Set Of Operators Research Articles

An emergent ontology for digitally modeling existing buildings: examining Kyoto’s Nishiki market

PurposeThis project examines digital modeling strategies for existing buildings. In this context, it aims to question assumptions about the need for geometric accuracy and the efficacy of predefined ontologies. As a counterpoint to prevailing digital modeling strategies, this project proposes a digital modeling approach using a project-specific, emergent ontology.Design/methodology/approachNishiki Market, in Kyoto, Japan, is studied as a test case. The emergent-ontology modeling process is introduced with an initial minimal set of operations including basic fold and trim operations applicable to surfaces. As the model develops iteratively, new situations are encountered for which existing rules are insufficient. In response, the model maker’s subjective judgment is invoked to introduce new operations, and ontological rules are allowed to expand.FindingsThe emergent-ontology approach, when executed on the Nishiki Market test case, enables representation of specific architectural qualities, highlighting semantic distinctions between digitally modeled elements of real-world features. The modeling approach generated project-specific knowledge, informing disciplinary understanding. Ontological emergence enabled semantic relationships to be disclosed and newly constructed.Originality/valueThe project proposes a novel methodology using an emergent ontology for digitally modeling existing buildings. Instead of remaining within the limitations a predefined ontology, the model maker’s subjective decisions shape the model’s ongoing development. This interpretive approach allows project-specific knowledge generation while challenging prevailing assumptions about accuracy and consistency in digital models of existing buildings.

On the set of supercyclic operators

AbstractIn this article, we address a problem posed by Bayart regarding the existence of an infinite‐dimensional closed vector subspace (excluding the null operator) within the set of supercyclic operators on Banach spaces. We fully resolve this problem by establishing the existence of the closed subspace. Furthermore, we prove that the set of supercyclic operators on contains, up to the null operator, an isometric copy of .

A New Melting Model and Its Implementation in Parameterized Forward Operators for Polarimetric Radar Data Simulation With Double Moment Microphysics Schemes

AbstractTo improve short‐term severe weather forecasts through assimilation of polarimetric radar data (PRD), the use of accurate and efficient forward operators for polarimetric radar variables is required. In this study, a new melting model is proposed to estimate the mixing ratio and number concentration of melting hydrometeor species and incorporated in a set of parameterized polarimetric radar forward operators. The new melting model depends only on the mixing ratio and number concentration of rain and ice species and is characterized by its independence from ambient temperature and its simplicity and ease of linearization. To assess the impact of this newly proposed melting model on the simulated polarimetric radar variables, a real mesoscale convective system is simulated using three double‐moment microphysics schemes. Compared with the output of the original implementation of the parameterized forward operators (PFO_Old) that rely on an “old” melting model which only estimates the mixing ratio of the melting species, the updated implementation with the new melting model (PFO_New) that estimates both the mixing ratio and number concentration of melting species eliminates the very large mass/volume‐weighted mean diameter (Dm) at the bottom of the melting layer and produces more reasonable melting layer signatures for all three double‐moment microphysics schemes that more closely match the corresponding radar observations. This suggests that the new melting model has more reasonable implicit estimates of mixing ratios and number concentrations of melting hydrometeor species than the “old” melting model.

Upper bounds for the norm of the sum and Kronecker product of Hilbert space operators

Upper bounds for the norm of the sum and Kronecker product of Hilbert space operators

Improving functional merit of commercially available pea protein isolate by employing a combination of physical, chemical, and enzymatic modification

