bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative evaluation of image quality between virtual grid and grid portable radiographic systems
Authors: Siddique Azmul H , Ge Gary , Zhang Jie 
Published: August 2024

Abstract

Purpose. Virtual Grid (VG) is an image processing technique designed to address scattered radiation from radiographic systems without a physical grid. It aims to eliminate artifacts caused by grid misalignment and enhance radiographic workflow efficiency. We intend to evaluate image quality between Virtual Grid and grid-based radiographic systems across various patient thicknesses. Methods. A Fujifilm Virtual Grid and GE AMX-4 portable radiographic system was used. Image quality was assessed using MTF, NPS, LCR, and CNR. MTF calculations employed an edge-device method with a 0.1 mmCu sheet. For NPS evaluation, uniform images were acquired with multiple 30 × 30 cm solid water blocks (2 cm thick), overlaid in 2 cm increments to simulate patient sizes from 2cm to 40 cm. LCR and CNR were evaluated using a CIRS test plate with 9-hole depths for a hole diameter of 0.375’. The test object was placed on top of the detector then water blocks, while maintaining the same SID, beam quality, and exposure between the units. Visual assessments were conducted by four readers, quantifying perceived hole numbers. The weighted Cohen’s Kappa and Welch’s T-test were utilized for statistical analysis. Results. At 80% MTF, VG exhibited high contrast resolution of 1.1 l p/mm compared to 1.2 l p/mm for the grid system. VG demonstrated lower noise levels across all frequencies for equivalent patient thicknesses. Welch’s T-test indicated no significant differences in LCR (P = 0.31) and CNR (P = 0.34) between the systems. However, qualitative observation demonstrated VG’s better low contrast response for patient sizes ≥10 cm. The average weighted Cohen’s Kappa value was 0.78. Conclusion. This work indicates the Virtual Grid technology can effectively mitigate scattered radiation to improve granularity and low-contrast resolution in an image compared to a grid system. Furthermore, it can potentially reduce patient dose.
Comparison review of image classification techniques for early diagnosis of diabetic retinopathy
Authors: Wangweera Chayarat , Zanini Plinio 
Published: August 2024

Abstract
Diabetic retinopathy (DR) is one of the leading causes of vision loss in adults and is one of the detrimental side effects of the mass prevalence of Diabetes Mellitus (DM). It is crucial to have an efficient screening method for early diagnosis of DR to prevent vision loss. This paper compares and analyzes the various Machine Learning (ML) techniques, from traditional ML to advanced Deep Learning models. We compared and analyzed the efficacy of Convolutional Neural Networks (CNNs), Capsule Networks (CapsNet), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), decision trees, and Random Forests. This paper also considers determining factors in the evaluation, including contrast enhancements, noise reduction, grayscaling, etc We analyze recent research studies and compare methodologies and metrics, including accuracy, precision, sensitivity, and specificity. The findings highlight the advanced performance of Deep Learning (DL) models, with CapsNet achieving a remarkable accuracy of up to 97.98% and a high precision rate, outperforming other traditional ML methods. The Contrast Limited Adaptive Histogram Equalization (CLAHE) preprocessing technique substantially enhanced the model’s efficiency. Each ML method’s computational requirements are also considered. While most advanced deep learning methods performed better according to the metrics, they are more computationally complex, requiring more resources and data input. We also discussed how datasets like MESSIDOR could be more straightforward and contribute to highly evaluated performance and that there is a lack of consistency regarding benchmark datasets across papers in the field. Using the DL models facilitates accurate early detection for DR screening, can potentially reduce vision loss risks, and improves accessibility and cost-efficiency of eye screening. Further research is recommended to extend our findings by building models with public datasets, experimenting with ensembles of DL and traditional ML models, and considering testing high-performing models like CapsNet.
Automatic motion estimation with applications to hiPSC-CMs
Authors: Finsberg Henrik , Charwat Verena , Healy Kevin E , Wall Samuel T 
Published: August 2024

