bluuebunny/crossref_metadata_2025_split huggingface dataset:
Generalizing to new geometries with Geometry-Aware Autoregressive Models (GAAMs) for fast calorimeter simulation
Authors: Liu Junze , Ghosh Aishik , Smith Dylan , Baldi Pierre , Whiteson Daniel 
Published: November 2023

AbstractGeneration of simulated detector response to collision products is crucial to data analysis in particle physics, but computationally very expensive. One subdetector, the calorimeter, dominates the computational time due to the high granularity of its cells and complexity of the interactions. Generative models can provide more rapid sample production, but currently require significant effort to optimize performance for specific detector geometries, often requiring many models to describe the varying cell sizes and arrangements, without the ability to generalize to other geometries. We develop ageometry-awareautoregressive model, which learns how the calorimeter response varies with geometry, and is capable of generating simulated responses to unseen geometries without additional training. The geometry-aware model outperforms a baseline unaware model by over 50% in several metrics such as the Wasserstein distance between the generated and the true distributions of key quantities which summarize the simulated response. A single geometry-aware model could replace the hundreds of generative models currently designed for calorimeter simulation by physicists analyzing data collected at the Large Hadron Collider. This proof-of-concept study motivates the design of a foundational model that will be a crucial tool for the study of future detectors, dramatically reducing the large upfront investment usually needed to develop generative calorimeter models.
CNR performance of semiconductor materials for X-ray imaging of breast calcifications
Authors: Mendoza L. , Avila C. , Rodríguez R. , Loaiza L. , Roque G. 
Published: November 2023

Abstract
We present the results of a GAMOS/GEANT4 computer simulation
of a standard X-ray mammography system, which consists of a Tungsten
28 kVp polychromatic X-ray source with a 50 μm Rh filter, a
mammography phantom with Al2O3 spherical specks of different
diameters, and a generic pixel detector
(55 μm × 55 μm pixel size) with different
types of semiconductor sensors. The number of photons simulated is
calibrated to produce similar entrance surface dose (ESD) as the one
used by a standard clinical mammography screening. Estimates of
Contrast to Noise Ratio (CNR) as a function of ESD, sensor thickness
and microcalcification diameter are presented for four different
sensor materials: Silicon (Si), Cadmium Telluride (CdTe), Gallium
Arsenide (GaAs) and Perovskite (MAPbI3). For the X-ray energy
spectrum and pixel size considered, and an ESD dose of 4 mGy, our
study shows that, with the exception of Si, these sensors, as thin
as 200 μm, are able to resolve (with at least 3 standard
deviations above background) Al2O3 spherical specks up to a
minimum diameter of 180 μm, having statistically compatible
CNR performance. The increase in substrate thickness has a
substantial improvement in the CNR values provided by the Si sensor,
while for the other cases the enhancement of CNR is marginal and
consistent with statistical uncertainties with the thinnest case
considered.
Environmental force sensing helps robots traverse cluttered large obstacles
Authors: Xuan Qihan , Li Chen 
Published: November 2023

Abstract
Robots can traverse sparse obstacles by sensing environmental geometry and avoiding contact with obstacles. However, for search and rescue in rubble, environmental monitoring through dense vegetation, and planetary exploration over Martian and lunar rocks, robots must traverse cluttered obstacles as large as themselves by physically interacting with them. Previous work discovered that the forest floor-dwelling discoid cockroach and a sensor-less minimalistic robot can traverse cluttered grass-like beam obstacles of various stiffness by transitioning across different locomotor modes. Yet the animal was better at traversal than the sensor-less robot, likely by sensing forces during obstacle interaction to control its locomotor transitions. Inspired by this, here we demonstrated in simulation that environmental force sensing helps robots traverse cluttered large obstacles. First, we developed a multi-body dynamics simulation and a physics model of the minimalistic robot interacting with beams to estimate beam stiffness from the sensed contact forces. Then, we developed a force feedback strategy for the robot to use the sensed beam stiffness to choose the locomotor mode with a lower mechanical energy cost. With feedforward pushing, the robot was stuck in front of stiff beams if it has a limited force capacity; without force limit, it traversed but suffered a high energy cost. Using obstacle avoidance, the robot traversed beams by avoiding beam contact regardless of beam stiffness, resulting in a high energy cost for flimsy beams. With force feedback, the robot determined beam stiffness, then traversed flimsy beams by pushing them over and stiff beams by rolling through the gap between them with a low energy cost. Stiffness estimation based on force sensing was accurate across varied body oscillation amplitude and frequency and position sensing uncertainty. Mechanical energy cost of traversal increased with sensorimotor delay. Future work should demonstrate cluttered large obstacle traversal using force feedback in a physical robot.
Understanding extreme precipitation scaling with temperature: insights from multi-spatiotemporal analysis in South Korea
Authors: Yoo Suhyun , Ahn Kuk-Hyun 
Published: November 2023

