cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Unsteady High-Lift Mechanisms from Heaving Flat Plate Simulations
Creators: name: Franck, Jennifer A.
name: Breuer, Kenneth
Publisher: name: arXiv
Year: 2017
Subjects: subject: Fluid Dynamics (physics.flu-dyn)
subject: FOS: Physical sciences

description: Flapping animal flight is often modeled as a combined pitching and heaving motion in order to investigate the unsteady flow structures and resulting forces that could augment the animal's lift and propulsive capabilities. This work isolates the heaving motion of flapping flight in order to numerically investigate the flow physics at a Reynolds number of 40,000, a regime typical for large birds and bats and challenging to simulate due to the added complexity of laminar to turbulent transition in which boundary layer separation and reattachment are traditionally more difficult to predict. Periodic heaving of a thin flat plate at fixed angles of attacks of 1, 5, 9, 13, and 18 degrees are simulated using a large-eddy simulation (LES). The heaving motion significantly increases the average lift compared with the steady flow, and also surpasses the quasi-steady predictions due to the formation of a leading edge vortex (LEV) that persists well into the static stall region. The progression of the high-lift mechanisms throughout the heaving cycle is presented over the range of angles of attack. Lift enhancement compared with the equivalent steady state flow was found to be up to 17% greater, and up to 24% greater than that predicted by a quasi-steady analysis. For the range of kinematics explored it is found that maximum lift enhancement occurs at an angle of attack of 13 degrees, with a maximum lift coefficient of 2.1, a mean lift coefficient of 1.04.
Global embedding of BTZ spacetime using generalized method of symmetric embeddings construction
Creators: name: Sheykin, A. A.
name: Markov, M. V.
name: Paston, S. A.
Publisher: name: arXiv
Year: 2021
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: Mathematical Physics (math-ph)
subject: FOS: Physical sciences

description: It is often easier to study pseudo-Riemannian manifolds by presenting them as surfaces in some ambient space. We propose an algorithm for construction of explicit isometric embeddings of pseudo-Riemannian manifolds with symmetries into an ambient space of higher dimension. While most of the existing methods are based on Gauss-Codazzi-Mainardi-Peterson equations, we do not use them and instead concentrate on a system of equations which connects the metric on the manifold and the embedding function of the surface. Our algorithm is based on the group theoretical method of separation of variables that we developed earlier (arXiv:1202.1204). The algorithm makes this method more convenient and simple to use. It allowed us to simplify the construction of many known embeddings as well as obtain some new ones. In particular, we obtain explicit global embeddings of spinning BTZ black hole in 7-dimensional flat space.
description: 16 pages
Solving the Spectral Problem via the Periodic Boundary Approximation in $ϕ^6$ Theory
Creators: name: Long, Lingxiao
name: Jiang, Yunguo
Publisher: name: arXiv
Year: 2024
Subjects: subject: High Energy Physics - Theory (hep-th)
subject: FOS: Physical sciences

description: In $ϕ^6$ theory, the resonance scattering structure is triggered by the so-calls delocalized modes trapped between the $\bar{K}K$ pair. The frequencies and configurations of such modes depend on the $\bar{K}K$ half-separation 2$a$, can be derived from the Schrödinger-like equation. We propose to use the periodic boundary conditions to connect the localized and delocalized modes, and use periodic boundary approximation (PBA) to solve the spectrum analytically. In detail, we derive the explicit form of frequencies, configurations and spectral wall locations of the delocalized modes. We test the analytical prediction with the numerical simulation of the Schrödinger-like equation, and obtain astonishing agreement between them at the long separation regime.
The challenge of studying perovskite solar cells’ stability with machine learning
Creators: name: Graniero, Paolo
name: Khenkin, Mark
name: Köbler, Hans
name: Hartono, Noor Titan Putri
name: Schlatmann, Rutger
name: Abate, Antonio
name: Unger, Eva
name: Jacobsson, T. Jesper
name: Ulbrich, Carolin
Publisher: name: Freie Universität Berlin
Year: 2023
Subjects: subject: perovskite solar cell
subject: stability
subject: machine learning
subject: figures of merit
subject: learning curves
subject: database
subject: feature importance analysis
subject: halide perovskite
subject: 500 Naturwissenschaften und Mathematik::530 Physik::537 Elektrizität, Elektronik

description: Perovskite solar cells are the most dynamic emerging photovoltaic technology and attracts the attention of thousands of researchers worldwide. Recently, many of them are targeting device stability issues–the key challenge for this technology–which has resulted in the accumulation of a significant amount of data. The best example is the “Perovskite Database Project,” which also includes stability-related metrics. From this database, we use data on 1,800 perovskite solar cells where device stability is reported and use Random Forest to identify and study the most important factors for cell stability. By applying the concept of learning curves, we find that the potential for improving the models’ performance by adding more data of the same quality is limited. However, a significant improvement can be made by increasing data quality by reporting more complete information on the performed experiments. Furthermore, we study an in-house database with data on more than 1,000 solar cells, where the entire aging curve for each cell is available as opposed to stability metrics based on a single number. We show that the interpretation of aging experiments can strongly depend on the chosen stability metric, unnaturally favoring some cells over others. Therefore, choosing universal stability metrics is a critical question for future databases targeting this promising technology.
Thin-shell wormholes constrained by cosmological observations
Creators: name: Wang, Deng
name: Meng, Xin-He
Publisher: name: arXiv
Year: 2017
Subjects: subject: General Relativity and Quantum Cosmology (gr-qc)
subject: FOS: Physical sciences

