bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dimer Algebras, Ghor Algebras, and Cyclic Contractions
Authors: Beil Charlie 
Published: September 2023

AbstractA ghor algebra is the path algebra of a dimer quiver on a surface, modulo relations that come from the perfect matchings of its quiver. Such algebras arise from abelian quiver gauge theories in physics. We show that a ghor algebra $$\Lambda $$
Λ
on a torus is a dimer algebra (a quiver with potential) if and only if it is noetherian, and otherwise $$\Lambda $$
Λ
is the quotient of a dimer algebra by homotopy relations. Furthermore, we classify the simple $$\Lambda $$
Λ
-modules of maximal dimension and give an explicit description of the center of $$\Lambda $$
Λ
using a special subset of perfect matchings. In our proofs we introduce formalized notions of Higgsing and the mesonic chiral ring from quiver gauge theory.
Court Decisions and Air Pollution: Evidence from Ten Million Penal Cases in India
Authors: Sarmiento Luis , Nowakowski Adam 
Published: September 2023

AbstractThis study explores the relationship between air pollution and judicial rulings. Although environmental factors should not affect judicial decisions, realists contend that there is substantial room for external factors to transpire into sentencing and sway human reasoning. We hypothesize that air pollution is one of these factors. Using Poisson panel models and instrumental variable techniques, we show that exposure leads to more convictions. We posit that this effect occurs because the impact of exposure on the central nervous system changes the cognitive performance and empathy of judges. Back-of-the-envelope calculations suggest that decreasing average air pollution in India by one standard deviation would lead to up to 145,000 fewer convictions regarding currently active cases.
Influence of Place of Birth on Adult Mortality: The Case of Spain
Authors: Aldea Néstor , Ordanovich Dariya , Palloni Alberto , Ramiro Diego , Viciana Francisco 
Published: September 2023

AbstractWe use a unique data set from Spain and we estimate life expectancy at age 50 for males and females by place of residence and place of birth. We show that, consistent with expectations regarding the influence of early conditions on adult health and mortality, the effects of place of birth on adult mortality are very strong, irrespective of place of residence. Furthermore, we find that mortality levels observed in a place are strongly influenced by the composition of migrants by place of birth. This is reflected in a new measure of heritability of early childhood conditions that attains a value in the range 0.42–0.43, implying that as much as 43 percent of the variance in Spain’s life expectancy at age 50 is explained by place of birth. Finally, we find evidence of the healthy migrant effect, that is, positive health selection of migrants, at a regional level.
Applications of Limiters, Neural Networks and Polynomial Annihilation in Higher-Order FD/FV Schemes
Authors: Hillebrand Dorian , Klein Simon-Christian , Öffner Philipp 
Published: September 2023

AbstractThe construction of high-order structure-preserving numerical schemes to solve hyperbolic conservation laws has attracted a lot of attention in the last decades and various different ansatzes exist. In this paper, we compare several completely different approaches, i.e. deep neural networks, limiters and the application of polynomial annihilation to construct high-order accurate shock capturing finite difference/volume (FD/FV) schemes. We further analyze their analytical and numerical properties. We demonstrate that all techniques can be used and yield highly efficient FD/FV methods but also come with some additional drawbacks which we point out. Our investigation of the different strategies should lead to a better understanding of those techniques and can be transferred to other numerical methods as well which use similar ideas.
A Spatial Equilibrium Problem for the European Pulp and Paper Industry Under the Emission Trading System
Authors: Allevi Elisabetta , Gnudi Adriana , Konnov Igor V. , Oggioni Giorgia , Riccardi Rossana 
Published: September 2023