Abstract
Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are an effective tool for studying cardiac function and disease, and hold promise for screening drug effects on human tissue. Understanding alterations in motion patterns within these cells is crucial for comprehending how the administration of a drug or the onset of a disease can impact the rhythm of the human heart. However, quantifying motion accurately and efficiently from optical measurements using microscopy is currently time consuming. In this work, we present a unified framework for performing motion analysis on a sequence of microscopically obtained images of tissues consisting of hiPSC-CMs. We provide validation of our developed software using a synthetic test case and show how it can be used to extract displacements and velocities in hiPSC-CM microtissues. Finally, we show how to apply the framework to quantify the effect of an inotropic compound. The described software system is distributed as a python package that is easy to install, well tested and can be integrated into any python workflow.
Bayesian optimization of non-classical optomechanical correlations
Authors: Pitchford Alexander , Rakhubovsky Andrey A , Mukherjee Rick , Moore Darren W , Sauvage Frédéric , Burgarth Daniel , Filip Radim , Mintert Florian 
Published: August 2024

Abstract
Nonclassical correlations provide a resource for many applications in quantum technology as well as providing strong evidence that a system is indeed operating in the quantum regime. Optomechanical systems can be arranged to generate nonclassical correlations (such as quantum entanglement) between the mechanical mode and a mode of travelling light. Here we propose automated optimization of the production of quantum correlations in such a system, beyond what can be achieved through analytical methods, by applying Bayesian optimization to the control parameters. A two-mode optomechanical squeezing experiment is simulated using a detailed theoretical model of the system and the measurable outputs fed to the Bayesian optimization process. This then modifies the controllable parameters in order to maximize the non-classical two-mode squeezing and its detection, independently of the inner workings of the model. We focus on a levitated nano-sphere system, but the techniques described are broadly applicable in optomechanical experiments, and also more widely, especially where no detailed theoretical treatment is available. We find that in the experimentally relevant thermal regimes, the ability to vary and optimize a broad array of control parameters provides access to large values of two-mode squeezing that would otherwise be difficult or intractable to discover via analytical or trial and error methods. In particular we observe that modulation of the driving frequency around the resonant sideband allows for stronger nonclassical correlations. We also observe that our optimization approach finds parameters that allow significant squeezing in the high temperature regime. This extends the range of experimental setups in which non-classical correlations could be generated beyond the region of high quantum cooperativity.
Effect of surface remelting on the characteristics of IN718 components fabricated using laser powder directed energy deposition
Authors: Thanumoorthy Raja S , Jadhav Satyajeet Vijay , Oyyaravelu R , Bontha Srikanth , A S S Balan 
Published: August 2024

Abstract
Laser Powder Directed Energy Deposition (LP-DED) fabricated components exhibit poor surface finish, necessitating additional post-processing steps prior to their practical application. Enhancing the surface quality of additively manufactured IN718 specimens through conventional post-processing methods is particularly challenging, given the material’s poor machinability and the complexity of the fabricated components. The current study is centered on comprehending the impact of Laser Surface Remelting (LSR) on the surface properties of Laser Powder Directed Energy Deposited (LP-DED) IN718 material. To gain insights into how remelting influences surface characteristics, remelting was carried out using various sets of parameters. The remelted zone exhibited a refined grain structure, leading to increased hardness. Moreover, significant reductions in surface roughness and residual stress were observed in the remelted samples. Regression analysis indicated that laser power played a pivotal role, with positive impact on surface finish and depth of influence but a negative impact on residual stress and hardness. Therefore, considering all the comparison metrics, remelting using laser power of 150 W and a scan speed of 1140 mm min−1 were found to yield optimal surface conditions.
Exploring the benefits of integrated energy-water management in reducing economic and environmental tradeoffs
Authors: Singh Samarth , Quinn Julianne , Kern Jordan , Cuppari Rosa , Characklis Greg 
Published: August 2024