description: We investigate the thin-shell wormholes constrained by cosmological observations for the first time in the literature. Without loss of generality, we study the thin-shell wormholes in $ω$CDM model and analyze their stability under perturbations preserving the symmetry. Firstly, we constrain the $ω$CDM model using a combination of Union 2.1 SNe Ia data, the latest $H(z)$ data and CMB data. Secondly, we use the constrained dark energy equation of state (EoS) $ω$ which lies in $[-1.05,-0.89]$ to investigate thin-shell wormholes of various black hole spacetimes. We find that the stable Schwarzschild and Reinssner-Nordström thin-shell wormholes constrained by cosmological observations do not exist. In addition, the method we developed can be applied into exploring the stable thin-shell wormholes from any black hole spacetime in the framework of any cosmological theory.
description: 9 pages, 13 figures
DSC #9: The Ghost in Anouk’s Laptop
Creators: name: Dombrowski, Quinn
name: Lang, Anouk
Publisher: name: Stanford Digital Repository
Year: 2024
Subjects: subject: Digital humanities
subject: Artificial intelligence--Data processing
subject: Natural language generation (Computer science)

description: In DSC #9: The Ghost in Anouk’s Laptop, Quinn Dombrowski and Anouk Lang look at 2020-2021 era AI tools for machine-generating text, including fine-tuning GPT-2 on the Baby-Sitters Club corpus.
Parallel Element-based Algebraic Multigrid for H(curl) and H(div) Problems Using the ParELAG Library
Creators: name: Kalchev, Delyan Z.
name: Vassilevski, Panayot S.
name: Villa, Umberto
Publisher: name: arXiv
Year: 2021
Subjects: subject: Numerical Analysis (math.NA)
subject: Mathematical Software (cs.MS)
subject: FOS: Mathematics
subject: FOS: Computer and information sciences

description: This paper presents the use of element-based algebraic multigrid (AMGe) hierarchies, implemented in the ParELAG (Parallel Element Agglomeration Algebraic Multigrid Upscaling and Solvers) library, to produce multilevel preconditioners and solvers for H(curl) and H(div) formulations. ParELAG constructs hierarchies of compatible nested spaces, forming an exact de Rham sequence on each level. This allows the application of hybrid smoothers on all levels and AMS (Auxiliary-space Maxwell Solver) or ADS (Auxiliary-space Divergence Solver) on the coarsest levels, obtaining complete multigrid cycles. Numerical results are presented, showing the parallel performance of the proposed methods. As a part of the exposition, this paper demonstrates some of the capabilities of ParELAG and outlines some of the components and procedures within the library.
Machine Learning in Viscoelastic Fluids via Energy-Based Kernel Embedding
Creators: name: Otto, Samuel E.
name: Oishi, Cassio M.
name: Amaral, Fabio
name: Brunton, Steven L.
name: Kutz, J. Nathan
Publisher: name: arXiv
Year: 2024
Subjects: subject: Fluid Dynamics (physics.flu-dyn)
subject: Computational Physics (physics.comp-ph)
subject: FOS: Physical sciences

description: The ability to measure differences in collected data is of fundamental importance for quantitative science and machine learning, motivating the establishment of metrics grounded in physical principles. In this study, we focus on the development of such metrics for viscoelastic fluid flows governed by a large class of linear and nonlinear stress models. To do this, we introduce a kernel function corresponding to a given viscoelastic stress model that implicitly embeds flowfield snapshots into a Reproducing Kernel Hilbert Space (RKHS) whose squared norm equals the total mechanical energy. Working implicitly with lifted representations in the RKHS via the kernel function provides natural and unambiguous metrics for distances and angles between flowfields without the need for hyperparameter tuning. Additionally, we present a solution to the preimage problem for our kernels, enabling accurate reconstruction of flowfields from their RKHS representations. Through numerical experiments on an unsteady viscoelastic lid-driven cavity flow, we demonstrate the utility of our kernels for extracting energetically-dominant coherent structures in viscoelastic flows across a range of Reynolds and Weissenberg numbers. Specifically, the features extracted by Kernel Principal Component Analysis (KPCA) of flowfield snapshots using our kernel functions yield reconstructions with superior accuracy in terms of mechanical energy compared to conventional methods such as ordinary Principal Component Analysis (PCA) with naïvely-defined state vectors or KPCA with ad-hoc choices of kernel functions. Our findings underscore the importance of principled choices of metrics in both scientific and machine learning investigations of complex fluid systems.
description: 33 pages, 10 figures, Revised version of the publication
Fed-ensemble: Improving Generalization through Model Ensembling in Federated Learning
Creators: name: Shi, Naichen
name: Lai, Fan
name: Kontar, Raed Al
name: Chowdhury, Mosharaf
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (stat.ML)
subject: Machine Learning (cs.LG)
subject: FOS: Computer and information sciences