Abstract
With global warming, the intensification of extreme precipitation events is anticipated to follow an exponential growth pattern aligned with the Clausius–Clapeyron (CC) scaling rate (approximately 7% per degree Celsius). However, the regional-scale response of extreme precipitation shows significant variability, deviating from the expected CC rate. This deviation is likely caused by diverse weather patterns and local fluctuations in thermodynamic influences, resulting in differences across seasons and within the region of interest. In this study, we examine the spatial distribution of scaling relationships between extreme precipitation and temperature in South Korea, considering daily and sub-daily scales, both annually and seasonally. For a thorough analysis, we utilize multiple precipitation accumulation periods, temperatures, and different conditional quantiles. Our results reveal that, at the annual scale, most scaling patterns exhibit a peak-like structure, with significant variations in breakpoints observed across temperature variables and regions. However, the southern area presents a notable exception with a positive scaling pattern, particularly with the dew point temperature. At the seasonal scale, we observe more variability, with notable shifts occurring during the wet season across different temperatures and regions. Lastly, we explore the long-term historical changes in the peak value in extreme precipitation and find significant increases at high quantiles in the southern area of South Korea. It informs that the observed peak like pattern does not impose a potential upper limit for extreme precipitation. Overall, our findings emphasize the need for cautious interpretation of precipitation scaling within specific spatiotemporal contexts, which could provide a solid basis for better understanding future extreme precipitation events in a changing climate.
Can shallow quantum circuits scramble local noise into global white noise?
Authors: Foldager Jonathan , Koczor Bálint 
Published: November 2023

Abstract
Shallow quantum circuits are believed to be the most promising candidates for achieving early practical quantum advantage—this has motivated the development of a broad range of error mitigation techniques whose performance generally improves when the quantum state is well approximated by a global depolarising (white) noise model. While it has been crucial for demonstrating quantum supremacy that random circuits scramble local noise into global white noise—a property that has been proved rigorously—we investigate to what degree practical shallow quantum circuits scramble local noise into global white noise. We define two key metrics as (a) density matrix eigenvalue uniformity and (b) commutator norm that quantifies stability of the dominant eigenvector. While the former determines the distance from white noise, the latter determines the performance of purification based error mitigation. We derive analytical approximate bounds on their scaling and find in most cases they nicely match numerical results. On the other hand, we simulate a broad class of practical quantum circuits and find that white noise is in certain cases a bad approximation posing significant limitations on the performance of some of the simpler error mitigation schemes. On a positive note, we find in all cases that the commutator norm is sufficiently small guaranteeing a very good performance of purification-based error mitigation. Lastly, we identify techniques that may decrease both metrics, such as increasing the dimensionality of the dynamical Lie algebra by gate insertions or randomised compiling.
Duality of O(N) and Sp(N) random tensor models: tensors with symmetries
Authors: Keppler H , Krajewski T , Muller T , Tanasa A 
Published: November 2023

Abstract
In a recent series of papers, a duality between orthogonal and symplectic random tensor models has been proven, first for quartic models and then for models with interactions of arbitrary order. However, the tensor models considered so far in the literature had no symmetry under permutation of the indices. In this paper, we generalize these results for tensors models with interactions of arbitrary order which further have non-trivial symmetry under the permutation of the indices. Totally symmetric and anti-symmetric tensors are thus treated as a particular case of our result.
A clinical proteomics study of exhaled breath condensate and biomarkers for pulmonary embolism
Authors: Gade Inger Lise , Riddersholm Signe Juul , Stilling-Vinther Thomas , Brøndum Rasmus Froberg , Bennike Tue Bjerg , Honoré Bent 
Published: November 2023

