General Framework Research Articles

A General Statistical Physics Framework for Assignment Problems

A General Statistical Physics Framework for Assignment Problems

A unified Bayesian framework for bias adjustment in multiple comparisons from clinical trials.

In clinical trials, multiple comparisons arising from various treatments/doses, subgroups, or endpoints are common. Typically, trial teams focus on the comparison showing the largest observed treatment effect, often involving a specific treatment pair and endpoint within a subgroup. These findings frequently lead to follow-up pivotal studies, many of which do not confirm the initial positive results. Selection bias occurs when the most promising treatment, subgroup, or endpoint is chosen for further development, potentially skewing subsequent investigations. Such bias can be defined as the deviation in the observed treatment effects from the underlying truth. In this article, we propose a general and unified Bayesian framework to address selection bias in clinical trials with multiple comparisons. Our approach does not require a priori specification of a parametric distribution for the prior, offering a more flexible and generalized solution. The proposed method facilitates a more accurate interpretation of clinical trial results by adjusting for such selection bias. Through simulation studies, we compared several methods and demonstrated their superior performance over the normal shrinkage estimator. We recommended the use of Bayesian Model Averaging estimator averaging over Gaussian Mixture Models as the prior distribution based on its performance and flexibility. We applied the method to a multicenter, randomized, double-blind, placebo-controlled study investigating the cardiovascular effects of dulaglutide.

On variable-order fractional linear viscoelasticity

AbstractA generalization of fractional linear viscoelasticity based on Scarpi’s approach to variable-order fractional calculus is presented. After reviewing the general mathematical framework, a variable-order fractional Maxwell model is analysed as a prototypical example for the theory. Some physical considerations are then provided concerning the fractionalisation procedure and the choice of the transition functions. Lastly, the material functions for the considered model are derived and numerically evaluated for exponential-type and Mittag-Leffler-type order functions.

Evolution-guided Bayesian optimization for constrained multi-objective optimization in self-driving labs

The development of automated high-throughput experimental platforms has enabled fast sampling of high-dimensional decision spaces. To reach target properties efficiently, these platforms are increasingly paired with intelligent experimental design. However, current optimizers show limitations in maintaining sufficient exploration/exploitation balance for problems dealing with multiple conflicting objectives and complex constraints. Here, we devise an Evolution-Guided Bayesian Optimization (EGBO) algorithm that integrates selection pressure in parallel with a q-Noisy Expected Hypervolume Improvement (qNEHVI) optimizer; this not only solves for the Pareto Front (PF) efficiently but also achieves better coverage of the PF while limiting sampling in the infeasible space. The algorithm is developed together with a custom self-driving lab for seed-mediated silver nanoparticle synthesis, targeting 3 objectives (1) optical properties, (2) fast reaction, and (3) minimal seed usage alongside complex constraints. We demonstrate that, with appropriate constraint handling, EGBO performance improves upon state-of-the-art qNEHVI. Furthermore, across various synthetic multi-objective problems, EGBO shows significative hypervolume improvement, revealing the synergy between selection pressure and the qNEHVI optimizer. We also demonstrate EGBO’s good coverage of the PF as well as comparatively better ability to propose feasible solutions. We thus propose EGBO as a general framework for efficiently solving constrained multi-objective problems in high-throughput experimentation platforms.

Rapid measurement method for key dimensions of train wheelset based on improved image processing algorithm

Abstract The key dimensions of train wheelsets change with the increase of running time. To ensure the safe operation of the train, the key dimensions should meet the relevant technical requirements. Maintenance of wheelsets and measurement of key dimensions are important part of the daily maintenance work. A rapid measurement method for key dimensions of hand-held wheelsets integrating multiple parameter measurements is studied. The general framework of the system is examined and the roles of the modules are described. A rapid measurement algorithm for key dimensions of wheelsets based on image processing algorithms is studied. The optical center for the initial position of the continuous line spot is searched. The continuous line spot center extraction algorithm during tread measurement is optimized. A three-dimensional model is reconstructed using point cloud data, then key dimensions are measured and calculated from the model. In order to verify the feasibility of the method, traditional measurement methods are used for comparison tests. The results show that the method is effective in the daily maintenance of trains.