Abstract
Integrated water-energy management is crucial for balancing socioeconomic and environmental objectives in multi-reservoir systems. Multipurpose reservoirs support clean energy production, recreation, navigation, and flood protection but also disrupt natural water flows and fish migration. As hydropower’s role evolves with grid decarbonization, managing these tradeoffs becomes increasingly complex. An integrated model combining economic and environmental factors is essential to inform how to adapt hydropower operations effectively to complement decarbonization of the electric grid. However, existing literature lacks such comprehensive models. This study introduces an integrated water-energy optimization model using the Columbia River Basin (CRB) and Mid-Columbia energy market as a case study. The model couples a simulation of operations of 47 CRB reservoirs with a unit commitment/economic dispatch model of the California and West Coast Power system. We employ Direct policy search and a multi-objective evolutionary algorithm to optimize four objectives: maximize economic benefits from energy production, minimize fossil fuel electricity generation, minimize environmental flow violations, and minimize peak flood levels. Our findings reveal that the integrated model discovers superior operational strategies compared to existing rules, with some policies outperforming current operations on all objectives simultaneously. Insights from the optimized policies include strategies for improved coordination of reservoir operations using storage and inflow data, and the strategic timing of water releases to ensure increased hydropower production leads to less fossil fuel dependence and greater revenue. These results highlight the potential of integrated models to enhance the sustainability of hydropower operations amid a transitioning energy landscape.
Interoceptive active inference
Authors: Gerrans Philip 
Published: August 2024

Abstract
Allostatic active inference is the name for the integrated processing that optimizes organismic function. It derives from the idea that, in order to maintain viability, organisms must implement a model that predicts the consequences of regulatory action. Bodily and affective experiences inform the subject of degree and nature of regulatory success and failure. The avatar is modelled as the entity whose experiences change as a result of internally generated action. It is both source and target of regulation. Predictive processing is a neurocomputational implementation of these ideas, providing testable hypotheses about the nature of neurocognitive processes.
Pain and the avatar
Authors: Gerrans Philip 
Published: August 2024

Abstract
In pain asymbolia, patients say that they are aware of pain but that it does not bother them or it feels as ‘if it’ is not their pain.. Pain asymbolia results from the processing of nociceptive signals not modelled as belonging to the self. Lack of affective response to pain in asymbolia is a consequence, not a cause, of inability to represent the nociceptive signal as ‘mine’. This interpretation is consistent with neural correlates not only of pain asymbolia, but also of a variety of pain and self-related conditions. It also reconciles depersonalization accounts of pain asymbolia with the classic sensorimotor-limbic disconnection accounts that explain it as a phenomenon of ‘hypoemotionality’.
Depersonalization disorder
Authors: Gerrans Philip 
Published: August 2024

Abstract
In depersonalization disorder, patients feel detached from experience and report feeling ‘as if’ experience is not happening to them. There is no deficit of cognition or interoception, but there is a characteristic flattening or absence of affective feeling.The nature of the avatar as an anchor of allostatic inference, neurally realized by circuitry centred on the insula, provides an explanation. The posterior insula is a hub of bodily self modelling. The anterior insula is a hub that: (1) integrates bodily signals with higher-level cognitive and emotional processing and (2) in the process becomes a key substrate for self attribution of affective experience. When the anterior insula is unpredictably and intractably hypoactivated, the subject still feels her bodily experience as her own, but the affective dimension of self modelling is absent. She reports the result in the language of depersonalization.
A vessel without a pilot
Authors: Gerrans Philip 
Published: August 2024

Abstract
In Cotard delusion, the subject believes that she has died or no longer exists. One explanation of the delusion proceeds from the fact that depersonalization is often reported as the experience of feeling ‘as if’ the subject no longer exists. In Cotard delusion, this depersonalization experience is coupled with a metacognitive deficit that leads the subject to endorse the experience or take it at face value. An alternative explanation treats depersonalization and Cotard delusion as products of different impairments to the avatar. Unlike depersonalization in which the avatar is largely intact, in Cotard delusion, the avatar degrades and disintegrates. The result is that the narrative ‘I’ remains as a purely verbal construct detached from an underlying sense of self as a unified persisting entity.
The social self
Authors: Gerrans Philip 
Published: August 2024