description: In this paper we propose Fed-ensemble: a simple approach that bringsmodel ensembling to federated learning (FL). Instead of aggregating localmodels to update a single global model, Fed-ensemble uses random permutations to update a group of K models and then obtains predictions through model averaging. Fed-ensemble can be readily utilized within established FL methods and does not impose a computational overhead as it only requires one of the K models to be sent to a client in each communication round. Theoretically, we show that predictions on newdata from all K models belong to the same predictive posterior distribution under a neural tangent kernel regime. This result in turn sheds light onthe generalization advantages of model averaging. We also illustrate thatFed-ensemble has an elegant Bayesian interpretation. Empirical results show that our model has superior performance over several FL algorithms,on a wide range of data sets, and excels in heterogeneous settings often encountered in FL applications.
Heisenberg-scaling measurement of the single-photon Kerr non-linearity using mixed states
Creators: name: Chen, Geng
name: Aharon, Nati
name: Sun, Yong-Nan
name: Zhang, Zi-Huai
name: Zhang, Wen-Hao
name: He, De-Yong
name: Tang, Jian-Shun
name: Kedem, Yaron
name: Li, Chuan-Feng
name: Guo, Guang-Can
Publisher: name: arXiv
Year: 2016
Subjects: subject: Quantum Physics (quant-ph)
subject: Optics (physics.optics)
subject: FOS: Physical sciences

description: Improving the precision of measurements is a significant scientific challenge. The challenge is twofold: first, overcoming noise that limits the precision given a fixed amount of a resource, N, and second, improving the scaling of precision over the standard quantum limit (SQL), 1/\sqrt{N}, and ultimately reaching a Heisenberg scaling (HS), 1/N. Here we present and experimentally implement a new scheme for precision measurements. Our scheme is based on a probe in a mixed state with a large uncertainty, combined with a post-selection of an additional pure system, such that the precision of the estimated coupling strength between the probe and the system is enhanced. We performed a measurement of a single photon's Kerr non-linearity with an HS, where an ultra-small Kerr phase of around 6 *10^{-8} rad was observed with an unprecedented precision of around 3.6* 10^{-10} rad. Moreover, our scheme utilizes an imaginary weak-value, the Kerr non-linearity results in a shift of the mean photon number of the probe, and hence, the scheme is robust to noise originating from the self-phase modulation.
Polarization textures in crystal supercells with topological bands
Creators: name: Jankowski, Wojciech J.
name: Bennett, Daniel
name: Agarwal, Aneesh
name: Chaudhary, Gaurav
name: Slager, Robert-Jan
Publisher: name: arXiv
Year: 2024
Subjects: subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: Materials Science (cond-mat.mtrl-sci)
subject: FOS: Physical sciences

description: Two-dimensional materials are a highly tunable platform for studying the momentum space topology of the electronic wavefunctions and real space topology in terms of skyrmions, merons, and vortices of an order parameter. Such textures for electronic polarization can exist in moiré heterostructures. A quantum-mechanical definition of local polarization textures in insulating supercells was recently proposed. Here, we propose a definition for local polarization that is also valid for systems with topologically non-trivial bands. We introduce semilocal hybrid polarizations, which are valid even when the Wannier functions in a system cannot be made exponentially localized in all dimensions. We use this definition to explicitly show that nontrivial real-space polarization textures can exist in topologically non-trivial systems with non-zero Chern number under (1) an external superlattice potential, and (2) under a stacking-induced moiré potential. In the latter, we find that while the magnitude of the local polarization decreases discontinuously across a topological phase transition from trivial to topologically nontrivial, the polarization does not completely vanish. Our findings suggest that band topology and real-space polar topology may coexist in real materials.
description: 7+7 pages, 3+2 figures
SurfaceNet: Adversarial SVBRDF Estimation from a Single Image
Creators: name: Vecchio, Giuseppe
name: Palazzo, Simone
name: Spampinato, Concetto
Publisher: name: arXiv
Year: 2021
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: In this paper we present SurfaceNet, an approach for estimating spatially-varying bidirectional reflectance distribution function (SVBRDF) material properties from a single image. We pose the problem as an image translation task and propose a novel patch-based generative adversarial network (GAN) that is able to produce high-quality, high-resolution surface reflectance maps. The employment of the GAN paradigm has a twofold objective: 1) allowing the model to recover finer details than standard translation models; 2) reducing the domain shift between synthetic and real data distributions in an unsupervised way. An extensive evaluation, carried out on a public benchmark of synthetic and real images under different illumination conditions, shows that SurfaceNet largely outperforms existing SVBRDF reconstruction methods, both quantitatively and qualitatively. Furthermore, SurfaceNet exhibits a remarkable ability in generating high-quality maps from real samples without any supervision at training time.
DSC Multilingual Mystery #1: Lee and the Missing Metadata
Creators: name: Dombrowski, Quinn
name: Skallerup Bessette, Lee
Publisher: name: Stanford Digital Repository
Year: 2024
Subjects: subject: Digital humanities
subject: Text corpora
subject: Translations
subject: Metadata

description: DSC #M1: Lee and the Missing Metadata introduces Lee Skallerup Bessette, the first Multilingual Mystery Data-Sitter, along with an expanded corpus of translations of the Baby-Sitters Club books.
Open-Set 3D Semantic Instance Maps for Vision Language Navigation -- O3D-SIM
Creators: name: Nanwani, Laksh
name: Gupta, Kumaraditya
name: Mathur, Aditya
name: Agrawal, Swayam
name: Hafez, A. H. Abdul
name: Krishna, K. Madhava
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: Robotics (cs.RO)
subject: FOS: Computer and information sciences

