Heuristics - Science topic

The book is devoted to the description and practical application of effective analytical methods for finding exact solutions to nonlinear partial differential equations. It covers the methods of generalized separation of variables, methods of functional separation of variables, the classical method of symmetry reductions, the direct method of symme...

Albeit August Comte, way back in the later part of 19 th century, conducted an array if heuristics, in Social physics in his path breaking work on Positive Philosophy, not much of progress has been there in this genre of research in India. The interchange ability of physics and social science has recently been drawn with the realms of social networ...

Dear Colleagues, As a critical concept in understanding the laws of nature, symmetry has been well-investigated in studies of mathematical optimizations. Over the past few decades, optimization has played a pivotal role in formulating and solving machine learning tasks, thus the connection between optimization and machine learning is becoming a po...

A tumor is an abnormal growth of cells, either cancerous or benign, that develops in an organ. Early detection and segmentation of brain tumors are crucial for effective treatment, but manual analysis by experts is a labor-intensive and time-consuming process. The proposed solution is a deep learning framework called RobU-Net, a modified U-Net, to...

This study examines the evolution of neurotrauma care across three historical eras, highlighting the gradual transition from empirical practice to formal surgical doctrine. It explores how early American surgeons balanced anatomical knowledge, clinical restraint, and surgical risk in managing complex injuries to the skull and spine. The evolutio...

Momentum-based optimizers are widely adopted for training neural networks. However, the optimal selection of momentum coefficients remains elusive. This uncertainty impedes a clear understanding of the role of momentum in stochastic gradient methods. In this paper, we present a frequency domain analysis framework that interprets the momentum method...

This article studies the effective design of content distribution networks over cloud computing platforms. This problem is relevant nowadays to provide fast and reliable access to content on the internet. A bio-inspired evolutionary multiobjective optimization approach is applied as a viable alternative to solve realistic problem instances where ex...

This conceptual paper presents a speculative account of how artificial intelligence (AI) systems might emulate emotion as experienced by humans—and presumably also by animals. It develops a thought experiment grounded in the assumption that natural emotions evolved as heuristics for rapid situational appraisal and action selection, supporting biolo...

The classical simulation of quantum circuits is of central importance for benchmarking near-term quantum devices. The fact that gates belonging to the Clifford group can be simulated efficiently on classical computers has motivated a range of methods that scale exponentially only in the number of non-Clifford gates. Here, we consider the expectatio...

Multi-agent pathfinding (MAPF) is a problem focused on coordinating multiple agents to navigate from starting positions to goals in shared environments while avoiding collisions. This capability is important for applications in areas such as computer gaming and robotics, where efficient and safe navigation in complex environments is required. Altho...

Blockchain Technology (BCT) operates on a distributed network of independent nodes that depend on mutual trust. To interact with external environments, these networks rely on blockchain oracles, which provide the external data required for the accurate and real-time execution of Smart Contracts. However, this introduces the "Oracle Problem," referr...

This study addresses the persisting issue of restaurant reservation no‐shows by applying the availability heuristic principle to analyze booking behaviors. Three experiments identified cancellation policy features and factors influencing booking and no‐show behaviors. Findings confirmed that a cancellation barrier (i.e., strict policy and complex c...

The most dangerous form of cancer is breast cancer. This disease is life-threatening because of its aggressive nature and high death rates. Therefore, early discovery increases the patient’s survival. Mammography has recently been recommended as diagnosis technique. Mammography, is expensive and exposure the person to radioactivity. Thermography is...

Optimizing communication channels in multi-hop wireless sensor networks (WSNs) is critical for improving network efficiency, energy consumption, and data transmission reliability. Traditional optimization methods often rely on heuristic algorithms, which may struggle with dynamic network conditions and high-dimensional feature spaces. This paper ex...

Denial of Wallet (DoW) attacks are a cyber threat designed to utilize and deplete an organization’s financial resources by generating excessive prices or charges in their cloud computing (CC) and serverless computing platforms. These threats are primarily appropriate in serverless manners because of features such as auto-scaling, pay-as-you-go, res...

Accurate cost estimation and optimization are crucial in engineering project management, as budget overruns and resource misallocations often lead to financial and operational inefficiencies. Traditional cost estimation methods, including regression models and heuristic approaches, struggle to adapt to the complex and dynamic nature of engineering...