AARF: Autonomous Attack Response Framework for Honeypots to Enhance Interaction Based on Multi-Agent Dynamic Game

Highly interactive honeypots can form reliable connections by responding to attackers to delay and capture intranet attacks. However, current research focuses on modeling the attacker as part of the environment and defining single-step attack actions by simulation to study the interaction of honeypots. It ignores the iterative nature of the attack and defense game, which is inconsistent with the correlative and sequential nature of actions in real attacks. These limitations lead to insufficient interaction of the honeypot response strategies generated by the study, making it difficult to support effective and continuous games with attack behaviors. In this paper, we propose an autonomous attack response framework (named AARF) to enhance interaction based on multi-agent dynamic games. AARF consists of three parts: a virtual honeynet environment, attack agents, and defense agents. Attack agents are modeled to generate multi-step attack chains based on a Hidden Markov Model (HMM) combined with the generic threat framework ATT&CK (Adversarial Tactics, Techniques, and Common Knowledge). The defense agents iteratively interact with the attack behavior chain based on reinforcement learning (RL) to learn to generate honeypot optimal response strategies. Aiming at the sample utilization inefficiency problem of random uniform sampling widely used in RL, we propose the dynamic value label sampling (DVLS) method in the dynamic environment. DVLS can effectively improve the sample utilization during the experience replay phase and thus improve the learning efficiency of honeypot agents under the RL framework. We further couple it with a classic DQN to replace the traditional random uniform sampling method. Based on AARF, we instantiate different functional honeypot models for deception in intranet scenarios. In the simulation environment, honeypots collaboratively respond to multi-step intranet attack chains to defend against these attacks, which demonstrates the effectiveness of AARF. The average cumulative reward of the DQN with DVLS is beyond eight percent, and the convergence speed is improved by five percent compared to a classic DQN.

Judicial precedent in the Romano-Germanic legal family

The article is devoted to the study of the institution of judicial precedent in the Romano- Germanic legal family. It is established that the countries of the Romano-Germanic legal family, unlike the countries of the Anglo-Saxon legal family under the influence of Roman law, in the context of codification of law, have determined for themselves the priority source of law - law, not precedent. However, there is a gradual increase in the role of judicial precedent in continental law countries due to a number of factors that contribute to the intensification of judicial rulemaking. First, in these countries, there are still branches of law that, for various reasons, either do not have legal regulation or are regulated only by general reference rules or framework provisions. For objective reasons, the legislator cannot foresee the entire variety of real-life phenomena. This pushes the courts to adopt legal provisions that can fill in the gaps in the legal regulation of social relations. Secondly, there is a noticeable lag between legislation and dynamically changing social relations, resulting in the formation of large segments of social life regulated by legal acts that have lost their relevance. The current realities, which are largely full of unpredictable events, highlight the need for legal systems to adapt. All of this contributes to the use of judicial precedents, which serve not only as a tool for resolving specific disputes, but also as a means of developing law, allowing to fill existing «gaps in law». In the Romano-Germanic legal family, the real legal force of a judicial precedent is determined not so much by its formal enshrinement in legal acts as by actual life circumstances. Real-life situations, as well as the practical necessity and demand for precedent as a source of law, play a key role in determining its true legal status and legal force. Judicial precedent in continental law countries is becoming increasingly important, becoming an integral element of dynamic and adaptive legal regulation over time, and the rules establishing the generally binding force of precedents and the discretionary powers of authorized bodies are increasingly common in both public and private law in these countries. Judicial precedent in these countries should be considered as a source of law from a practical point of view, focusing on solving problems arising in the process of law enforcement.

Classification of classical spin liquids: Detailed formalism and suite of examples

The hallmark of highly frustrated systems is the presence of many states close in energy to the ground state. Fluctuations between these states can preclude the emergence of any form of order and lead to the appearance of spin liquids. Even on the classical level, spin liquids are not all alike: they may have algebraic or exponential correlation decay, and various forms of long wavelength description, including vector or tensor gauge theories. Here, we introduce a classification scheme, allowing us to fit the diversity of classical spin liquids (CSLs) into a general framework as well as predict and construct new kinds. CSLs with either algebraic or exponential correlation-decay can be classified via the properties of the bottom flat band(s) in their soft-spin Hamiltonians. The classification of the former is based on the algebraic structures of gapless points in the spectra, which relate directly to the emergent generalized Gauss's laws that control the low-temperature physics. The second category of CSLs, meanwhile, are classified by the fragile topology of the gapped bottom band(s). Utilizing the classification scheme we construct new models realizing exotic CSLs, including one with anisotropic generalized Gauss's laws and charges with subdimensional mobility, one with a network of pinch-line singularities in its correlation functions and a series of fragile topological CSLs connected by zero-temperature transitions. Published by the American Physical Society 2024

Codesigning Online Continuing Medical Education on Social Health Integration and Social Risk-Informed Care for Primary Care Providers.