description: Humans excel at forming mental maps of their surroundings, equipping them to understand object relationships and navigate based on language queries. Our previous work SI Maps [1] showed that having instance-level information and the semantic understanding of an environment helps significantly improve performance for language-guided tasks. We extend this instance-level approach to 3D while increasing the pipeline's robustness and improving quantitative and qualitative results. Our method leverages foundational models for object recognition, image segmentation, and feature extraction. We propose a representation that results in a 3D point cloud map with instance-level embeddings, which bring in the semantic understanding that natural language commands can query. Quantitatively, the work improves upon the success rate of language-guided tasks. At the same time, we qualitatively observe the ability to identify instances more clearly and leverage the foundational models and language and image-aligned embeddings to identify objects that, otherwise, a closed-set approach wouldn't be able to identify.
A Compositional Framework for Reaction Networks
Creators: name: Baez, John C.
name: Pollard, Blake S.
Publisher: name: arXiv
Year: 2017
Subjects: subject: Mathematical Physics (math-ph)
subject: FOS: Physical sciences

description: Reaction networks, or equivalently Petri nets, are a general framework for describing processes in which entities of various kinds interact and turn into other entities. In chemistry, where the reactions are assigned "rate constants", any reaction network gives rise to a nonlinear dynamical system called its "rate equation". Here we generalize these ideas to "open" reaction networks, which allow entities to flow in and out at certain designated inputs and outputs. We treat open reaction networks as morphisms in a category. Composing two such morphisms connects the outputs of the first to the inputs of the second. We construct a functor sending any open reaction network to its corresponding "open dynamical system". This provides a compositional framework for studying the dynamics of reaction networks. We then turn to statics: that is, steady state solutions of open dynamical systems. We construct a "black-boxing" functor that sends any open dynamical system to the relation that it imposes between input and output variables in steady states. This extends our earlier work on black-boxing for Markov processes.
description: 44 pages LaTeX
A Low-Cost Neural ODE with Depthwise Separable Convolution for Edge Domain Adaptation on FPGAs
Creators: name: Kawakami, Hiroki
name: Watanabe, Hirohisa
name: Sugiura, Keisuke
name: Matsutani, Hiroki
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (cs.LG)
subject: Hardware Architecture (cs.AR)
subject: FOS: Computer and information sciences

description: High-performance deep neural network (DNN)-based systems are in high demand in edge environments. Due to its high computational complexity, it is challenging to deploy DNNs on edge devices with strict limitations on computational resources. In this paper, we derive a compact while highly-accurate DNN model, termed dsODENet, by combining recently-proposed parameter reduction techniques: Neural ODE (Ordinary Differential Equation) and DSC (Depthwise Separable Convolution). Neural ODE exploits a similarity between ResNet and ODE, and shares most of weight parameters among multiple layers, which greatly reduces the memory consumption. We apply dsODENet to a domain adaptation as a practical use case with image classification datasets. We also propose a resource-efficient FPGA-based design for dsODENet, where all the parameters and feature maps except for pre- and post-processing layers can be mapped onto on-chip memories. It is implemented on Xilinx ZCU104 board and evaluated in terms of domain adaptation accuracy, inference speed, FPGA resource utilization, and speedup rate compared to a software counterpart. The results demonstrate that dsODENet achieves comparable or slightly better domain adaptation accuracy compared to our baseline Neural ODE implementation, while the total parameter size without pre- and post-processing layers is reduced by 54.2% to 79.8%. Our FPGA implementation accelerates the inference speed by 23.8 times.
Next-generation humanized NSG-SGM3 mice are highly susceptible to Staphylococcus aureus infection
Creators: name: Hung, Sophia
name: Kasperkowitz, Amelie
name: Kurz, Florian
name: Dreher, Liane
name: Diessner, Joachim
name: Ibrahim, Eslam S.
name: Schwarz, Stefan
name: Ohlsen, Knut
name: Hertlein, Tobias
Publisher: name: Freie Universität Berlin
Year: 2023
Subjects: subject: humanized mice
subject: Staphylococcus aureus
subject: MRSA
subject: NSG
subject: NSG-SGM3
subject: staphylococcal abscess
subject: Staphylococcus aureus immune response
subject: humanized hemato-lymphoid mice
subject: 600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::616 Krankheiten
subject: 600 Technik, Medizin, angewandte Wissenschaften::630 Landwirtschaft::632 Schäden, Krankheiten, Schädlinge an Pflanzen