Abstract
Pulmonary embolism (PE) can be a diagnostic challenge. Current diagnostic markers for PE are unspecific and new diagnostic tools are needed. The air we exhale is a possible new source for biomarkers which can be tapped into by analysing the exhaled breath condensate (EBC). We analysed the EBC from patients with PE and controls to investigate if the EBC is a useful source for new diagnostic biomarkers of PE. We collected and analysed EBC samples from patients with suspected PE and controls matched on age and sex. Patients in whom PE was ruled out after diagnostic work-up were included in the control group to increase the sensitivity and generalizability of the identified markers. EBC samples were collected using an RTube™. The protein composition of the EBCs were analysed using data dependent label-free quantitative nano liquid chromatography–tandem mass spectrometry. EBC samples from 28 patients with confirmed PE, and 49 controls were analysed. A total of 928 EBC proteins were identified in the 77 EBC samples. As expected, a low protein concentration was determined which resulted in many proteins with unmeasurable levels in several samples. The levels of HSPA5, PEBP1 and SFTPA2 were higher and levels of POF1B, EPPK1, PSMA4, ALDOA, and CFL1 were lower in PE compared with controls. In conclusion, the human EBC contained a variety of endogenous proteins and may be a source for new diagnostic markers of PE and other diseases.
Valorisation of pomegranate processing waste for the synthesis of ZnO nanoparticles: antioxidant and antimicrobial properties against food pathogens
Authors: Leta Tshiamo B , Adeyemi Jerry O , Fawole Olaniyi A 
Published: November 2023

Abstract
The sustainable management of food waste is a pressing concern, with fruit waste valorisation emerging as a viable strategy to address this challenge. This study investigated the potential of pomegranate peel waste (PPW) and pomegranate seed waste (PSW) as mediating agents for the biosynthesis of zinc oxide (ZnO) nanoparticles (NPs); ZnO-PPW and ZnO-PSW, respectively, for potential utilization as additives in various polymer matrices for food packaging materials. The resulting physicochemical characteristics were ascertained using Ultraviolet visible spectroscopy (UV–vis), x-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), x-ray diffraction (XRD), Scanning electron microscope (SEM) and Energy Dispersive x-Ray Analysis (EDX). The obtained indexed diffractogram from the XRD analysis for both ZnO-PPW and ZnO-PSW confirmed the wurtzite crystalline structure of ZnO NPs. The observed morphology from the TEM and SEM analysis showed a similar spherical shaped structure, with agglomerations. However, ZnO-PSW, had a smaller size (58 nm) in comparison to ZnO-PPW (59 nm). Total phenolic content (TPC) for ZnO-PPW and ZnO-PSW ranged from 16.87–54.4 μg GAE/g DM, respectively. Also, the estimated minimum inhibitory concentration at 50% (IC50) for both DPPH and ABTS are 2.97 and 2.57 mg ml−1 for ZnO-PPW; and 3.43 and 3.33 mg ml−1 for ZnO-PSW, respectively. Moreover, due to its smaller size, ZnO-PSW demonstrated superior antimicrobial activity against five foodborne microorganisms. These findings suggest that pomegranate waste derived ZnO NPs could be beneficial for developing active food packaging materials.
Chinese rose-derived nanostructure carbon as new anode material for lithium-ion batteries
Authors: Qu Ye , Zhu Xinyu , Qiang Yaochun , Chang Junwei , Pan Jinping 
Published: November 2023