This study presents a theoretical analysis of partitional clustering on networks. Different versions of the problem are studied considering different assignment schemes (hard and soft) and different objective functions. Cluster centers are not restricted to only the set of nodes, it is assumed that centers can also be at the edges of the network. F...

Digital nudges have gained much attention in recent years due to their potential to influence decision-making of consumers in an online environment. This study focuses on the impact of two specific digital nudges: anchor bias and affect heuristics (reviews), both individually and in combination, on customers' online purchase intentions. We conducte...

Powdered iron is being investigated for its potential use as a carbon-free fuel due to its ability to burn heterogeneously and produce oxide particles, which can be collected, reduced back to iron and burned again. However, high temperature oxidation of iron particles can induce partial vaporization/decomposition and evolution of nanometric iron ox...

We present a comprehensive, step-by-step framework for detecting “spectral finger‑prints” of new physics via deviations from the universal 1/E^2 suppression law. Our protocol encompasses: (i) a synthetic demonstration of changepoint detection methods; (ii) quantitative comparison of leading detection algorithms; (iii) a standardized cross-domain da...

Data quality and diversity are key to the construction of effective instruction-tuning datasets. % With the increasing availability of open-source instruction-tuning datasets, it is advantageous to automatically select high-quality and diverse subsets from a vast amount of data. % Existing methods typically prioritize instance quality and use heuri...

This paper introduces the Susceptible-Infected-Removed Optimizer (SIRO), a novel learned heuristic inspired by biological systems and deep learning techniques. SIRO models its search process after the SIR epidemiological compartmental model, predicting the susceptibility, infection, and recovery dynamics of solutions. To enhance its efficiency, SIR...

Cryptocurrency blockchains, beyond their primary role as distributed payment systems, are increasingly used to store and share arbitrary content, such as text messages and files. Although often non-financial, this hidden content can impact price movements by conveying private information, shaping sentiment, and influencing public opinion. However,...

The dynamics of a Brownian particle immersed in a bath with variable temperature where both subsystems interact with an external parabolic field are analyzed under the framework of the generalized Langevin equation. This analysis is based on adopting a heuristic method applied to the classical Zwanzig version formulated in 1990 by Brey and Casado....

Optimization of the best policy has a long history in Reinforcement Learning (RL). However due to No Free Lunch theorem, no optimizer guaranties to find the optimal solution for all possible problems. Problems like Low Exploration/Exploitation, non-Markovian behavior and wideness of policy/state space can affect a lot on the speed and performance o...

This paper introduces a pioneering model for short-term planning of an energy hub (EH) that goes beyond traditional approaches by considering a comprehensive multicarrier energy system. The proposed model focuses on minimizing energy buffering costs while ensuring system operation and optimizing economic performance. The novelty of this study lies...

Variational Quantum Eigensolver (VQE) faces significant challenges due to hardware noise and the presence of barren plateaus and local traps in the optimization landscape. To mitigate the detrimental effects of these issues, we introduce a general formalism that optimizes hardware resource utilization and accuracy by projecting VQE optimizations on...

As an extension of the well-known job shop problem through introducing no-wait constraints, the no-wait job shop problem (NWJSP) is one of the most difficult combinatorial problems. In this study, we consider the NWJSP with the objective of minimizing total weighted tardiness and develop a mixed integer linear programming model (MILP) to formulate...

This study presents an NNTile framework for training large deep neural networks in heterogeneous clusters. The NNTile is based on a StarPU library, which implements task-based parallelism and schedules all provided tasks onto all available processing units (CPUs and GPUs). It means that a particular operation, necessary to train a large neural netw...

Late interaction neural IR models like ColBERT offer a competitive effectiveness-efficiency trade-off across many benchmarks. However, they require a huge memory space to store the contextual representation for all the document tokens. Some works have proposed using either heuristics or statistical-based techniques to prune tokens from each documen...

The reliability of a system can be improved by the addition of redundant elements, giving rise to the well-known redundancy allocation problem (RAP), which can be seen as a design problem. We propose a novel extension to the RAP called the Bi-Objective Integrated Design and Dynamic Maintenance Problem (BO-IDDMP) which allows for future dynamic main...

