cometadata/datacite-rtg-text-other-reclassification-records huggingface dataset:
Feynman symmetries of the Martin and $c_2$ invariants of regular graphs
Creators: name: Panzer, Erik
name: Yeats, Karen
Publisher: name: arXiv
Year: 2023
Subjects: subject: Combinatorics (math.CO)
subject: High Energy Physics - Theory (hep-th)
subject: Mathematical Physics (math-ph)
subject: Algebraic Geometry (math.AG)
subject: FOS: Mathematics
subject: FOS: Physical sciences
subject: 81Q30 (Primary) 05C70, 05C45 (Secondary)

description: For every regular graph, we define a sequence of integers, using the recursion of the Martin polynomial. This sequence counts spanning tree partitions and constitutes the diagonal coefficients of powers of the Kirchhoff polynomial. We prove that this sequence respects all known symmetries of Feynman period integrals in quantum field theory. We show that other quantities with this property, the $c_2$ invariant and the extended graph permanent, are essentially determined by our new sequence. This proves the completion conjecture for the $c_2$ invariant at all primes, and also that it is fixed under twists. We conjecture that our invariant is perfect: Two Feynman periods are equal, if and only if, their Martin sequences are equal.
description: 78 pages, 30 figures, 7 tables, 2 ancillary files with data for 3- and 4-regular graphs
Thai national mental health survey 2013: methodology and procedure
Creators: name: Phunnapa Kittirattanapaiboon
name: Nopporn Tantirangsee
name: Worawan Chutha
name: Sawitri Assanangkornchai
name: Suttha Supanya
Publisher: name: Department of Mental Health Thailand
Year: 2016
Subjects: CIDI,epidemiology,mental health,national survey

description: 1, 24, Journal of Mental Health of Thailand
Structural and Physical Properties of Biofield Treated Thymol and Menthol
Creators: name: Trivedi, Mahendra
Publisher: name: SMN - The ScienceMedia Network
Year: 2016
Subjects: Natural Sciences
Chemistry

description: Thymol and menthol are naturally occurring plant derived compounds, which have excellent pharmaceutical and antimicrobial applications. The aim of this work was to evaluate the impact of biofield energy on physical and structural characteristics of thymol and menthol. The control and biofield treated compounds (thymol and menthol) were characterized by X-ray diffraction (XRD), Differential Scanning Calorimetry (DSC), Thermogravimetric analysis (TGA), and Fourier Transform Infrared Spectroscopy (FT-IR). XRD study revealed increase in intensity of the XRD peaks of treated thymol, which was correlated to high crystallinity of the treated sample. The treated thymol showed significant increase in crystallite size by 50.01% as compared to control. However, the treated menthol did not show any significant change in crystallite size as compared to control. DSC of treated menthol showed minimal increase in melting temperature (45ºC) as compared to control (44ºC). The enthalpy (ΔH) of both the treated compounds (thymol and menthol) was decreased as compared to control samples which could be due the high energy state of the powders. TGA analysis showed that thermal stability of treated thymol was increased as compared to control; though no change in thermal stability was noticed in treated menthol. FT-IR spectrum of treated thymol showed increase in wave number of –OH stretching vibration peak (14 cm-1) as compared to control. Whereas, the FT-IR spectrum of treated menthol showed appearance of new stretching vibration peaks in the region of 3200-3600 cm-1 which may be attributed to the presence of hydrogen bonding in the sample as compared to control. Overall, the result showed that biofield treatment has substantially changed the structural and physical properties of thymol and menthol.
Supplementary document for Polarizer-free measurement of the full Stokes vector using a fiber-coupled superconducting nanowire single photon detector with a polarization extinction ratio of ~2 - 6197238.pdf
Creators: name: Fei, Yue
name: Ji, Tianhao
name: Zhu, Guanghao
name: Zhang, Labao
name: Zhang, Lijian
name: tan, jingrou
name: chen, qi
name: Guan, Yanqiu
name: Yin, Rui
name: Wang, Hao
Publisher: name: Optica Publishing Group
Year: 2025
Subjects: Uncategorized

description: Supplemental document of theory model
A Simulation of a Simulation : Algorithms for Symmetry-Protected Measurement-Based Quantum Computing Experiments
Creators: name: Weil, Ryohei
Publisher: name: The University of British Columbia
Year: 2022