AbstractWe propose a spatial equilibrium problem for a pulp and paper supply chain network under the Emission Trading System (ETS) and the circular economy principles. The developed closed-loop supply chain model, based on the variational inequality theory, accounts for the forest deployment and the production of paper using both virgin pulp and recyclable paper waste in an area that is subject to the ETS. In addition, it considers the exchanges of virgin pulp, paper, recyclable and not-recyclable paper waste between this regulated area and geographical zones where no environmental policies apply. The aim of this paper is to investigate the ETS impacts on the pulp and paper industries located in an environmentally regulated area in terms of operating costs, production and trading volumes of the aforementioned commodities, and emissions generated. The case study focuses on the European pulp and paper companies, which are among the energy-intensive industries deemed to be exposed to the carbon leakage risk as a consequence of the ETS application. Sensitivity analyses on the carbon price value and on the trade flexibility are conducted. Our results show that the ETS implies additional costs for the European pulp and paper companies and induces them to resort more on imports of virgin pulp and paper from not-regulated zones.
Sfnet: Faster and Accurate Semantic Segmentation Via Semantic Flow
Authors: Li Xiangtai , Zhang Jiangning , Yang Yibo , Cheng Guangliang , Yang Kuiyuan , Tong Yunhai , Tao Dacheng 
Published: September 2023

AbstractIn this paper, we focus on exploring effective methods for faster and accurate semantic segmentation. A common practice to improve the performance is to attain high-resolution feature maps with strong semantic representation. Two strategies are widely used: atrous convolutions and feature pyramid fusion, while both are either computationally intensive or ineffective. Inspired by the Optical Flow for motion alignment between adjacent video frames, we propose a Flow Alignment Module (FAM) to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high-resolution features effectively and efficiently. Furthermore, integrating our FAM to a standard feature pyramid structure exhibits superior performance over other real-time methods, even on lightweight backbone networks, such as ResNet-18 and DFNet. Then to further speed up the inference procedure, we also present a novel Gated Dual Flow Alignment Module to directly align high-resolution feature maps and low-resolution feature maps where we term the improved version network as SFNet-Lite. Extensive experiments are conducted on several challenging datasets, where results show the effectiveness of both SFNet and SFNet-Lite. In particular, when using Cityscapes test set, the SFNet-Lite series achieve 80.1 mIoU while running at 60 FPS using ResNet-18 backbone and 78.8 mIoU while running at 120 FPS using STDC backbone on RTX-3090. Moreover, we unify four challenging driving datasets (i.e., Cityscapes, Mapillary, IDD, and BDD) into one large dataset, which we named Unified Driving Segmentation (UDS) dataset. It contains diverse domain and style information. We benchmark several representative works on UDS. Both SFNet and SFNet-Lite still achieve the best speed and accuracy trade-off on UDS, which serves as a strong baseline in such a challenging setting. The code and models are publicly available at https://github.com/lxtGH/SFSegNets.
Contract-based specification of mode-dependent timing behavior
Authors: Kröger Janis , Koopmann Björn , Stierand Ingo , Fränzle Martin 
Published: September 2023

AbstractThe design of safety-critical systems calls for rigorous application of specification and verification methods. In this context, a comprehensive consideration of safety aspects, which inevitably include timing properties, requires explicit addressing of operating modes and their transitions in the system model as well as in the respective specifications. As a side effect, this helps to reduce verification complexity. This paper presents an extension of a framework for the specification of timing properties following the contract-based design paradigm. It provides enhancements of the underlying specification language, which enables specifying modes, mode transitions, and mode-dependent behavior. A formal semantics is given in order to enable reasoning about such specifications as well as about contract operations like refinement and composition, thus enabling to make statements about mode composition. The results are discussed using a real-world example.
Automatic detection of ictal activity in EEG using synchronization and chaos-based attributes
Authors: Mahgoub Asma , Qaraqe Marwa 
Published: September 2023

