bluuebunny/crossref_metadata_2025_split huggingface dataset:
A detailed look at the most obscured galactic nuclei in the mid-infrared
Authors: Donnan F. R. , Rigopoulou D. , García-Bernete I. , Pereira-Santaella M. , Alonso-Herrero A. , Roche P. F. , Aalto S. , Hernán-Caballero A. , Spoon H. W. W. 
Published: November 2022

Context. Compact obscured nuclei (CONs) represent an extreme phase of galaxy evolution where rapid supermassive black hole growth and/or compact star-forming activity is completely obscured by gas and dust.
Aims. We investigate the properties of CONs in the mid-infrared and explore techniques aimed at identifying these objects, such as through the equivalent width (EW) ratios of their polycyclic aromatic hydrocarbon (PAH) features.
Methods. We modelled Spitzer spectra by decomposing the continua into nuclear and star-forming components, from which we then measured the nuclear optical depth, τN, of the 9.8 μm silicate absorption feature. We also used Spitzer spectral maps to investigate how PAH EW ratios vary with aperture size for objects that host CONs.
Results. We find that the nuclear optical depth, τN, strongly correlates with the HCN-vib emission line in the millimetre for CONs, with a Pearson correlation coefficient of 0.91. We find the PAH EW ratio technique to be effective at selecting CONs and robust against highly inclined galaxies, where strong dust lanes may mimic a CON-like spectrum by producing a high τN. Our analysis of the Spitzer spectral maps shows that the efficacy of the PAH EW ratios in isolating CONs is reduced when there is a strong star-forming component from the host galaxy. In addition, we find that the use of the inferred nuclear optical depth is a reliable method for identifying CONs in 36−7+8% of ultra-luminous infrared galaxies and 17−3+3% of luminous infrared galaxies, consistent with previous work.
Conclusions. We confirm mid-infrared spectra to be a powerful diagnostic of CONs. The increased sensitivity of JWST will allow the identification of CONs at cosmic noon, revealing this extreme but hidden phase of galaxy evolution.
Challenging Myelopathy Cases
Authors: Mustafa Rafid , Zalewski Nicholas L. , Flanagan Eoin P. , Kumar Neeraj 
Published: November 2022

AbstractMisdiagnosis of myelopathies is common and can lead to irreversible disability when diagnosis- and disease-specific treatments are delayed. Therefore, quickly determining the etiology of myelopathy is crucial. Clinical evaluation and MRI spine are paramount in establishing the correct diagnosis and subsequently an appropriate treatment plan. Herein, we review an approach to myelopathy diagnosis focused on the time course of neurologic symptom progression and neuroimaging pearls, and apply them to a variety of inflammatory, structural, and vascular myelopathy cases.
Challenging Cases in Cerebrovascular Disease
Authors: Kelly Michael A. , Singh Jasmine , Balabanov Alexandra , Wadina Adam , Dasovic Braden 
Published: November 2022

AbstractDisorders involving the vascular system of the brain are numerous and sundry. Atherosclerotic thromboembolism of large vessels and lacunar infarctions of small vessel disease are well known. Brain infarction due to cardioembolism is common as well, and even more so when diligently sought. Rupture of intracranial blood vessels results in subarachnoid and intraparenchymal hemorrhage. We present four cases of stroke of uncommon cause and remind clinicians to be open minded to the many possible causes of stroke, in particular because early recognition and treatment is often critical. Case 1 discusses a patient with inflammatory cerebral amyloid angiopathy. The presentation, ability to recur, and current treatment considerations are reviewed. Case 2 discusses microangiopathic thrombotic angiopathy. Diagnosis and treatment are considered. An association with interferon therapy and the evolving terminology of this and related conditions are discussed. Case 3 discusses intracranial hemorrhage secondary to acute promyelocytic leukemia. Patients with acute leukemias require aggressive management of their coagulopathy, thrombocytopenia, and the disease itself. Finally, Case 4 discusses ischemic stroke due to a paradoxical embolism in the setting of a patent foramen ovale (PFO). Both medical and surgical management of a PFO for stroke prevention are considered.
Brachistochrone of off-centered cylinders
Authors: Sambath Krishnaraj , Nagarajan Vidhya 
Published: November 2022

