merely as sources of doctrine and resolvers of disputes — has languished. This Article remedies that oversight by drawing attention for the first time to the wide array of roles state courts serve, and by evaluating the suitability of both the allocation of these tasks and the various procedures by which they are carried out across the country. In every state, on top of the ordinary adversarial dispute-resolution function that we expect judges to serve, it is state court judges who are charged with administrative functions like approving applications to change one’s name, to enter the legal profession, or to exercise constitutional rights like accessing abortion care without parental knowledge or consent. And it is often state court judges who are charged with or who have taken on a range of legislative and policymaking functions like redistricting and establishing specialized criminal courts for veterans, persons in need of drug treatment, and others. And in some states, it is state court judges who have the law enforcement power to decide whether a prosecutor’s charging choice was a wise exercise of her discretion. These are not mere odds and ends of governing either; weighty interests hang in the balance across the board. In addition to developing this more complete portrait of the state courts — and of important variation in how these roles are structured across the states — this Article examines whether the interests at stake in each context are appropriately served when state court judges handle them. In some arenas, they are, and this Article places these facets of state court practice on firmer theoretical footing. In others, however, there is cause for concern. With respect to these tasks, this Article argues that state court judges need to be better guided by statute and subject to reason-giving and record-developing requirements that would channel their discretion, improve their decisionmaking, and enable more rigorous appellate review. But most important of all, this Article calls for states to make more conscious choices about structuring the roles they assign to state courts, and for scholars to devote more careful attention to these powerful and nuanced institutions.