3D Gaussian Splatting (3DGS) is widely used for novel view synthesis due to its high rendering quality and fast inference time. However, 3DGS predominantly relies on first-order optimizers such as Adam, which leads to long training times. To address this limitation, we propose a novel second-order optimization strategy based on Levenberg-Marquardt...

In the following sections, I will explain in great detail the core content of Planck's paper, titled 'On the Law of Distribution of Energy in the Normal Spectrum,' in which he derived the blackbody radiation law, marking the birth of quantum physics. I will show how and why the quantization assumption came about. Then, I will explain a major conten...

Vehicle routing problem has been widely concerned because of the development of transportation industry, and simulated annealing algorithm as a tool to solve it has also been deeply studied. This work introduces the background information of simulated annealing algorithm and the process of annealing in physics. In order to confirm the effectiveness...

Cell-free massive multiple-input multiple-output (CF-mMIMO) surmounts conventional cellular network limitations in terms of coverage, capacity, and interference management. This paper aims to introduce a novel unsupervised learning framework for the downlink (DL) power allocation problem in CF-mMIMO networks, utilizing only large-scale fading (LSF)...

Under current process models of reasoning, detecting conflict between beliefs and logic is a key step that determines whether people will engage in reflective thinking. Conflict detection has been found across many tasks, but it is less reliably observed with syllogistic reasoning. ‘Reverse’ detection effects have also been found in some studies, w...

A vast majority of the current research in the field of machine learning is done using algorithms with strong arguments pointing to their biological implausibility such as backpropagation, deviating the field’s focus from understanding its original organic inspiration to a compulsive search for optimal performance. Yet there have been a few propose...

Standalone energy supply system has been in the limelight, especially in rural areas since it can provide cheap electrical supply to the local communities. Energy supply is always a problematic condition in many developing countries that significantly affects progress and inclusive economic development. Furthermore, excess energy generated is waste...

We present a general method to determine the probability that stochastic Monte Carlo data, in particular those generated in a lattice QCD calculation, would have been obtained were that data drawn from the distribution predicted by a given theoretical hypothesis. Such a probability, or p -value, is often used as an important heuristic measure of th...

The main purpose of this research is to build a framework, which explains the Bandwagon Effect on consumer purchase decisions. The framework aims to explain the purchase loop, which begins with an information search and ends with the post-purchase outcome, the sense of value or guilt. This research is purely qualitative in nature considering deep s...

Postulations to guide researchers approaching psycho-sociological parameters from the standpoint of waning or blooming relationships measurable in terms of time, space, and heuristics.

Mathematical optimization, although often leading to NP-hard models, is now capable of solving even large-scale instances within reasonable time. However, the primary focus is often placed solely on optimality. This implies that while obtained solutions are globally optimal, they are frequently not comprehensible to humans, in particular when obtai...

Regardless of past learning, an agent in an open world will face unfamiliar situations and events outside of prior experience, existing models, or policies. Further, the agent will sometimes lack relevant knowledge and/or sufficient time to assess the situation, generate and evaluate options, and pursue a robustly considered course of action. How c...

Memory and cache optimization are crucial in various areas of computing, including multicore processors and operating systems handling diverse workloads. Traditional memory management techniques such as Least Recently Used (LRU) and First-In First-Out (FIFO) are still widely used today. However, these methods do not adapt to changing workload patte...

Zero-knowledge (ZK) circuits enable privacy-preserving computations and are central to many cryptographic protocols. Systems like Circom simplify ZK development by combining witness computation and circuit constraints in one program. However, even small errors can compromise security of ZK programs --under-constrained circuits may accept invalid wi...

Pharmaceutical marketing plays a main role in informing ideas and attitudes having to do with drug efficiency and loyalty. This paper tests the influence of service biases and heuristics on efficiency, faith, and cure loyalty in the context of pharmaceutical marketing game plans. Drawing on emotional theories of responsibility, regarding the mann...

The issue of behavioral finance in investment has become the center of debate recently. Several factors has been included while making decision to invest and how does it influence while residential investors makes their investment decision. The existence of these factors is thought to be helpful to the investors in reducing their irrationality in i...