description: Humanized hemato-lymphoid system mice, or humanized mice, emerged in recent years as a promising model to study the course of infection of human-adapted or human-specific pathogens. Though Staphylococcus aureus infects and colonizes a variety of species, it has nonetheless become one of the most successful human pathogens of our time with a wide armory of human-adapted virulence factors. Humanized mice showed increased vulnerability to S. aureus compared to wild type mice in a variety of clinically relevant disease models. Most of these studies employed humanized NSG (NOD-scid IL2Rgnull) mice which are widely used in the scientific community, but show poor human myeloid cell reconstitution. Since this immune cell compartment plays a decisive role in the defense of the human immune system against S. aureus, we asked whether next-generation humanized mice, like NSG-SGM3 (NOD-scid IL2Rgnull-3/GM/SF) with improved myeloid reconstitution, would prove to be more resistant to infection. To our surprise, we found the contrary when we infected humanized NSG-SGM3 (huSGM3) mice with S. aureus: although they had stronger human immune cell engraftment than humanized NSG mice, particularly in the myeloid compartment, they displayed even more pronounced vulnerability to S. aureus infection. HuSGM3 mice had overall higher numbers of human T cells, B cells, neutrophils and monocytes in the blood and the spleen. This was accompanied by elevated levels of pro-inflammatory human cytokines in the blood of huSGM3 mice. We further identified that the impaired survival of huSGM3 mice was not linked to higher bacterial burden nor to differences in the murine immune cell repertoire. Conversely, we could demonstrate a correlation of the rate of humanization and the severity of infection. Collectively, this study suggests a detrimental effect of the human immune system in humanized mice upon encounter with S. aureus which might help to guide future therapy approaches and analysis of virulence mechanisms.
DSC Multilingual Mystery #2: Beware, Lee and Quinn!
Creators: name: Skallerup Bessette, Lee
name: Dombrowski, Quinn
Publisher: name: Stanford Digital Repository
Year: 2024
Subjects: subject: Digital humanities
subject: Text data mining
subject: Translations
subject: Named entity recognition

description: In DSC Multilingual Mystery #2: Beware, Lee and Quinn!, Lee and Quinn look at the translators' decisions about how to handle character names in the Baby-Sitters Club French translations, and try named entity recognition in both English and French.
Asymmetric noise-induced large fluctuations in coupled systems
Creators: name: Schwartz, Ira B.
name: Szwaykowska, Klimka
name: Carr, Thomas W.
Publisher: name: arXiv
Year: 2017
Subjects: subject: Statistical Mechanics (cond-mat.stat-mech)
subject: FOS: Physical sciences

description: Networks of interacting, communicating subsystems are common in many fields, from ecology, biology, epidemiology to engineering and robotics. In the presence of noise and uncertainty, inter- actions between the individual components can lead to unexpected complex system-wide behaviors. In this paper, we consider a generic model of two weakly coupled dynamical systems, and show how noise in one part of the system is transmitted through the coupling interface. Working synergistically with the coupling, the noise on one system drives a large fluctuation in the other, even when there is no noise in the second system. Moreover, the large fluctuation happens while the first system exhibits only small random oscillations. Uncertainty effects are quantified by showing how characteristic time scales of noise induced switching scale as a function of the coupling between the two coupled parts of the experiment. In addition, our results show that the probability of switching in the noise-free system scales inversely as the square of reduced noise intensity amplitude, rendering the virtual probability of switching to be an extremely rare event. Our results showing the interplay between transmitted noise and coupling are also confirmed through simulations, which agree quite well with analytic theory.
description: 12 pages 11 figures
Two atoms in a harmonic trap with spin-orbital-angular-momentum coupling
Creators: name: Chen, Xiao-Long
name: Chen, Aixi
name: Peng, Shi-Guo
Publisher: name: arXiv
Year: 2024
Subjects: subject: Quantum Gases (cond-mat.quant-gas)
subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: We study the problem of two harmonically trapped atoms in the presence of spin-orbital-angular-momentum (SOAM) coupling. The two-body energy spectrum is numerically calculated by utilizing the exact diagonalization method. We analyze how the degeneracy of energy levels is lifted under the interplay between the interatomic interaction and SOAM coupling. The exact numerical results show excellent agreement with that of perturbation theory in the weak-interaction limit as well as that in the absence of SOAM coupling. The properties of correlations between the two atoms are also discussed with respect to the interaction strength. The findings in this paper may provide valuable insights into few-body physics subjected to SOAM coupling and the possible experimental detection of spectrum functions in many-body systems, such as radiofrequency spectroscopy.
description: 12 pages, 5 figures
On Selkow's Bound on the Independence Number of Graphs
Creators: name: Harant, Jochen
name: Mohr, Samuel
Publisher: name: arXiv
Year: 2017
Subjects: subject: Combinatorics (math.CO)
subject: FOS: Mathematics

description: For a graph $G$ with vertex set $V(G)$ and independence number $α(G)$, S. M. Selkow (Discrete Mathematics, 132(1994)363--365) established the famous lower bound $\sum\limits_{v\in V(G)}\frac{1}{d(v)+1}(1+\max\{\frac{d(v)}{d(v)+1}-\sum\limits_{u\in N(v)}\frac{1}{d(u)+1},0 \})$ on $α(G)$, where $N(v)$ and $d(v)=|N(v)|$ denote the neighborhood and the degree of a vertex $v\in V(G)$, respectively. However, Selkow's original proof of this result is incorrect. We give a new probabilistic proof of Selkow's bound here.
Chemical priming of plant defense responses to pathogen attacks
Creators: name: Hönig, Martin
name: Roeber, Venja M.
name: Schmülling, Thomas
name: Cortleven, Anne
Publisher: name: Freie Universität Berlin
Year: 2023
Subjects: subject: Arabidopsis thaliana
subject: biotic stress
subject: chemical priming
subject: defense priming
subject: induced systemic resistance
subject: pathogen attack
subject: priming
subject: systemic acquired resistance
subject: 500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie

description: Plants can acquire an improved resistance against pathogen attacks by exogenous application of natural or artificial compounds. In a process called chemical priming, application of these compounds causes earlier, faster and/or stronger responses to pathogen attacks. The primed defense may persist over a stress-free time (lag phase) and may be expressed also in plant organs that have not been directly treated with the compound. This review summarizes the current knowledge on the signaling pathways involved in chemical priming of plant defense responses to pathogen attacks. Chemical priming in induced systemic resistance (ISR) and systemic acquired resistance (SAR) is highlighted. The roles of the transcriptional coactivator NONEXPRESSOR OF PR1 (NPR1), a key regulator of plant immunity, induced resistance (IR) and salicylic acid signaling during chemical priming are underlined. Finally, we consider the potential usage of chemical priming to enhance plant resistance to pathogens in agriculture.
GIFAIR-FL: A Framework for Group and Individual Fairness in Federated Learning
Creators: name: Yue, Xubo
name: Nouiehed, Maher
name: Kontar, Raed Al
Publisher: name: arXiv
Year: 2021
Subjects: subject: Machine Learning (cs.LG)
subject: Distributed, Parallel, and Cluster Computing (cs.DC)
subject: FOS: Computer and information sciences

description: In this paper we propose \texttt{GIFAIR-FL}: a framework that imposes \textbf{G}roup and \textbf{I}ndividual \textbf{FAIR}ness to \textbf{F}ederated \textbf{L}earning settings. By adding a regularization term, our algorithm penalizes the spread in the loss of client groups to drive the optimizer to fair solutions. Our framework \texttt{GIFAIR-FL} can accommodate both global and personalized settings. Theoretically, we show convergence in non-convex and strongly convex settings. Our convergence guarantees hold for both $i.i.d.$ and non-$i.i.d.$ data. To demonstrate the empirical performance of our algorithm, we apply our method to image classification and text prediction tasks. Compared to existing algorithms, our method shows improved fairness results while retaining superior or similar prediction accuracy.
Adaptive cluster approximation for reduced density-matrix functional theory
Creators: name: Schade, Robert
name: Blöchl, Peter E.
Publisher: name: arXiv
Year: 2016
Subjects: subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: FOS: Physical sciences

description: A method, called the adaptive cluster approximation (ACA), for single-impurity Anderson models is proposed. It is based on reduced density-matrix functional theory, where the one-particle reduced density matrix is used as the basic variable. The adaptive cluster approximation introduces a unitary transformation of the bath states such that the effect of the bath is concentrated to a small cluster around the impurity. For this small effective system one can then either calculate the reduced density-matrix functional numerically exact from Levy's constrained-search formalism or approximate it by an implicit approximation of the reduced density-matrix functional. The method is evaluated for single-impurity Anderson models with finite baths. The method converges rapidly to the exact result with the size of the effective bath.
description: 15 pages, 8 figures
On the (in)consistency of perturbation theory at finite temperature
Creators: name: Lowdon, Peter
name: Philipsen, Owe
Publisher: name: arXiv
Year: 2024
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: High Energy Physics - Lattice (hep-lat)
subject: High Energy Physics - Theory (hep-th)
subject: Nuclear Theory (nucl-th)
subject: FOS: Physical sciences

description: A well-known difficulty of perturbative approaches to quantum field theory at finite temperature is the necessity to address theoretical constraints that are not present in the vacuum theory. In this work, we use lattice simulations of scalar correlation functions in massive $ϕ^{4}$ theory to analyse the extent to which these constraints affect the perturbative predictions. We find that the standard perturbative predictions deteriorate even in the absence of infrared divergences at relatively low temperatures, and that this is directly connected to the analytic structure of the propagators used in the expansion. This suggests that the incorporation of non-perturbative thermal effects in the propagators is essential for a consistent perturbative formulation of scalar quantum field theories at finite temperature. By utilising the spectral constraints imposed on finite-temperature correlation functions, we explore how these effects manifest themselves in the lattice data, and discuss why the presence of distinct thermoparticle excitations provides a potential resolution to these issues.
description: 22 pages, 8 figures; v2: additional discussion added, matches published version. Figure data attached
MPEC 2023-E78 : 2023 EY
Creators: name: Minor Planet Center Staff
Publisher: name: The Minor Planet Center
Year: 2023
Subjects: subject: Small Solar System bodies
valueUri: http://astrothesaurus.org/uat/1469
subject: 2023 EY

description: The Minor Planet Electronic Circulars contain information on unusual minor planets, routine data on comets and natural satellites, and occasional editorial announcements.  They are published on behalf of Division F of the International Astronomical Union by the Minor Planet Center, Smithsonian Astrophysical Observatory, Cambridge, MA 02138, U.S.A.
Whole-genome sequencing of Listeria innocua recovered from retail milk and dairy products in Egypt
Creators: name: Ramadan, Hazem
name: Al-Ashmawy, Maha
name: Soliman, Ahmed M.
name: Elbediwi, Mohammed
name: Sabeq, Islam
name: Yousef, Mona
name: Algammal, Abdelazeem M.
name: Hiott, Lari M.
name: Berrang, Mark E.
name: Frye, Jonathan G.
name: Jackson, Charlene R.
Publisher: name: Freie Universität Berlin
Year: 2023
Subjects: subject: Listeria innocua
subject: whole-genome sequencing
subject: genetic context
subject: clpL gene
subject: phylogenetic analysis
subject: milk
subject: Egypt
subject: 500 Naturwissenschaften und Mathematik::570 Biowissenschaften; Biologie::570 Biowissenschaften; Biologie