Abstract
Intuitively and clinically, people with social anxiety disorder (SAD) fear social interaction because they overestimate the adversity of the social world. They retreat from a world that they misrepresent as hostile and intractable. However, SAD is not essentially a problem of misrepresentation of the social world. Rather inconsistent and fragile self modelling leads to misrepresentation of the social world. The SAD subject represents the social world as essential to her well-being because she lacks confidence in her capacity for independent coping. However, simultaneously, she predicts that engaging with others will lead to humiliation and ostracism. A weak and unstable avatar that leads to conflicting social motivation manifests as SAD symptoms and makes it impossible for her to adaptively regulate or reinterpret her distressing experience.
Una nota supra La/Semper est canendum Fa
Authors: Chan Eleanor 
Published: August 2024

Abstract
One thing above others is known widely about the so-called English cadence: that it is not English. Ever since musicologists of the mid-twentieth century named it, the cadence has laboured under the guise of its deceptiveness. However, as argued in this chapter, the cadence has much to tell us about the musical culture of England at the end of the sixteenth and into the seventeenth century, during the height of its popularity. Using the ‘English’ cadence as its lodestar, this chapter examines the dual accusations of archaism and strangeness levelled at false relations by Thomas Morley in his A Plaine and Easie Practicall Introduction. Ultimately, it demonstrates that the ‘English’ cadence shines a light on the movement towards tonality, the twilight period of solmization, and on the turbulent early years of the Church of England, debates over music in liturgy, and this poignant period of history in general.
Introduction
Authors: Zinkin Melissa 
Published: August 2024

Abstract
This introduction provides a definition of the concept of depth and explains why it is valuable. It also begins to make the case that Kant considers reason to be valuable because it is the source of depth. It discusses passages from Kant’s Lectures on Logic, where he uses the analogy of the depths of a body of water to discuss the thoroughness of cognition, and a passage from the Critique of Pure Reason where Kant distinguishes a presumably superficial “plaster cast of a living human being” from a real “living being” with regard to whether or not their cognition is from principles and is rational. The introduction also provides an overview of the chapters.
Reason, Systematicity, Judgment, and Depth in Kant’s Critique of Pure Reason
Authors: Zinkin Melissa 
Published: August 2024

Abstract
This chapter argues that for Kant reason is the faculty for making deep, systematic judgments. It focuses on Kant’s discussion of the principle of systematicity in the Appendix to the Critique of Pure Reason. It argues that whereas the faculty of understanding is the faculty for judging what something is, reason is the faculty for judging why something is what it is and comprehending it. Once we understand reason to be the faculty for systematically reflecting on something in order to judge why it is what it is, we can make sense of Kant’s statement in the Critique of the Power of Judgment that a critique of the pure power of judgment is a part of the critique of pure reason; to have good judgment is to judge according to the principle of systematicity, which is the principle of reason.
What is a civil war?
Authors: Toft Monica Duffy 
Published: August 2024

Abstract
It should come as no surprise that the definition of “civil war” is hard to pin down. Civil war can be defined as “any armed conflict that involves (a) military action internal to the metropole, (b) the active participation of the national government, and (c) effective resistance by both sides.” This distinguishes civil wars from international, or interstate wars, by the fact that the violence occurred within the territory of a sovereign state, and that the government participated as a combatant.
Causes of civil war
Authors: Toft Monica Duffy 
Published: August 2024

Abstract
The reasons why civil wars occur are highly disputed. There are even disagreements over how to classify different types of civil wars. Still, this debate is relatively new in civil war scholarship. A focus on the different types and risk factors of civil war only emerged in the early twenty-first century. Up until then, studies mainly focused on risk factors without considering that there may be more than one “type” of civil war, with a significant amount of scholarship distinguishing between ethnic and non-ethnic wars, or identity versus non-identity wars.
External involvement
Authors: Toft Monica Duffy 
Published: August 2024