Abstract
Renewable biomass carbon materials are of wide interest for energy storage applications. Using high-temperature pyrolysis, we carbonized Chinese rose for the first time at different temperatures to investigate the performance of lithium-ion batteries (LIBs) of the resulting materials under different temperature conditions. The fluffy folded structure after carbonization exhibits multiple active sites, which helps to improve the electrical conductivity and mitigate the damage to the material structure caused by electrode expansion. The negative electrode made from the 1000 °C carbonized material can provide a high specific capacity of 725 mAh g−1 after 250 cycles at 0.1 A g−1, and maintains a specific capacity of 373 mAh g−1 at a high current density of 1 A g−1, with excellent rate performance. The results show that Chinese rose-derived carbon (CRDC) materials can be naturally green anode materials for next-generation lithium-ion batteries.
Monitoring self-discharge in a dual-ion battery using in situ Raman spectro-electrochemistry
Authors: Hassan Ismail Yussuf , van Ekeren Wessel W A , Kotronia Antonia , Hahlin Maria , Asfaw Habtom D 
Published: November 2023

Abstract
A dual-ion battery employs two graphite electrodes to host cations and anions from the electrolyte. The high potential required to intercalate anions in graphite fully, typically > 5 V versus Li+/Li, triggers electrolyte decomposition and dissolution of the aluminium current collector. Such unwanted reactions significantly aggravate self-discharge, leading to low energy efficiency and shorter cycle life. This study investigates changes in graphite structure during the intercalation of bis(fluorosulfonyl)imide (FSI) anion in 4 M LiFSI in ethyl methyl carbonate (EMC) and evaluates the stability of the associated FSI-intercalated graphite compounds using in situ Raman spectroscopy. The results highlight the critical importance of the duration the GICs remain in contact with the electrolyte, before the acquisition of the Raman spectra. Accordingly, the GICs with high FSI anion content exhibited only short-term stability and lost anions during open-circuit potential relaxation; only dilute GIC phases (stages ≥ IV) were sufficiently stable in the presence of the concentrated electrolyte. Furthermore, the formation of gaseous products during the charge–discharge cycles was verified using a 3-electrode cell with a pressure sensor. Future studies can adopt the experimental strategy developed in this work to assess the efficacy of electrolyte additives in mitigating self-discharge in DIBs.
Minimization of hypersurfaces
Authors: Elsenhans Andreas-Stephan , Stoll Michael 
Published: November 2023

Let

F
∈

Z

[

x
0

,
…
,

x
n

]

F \in \mathbb {Z}[x_0, \ldots , x_n]

be homogeneous of degree

d
d

and assume that

F
F

is not a ‘nullform’, i.e., there is an invariant

I
I

of forms of degree

d
d

in

n
+
1

n+1

variables such that

I
(
F
)
≠
0

I(F) \neq 0

. Equivalently,

F
F

is semistable in the sense of Geometric Invariant Theory. Minimizing

F
F

at a prime

p
p

means to produce

T
∈
M
a
t
(
n
+
1
,

Z

)
∩
G
L
(
n
+
1
,

Q

)

T \in Mat(n+1, \mathbb {Z}) \cap GL(n+1, \mathbb {Q})

and

e
∈

Z

≥
0

e \in \mathbb {Z}_{\ge 0}

such that

F
1

=

p

−
e

F
(
[

x
0

,
…
,

x
n

]
⋅
T
)

F_1 = p^{-e} F([x_0, \ldots , x_n] \cdot T)

has integral coefficients and

v
p

(
I
(

F
1

)
)

v_p(I(F_1))

is minimal among all such

F
1

F_1

. Following Kollár [Electron. Res. Announc. Amer. Math. Soc. 3 (1997), pp. 17–27], the minimization process can be described in terms of applying weight vectors

w
∈

Z

≥
0

n
+
1

w \in \mathbb {Z}_{\ge 0}^{n+1}

to

F
F

. We show that for any dimension

n
n

and degree

d
d

, there is a complete set of weight vectors consisting of

[
0
,

w
1

,

w
2

,
…
,

w
n

]

[0,w_1,w_2,\dots ,w_n]

with

0
≤

w
1

≤

w
2

≤
⋯
≤

w
n

≤
2
n

d

n
−
1

0 \le w_1 \le w_2 \le \dots \le w_n \le 2 n d^{n-1}

. When

n
=
2

n = 2

, we improve the bound to

d
d

. This answers a question raised by Kollár. These results are valid in a more general context, replacing

Z

\mathbb {Z}

and

p
p

by a PID

R
R

and a prime element of

R
R

.