AbstractAutomatic seizure onset detectors (SODs) have been proposed to alert epileptic patients when a seizure is about to happen and in turn improve their quality of life. Yet, the detectors proposed in literature are complex and difficult to implement in real-time as they utilize large feature sets with redundant and irrelevant features. Hence, the aim of this work is to propose a simple and lightweight SOD that exploits two characteristics that reflect the neuronal behavior during a seizure. Namely, the synchronization between EEG channels and the chaoticity of the EEG; synchronization was measured by the condition number while the recurrence period density entropy estimated the chaoticity of an EEG signal. A support vector machine was trained and tested on 10 patients from a scalp EEG dataset and was able to detect the considered seizures with a sensitivity of 100% and a false positives rate of 0.5 per hour. The results indicate that synchronization and chaos attributes can reflect the manifestation of seizures in EEG data and can be used to develop SODs. This work emphasizes that even a single relevant feature can produce an SOD with comparable performance to SODs that use many features.
Graphical Abstract
Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma
Authors: Ma Xijuan , Qian Xianling , Wang Qing , Zhang Yunfei , Zong Ruilong , Zhang Jia , Qian Baoxin , Yang Chun , Lu Xin , Shi Yibing 
Published: September 2023

Abstract
Objective
Microvascular invasion (MVI) is a significant adverse prognostic indicator of intrahepatic cholangiocarcinoma (ICC) and affects the selection of individualized treatment regimens. This study sought to establish a radiomics nomogram based on the optimal VOI of multi-sequence MRI for predicting MVI in ICC tumors.

Methods
160 single ICC lesions with MRI scanning confirmed by postoperative pathology were randomly separated into training and validation cohorts (TC and VC). Multivariate analysis identified independent clinical and imaging MVI predictors. Radiomics features were obtained from images of 6 MRI sequences at 4 different VOIs. The least absolute shrinkage and selection operator algorithm was performed to enable the derivation of robust and effective radiomics features. Then, the best three sequences and the optimal VOI were obtained through comparison. The MVI prediction nomogram combined the independent predictors and optimal radiomics features, and its performance was evaluated via the receiver operating characteristics, calibration, and decision curves.

Results
Tumor size and intrahepatic ductal dilatation are independent MVI predictors. Radiomics features extracted from the best three sequences (T1WI-D, T1WI, DWI) with VOI10mm (including tumor and 10 mm peritumoral region) showed the best predictive performance, with AUCTC = 0.987 and AUCVC = 0.859. The MVI prediction nomogram obtained excellent prediction efficacy in both TC (AUC = 0.995, 95%CI 0.987–1.000) and VC (AUC = 0.867, 95%CI 0.798–0.921) and its clinical significance was further confirmed by the decision curves.

Conclusion
A nomogram combining tumor size, intrahepatic ductal dilatation, and the radiomics model of MRI multi-sequence fusion at VOI10mm may be a predictor of preoperative MVI status in ICC patients.
Subjektivierungsprozesse neu zugewanderter Jugendlicher im Kontext schulischer Übergänge – Potenziale eines methodenpluralen Zugangs
Authors: Fritzsche Bettina , Warkentin Stephanie 
Published: September 2023

ZusammenfassungIm Sinne einer subjektivierungstheoretisch ausgerichteten Übergangsforschung werden in diesem Beitrag organisationale Praktiken schulischer Übergänge und die mit diesen verbundenen Formen der Adressierung neu zugewanderter Jugendlicher zu deren subjektiv erlebten Handlungsmöglichkeiten oder auch -beschränkungen in Beziehung gesetzt. Ziel ist es hierbei, das subjektivierende Potenzial einer organisationalen Gestaltung schulischer Übergänge zu erfassen. Untersuchungsgegenstand sind die bildungsbiographisch höchst relevanten schulischen Übergänge neu zugewanderter Jugendlicher. Unter Bezug auf eine laufende ethnographische Studie werden anhand eines Fallbeispiels Subjektivierungsprozesse nachgezeichnet, die sich im Spannungsfeld zwischen einer sozialisationsbedingt erworbenen bildungsaffinen meritokratischen Haltung und machtvollen organisationalen Zuweisungspraktiken vollziehen. Auf dieser Grundlage wird abschließend auch auf das spezifische Potenzial des gewählten methoden- und perspektivenpluralen Zuganges für eine Analyse des Zusammenspiels einer strukturellen und organisationalen Ebene und der Subjektebene bei Subjektivierungsprozessen eingegangen.
Körper-Interventionen in der psychodramatischen Paar- und Sexualtherapie
Authors: Suchanek Gilbert 
Published: September 2023