Pea protein-based diets have garnered global attention as a viable alternative to meat or primary protein sources. Nevertheless, the utilization of pea protein is hindered by issues such as solubility, and emulsifying properties. This research presents a combined set of techniques employed to commercially available Pea Protein Isolate (PPI) to effectively address major setbacks associated with the usage of pea protein in diverse industrial sectors, especially meat. The set integrates pH at 10.0, sonication at three different magnitudes (1,2 and 4 W/mL), heat at 60 °C, and enzyme hydrolysis through Flavourzyme at three different enzyme-to-substrate ratios (0.25%, 0.5%, and 1.0%). Among the twelve treatments, PPI11, PPI12, and PPI13 showed promising outcomes for artificial meat. The treatments exhibited favorable solubility, gelling, emulsifying, and foaming properties while containing the optimum and desirable protein size after undergoing partial hydrolysis comprehended in the gel electrophoresis results. Solubility (for PPI11, PPI12, PPI13) had seen significant (p < 0.05) improvement from 31.03% ± 2.11% (Control) to 64.43% ± 2.09%, 66.23% ± 2.11%, and 69.87% ± 1.57%, accompanied by changes in protein morphology in scanning electron microscopy. Furthermore, treated PPIs were applied to stabilize sunflower oil-in-water emulsion, showing a significant escalation in emulsion capacity (38.46%, 39.42% and 66.61% increase for PPI11, PPI12, PPI13), foaming capacity (46.88%, 51.20%, and 69.60% increase for PPI11, PPI12, PPI13), and foaming stability (30 min) (23.97%, 28.70%, and 30.22% increase for PPI11, PPI12, PPI13). Gelling properties were decisive in this study (2.512 ± 0.1 N, 2.604 ± 0.1 N, and 2.168 ± 0.3 N, for PPI11, PPI12, and PPI13), predominantly in plant-based meat analog perspective. So the mechanism proposed through this research will enable pea protein treated with this set of operations, combining physical, chemical and enzymatic treatments, to further its journey towards considerably improved meat analogs.

Operators on L-algebras

The aim of this paper is to investigate several operators on L-algebras. At first, closure (interior) operators on L-algebras are defined and some properties of them are obtained. Then, existential operators and universal operators on L-algebras are studied, a one-to-one correspondence between the set of all quantifier operators and the set of all relative complete subalgebras of CKL-algebras is constructed. Furthermore, very true operators on L-algebras are investigated and by giving a very true ideal of a very true L-algebra, quotient structures on very true L-algebras are established.

Parent Hamiltonian Reconstruction via Inverse Quantum Annealing.

Finding a local Hamiltonian H[over ^] that has a given many-body wave function |ψ⟩ as its ground state, i.e., a parent Hamiltonian, is a challenge of fundamental importance in quantum technologies. Here we introduce a numerical method, inspired by quantum annealing, that efficiently performs this task through an artificial inverse dynamics: a slow deformation of the states |ψ(λ(t))⟩, starting from a simple state |ψ_{0}⟩ with a known H[over ^]_{0}, generates an adiabatic evolution of the corresponding Hamiltonian. We name this approach inverse quantum annealing. The method, implemented through a projection onto a set of local operators, only requires the knowledge of local expectation values, and, for long annealing times, leads to an approximate parent Hamiltonian whose degree of locality depends on the correlations built up by the states |ψ(λ)⟩. We illustrate the method on two paradigmatic models: the Kitaev fermionic chain and a quantum Ising chain in longitudinal and transverse fields.

AutoEFT: Automated operator construction for effective field theories

The program AutoEFT is described. It allows one to generate Effective Field Theories (EFTs) from a given set of fields and symmetries. Allowed fields include scalars, spinors, gauge bosons, and gravitons. The symmetries can be local or global Lie groups based on U(1) and SU(N). The mass dimension of the EFT is limited only by the available computing resources. The operators are stored in a compact, human and machine-readable format. Aside from the program itself, we provide input files for EFTs based on the Standard Model and a number of its extensions. These include additional particles and symmetries, EFTs with minimal flavor violation, and gravitons. Program summaryProgram title:AutoEFTCPC Library link to program files:https://doi.org/10.17632/z8xm2hpbsp.1Developer's repository link:https://gitlab.com/auto_eft/autoeftLicensing provisions: MIT licenseProgramming language:PythonNature of problem: The bottom-up construction of an Effective Field Theory (EFT) which describes physics below a certain energy scale Λ requires obtaining a set of operators, composed of fields with mass m≪Λ, that are invariant under certain symmetries. One is primarily interested in complete sets of independent operators, called operator bases. Their construction for a given mass dimension of the operators is nontrivial due to algebraic and kinematic relations that may render different operators redundant. Except for the lowest mass dimensions, the number of operators is so large that the task of constructing an explicit EFT operator basis requires a high degree of automation on a computer. In addition, an automated approach will allow one to immediately take into account newly postulated or discovered light particles beyond the Standard Model.Solution method: Based on the group theoretical techniques and concepts established in Refs. [1–9] and in particular Refs. [10,11], we developed the program AutoEFT, capable of constructing a non-redundant on-shell operator basis for general EFTs and arbitrary mass dimension. Provided a suitable model file, the respective operator basis is generated explicitly, including contractions of the symmetry group indices, in a fully automated fashion. Due to the generality of the algorithm, it can be applied to a variety of low-energy scenarios. The underlying low-energy theory is encoded in a model file which defines the symmetries and the field content. The fairly simple format enables the user to compose their own model files and to construct the respective operator basis with minimal effort.Additional comments including restrictions and unusual features: In its current form, AutoEFT is restricted to theories including particles with spin 0, 1/2, 1, and 2, where the latter two are considered massless. In addition, AutoEFT only constructs operators that mediate proper interactions, meaning that any operator must be composed of at least three fields. The internal symmetries must be given as factors of U(1) and SU(N) groups. In principle, operator bases can be generated for any mass dimension, which is, however, limited by the available computing resources.