The paper presents a comparative analysis of existing milling technologies. The influence of the main negative factors affecting the quality of machined surfaces is revealed. Optimal schemes for machining with opposed cutters are proposed. The modeling of milling processes with oppositely located cutters of thin-walled extended aircraft parts is ca...

Scientific discovery aspires to objectivity – but reputational hierarchies and institutional inertia often shield foundational theories from scrutiny. This paper explores how a new class of large language models – reasoning agents – can operate outside these constraints, exposing not only a specific structural inconsistency in the transformation eq...

My academic and professional journey has been shaped by a deep commitment to learning, leadership, and transformation. As a doctoral candidate in Educational Technology at the University of Phoenix, I am driven by the belief that education is not merely a means to transmit knowledge but a powerful, humanizing process capable of awakening personal a...

Offline reinforcement learning (RL) learns effective policies from pre-collected datasets, offering a practical solution for applications where online interactions are risky or costly. Model-based approaches are particularly advantageous for offline RL, owing to their data efficiency and generalizability. However, due to inherent model errors, mode...

As web applications grow in complexity, managing CSS specificity and inheritance becomes increasingly challenging, often leading to bloated stylesheets, unpredictable overrides, and maintenance overhead. This paper explores the application of intelligent systems-leveraging machine learning, static analysis, and heuristic algorithms-to optimize CSS...

Long-term multivariate time series forecasting is critical in domains like finance, climate science, and infrastructure planning, but it faces challenges like high dimensionality, computational inefficiency, and long-range dependency capture. While transformers excel at modelling sequential data, their quadratic complexity and reliance on heuristic...

In many optimization domains, there are multiple different solvers that contribute to the overall state-of-the-art, each performing better on some, and worse on other types of problem instances. Meta-algorithmic approaches, such as instance-based algorithm selection, configuration and scheduling, aim to close this gap by extracting the most perform...

This paper introduces a vehicle‐assisted multi‐drone inspection routing problem (VAMDIRP), which enables the vehicle to repeatedly traverse roads, thereby reducing task completion time. Firstly, a mixed‐integer linear programming (MILP) model is constructed for the problem using a series of decision variables and auxiliary variables. The model can...

The attached document, Reflections of Me: An Autoethnographic Journey into Identity, Culture, and Belonging by Justin-Ames Gamache, is a qualitative dissertation that explores the complexities of identity formation, cultural integration, and the pursuit of belonging through the author’s own lived experiences. Employing autoethnography and heuristic...

The advancement of robotics has been significantly driven by the development of efficient algorithms for path planning, control, and decision-making. These algorithms are critical in enabling robots to navigate complex environments, perform tasks autonomously, and adapt to dynamic conditions. This paper explores various approaches to designing and...

We study frugal splitting algorithms with minimal lifting for solving monotone inclusion problems involving sums of maximal monotone and cocoercive operators. Building on a foundational result by Ryu, we fully characterize all methods that use only individual resolvent evaluations, direct evaluations of cocoercive operators, and minimal memory reso...

Intrusion detection is crucial for modern cyber-security, aiming to detect and mitigate unauthorized access and malicious activities within networks. Traditional systems often rely on linear models, which may fail to capture complex patterns in advanced threats. This study proposes a heuristic approach to select the best deep features through an en...

This study presents a model for emergency medical assistance services (EMAS) in a smart city efficiently by integrating Internet of Things (IoT) technology with the scheduling approach travelling salesman problem (TSP). The system utilizes real-time data from IoT-enabled medical devices attached with the beneficiaries and GIS (geographic informatio...

Quantum algorithms based on the variational principle have found applications in diverse areas with a huge flexibility. But as the circuit size increases the variational landscapes become flattened, causing the so-called Barren plateau phenomena. This will lead to an increased difficulty in the optimization phase, due to the reduction of the cost f...

The quest for innovative and efficient algorithmic structures has traditionally relied on human expertise and heuristic approaches. However, recent advancements in deep learning offer a transformative paradigm-automated discovery of algorithmic structures that can surpass handcrafted designs in both performance and efficiency. This research explore...

The Fourier transform over finite groups has proved to be a useful tool for analyzing combinatorial optimization problems. However, few heuristic and metaheuristic algorithms have been proposed in the literature that utilize the information provided by this technique to guide the search process. In this work, we attempt to address this research gap...