Abstract
We consider the problem of finding paths of shortest transit time between two points (popularly known as brachistochrone) for cylinders with off-centered center of mass, rolling down without slip, subject solely to the force of gravity. This problem is set up using principles of classical rigid body dynamics and the desired path function is solved for numerically using the method of discrete calculus of variations. We discover a distinct array of brachistochrone trajectories for off-centered cylinders, demonstrate a critical dependence of such paths on the initial location and orientation of cylinders’ centers of mass and bring new insights into the family of brachistochrone problems and solutions.
Convergence rate analysis of the coefficient identification problems in a Kuramoto–Sivashinsky equation
Authors: Cao Kai 
Published: November 2022

Abstract
The convergence rate results of the mixed total variation (TV) and L

p
regularization to the identification of the unknown anti-diffusion coefficient, and the mixed H
2-L

p
regularization for the identification of the unknown diffusion coefficient in a Kuramoto–Sivashinsky (KS) equation are investigated with some additional observation. Under suitable source conditions, the convergence rates to regularized solutions for the TV-L

p
regularization of the anti-diffusion coefficient are obtained. Analogously, for the identification of diffusion coefficient with H
2-L

p
regularization, the convergence rates are derived with proper source conditions. In addition, simple and easily interpretable source conditions are also established to deduce the same convergence rates under some assumptions to the sought coefficients. For the source conditions in both two coefficient identification problems, no restrictive smallness condition to the source functions is required.
A facile covalent strategy for ultrafast negative photoconductance hybrid graphene/porphyrin-based photodetector
Authors: Li Mengshan , Ma Xiaoqing , Mu Yanqi , Xie Guancai , Wan Hongfeng , Tao Minli , Guo Beidou , Gong Jian Ru 
Published: November 2022

Abstract
As a powerful complement to positive photoconductance (PPC), negative photoconductance (NPC) holds great potential for photodetector. However, the slow response of NPC relative to PPC devices limits their integration. Here, we propose a facile covalent strategy for an ultrafast NPC hybrid 2D photodetector. Our transistor-based graphene/porphyrin model device with a rise time of 0.2 ms and decay time of 0.3 ms has the fastest response time in the so far reported NPC hybrid photodetectors, which is attributed to efficient photogenerated charge transport and transfer. Both the photosensitive porphyrin with an electron-rich and large rigid structure and the built-in graphene frame with high carrier mobility are prone to the photogenerated charge transport. Especially, the intramolecular donor-acceptor system formed by graphene and porphyrin through covalent bonding promotes photoinduced charge transfer. This covalent strategy can be applied to other nanosystems for high-performance NPC hybrid photodetector.
Output characteristics of series-parallel triboelectric nanogenerators
Authors: He Xinsheng , Zhang Huaiyi , Jiang Jiajie , Liu Xin 
Published: November 2022

Abstract
To tackle the problem of the low output power of triboelectric nanogenerators (TENGs), the output characteristics of multiple TENGs in series and parallel were explored. An experimental device for multilayer TENGs was developed. Through the method of experimental modeling, the equivalent circuits and mathematical models in series and parallel are established respectively, and the working principle is expounded. The experiment found that when multiple TENGs are connected in parallel, the voltage, current, and output power can be regularly increased. When the TENGs are connected in series, the output power is only related to the performance of the first and last two triboelectric generators and has nothing to do with the number of series-connected TENGs. This finding has a certain reference value for the design of high-efficiency triboelectric nanogenerators.
Automatic segmentation for plan-of-the-day selection in CBCT-guided adaptive radiation therapy of cervical cancer
Authors: Zhang Chen , Lafond Caroline , Barateau Anaïs , Leseur Julie , Rigaud Bastien , Chan Sock Line Diane Barbara , Yang Guanyu , Shu Huazhong , Dillenseger Jean-Louis , de Crevoisier Renaud , Simon Antoine 
Published: November 2022