Supporting Better Insights of Data Science Pipelines with Fine-grained Provenance

Successful data-driven science requires complex data engineering pipelines to clean, transform, and alter data in preparation for machine learning, and robust results can only be achieved when each step in the pipeline can be justified, and its effect on the data explained. In this framework, we aim at providing data scientists with facilities to gain an in-depth understanding of how each step in the pipeline affects the data, from the raw input to training sets ready to be used for learning. Starting from an extensible set of data preparation operators commonly used within a data science setting, in this work we present a provenance management infrastructure for generating, storing, and querying very granular accounts of data transformations, at the level of individual elements within datasets whenever possible. Then, from the formal definition of a core set of data science preprocessing operators, we derive a provenance semantics embodied by a collection of templates expressed in PROV, a standard model for data provenance. Using those templates as a reference, our provenance generation algorithm generalises to any operator with observable input/output pairs. We provide a prototype implementation of an application-level provenance capture library to produce, in a semi-automatic way, complete provenance documents that account for the entire pipeline. We report on the ability of that reference implementation to capture provenance in real ML benchmark pipelines and over TCP-DI synthetic data. We finally show how the collected provenance can be used to answer a suite of provenance benchmark queries that underpin some common pipeline inspection questions, as expressed on the Data Science Stack Exchange.

A reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses

Globally, e-commerce warehouses have begun implementing robotic mobile fulfillment systems (RMFS), which can improve order-picking efficiency by using automated guided vehicles (AGVs) to realize operations from parts to pickers. AGVs depart from their initial points, move to a target rack position, and subsequently transport racks to picking stations. The AGVs return the racks to their original positions after the workers pick them up. When all tasks are completed, the AGVs return to their starting point. In this context, the main challenge is the task assignment and route planning of multiple AGVs to minimize travel times. We formulate a mixed-integer linear programming (MILP) model with valid inequalities to solve small problem instances optimally. We introduce a reinforcement learning (RL)-based hyper-heuristic (HH) framework to solve large instances to near-optimality. A typical HH framework comprises two levels: high-level heuristics (HLH) and low-level heuristics (LLH). The framework starts from an initial solution and improves iteratively through LLHs, while the HLH invokes a selection strategy and an acceptance criterion to generate a new solution. We propose a novel selection strategy based on the improved Multi-Armed Bandits algorithm called Co-SLMAB and Exponential Monte Carlo with counters (EMCQ) as the acceptance criterion. The corresponding collision avoidance rules are then formulated for different conflicts to construct a conflict-free traveling route for AGVs. Besides testing the proposed framework’s effectiveness in real-life warehouse layouts, we perform extensive computational experiments and a thorough sensitivity analysis. The results show that (i) the proposed valid inequalities aid in obtaining better lower bounds and significantly speed up the solution process; (ii) the Co-SLMAB-HH framework is quite competitive compared to CPLEX, outperforming the other tested hyper-heuristics and the problem-specific heuristic regarding convergence and computation time; and (iii) a pool of LLHs consisting of a wide range of different operators is advantageous over a limited set of simple operators while solving problems using hyper-heuristics.