Abstract
A common phenomenon in the modern history of civil wars has been third-party intervention, or an outside state or international body deciding to get involved in another state’s internal conflict. But not all interventions are due to civil war externalities like drug smuggling or refugees. Individual states, the United Nations, or regional organizations may choose to intervene for geopolitical gain, humanitarian concerns, or economic benefit, along with many other reasons. This chapter outlines the rationales for intervention, and how they affect civil wars.
A puzzle about weak belief
Authors: Pearson Joshua Edward 
Published: August 2024

Abstract
I present an intractable puzzle for the currently popular view that belief is weak – the view that expressions like ‘S believes p’ ascribe to S a doxastic attitude towards p that is rationally compatible with low credence that p. The puzzle concerns issues that arise on considering beliefs in conditionals. I show that proponents of weak belief either cannot consistently apply their preferred methodology when accommodating beliefs in conditionals, or must deny that beliefs in conditionals can be used in reasoning.
Triangulating evidence in health sciences with Annotated Semantic Queries
Authors: Liu Yi , Gaunt Tom R 
Published: August 2024

Abstract

Motivation
Integrating information from data sources representing different study designs has the potential to strengthen evidence in population health research. However, this concept of evidence “triangulation” presents a number of challenges for systematically identifying and integrating relevant information. These include the harmonization of heterogenous evidence with common semantic concepts and properties, as well as the priortization of the retrieved evidence for triangulation with the question of interest.

Results
We present Annotated Semantic Queries (ASQ), a natural language query interface to the integrated biomedical entities and epidemiological evidence in EpiGraphDB, which enables users to extract “claims” from a piece of unstructured text, and then investigate the evidence that could either support, contradict the claims, or offer additional information to the query. This approach has the potential to support the rapid review of preprints, grant applications, conference abstracts, and articles submitted for peer review. ASQ implements strategies to harmonize biomedical entities in different taxonomies and evidence from different sources, to facilitate evidence triangulation and interpretation.

Availability and implementation
ASQ is openly available at https://asq.epigraphdb.org and its source code is available at https://github.com/mrcieu/epigraphdb-asq under GPL-3.0 license.
LineageVAE: reconstructing historical cell states and transcriptomes toward unobserved progenitors
Authors: Majima Koichiro , Kojima Yasuhiro , Minoura Kodai , Abe Ko , Hirose Haruka , Shimamura Teppei 
Published: August 2024

Abstract

Motivation
Single-cell RNA sequencing (scRNA-seq) enables comprehensive characterization of the cell state. However, its destructive nature prohibits measuring gene expression changes during dynamic processes such as embryogenesis or cell state divergence due to injury or disease. Although recent studies integrating scRNA-seq with lineage tracing have provided clonal insights between progenitor and mature cells, challenges remain. Because of their experimental nature, observations are sparse, and cells observed in the early state are not the exact progenitors of cells observed at later time points. To overcome these limitations, we developed LineageVAE, a novel computational methodology that utilizes deep learning based on the property that cells sharing barcodes have identical progenitors.

Results
LineageVAE is a deep generative model that transforms scRNA-seq observations with identical lineage barcodes into sequential trajectories toward a common progenitor in a latent cell state space. This method enables the reconstruction of unobservable cell state transitions, historical transcriptomes, and regulatory dynamics at a single-cell resolution. Applied to hematopoiesis and reprogrammed fibroblast datasets, LineageVAE demonstrated its ability to restore backward cell state transitions and infer progenitor heterogeneity and transcription factor activity along differentiation trajectories.

Availability and implementation
The LineageVAE model was implemented in Python using the PyTorch deep learning library. The code is available on GitHub at https://github.com/LzrRacer/LineageVAE/.