Based on this result and a further study of the minimization process in the planar case

n
=
2

n = 2

, we devise an efficient minimization algorithm for ternary forms (equivalently, plane curves) of arbitrary degree

d
d

. We also describe a similar algorithm that allows to minimize (and reduce) cubic surfaces. These algorithms are available in the computer algebra system Magma.
Altitudinal differences in foraging decisions under predation risk in great tits
Authors: Crouchet Thomas , Heeb Philipp , Chaine Alexis S 
Published: November 2023

Abstract
Foraging decisions under risk of predation are crucial for survival as predation risk can contribute to a reduction of food intake over time leading to a trade-off between starvation and predation. Environmental variation can provoke changes in food accessibility or predation risk that will in turn affect foraging decisions. Specifically, less predictable or harsher environments, such as those found at high elevation, should lead to more risk-prone foraging in order to prevent risk of starvation, but empirical confirmation of this hypothesis is lacking. In the current study, we used video playbacks combined with an automatic feeder to measure continuous foraging choices between control and predator videos by wild great tits originating from high and low elevations and tested under controlled conditions. Great tits discriminated between two conditions representing differences in predation risk and visited the feeder less frequently when a predator was shown. Moreover, we found that birds from low elevation populations were more risk-averse and visited the feeder significantly less when a predator video playback was broadcasted compared with high elevation individuals. This elevation-related contrast was also dependent on the season, body mass, and fat reserves of individuals, and was more marked in females. Furthermore, adults visited the feeder less in the presence of a predator compared with yearlings. These results are consistent with predictions from life-history theory and starvation–predation trade-off hypotheses and could have implications for individual movements and population dynamics in changing environments.
scMHNN: a novel hypergraph neural network for integrative analysis of single-cell epigenomic, transcriptomic and proteomic data
Authors: Li Wei , Xiang Bin , Yang Fan , Rong Yu , Yin Yanbin , Yao Jianhua , Zhang Han 
Published: November 2023

Abstract
Technological advances have now made it possible to simultaneously profile the changes of epigenomic, transcriptomic and proteomic at the single cell level, allowing a more unified view of cellular phenotypes and heterogeneities. However, current computational tools for single-cell multi-omics data integration are mainly tailored for bi-modality data, so new tools are urgently needed to integrate tri-modality data with complex associations. To this end, we develop scMHNN to integrate single-cell multi-omics data based on hypergraph neural network. After modeling the complex data associations among various modalities, scMHNN performs message passing process on the multi-omics hypergraph, which can capture the high-order data relationships and integrate the multiple heterogeneous features. Followingly, scMHNN learns discriminative cell representation via a dual-contrastive loss in self-supervised manner. Based on the pretrained hypergraph encoder, we further introduce the pre-training and fine-tuning paradigm, which allows more accurate cell-type annotation with only a small number of labeled cells as reference. Benchmarking results on real and simulated single-cell tri-modality datasets indicate that scMHNN outperforms other competing methods on both cell clustering and cell-type annotation tasks. In addition, we also demonstrate scMHNN facilitates various downstream tasks, such as cell marker detection and enrichment analysis.
Automated and reproducible cell identification in mass cytometry using neural networks
Authors: Saihi Hajar , Bessant Conrad , Alazawi William 
Published: November 2023

Abstract
The principal use of mass cytometry is to identify distinct cell types and changes in their composition, phenotype and function in different samples and conditions. Combining data from different studies has the potential to increase the power of these discoveries in diverse fields such as immunology, oncology and infection. However, current tools are lacking in scalable, reproducible and automated methods to integrate and study data sets from mass cytometry that often use heterogenous approaches to study similar samples. To address these limitations, we present two novel developments: (1) a pre-trained cell identification model named Immunopred that allows automated identification of immune cells without user-defined prior knowledge of expected cell types and (2) a fully automated cytometry meta-analysis pipeline built around Immunopred. We evaluated this pipeline on six COVID-19 study data sets comprising 270 unique samples and uncovered novel significant phenotypic changes in the wider immune landscape of COVID-19 that were not identified when each study was analyzed individually. Applied widely, our approach will support the discovery of novel findings in research areas where cytometry data sets are available for integration.