ZusammenfassungAusgehend von den Ergebnissen einer deskriptiven Studie zum Thema „Psychodrama-Körperpsychotherapie – Arbeit mit somatischen Rollen im Psychodrama“ (Suchanek 2022) wird in diesem Artikel der Zeitschrift für Psychodrama und Soziometrie die Verwendung von körperbezogenen Interventionen in der psychodramatischen Paar- und Sexualtherapie thematisiert. Vorgestellt werden ein Überblick über die häufigsten Anwendungsbereiche, sowie ein Modell zur Differenzierung zwischen Anwärmtechniken und vertiefter Prozessarbeit in der Arbeit mit somatischen Rollen.
Radicalizing Hope
Authors: Chapman Michael , Komesaroff Paul 
Published: September 2023

AbstractThe race against COVID-19 has been intense and painful and many of us are now looking for a way to move on. We may try to seize a degree of comfort and security by convincing ourselves that we are among the “fittest”—that is, among those who have managed to survive—who can now hope for a “new-normal” time, relatively unscathed. But this isn’t what we should be hoping for. Our world, and ourselves, will never be free of COVID-19 or its insidious effects. COVID-19, like climate change, is a threat multiplier and the challenges it has raised are now indelibly engraved in our vulnerable, interconnected lives. Rather than vainly hoping for a return to an imaginary, erstwhile “normal” what we need is something more fundamental: a new version of hope that embraces a courage to learn what we need to do, to enable us to live a future to which we aspire. Perhaps counter-intuitively, we need to accept that the COVID-19 experience has already changed us deeply and hope that we can learn from this and from the future changes that the pandemic will give rise to. We need to radicalize our responses to the challenges, enabling ourselves to learn new lessons about old but increasingly pertinent topics, such as the realities of human fragility, and inter-connection.
A causal hidden Markov model for assessing effects of multiple direct mail campaigns
Authors: Pennoni Fulvia , Paas Leonard J. , Bartolucci Francesco 
Published: September 2023

AbstractWe propose assessing the causal effects of a dynamic treatment in a longitudinal observational study, given observed confounders under suitable assumptions. The causal hidden Markov model is based on potential versions of discrete latent variables, and it accounts for the estimated propensity to be assigned to each treatment level over time using inverse probability weighting. Estimation of the model parameters is carried out through a weighted maximum log-likelihood approach. Standard errors for the parameter estimates are provided by nonparametric bootstrap. The proposal is validated through a simulation study aimed at comparing different model specifications. As an illustrative example, we consider a marketing campaign conducted by a large European bank over time on its customers. Findings provide straightforward managerial implications.
Brain Sagging Dementia
Authors: Lashkarivand Aslan , Eide Per Kristian 
Published: September 2023

Abstract
Purpose of Review
Brain sagging dementia (BSD) is a rare but devastating form of early-onset dementia characterized by intracranial hypotension and behavioral changes resembling behavioral variant frontotemporal dementia. This review aims to provide a comprehensive overview of BSD, highlighting its pathomechanism, diagnostic tools, and available treatment options.

Recent Findings
BSD exhibits a complex clinical manifestation with insidious onset and gradual progression of behavioral disinhibition, apathy, inertia, and speech alterations. Additionally, patients may exhibit brainstem and cerebellar signs such as hypersomnolence and gait disturbance. Although headaches are common, they may not always demonstrate typical orthostatic features. Recent radiological advances have improved the detection of CSF leaks, enabling targeted treatment and favorable outcomes.

Summary
Understanding the pathomechanism and available diagnostic tools for BSD is crucial for a systematic approach to timely diagnosis and treatment of this reversible form of early-onset dementia, as patients often endure a complex and lengthy clinical course.
An updated algorithm for an effective choice of continuous glucose monitoring for people with insulin-treated diabetes
Authors: Maiorino Maria Ida , Buzzetti Raffaella , Irace Concetta , Laviola Luigi , Napoli Nicola , Pitocco Dario , Esposito Katherine ,   on behalf of LIVE CGM working group
Published: September 2023

Abstract
Purpose
Continuous Glucose Monitoring (CGM) is a key tool for insulin-treated people with diabetes (PwD). CGM devices include both real-time CGM (rtCGM) and intermittently scanned CGM (isCGM), which are associated with an improvement of glucose control and less hypoglycemia in clinical trials of people with type 1 and type 2 diabetes.