Adapting clinical care decisions for patient-reported social risks is essential to social health integration and patient-centered care. Most research in this area focuses on awareness and assistance (social-needs-targeted care), such as screening and referral to food, financial, and other resources. Limited evidence for adjustment strategies (social risk-informed care) or adapting care for social risks made it difficult for Kaiser Permanente to implement new initiatives. This article describes a co-design process to build a novel, adjustment-focused continuing medical education course. The authors co-developed the online continuing medical education course with patients and clinicians using user-centered design. Transcripts from co-design activities were coded and analyzed by thematic analysis to identify major themes, including perceptions of social risk-informed care and barriers to care adjustment. Practical hurdles for implementing social risk-informed care emerged, including clinicians' concerns about the ethics of adjustment as substandard care, particularly without robust assistance activities. However, patients expressed a desire for their care to be adapted to their social circumstances, to allow for more realistic care plans. Implementation barriers identified from the co-design were addressed through an interactive, case-study approach. Existing evidence on contextualized care and shared decision making informed a general framework for primary care providers to engage in awareness and adjustment activities, paired with 3 interactive case studies based on real-world, clinician-supplied scenarios. The authors recommend that multiple stakeholder perspectives be incorporated during the development of social health integration initiatives, particularly adjustment. Education complemented by active, nuanced, flexible implementation strategies may be necessary for the successful uptake of care-delivery-based social health integration activities.

The impact of nuclear reactions on the neutron-star g-mode spectrum

Abstract Mature neutron stars are expected to exhibit gravity g-modes due to stratification caused by a varying matter composition. These modes are affected by nuclear reactions, leading to complex (damped) mode frequencies and the suppression of high order g-modes. This is in contrast with the common non-dissipative analysis which leads to an infinite g-mode spectrum. Focusing on the transition between the fast- and slow-reaction regimes, we examine the precise impact of nuclear reactions on the g-mode spectrum. The general framework for the analysis is presented along with sample numerical results for a matter model based on the BSk21 equation of state with a suitable parameterisation for the reaction rates.

What’s the Point of Authors?

In this paper, I want to start to untangle some of the philosophical issues associated with our practices of ascribing authorship for collaborative work, with an eye to formulating better guidelines for authorship. I will focus on the following questions: 1. What epistemic, social, and ethical functions are played by our practices of ascribing authorship for academic papers? 2. What ways of ascribing authorship would best address these functions? Can any one way of ascribing authorship address all of the functions? I hope to make three contributions. First, I hope to build on established debates about authorship to offer a general framework for assessing practices of assigning authorship. Secondly, to argue that the different functions of authorship are incoherent, making different predictions about who should be an author. Thirdly, I will argue that this incoherence points in the direction of replacing the role of an author with a number of different roles which address the different functions. In the final section I will set out a proposal that does just this, which I will call the no author account of authorship.

Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition

Vibrational spectroscopy allows us to understand complex physical and chemical interactions of molecular crystals and liquids such as ammonia, which has recently emerged as a strong hydrogen fuel candidate to support a sustainable society. We report inelastic neutron scattering measurement of vibrational properties of ammonia along the solid-to-liquid phase transition with high enough resolution for direct comparisons to ab-initio simulations. Theoretical analysis reveals the essential role of nuclear quantum effects (NQEs) for correctly describing the intermolecular spectrum as well as high energy intramolecular N-H stretching modes. This is achieved by training neural network models using ab-initio path-integral molecular dynamics (PIMD) simulations, thereby encompassing large spatiotemporal trajectories required to resolve low energy dynamics while retaining NQEs. Our results not only establish the role of NQEs in ammonia but also provide general computational frameworks to study complex molecular systems with NQEs.