Peer review is a cornerstone of quality control in scientific publishing. With the increasing workload, the unintended use of `quick' heuristics, referred to as lazy thinking, has emerged as a recurring issue compromising review quality. Automated methods to detect such heuristics can help improve the peer-reviewing process. However, there is limit...

Although existing garbage collectors (GCs) perform extremely well on typical programs, there still exist pathological programs for which modern GCs significantly degrade performance. This observation begs the question: might there exist a 'holy grail' GC algorithm, as yet undiscovered, guaranteeing both constant-length pause times and that memory i...

Large language model unlearning has become a critical challenge in ensuring safety and controlled model behavior by removing undesired data-model influences from the pretrained model while preserving general utility. Significant recent efforts have been dedicated to developing LLM unlearning benchmarks such as WMDP (Weapons of Mass Destruction Prox...

This study addresses the Aircraft Reactive Scheduling Problem (ARSP) on multiple parallel runways in response to operational disruptions. We specifically consider three disruptive event types; flight cancelations, delays and unexpected arrivals. Interruptions to aircraft schedules due to various reasons (e.g. bad weather conditions) may render the...

The Senate and the Chamber of Deputies constitute the Brazilian Congress and are responsible for the Brazilian legislative management. Complex networks were shown to be a suitable tool to analyze this type of system. Several researches explored party dynamics in the Chamber of Deputies, however, no attention has been given to the Senate. Previous w...

One of the time-consuming and expensive phases in software development is software testing, which is used to improve the quality of software systems. Therefore, Software test automation is a helpful technique that can alleviate testing time. Several techniques based on evolutionary and heuristic algorithms have been put forth to produce maximum cov...

The continuous network design problem (CNDP) has been recognized as one of the most challenging issues in the field of transportation. Existing approaches to solving the CNDP are primarily heuristic without convergence guarantee or suitable for handling small networks because of the inherent nonconvexity arising from its bilevel hierarchical struct...

With the continuous deepening of human exploration and utilization of marine resources, activities such as maritime trade, sea transportation, and scientific research have been increasing. However, the uncertainty of marine climate and surface environments has led to frequent maritime accidents. It is particularly important to ensure the safety of...

The performance of distributed averaging depends heavily on the underlying topology. In various fields, including compressed sensing, multi-party computation, and abstract graph theory, graphs may be expected to be free of short cycles, i.e. to have high girth. Though extensive analyses and heuristics exist for optimising the performance of distrib...

Scheduling a stream of jobs whose holding cost changes over time is a classic and practical problem. Specifically, each job is associated with a holding cost (penalty), where a job's instantaneous holding cost is some increasing function of its class and current age (the time it has spent in the system since its arrival). The goal is to schedule th...

While recent advances in large language models (LLMs) have significantly enhanced performance across diverse natural language tasks, the high computational and financial costs associated with their deployment remain substantial barriers. Existing routing strategies partially alleviate this challenge by assigning queries to cheaper or specialized mo...

This study presents a novel empirical investigation into the impact of psychological biases on investment decision-making within the Pakistan Stock Exchange (PSX), integrating Kahneman and Tversky’s Prospect Theory and Heuristics Theory into a unified behavioral finance framework. Departing from conventional analyses, this research employs a dual-m...

People, across a wide range of personal and professional domains, need to accurately detect whether the state of the world has changed. Previous research has documented a systematic pattern of over- and under-reaction to signals of change due to system neglect , the tendency to overweight the signals and underweight the system producing the signals...

The integration of traditional machine learning (ML) techniques with large language models (LLMs) has emerged as a promising approach to enhance the efficiency, scalability, and adaptability of artificial intelligence systems. Hybrid AI leverages the structured data processing power of traditional ML models (such as Bayesian networks and support ve...

Neural activity at the population level is commonly studied experimentally through measurements of electric brain signals like local field potentials (LFPs), or electroencephalography (EEG) signals. To allow for comparison between observed and simulated neural activity it is therefore important that simulations of neural activity can accurately pre...