Methods
This is an expert position to update a previous algorithm on the most suitable choice of CGM for insulin-treated PwD in light of the recent evidence and clinical practice.

Results
We identified six different clinical scenarios, including type 1 diabetes, type 2 diabetes, pregnancy on intensive insulin therapy, regular physical exercise, new onset of diabetes, and frailty. The use of rtCGM or isCGM is suggested, on the basis of the predominant clinical issue, as suboptimal glucose control or disabling hypoglycemia, regardless of baseline HbA1c or individualized HbA1c target.

Conclusion
The present algorithm may help to select the best CGM device based on patients’ clinical characteristics, needs and clinical context, offering a further opportunity of a “tailored” therapy for people with insulin-treated diabetes.
ANCA detection with solid phase chemiluminescence assay: diagnostic and severity association in vasculitis
Authors: Renuncio-García Mónica , Calvo-Río Vanesa , Benavides-Villanueva Fabricio , Al Fazazi Salma , Rodríguez-Vidriales María , Escagedo-Cagigas Clara , Martín-Penagos Luis , Irure-Ventura Juan , López-Hoyos Marcos , Blanco Ricardo 
Published: September 2023

AbstractANCA-associated vasculitis (AAV) comprises a group of necrotizing vasculitis that mainly affects small- and medium-sized vessels. Serum anti-neutrophil cytoplasmic antibodies (ANCA), mainly anti-myeloperoxidase (anti-MPO) and anti-proteinase 3 (anti-PR3), levels may correlate to severity, prognosis, and recurrence of the disease. A retrospective analysis of 101 patients with MPO-positive and 54 PR3-positive vasculitis was performed, using laboratory established cut-off value, measured by chemiluminescence. Furthermore, data of renal disease and pulmonary involvement were collected at vasculitis diagnosis, as well as the progress, requiring dialysis, transplant, or mortality. For anti-MPO antibodies with a diagnosis of vasculitis (n = 77), an area under the curve (AUC) was calculated (AUC = 0.8084), and a cut-off point of 41.5 IU/ml was determined. There were significant differences in anti-MPO levels between patients with renal or pulmonary dysfunction (n = 65) versus those without them (n = 36) (p = 0.0003), and a cut-off threshold of 60 IU/ml was established. For anti-PR3 antibodies with a diagnosis of vasculitis (n = 44), an area under the curve (AUC) was calculated (AUC = 0.7318), and a cut-off point of 20.5 IU/ml was determined. Significant differences in anti-PR3 levels were observed between those patients with renal or pulmonary dysfunction (n = 30) and those without them (n = 24) (p = 0.0048), and a cut-off threshold of 41.5 IU/ml was established. No significant differences between those patients who had a worse disease progression and those who did not were found for anti-MPO and anti-PR3. Anti-MPO and anti-PR3 levels at the moment of vasculitis diagnosis are related with disease severity but not with disease outcome or vasculitis recurrence.
Deep Reinforcement Learning for Robotic Approaching Behavior Influenced by User Activity and Disengagement
Authors: Raggioli Luca , D’Asaro Fabio Aurelio , Rossi Silvia 
Published: September 2023

AbstractA robot intended to monitor human behavior must account for the user’s reactions to minimize his/her perceived discomfort. The possibility of learning user interaction preferences and changing the robot’s behavior accordingly may positively impact the perceived quality of the interaction with the robot. The robot should approach the user without causing any discomfort or interference. In this work, we contribute and implement a novel Reinforcement Learning (RL) approach for robot navigation toward a human user. Our implementation is a proof-of-concept that uses data gathered from real-world experiments to show that our algorithm works on the kind of data that it would run on in a realistic scenario. To the best of our knowledge, our work is one of the first attempts to provide an adaptive navigation algorithm that uses RL to account for non-deterministic phenomena.