МАРКЕТИНГОВІ ТЕХНОЛОГІЇ ФОРМУВАННЯ ІМІДЖУ ТА УКРІПЛЕННЯ БРЕНДУ ЗАКЛАДІВ ВИЩОЇ ОСВІТИ

Formulation of the problem. The relevance of the article is determined by the role of special marketing technologies, which relate to various aspects of the functioning and development of higher education institutions, including communications, image, reputation and brand. The purpose of the article is to study the directions and features of the application of marketing technologies in the communication, image and brand policy of institutions of higher education, to develop recommendations for the use of marketing tools. The object of the study is the processes of applying marketing technologies and marketing tools by institutions of higher education. Research methods include desk research, theoretical generalization, system analysis, observation, synthesis, abstract-logical. The research hypothesis was formed in the direction of understanding marketing technologies (MarTech) as a set of knowledge, processes, specific methods, measures, operations and techniques, tools, programs, technical tools that allow higher education institutions to achieve marketing goals. Presenting main material. The authors emphasized that institutions of higher education use MarTech to organize, implement, coordinate, administer and evaluate the effectiveness of marketing campaigns, personalized experience, image formation, branding, and online and offline content creation. The application of educational technology (EdTech) marketing to create an engaging, inclusive, and personalized educational experience is considered. Several EdTech trends have been identified that are already creating value propositions for the learning of the future. Originality and practical importance are important. It was concluded that marketing technologies allow higher education institutions to form a successful competitive educational brand, build an attractive image, maintain a positive reputation, arouse the interest of the target audience and present the uniqueness of the university. The trends that higher education institutions should take into account when forming an image communication policy and branding are highlighted. Conclusions and prospects for further research. Marketing technologies and tools are considered, which, in the opinion of the author, will increase the recognition of the ZVO brand and promote the promotion of its individual specialties. It is recommended that institutions of higher education choose those marketing technologies that take into account the new conditions of competition, are based on non-standard approaches and creative ideas, are formed within the framework of a creative concept of marketing and correspond to the new generation of customers

Improved Evolutionary Operators for Sparse Large-Scale Multiobjective Optimization Problems

Many critical science, societal, and engineering fields contain large-scale multiobjective optimization problems (LSMOPs), comprised of many decision variables. However, as the number of decision variables increases, optimization algorithms face exponentially large search spaces, thereby exhibiting a degraded performance. Nonetheless, LSMOPs whose optimal solutions correspond to sparse variable vectors can be solved more efficiently by evolutionary multiobjective optimization (EMO) algorithms. Despite the great recent strides in developing generic EMO algorithms for sparse LSMOPs, there is still room for improvement. Specifically, algorithms still struggle to find convergent and diverse Pareto fronts in an acceptable amount of time when solving sparse LSMOPs with thousands of decision variables. To better solve sparse LSMOPs, we propose a novel set of evolutionary operators to adapt small-scale EMO algorithms for sparse LSMOPs. These simple, novel, and effective operators include varied striped sparse population sampling (VSSPS), sparse simulated binary crossover (S-SBX), and sparse polynomial mutation (S-PM). These operators, combined with NSGA-II, make the proposed S-NSGA-II algorithm. S-NSGA-II runs near-universally faster than existing methods for problems containing up to 6,400 decision variables, while performing as well as or better than contemporary sparse LSMOP algorithms with respect to hypervolume, especially with problems with larger than 5,000 decision variables.

Probabilistic Interpolation of Quantum Rotation Angles.