Abstract

Objective. Plan-of-the-day (PoD) adaptive radiation therapy (ART) is based on a library of treatment plans, among which, at each treatment fraction, the PoD is selected using daily images. However, this strategy is limited by PoD selection uncertainties. This work aimed to propose and evaluate a workflow to automatically and quantitatively identify the PoD for cervix cancer ART based on daily CBCT images. Approach. The quantification was based on the segmentation of the main structures of interest in the CBCT images (clinical target volume [CTV], rectum, bladder, and bowel bag) using a deep learning model. Then, the PoD was selected from the treatment plan library according to the geometrical coverage of the CTV. For the evaluation, the resulting PoD was compared to the one obtained considering reference CBCT delineations. Main results. In experiments on a database of 23 patients with 272 CBCT images, the proposed method obtained an agreement between the reference PoD and the automatically identified PoD for 91.5% of treatment fractions (99.6% when considering a 5% margin on CTV coverage). Significance. The proposed automatic workflow automatically selected PoD for ART using deep-learning methods. The results showed the ability of the proposed process to identify the optimal PoD in a treatment plan library.
Implementation of the surface gradiometer receive coils for the improved detection limit and sensitivity in the single-sided MPI scanner
Authors: McDonough C , Pagan J , Tonyushkin A 
Published: November 2022

Abstract

Objective. Magnetic Particle Imaging (MPI) promises to enhance diagnostic capabilities of the existing clinical imaging modalities. Traditional MPI scanners utilize cylindrical bore geometry that prevents scaling up the MPI to accommodate full human subject. Single-sided geometry, on the other hand, has all the hardware located on one side providing an unrestricted imaging volume. Approach. Our single-sided MPI device utilizes a field-free line topology with a single drive coil and a surface receive coil, which is used to detect the nanoparticles. Unlike closed bore systems, single-sided devices cannot adapt well established solenoid gradiometer receive coil, which result in impinging potential sensitivity gain. Main results. In this work we study multiple receive coil configurations with compensation for the purpose of removing feedthrough, whilst preserving the superparamagnetic iron oxide nanoparticle signal. Moreover, we present a compensated surface receive coil design that provides highest sensitivity in the single-sided geometry and demonstrate a new detection limit in a single-sided scanner of 100 ng of iron. In addition, we demonstrate 1D imaging of a sample without use of receive filter recovering signal at fundamental harmonic. Significance. These advancements in the receive chain are crucial for developing a practical MPI scanner with a single-sided geometry.
Application of the triple-probe technique to magnetized plasmas
Authors: Dimitrova M , Popov Tsv K , Dejarnac R , Kovačič J , Ivanova P , Gyergyek T , Losada U , Hidalgo C , Panek R , Stöckel J 
Published: November 2022

Abstract
The triple-probe technique (TPT) is a diagnostic widely used to determine the electron temperature in various devices and plasma conditions. It was developed for measurements in low-pressure gas-discharge plasmas in the absence of a magnetic field. This paper presents a comparison of the experimental results obtained by swept Langmuir probe (LP) measurements with those obtained by the TPT in magnetized plasmas in three experimental machines with different magnetic field magnitudes (0.01–1.15 T). The reliability of the triple-probe results for the electron temperature in tokamak plasmas at higher magnetic fields is discussed. It was found that the larger the magnetic field, the more the TPT overestimates the electron temperature compared with single swept LPs. The explanation proposed in this paper is based on a shift in the floating potential towards the plasma potential in the presence of a magnetic field, yielding a more positive voltage measured by the TPT and therefore higher electron temperatures. Using the extended formula for the electron probe current in the presence of a magnetic field a correction factor is derived such that the TPT yields a temperature similar to that of the swept LP techniques.
Cortical theta–gamma coupling tracks the mental workload as an indicator of mental schema development during simulated quadrotor UAV operation
Authors: Gu Heng , Chen He , Yao Qunli , Wang Shaodi , Ding Zhaohuan , Yuan Ziqian , Zhao Xiaochuan , Li Xiaoli 
Published: November 2022