description: The similarity of the Listeria innocua genome with Listeria monocytogenes and their presence in the same niche may facilitate gene transfer between them. A better understanding of the mechanisms responsible for bacterial virulence requires an in-depth knowledge of the genetic characteristics of these bacteria. In this context, draft whole genome sequences were completed on five L. innocua isolated from milk and dairy products in Egypt. The assembled sequences were screened for antimicrobial resistance and virulence genes, plasmid replicons and multilocus sequence types (MLST); phylogenetic analysis of the sequenced isolates was also performed. The sequencing results revealed the presence of only one antimicrobial resistance gene, fosX, in the L. innocua isolates. However, the five isolates carried 13 virulence genes involved in adhesion, invasion, surface protein anchoring, peptidoglycan degradation, intracellular survival, and heat stress; all five lacked the Listeria Pathogenicity Island 1 (LIPI-1) genes. MLST assigned these five isolates into the same sequence type (ST), ST-1085; however, single nucleotide polymorphism (SNP)-based phylogenetic analysis revealed 422–1,091 SNP differences between our isolates and global lineages of L. innocua. The five isolates possessed an ATP-dependent protease (clpL) gene, which mediates heat resistance, on a rep25 type plasmids. Blast analysis of clpL-carrying plasmid contigs showed approximately 99% sequence similarity to the corresponding parts of plasmids of L. monocytogenes strains 2015TE24968 and N1-011A previously isolated from Italy and the United States, respectively. Although this plasmid has been linked to L. monocytogenes that was responsible for a serious outbreak, this is the first report of L. innocua containing clpL-carrying plasmids. Various genetic mechanisms of virulence transfer among Listeria species and other genera could raise the possibility of the evolution of virulent strains of L. innocua. Such strains could challenge processing and preservation protocols and pose health risks from dairy products. Ongoing genomic research is necessary to identify these alarming genetic changes and develop preventive and control measures.
DSC Multilingual Mystery #3: Quinn and Lee Clean Up Ghost Cat Data-Hairballs
Creators: name: Dombrowski, Quinn
name: Skallerup Bessette, Lee
Publisher: name: Stanford Digital Repository
Year: 2024
Subjects: subject: Digital humanities
subject: Text data mining
subject: Metadata
subject: Web scraping
subject: COVID-19 Pandemic (2020-)

description: In DSC Multilingual Mystery #3: Quinn and Lee Clean Up Ghost Cat Data-Hairballs, Quinn Dombrowski and Lee Skallerup Bessette work on creating a metadata spreadsheet for the Baby-Sitters Club corpus in translation using web scraping and OpenRefine. There is also some commentary from the beginning of the COVID-19 pandemic.
Atividade de enzimas ligadas ao escurecimento de polpa em genótipos brasileiros de goiabeira serrana
Creators: name: Freitas, Diana Carolina Lima
name: Benincá, Thalita Dal Toé
name: Lima, Laís Dieb
name: Mosquera, Deysi Jhoana Camayo
name: Schuerne, Isadora Dalbem
name: Vilvert, João Claudio
name: Amarante, Cassandro Vidal Talamini do
Publisher: name: figshare
Year: 2022
Subjects: subject: 70605 Post Harvest Horticultural Technologies (incl. Transportation and Storage)
subject: FOS: Agriculture, forestry and fisheries

description: Article published in 2017 at the Revista da Jornada da Pós-Graduação e Pesquisa - Congrega Urcamp
DSC Multilingual Mystery #4: Isabelle and the Missing Spaghetti-O's
Creators: name: Dombrowski, Quinn
name: Skallerup Bessette, Lee
name: Gribomont, Isabelle
Publisher: name: Stanford Digital Repository
Year: 2024
Subjects: subject: Digital humanities
subject: Text data mining
subject: Food
subject: Translations

description: In DSC Multilingual Mystery #4: Isabelle and the Missing Spaghetti-O's, Lee Skallerup Bessette and Quinn Dombrowski work with guest Data-Sitter Isabelle Gribomont to look at how American foods are adapted into French translations of the Baby-Sitters Club, using text alignment after they fail to train a food-detection NER model. They make some discoveries about the identities of the Belgian translators by looking at translation strategies for foods.
Flavor Gauge Models Below the Fermi Scale
Creators: name: Babu, K. S.
name: Friedland, A.
name: Machado, P. A. N.
name: Mocioiu, I.
Publisher: name: arXiv
Year: 2017
Subjects: subject: High Energy Physics - Phenomenology (hep-ph)
subject: FOS: Physical sciences