Quantum computing requires a universal set of gate operations; regarding gates as rotations, any rotation angle must be possible. However a real device may only be capable of B bits of resolution, i.e., it might support only 2^{B} possible variants of a given physical gate. Naive discretization of an algorithm's gates to the nearest available options causes coherent errors, while decomposing an impermissible gate into several allowed operations increases circuit depth. Conversely, demanding higher B can greatly complexify hardware. Here, we explore an alternative: probabilistic angle interpolation (PAI). This effectively implements any desired, continuously parametrized rotation by randomly choosing one of three discretized gate settings and postprocessing individual circuit outputs. The approach is particularly relevant for near-term applications where one would in any case average over many runs of circuit executions to estimate expected values. While PAI increases that sampling cost, we prove that (a)the approach is optimal in the sense that PAI achieves the least possible overhead and (b)the overhead is remarkably modest even with thousands of parametrized gates and only seven bits of resolution available. This is a profound relaxation of engineering requirements for first generation quantum computers where even 5-6bits of resolution may suffice and, as we demonstrate, the approach is many orders of magnitude more efficient than prior techniques. Moreover we conclude that, even for more mature late noisy intermediate-scale quantum era hardware, no more than nine bits will be necessary.

Amplitude basis for conformal correlators

We present a classification of conformally-invariant three-point tensor structures in d dimensions that parallels the classification of three-particle scattering amplitudes in d + 1 dimensions. Using a set of canonically-normalized weight-shifting operators, we construct a basis of three-point structures involving conserved currents or stress tensors and non-conserved spinning operators, directly from their amplitude counterparts. As an application, we also examine the conformal block expansion of the four-point functions of external currents and stress tensors in this amplitude basis. Our results can be useful for conformal bootstrap applications involving spinning correlators as well as Witten diagram computations in anti-de Sitter space.

Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior

We propose a framework for the design of feedback controllers that combines the optimization-driven and model-free advantages of deep reinforcement learning with the stability guarantees provided by using the Youla-Kučera parameterization to define the search domain. Recent advances in behavioral systems allow us to construct a data-driven internal model; this enables an alternative realization of the Youla-Kučera parameterization based entirely on input–output exploration data. Perhaps of independent interest, we formulate and analyze the stability of such data-driven models in the presence of noise. The Youla-Kučera approach requires a stable “parameter” for controller design. For the training of reinforcement learning agents, the set of all stable linear operators is given explicitly through a matrix factorization approach. Moreover, a nonlinear extension is given using a neural network to express a parameterized set of stable operators, which enables seamless integration with standard deep learning libraries. Finally, we show how these ideas can also be applied to tune fixed-structure controllers

Abstract 2341: Morph: A feature extraction toolset for spatial transcriptomics

Abstract Accurately defining spatial characteristics of tumors has been a challenge in cancer research. Specifically, there is still a lack of spatial transcriptomic (ST) bioinformatic methods that infer tumor boundaries, a necessity for tumor microenvironment (TME) analyses, that are fully automated and handle non-rectangular grids (like the one found in Visium). Here we introduce Morph, a toolset that not only addresses these limitations, but also accurately extracts tumor regions, layers surrounding them, and distances related to such regions. Morph was tested on a dataset composed of 117 ST slides across 6 different cancer types, including primary and metastatic tumors. More broadly, Morph is a computationally efficient tool based on mathematical morphology that is designed to work without any approximations on ST slides (that can be composed of spots in a hexagonal lattice) of any resolution. The toolset accepts a variety of input types, such as tumor purity and manual annotation, and can perform a diverse set of morphological operations, such as erosion, dilation, opening and closing, using a number of structuring elements with different shapes and sizes, such as a hexagon of side 1. Moreover, Morph runs quickly (seconds per sample). Its main focus is to unveil the aforementioned features of any spatially distinct tumor region (here defined as microregion) for various downstream analyses, such as spatially-varying copy number variations, cell-type distribution, and tumor-microenvironment interaction. Overall, we developed a spatial transcriptomics toolset that is not limited to any specific technology platform, and that can accurately extract ST features facilitating the discovery of spatial transcriptomic and interaction patterns. Citation Format: André Luiz N. Targino da Costa, Jingxian Liu, Chia-Kuei Mo, Erik Storrs, Austin N. Southard-Smith, Reyka G. Jayasinghe, Julia T. Wang, Michael D. Iglesia, Michael Wendl, Siqi Chen, Andrew Houston, Alla Karpova, Yize Li, Li Ding. Morph: A feature extraction toolset for spatial transcriptomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2341.