Abstract

Objective. In the emerging field of neuroergonomics, mental workload assessment is one of the most important problems. Previous studies have made some progress on the relationship between task difficulties and mental workload, but how the mental schema, a reflection of the understanding and mastery degree of a task, affects mental workload has not been clearly discussed. Approach. There is emerging appreciation for the role of theta–gamma coupling (TGC) in high-level cognitive functions. Here, we attempt to further our understanding of how mental schema development and task difficulty had an impact on mental workload from the perspective of TGC. Specifically, the variation of TGC coupling strength and coupling pattern was estimated with different test orders and task difficulties performed by 51 students in a ten-day simulated quadrotor unmanned aerial vehicle flight training and test tasks. Main results. During the training, TGC increased with mental schema development. For the test tasks, TGC did not change with increasing task difficulty before the operator formed a mental schema but decreased with the increasing mental workload after the formation of the mental schema. Significance. Our results suggest that TGC was a robust indicator of mental schema development and could be biased by task difficulty. In conclusion, TGC can be a promising measure of mental workload, but only for experienced operators.
Learning curves of generic features maps for realistic datasets with a teacher-student model*
Authors: Loureiro Bruno , Gerbelot Cédric , Cui Hugo , Goldt Sebastian , Krzakala Florent , Mézard Marc , Zdeborová Lenka 
Published: November 2022

Abstract
Teacher-student models provide a framework in which the typical-case performance of high-dimensional supervised learning can be described in closed form. The assumptions of Gaussian i.i.d. input data underlying the canonical teacher-student model may, however, be perceived as too restrictive to capture the behaviour of realistic data sets. In this paper, we introduce a Gaussian covariate generalisation of the model where the teacher and student can act on different spaces, generated with fixed, but generic feature maps. While still solvable in a closed form, this generalization is able to capture the learning curves for a broad range of realistic data sets, thus redeeming the potential of the teacher-student framework. Our contribution is then two-fold: first, we prove a rigorous formula for the asymptotic training loss and generalisation error. Second, we present a number of situations where the learning curve of the model captures the one of a realistic data set learned with kernel regression and classification, with out-of-the-box feature maps such as random projections or scattering transforms, or with pre-learned ones—such as the features learned by training multi-layer neural networks. We discuss both the power and the limitations of the framework.
Deep networks on toroids: removing symmetries reveals the structure of flat regions in the landscape geometry*
Authors: Pittorino Fabrizio , Ferraro Antonio , Perugini Gabriele , Feinauer Christoph , Baldassi Carlo , Zecchina Riccardo 
Published: November 2022

Abstract
We systematize the approach to the investigation of deep neural network landscapes by basing it on the geometry of the space of implemented functions rather than the space of parameters. Grouping classifiers into equivalence classes, we develop a standardized parameterization in which all symmetries are removed, resulting in a toroidal topology. On this space, we explore the error landscape rather than the loss. This lets us derive a meaningful notion of the flatness of minimizers and of the geodesic paths connecting them. Using different optimization algorithms that sample minimizers with different flatness we study the mode connectivity and relative distances. Testing a variety of state-of-the-art architectures and benchmark datasets, we confirm the correlation between flatness and generalization performance; we further show that in function space flatter minima are closer to each other and that the barriers along the geodesics connecting them are small. We also find that minimizers found by variants of gradient descent can be connected by zero-error paths composed of two straight lines in parameter space, i.e. polygonal chains with a single bend. We observe similar qualitative results in neural networks with binary weights and activations, providing one of the first results concerning the connectivity in this setting. Our results hinge on symmetry removal, and are in remarkable agreement with the rich phenomenology described by some recent analytical studies performed on simple shallow models.
Particle dual averaging: optimization of mean field neural network with global convergence rate analysis*
Authors: Nitanda Atsushi , Wu Denny , Suzuki Taiji 
Published: November 2022

Abstract
We propose the particle dual averaging (PDA) method, which generalizes the dual averaging method in convex optimization to the optimization over probability distributions with quantitative runtime guarantee. The algorithm consists of an inner loop and outer loop: the inner loop utilizes the Langevin algorithm to approximately solve for a stationary distribution, which is then optimized in the outer loop. The method can thus be interpreted as an extension of the Langevin algorithm to naturally handle nonlinear functional on the probability space. An important application of the proposed method is the optimization of neural network in the mean field regime, which is theoretically attractive due to the presence of nonlinear feature learning, but quantitative convergence rate can be challenging to obtain. By adapting finite-dimensional convex optimization theory into the space of measures, we analyze PDA in regularized empirical/expected risk minimization, and establish quantitative global convergence in learning two-layer mean field neural networks under more general settings. Our theoretical results are supported by numerical simulations on neural networks with reasonable size.
Relative stability toward diffeomorphisms indicates performance in deep nets*
Authors: Petrini Leonardo , Favero Alessandro , Geiger Mario , Wyart Matthieu 
Published: November 2022