Anomaly Detection in Blockchain Networks Using Unsupervised Learning: A Survey

In decentralized systems, the quest for heightened security and integrity within blockchain networks becomes an issue. This survey investigates anomaly detection techniques in blockchain ecosystems through the lens of unsupervised learning, delving into the intricacies and going through the complex tapestry of abnormal behaviors by examining avant-garde algorithms to discern deviations from normal patterns. By seamlessly blending technological acumen with a discerning gaze, this survey offers a perspective on the symbiotic relationship between unsupervised learning and anomaly detection by reviewing this problem with a categorization of algorithms that are applied to a variety of problems in this field. We propose that the use of unsupervised algorithms in blockchain anomaly detection should be viewed not only as an implementation procedure but also as an integration procedure, where the merits of these algorithms can effectively be combined in ways determined by the problem at hand. In that sense, the main contribution of this paper is a thorough study of the interplay between various unsupervised learning algorithms and how this can be used in facing malicious activities and behaviors within public and private blockchain networks. The result is the definition of three categories, the characteristics of which are recognized in terms of the way the respective integration takes place. When implementing unsupervised learning, the structure of the data plays a pivotal role. Therefore, this paper also provides an in-depth presentation of the data structures commonly used in unsupervised learning-based blockchain anomaly detection. The above analysis is encircled by a presentation of the typical anomalies that have occurred so far along with a description of the general machine learning frameworks developed to deal with them. Finally, the paper spotlights challenges and directions that can serve as a comprehensive compendium for future research efforts.

Combining statistical dialog management and intent recognition for enhanced response selection

Abstract Conversational interfaces are becoming ubiquitous in an increasing number of application domains as Artificial Intelligence, Natural Language Processing and Machine Learning methods associated with the recognition, understanding and generation of natural language advance by leaps and bounds. However, designing the dialog model of these systems is still a very demanding task requiring a great deal of effort given the number of information sources to be considered related to the analysis of user utterances, interaction context, information repositories, etc. In this paper, we present a general framework for increasing the quality of the system responses by combining a statistical dialog management technique and a deep learning-based intention recognizer that allow replacing the system responses initially selected by the statistical dialog model with other presumably better candidates. This approach is portable to different task-oriented domains, a diversity of methodologies for dialog management and intention estimation techniques. We have evaluated our two-step proposal using two conversational systems, assessed several intention recognition methodologies and used the developed modules to dynamically select the system responses. The results of the evaluation show that the proposed framework achieves satisfactory results by making it possible to reduce the number of non-coherent dialog responses by replacing them by more coherent alternatives.

Cooperative effects in dense cold atomic gases including magnetic dipole interactions

We theoretically investigate cooperative effects in cold atomic gases exhibiting both electric and magnetic dipole-dipole interactions, such as occurs, for example, in clouds of dysprosium atoms. After introducing a general framework capturing both the quantum degenerate and nondenegerate cases, we focus on the emergence of tailorable spin models in the quantum nondegenerate regime. In the low-excitation limit, we provide analytical and numerical results detailing the effect of magnetic interactions on the directionality of scattered light and characterize sub and superradiant effects. Published by the American Physical Society 2024

Vectorization of the density matrix and quantum simulation of the von Neumann equation of time-dependent Hamiltonians

Based oh the properties of Lie algebras, in this work we develop a general framework to linearize the von Neumann equation rendering it in a suitable form for quantum simulations. Departing from the conventional method of expanding the density matrix in the Liouville space formed by matrices unit we express the von Neumann equation in terms of Pauli strings. This provides several advantages related to the quantum tomography of the density matrix and the formulation of the unitary gates that generate the time evolution. The use of Pauli strings facilitates the quantum tomography of the density matrix whose elements are purely real. As for any other basis of Hermitian matrices, this eliminates the need to calculate the phase of the complex entries of the density matrix. This approach also enables to express the evolution operator as a sequence of commuting Hamiltonian gates of Pauli strings that can readily be synthetized using Clifford gates. Additionally, the fact that these gates commute with each other along with the unique properties of the algebra formed by Pauli strings allows to avoid the use of Trotterization hence considerably reducing the circuit depth. The algorithm is demonstrated for three Hamiltonians using the IBM noisy quantum circuit simulator.

FGO-MFI: factor graph optimization-based multi-sensor fusion and integration for reliable localization