Classical shadows based on locally-entangled measurements

We study classical shadows protocols based on randomized measurements in n-qubit entangled bases, generalizing the random Pauli measurement protocol (n=1). We show that entangled measurements (n&amp;#x2265;2) enable nontrivial and potentially advantageous trade-offs in the sample complexity of learning Pauli expectation values. This is sharply illustrated by shadows based on two-qubit Bell measurements: the scaling of sample complexity with Pauli weight k improves quadratically (from &amp;#x223C;3k down to &amp;#x223C;3k/2) for many operators, while others become impossible to learn. Tuning the amount of entanglement in the measurement bases defines a family of protocols that interpolate between Pauli and Bell shadows, retaining some of the benefits of both. For large n, we show that randomized measurements in n-qubit GHZ bases further improve the best scaling to &amp;#x223C;(3/2)k, albeit on an increasingly restricted set of operators. Despite their simplicity and lower hardware requirements, these protocols can match or outperform recently-introduced "shallow shadows" in some practically-relevant Pauli estimation tasks.

The use of evidence in criminal proceedings as a component of the evidetiary process

The article is devoted to elucidating the essence and content of the use of evidence, as well as to justifying the identification of such a category as a separate component of evidentiary process in criminal proceedings. It was found that despite the absence of the category "use of evidence” among the provisions of Part 2 of Art. 91 of the Criminal Procedure Code of Ukraine, the term is used by the legislator in a number of articles of the current CPC of Ukraine. The positions of domestic scientists regarding the essence of the use of evidence were analyzed. It was found that the majority of doctrinal definitions of this term boil down to the understanding of the use of evidence as the manipulation of it (evidence, their procedural sources, evidentiary information) for the performance of intermediate and final tasks (that is, the achievement of certain positive results) in criminal proceedings.&#x0D; Based on the study of the provisions of the current legislation and doctrinal sources, it was established that evidence is used by justifying, making, and implementing decisions in criminal proceedings. The following forms of evidence use are singled out: procedural decisions, decisions on submitting evidence to the court and substantiating legal positions with them, on submitting motions and complaints during the pre-trial investigation, tactical decisions of the prosecution and other subjects of evidence. The general mechanism of making the listed decisions and the place of use of evidence in the system of components of evidentiary activity are considered.&#x0D; The use of evidence is defined as a component of the evidentiary process, a set of logical and instrumental operations consisting in justifying with evidence (evidential information), making and implementing decisions by the subject of proof in order to fulfill their own tasks in criminal proceedings.&#x0D; Further research prospects in the analyzed field are related to the scientific and theoretical study of the essence, types and mechanism of decision­making in criminal proceedings as a form of evidence use.

Precise certification of a qubit space

We demonstrate an implementation of the precise test of dimension on the qubit, using the public IBM quantum computer, using the determinant dimension witness. The accuracy is below 10−3 comparing to maximal possible value of the witness in higher dimension. The test involving minimal independent sets of preparation and measurement operations (gates) is applied both for specific configurations and parametric ones. The test is robust against nonidealities such as incoherent leakage and erroneous gate execution. Two of the IBM devices failed the test by more than 5 standard deviations, which has no simple explanation.

Molybdenum carbide nanoclusters with ultrafast electron transfer ability promotes molecular oxygen selective electrocatalytic reduction to hydroxyl radicals for pollutant control

A treatment strategy of in situ generation and activation of hydrogen peroxide to degrade pollutants by the heterogeneous Fenton-like technology is promising for water pollution. However, the selective activation of oxygen to hydroxyl radicals (OH) by cathode materials is still challenging. Here, molybdenum carbide (MoC) supported on a heteroatoms-doped (N, P, S) carbon carrier catalyst was successfully prepared, which rapidly produced OH and degraded paracetamol (APAP) pollutants. A high APAP degradation efficiency with a specific energy consumption of 0.24 kWh g−1 TOC was achieved. After 10 sets of continuous operations, the APAP degradation rate remains unchanged. Combined with density functional theory calculations, molecular oxygen adsorption obtains one electron with proton hydrogen transfer to hydroperoxyl (OOH) on the MoC surface, and the adsorbed OOH directly activate into OH due to the ultrafast electron transfer ability of MoC. Differential charge density analysis also shows that Mo transfers electrons to O. This strategy provides a new idea for optimizing catalysts to improve the heterogeneous Fenton-like performance.