Abstract
Understanding why deep nets can classify data in large dimensions remains a challenge. It has been proposed that they do so by becoming stable to diffeomorphisms, yet existing empirical measurements support that it is often not the case. We revisit this question by defining a maximum-entropy distribution on diffeomorphisms, that allows to study typical diffeomorphisms of a given norm. We confirm that stability toward diffeomorphisms does not strongly correlate to performance on benchmark data sets of images. By contrast, we find that the stability toward diffeomorphisms relative to that of generic transformations
R

f
correlates remarkably with the test error ϵ
t. It is of order unity at initialization but decreases by several decades during training for state-of-the-art architectures. For CIFAR10 and 15 known architectures we find

ϵ

t

≈
0.2

R

f

, suggesting that obtaining a small R

f
is important to achieve good performance. We study how R

f
depends on the size of the training set and compare it to a simple model of invariant learning.
A preliminary study on the hollow anode of a 450 kV rod-pinch diode
Authors: Qu J.F. , Feng Y.W. , Ma X. , Li H.T. 
Published: November 2022

Abstract
This paper proposes a hollow rod as the anode of a rod-pinch diode (RPD) at about 450 kV voltage. Two kinds of hollow rod anodes that may enhance the central radiation dose are obtained through PIC and Monte Carlo simulations. The experimental results show that the hollow anode made by Electrical Discharge Machining (EDM) can effectively enhance the forward central dose of the 450 kV rod-pinch diode, which helps to improve the radiation imaging definition.
Fire severity and the legacy of mountain pine beetle outbreak: high-severity fire peaks with mixed live and dead vegetation
Authors: Talucci Anna C , Meigs Garrett W , Knudby Anders , Krawchuk Meg A 
Published: November 2022

Abstract
Bark beetle outbreaks and wildfires are two of the most prevalent disturbances that influence tree mortality, regeneration, and successional trajectories in western North American forests. Subboreal forests have experienced broad overlaps in these disturbances, and recent wildfires have burned through landscapes with substantial tree mortality from prior outbreaks. This study investigated how fuel conditions associated with mountain pine beetle outbreaks influence the probability of high burn severity (i.e. stand-replacing fire) across a range of fire weather conditions in subboreal forests of central interior British Columbia, Canada. We focused on three large fires that occurred in 2012, 2013, and 2014. We characterized outbreak severity, outbreak-influenced prefire vegetation, and subsequent burn severity using Landsat spectral vegetation indices, high-resolution imagery, and field observations. Substantial portions of the prefire landscape contained mixtures of live and dead vegetation created by variable beetle damage and vegetation response—spatial patterns that are related to, but distinct from, peak outbreak severity. We evaluated drivers—fuels, weather, and topography—of high-severity fire under ‘extreme,’ ‘moderate,’ and ‘benign’ fire weather conditions (i.e. burning conditions) using Boosted Regression Trees. While fire weather was a primary driver in most cases, prefire vegetation was an influential predictor variable across all burning conditions, and the probability of high-severity fire was highest when prefire vegetation was a mixture of tree mortality from bark beetles and live vegetation. Thus, while weather and drought are important drivers of wildfires in subboreal forests, bottom-up drivers of elevation and vegetation, including the fuel legacies of bark beetle outbreaks, are crucial factors influencing high-severity burning. The legacy of recent bark beetle outbreaks will continue for decades on these landscapes, affecting fuel structures, future wildfires, forest dynamics, and the broader social-ecological systems of the region.
On the geometry of the Titchmarsh counterexample
Authors: Schleich W P , Tkáčová I , Maier H 
Published: November 2022

Abstract
We study the lines of constant phase corresponding to the ratio formed by the building blocks of the Titchmarsh counterexample, that is by two Dirichlet L-functions whose characters are the complex conjugate of each other. This ratio on the critical line is sensitive to zeros off the critical line.