Reliable and precise information pertaining to the position, velocity, and attitude is essential for automated driving. This paper proposes FGO-MFI, a cost-effective and robust multi-sensor fusion and integration localization framework that utilizes factor graph optimization. Firstly, a tightly coupled Global Navigation Satellite Systems (GNSS)/on-board sensor fusion localization framework is established to estimate vehicle states, including position, velocity, and attitude. To address the large drift rate of the Inertial Measurement Unit (IMU), this study introduces a novel IMU/Dynamics pre-integration method based on the vehicle dynamics model. We establish a two-degree-of-freedom vehicle dynamics model utilizing measurements from the wheel speed sensor and steering wheel angle sensor. The IMU/Dynamics factor is devised through a close integration of the model output and IMU pre-integration, enabling the construction of precise odometry with low-cost on-board sensors. Then, to address the issue of the non-Gaussian distribution of GNSS pseudorange error, this paper employs a Gaussian Mixture Model (GMM) to characterize the pseudorange noise, which is then applied to further sensor fusion. Given the time-varying nature of pseudorange noise, the expectation maximization algorithm is utilized to estimate GMM parameters online, leveraging the pseudorange residuals within a sliding window. Comprehensive experiments, inclusive of challenging scenarios such as urban canyons, tunnels, and wooded areas, have been carried out. They affirm the superior performance of the proposed method. Experiments have shown that our method demonstrates reliable localization across different statuses of GNSS signal, exhibiting a 39.4% improvement in the root mean square error of position error when compared to the state-of-the-art. Additionally, this FGO-MFI is a general sensor data fusion framework and is able to incorporate diverse sensor measurements, for example, from cameras and LiDARs to provide more reliable and accurate localization information.

Limiting behavior of quasilinear wave equations with fractional-type dissipation

Abstract In this work, we investigate a class of quasilinear wave equations of Westervelt type with, in general, nonlocal-in-time dissipation. They arise as models of nonlinear sound propagation through complex media with anomalous diffusion of Gurtin–Pipkin type. Aiming at minimal assumptions on the involved memory kernels – which we allow to be weakly singular – we prove the well-posedness of such wave equations in a general theoretical framework. In particular, the Abel fractional kernels, as well as Mittag-Leffler-type kernels, are covered by our results. The analysis is carried out uniformly with respect to the small involved parameter on which the kernels depend and which can be physically interpreted as the sound diffusivity or the thermal relaxation time. We then analyze the behavior of solutions as this parameter vanishes, and in this way relate the equations to their limiting counterparts. To establish the limiting problems, we distinguish among different classes of kernels and analyze and discuss all ensuing cases.

Single-nucleus RNA transcriptome profiling reveals murine adipose tissue endothelial cell proliferation gene networks involved in obesity development

Endothelial cells play an important role in the metabolism of adipose tissue (AT). This study aimed to analyze the changes that adipose tissue in AT endothelial cells undergo during the development of obesity, using single-nucleus RNA sequence (snRNA-seq). Mouse paraepididymal AT cells were subjected to snRNA-seq with the 10X Genomics platform. The cell types were then clustered using t-distributed stochastic neighbor embedding and unbiased computational informatics analyses. Protein–protein interactions network was established using the STRING database and visualized using Cytoscape. The dataset was subjected to differential gene enrichment analysis. In total, 21,333 cells acquired from 24 mouse paraepididymal AT samples were analyzed using snRNA-seq. This study identified 18 distinct clusters and annotated macrophages, fibroblasts, epithelial cells, T cells, endothelial cells, stem cells, neutrophil cells, and neutrophil cell types based on representative markers. Cluster 12 was defined as endothelial cells. The proportion of endothelial cells decreased with the development of obesity. Inflammatory factors, such as Vegfa and Prdm16 were upregulated in the medium obesity group but downregulated in the obesity group. Genes, such as Prox1, Erg, Flt4, Kdr, Flt1, and Pecam1 promoted the proliferation of AT endothelial cells and maintained the internal environment of AT. This study established a reference model and general framework for studying the mechanisms, biomarkers, and therapeutic targets of endothelial cell dysfunction-related diseases at the single-cell level.

Public infrastructure provision in the presence of terms‐of‐trade effects and tax competition

AbstractThis paper analyses and compares the performance of resource taxes and capital taxes in financing public goods while considering the positive effects of public expenditure on firm productivity. It is motivated by Franks et al. (2017), who argue that the advantage of the resource tax consists in its potential to reap foreign resource rents. I employ an analytical general equilibrium framework of identical resource‐poor countries, where local firms use internationally mobile capital and a net imported resource in production as well as local public infrastructure. The latter is financed solely by either taxing the input of the resource or capital. The choice of the policy instrument is exogenous to policy makers and symmetric across countries. I find that expenditure on infrastructure renders the impact of fiscal policy on the terms of trade ambiguous under resource taxation and negative under capital taxation. Moreover, public expenditure weakens the outflow of factors moderating the deficit of public spending caused by tax competition. This holds for both policy scenarios. Considering both effects simultaneously, resource taxation cannot generally be identified as the policy to provide higher provision or efficiency. A numerical exercise shows cases for higher provision of either policy.