Mathilde Tahar’s Du finalisme en biologie. Bergson et la théorie de l’évolution offers a bold reinterpretation of Henri Bergson’s Creative Evolution (1907) in light of contemporary biology. She reconstructs Bergson’s critique of Darwinism, mutationism, orthogenesis, and neo-Lamarckism, arguing that his Élan vital is neither a metaphysical force nor...

Background: Intralogistics systems face growing challenges from globalization, individualization, and shorter product life cycles, demanding flexible and responsive solutions beyond traditional centralized control. Decentralized, agent-based approaches offer potential advantages, especially for Automated Guided Vehicle (AGV) systems where managing...

This paper proposes a novel nonlinear programming model to capture the equilibrium state of complex supply chain networks. The model, equivalent to a variational inequality model, relaxes traditional strict assumptions to accommodate real-world complexities, such as nonlinear, non-convex, and non-smooth relationships between production, consumption...

Anchoring bias is one of the most prevalent biases within forecasting. It distorts managers' estimations whenever context-driven intervention to the statistical model output is required. Consequences extend beyond a single organization since forecasting affects order quantity decisions and, therefore, the relations among suppliers, potentially gene...

Radio Frequency Interference (RFI) from anthropogenic radio sources poses significant challenges to current and future radio telescopes. Contemporary approaches to detecting RFI treat the task as a semantic segmentation problem on radio telescope spectrograms. Typically, complex heuristic algorithms handle this task of `flagging' in combination wit...

The reliance on translated or adapted datasets from English or multilingual resources introduces challenges regarding linguistic and cultural suitability. This study addresses the need for robust and culturally appropriate benchmarks by evaluating the quality of 17 commonly used Turkish benchmark datasets. Using a comprehensive framework that asses...

Large-scale pre-trained diffusion models have produced excellent results in the field of conditional image generation. However, restoration of ancient murals, as an important downstream task in this field, poses significant challenges to diffusion model-based restoration methods due to its large defective area and scarce training samples. Condition...

The role of mental simulation in human physical reasoning is widely acknowledged, but whether it is employed across scenarios with varying simulation costs and where its boundary lies remains unclear. Using a pouring-marble task, our human study revealed two distinct error patterns when predicting pouring angles, differentiated by simulation time....

Generating realistic building layouts for automatic building design has been studied in both the computer vision and architecture domains. Traditional approaches from the architecture domain, which are based on optimization techniques or heuristic design guidelines, can synthesize desirable layouts, but usually require post-processing and involve h...

Facing the challenges of delivery cost, efficiency, and security in modern logistics systems, this study investigates a realistic scenario where multiple logistics companies jointly serve a common customer base, and customers' parcels can be picked up by their neighboring customers. By integrating mobile lockers and drones into the delivery process...

The paper's author suggests considering that Rüdiger Inhetveen's (ex-post) heuristics can be everything that could be named “background.” This may include, for instance, insight, intuition, experience, language competencies, the specific nature of assimilation and socialization, and other social and cultural influences. The author would also includ...

A structural model for Gettier cases is proposed, based on four dimensions of epistemic (in)stability: justification, truth, temporal dynamics, and context. Binary coding along these axes enables classification and systematic comparison of epistemically ambiguous cases. The framework integrates perspectival shifts, and temporal dynamics, revealing...

This study proposes a novel machine learning framework to enhance blood bank management, focusing on inventory optimization and shortage prediction. By leveraging big data analytics, the model identifies donor trends, seasonal fluctuations, and real-time hospital demand to dynamically adjust blood supply. The predictive accuracy of our machine lear...

The Stackelberg knapsack game with weight selection (SKPW) is a variation of the bilevel knapsack problem in which the leader must determine the weights of a given subset of items, and then, the follower solves the knapsack problem to maximize the profit sum. The leader’s objective is to maximize the sum of the weights of the leader’s items include...

With the rapid development of smart cities, edge computing is confronted with the challenges of a sharp increase in the number of devices and computing tasks. How to efficiently perform task offloading to optimize the utilization of computing resources and reduce latency and energy consumption has become an urgent problem to be solved. This paper p...

Quantum computing promises to revolutionize problem-solving through quantum mechanics, but current NISQ devices face limitations in qubit count and error rates, hindering the execution of large-scale quantum circuits. To address these challenges and improve scalability, two main circuit cutting strategies have emerged: the gate-cut approach, which...