description: The mass and weak interaction eigenstates for the quarks of the third generation are very well aligned, an empirical fact for which the Standard Model offers no explanation. We explore the possibility that this alignment is due to an additional gauge symmetry in the third generation. Specifically, we construct and analyze an explicit, renormalizable model with a gauge boson, $X$, corresponding to the $B-L$ symmetry of the third family. Having a relatively light (in the MeV to multi-GeV range), flavor-nonuniversal gauge boson results in a variety of constraints from different sources. By systematically analyzing 20 different constraints, we identify the most sensitive probes: kaon, $B^+$, $D^+$ and Upsilon decays, $D-\bar{D}^0$ mixing, atomic parity violation, and neutrino scattering and oscillations. For the new gauge coupling $g_X$ in the range $(10^{-2} - 10^{-4})$ the model is shown to be consistent with the data. Possible ways of testing the model in $b$ physics, top and $Z$ decays, direct collider production and neutrino oscillation experiments, where one can observe nonstandard matter effects, are outlined. The choice of leptons to carry the new force is ambiguous, resulting in additional phenomenological implications, such as non-universality in semileptonic bottom decays. The proposed framework provides interesting connections between neutrino oscillations, flavor and collider physics.
description: 44 pages, 7 figures, 3 tables; B physics constraints and references added, conclusions unchanged
Field-of-View Extension for Brain Diffusion MRI via Deep Generative Models
Creators: name: Gao, Chenyu
name: Bao, Shunxing
name: Kim, Michael
name: Newlin, Nancy
name: Kanakaraj, Praitayini
name: Yao, Tianyuan
name: Rudravaram, Gaurav
name: Huo, Yuankai
name: Moyer, Daniel
name: Schilling, Kurt
name: Kukull, Walter
name: Toga, Arthur
name: Archer, Derek
name: Hohman, Timothy
name: Landman, Bennett
name: Li, Zhiyuan
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: Purpose: In diffusion MRI (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field-of-view (FOV). This work aims to develop a method for imputing the missing slices directly from existing dMRI scans with an incomplete FOV. We hypothesize that the imputed image with complete FOV can improve the whole-brain tractography for corrupted data with incomplete FOV. Therefore, our approach provides a desirable alternative to discarding the valuable dMRI data, enabling subsequent tractography analyses that would otherwise be challenging or unattainable with corrupted data. Approach: We propose a framework based on a deep generative model that estimates the absent brain regions in dMRI scans with incomplete FOV. The model is capable of learning both the diffusion characteristics in diffusion-weighted images (DWI) and the anatomical features evident in the corresponding structural images for efficiently imputing missing slices of DWI outside of incomplete FOV. Results: For evaluating the imputed slices, on the WRAP dataset the proposed framework achieved PSNRb0=22.397, SSIMb0=0.905, PSNRb1300=22.479, SSIMb1300=0.893; on the NACC dataset it achieved PSNRb0=21.304, SSIMb0=0.892, PSNRb1300=21.599, SSIMb1300= 0.877. The proposed framework improved the tractography accuracy, as demonstrated by an increased average Dice score for 72 tracts (p < 0.001) on both the WRAP and NACC datasets. Conclusions: Results suggest that the proposed framework achieved sufficient imputation performance in dMRI data with incomplete FOV for improving whole-brain tractography, thereby repairing the corrupted data. Our approach achieved more accurate whole-brain tractography results with extended and complete FOV and reduced the uncertainty when analyzing bundles associated with Alzheimer's Disease.
description: 20 pages, 11 figures
Lower visual processing speed relates to greater subjective cognitive complaints in community-dwelling healthy older adults
Creators: name: Marrero-Polegre, Daniela
name: Finke, Kathrin
name: Roaschio, Naomi
name: Haupt, Marleen
name: Reyes-Moreno, Cristian
name: Ruiz-Rizzo, Adriana L.
Publisher: name: Freie Universität Berlin
Year: 2023
Subjects: subject: aging
subject: memory complaints
subject: subjective function
subject: subjective cognitive decline
subject: visual attention
subject: visual processing speed
subject: 100 Philosophie und Psychologie::150 Psychologie::150 Psychologie

description: Introduction: Subjective cognitive complaints in older age may reflect subtle objective impairments in basic cognitive functions that might foreshadow broader cognitive problems. Such cognitive functions, however, are not captured by standard neuropsychological testing. Visual processing speed is a basic visual attention function that underlies the performance of cognitive tasks relying on visual stimuli. Here, we test the hypothesis that lower visual processing speed correlates with greater subjective cognitive complaints in healthy older adults from the community. Methods: To do so, we assessed a sample of 30 healthy, cognitively normal older adults (73.07 ± 7.73 years old; range: 60–82; 15 females) with respect to individual subjective cognitive complaints and visual processing speed. We quantified the degree of subjective cognitive complaints with two widely-used questionnaires: the Memory Functioning Questionnaire and the Everyday Cognition. We used verbal report tasks and the theory of visual attention to estimate a visual processing speed parameter independently from motor speed and other visual attention parameters, i.e., visual threshold, visual short-term memory storage capacity, top-down control, and spatial weighting. Results: We found that lower visual processing speed correlated with greater subjective complaints and that this relationship was not explained by age, education, or depressive symptoms. The association with subjective cognitive complaints was specific to visual processing speed, as it was not observed for other visual attention parameters. Discussion: These results indicate that subjective cognitive complaints reflect a reduction in visual processing speed in healthy older adults. Together, our results suggest that the combined assessment of subjective cognitive complaints and visual processing speed has the potential to identify individuals at risk for cognitive impairment before the standard tests show any abnormal results.