description: The paradigm of measurement-based quantum computing (MBQC) provides an ideal theoretical playground to characterize quantum computational resources. Recent advances have yielded a formalism to characterize the computational power of finite one-dimensional MBQC resource states, extending prior classification for infinite systems based on symmetry-protected topological (SPT) phases. In this work, we develop techniques for the experimental realization of these results on Noisy Intermediate-Scale Quantum (NISQ) devices. We demonstrate a post-processing algorithm for bypassing the generally inefficient transformation to the resource states of interest. We also develop and perform a variational algorithm for obtaining the coefficients of this transformation. Our results demonstrate the capability of NISQ devices for showcasing phenomena relating to quantum computational resource characterization.
Systematic design of particle dampers for passive vibration attenuation
Creators: name: Meyer, Niklas Joachim
Publisher: name: TUHH Universitätsbibliothek
Year: 2023
Subjects: subject: Particle damper
subject: DEM
subject: Design methodology
subject: Toolchain
subject: Experimental and numerical results
classificationCode: 600
subject: Technik
classificationCode: 620
subject: Ingenieurwissenschaften

description: Das nichtlineare Dämpfungsvehalten von Partikeldämpfern und die Vielzahl an Einflussparametern hindern derzeit deren weit verbreitete industrielle Nutzung. Das Ziel dieser Arbeit ist daher die Entwicklung eines neuen Entwurfskonzeptes in Form einer Toolchain, um eine systematische Auslegung von Partikeldämpfern zu ermöglichen. Dabei werden verschiedene experimentelle als auch numerische Modelle miteinander kombiniert. Die Toolchain beginnt mit der Analyse des makroskopischen Verhaltens einzelner Partikelstöße, führt über die Analyse der Eigenschaften von vibrierenden granularen Medien und endet mit der Auslegung des Partikeldämpfers für die gewünschte Struktur. Die Wirksamkeit der Toolchain wird schlussendlich anhand von verschiedenen Beispielen demonstriert.
description: The nonlinear damping behavior of particle dampers and the multitude of influence parameters hinder their wide industrial usage. Hence, the goal of this work is the development of a new design methodology in form of a toolchain enabling a systematic damper design. On that account, various numerical models and experimental tests are combined. The toolchain commences with investigations of the micro-mechanical behavior of single particle impacts, continues with analyzing the properties of vibrating granular matter inside different containers and ends with designing a particle damper for a desired vibrating structure. The good agreement of the experimental and numerical models and their efficiency for the damper design and dimensioning are demonstrated on multiple examples.
A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU
Creators: name: Shiri, Farhad Mortezapour
name: Perumal, Thinagaran
name: Mustapha, Norwati
name: Mohamed, Raihani
Publisher: name: arXiv
Year: 2023
Subjects: subject: Machine Learning (cs.LG)
subject: Artificial Intelligence (cs.AI)
subject: FOS: Computer and information sciences

description: Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and artificial intelligence (AI), outperforming traditional ML methods, especially in handling unstructured and large datasets. Its impact spans across various domains, including speech recognition, healthcare, autonomous vehicles, cybersecurity, predictive analytics, and more. However, the complexity and dynamic nature of real-world problems present challenges in designing effective deep learning models. Consequently, several deep learning models have been developed to address different problems and applications. In this article, we conduct a comprehensive survey of various deep learning models, including Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Temporal Convolutional Networks (TCN), Transformer, Kolmogorov-Arnold networks (KAN), Generative Models, Deep Reinforcement Learning (DRL), and Deep Transfer Learning. We examine the structure, applications, benefits, and limitations of each model. Furthermore, we perform an analysis using three publicly available datasets: IMDB, ARAS, and Fruit-360. We compared the performance of six renowned deep learning models: CNN, RNN, Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU alongside two newer models, TCN and Transformer, using the IMDB and ARAS datasets. Additionally, we evaluated the performance of eight CNN-based models, including VGG (Visual Geometry Group), Inception, ResNet (Residual Network), InceptionResNet, Xception (Extreme Inception), MobileNet, DenseNet (Dense Convolutional Network), and NASNet (Neural Architecture Search Network), for image classification tasks using the Fruit-360 dataset.
description: 62 pages, 37 figures
Mathematics -- an imagined tool for rational cognition
Creators: name: Čulina, Boris
Publisher: name: arXiv
Year: 2023
Subjects: subject: History and Overview (math.HO)
subject: FOS: Mathematics

description: Analysing several characteristic mathematical models: natural and real numbers, Euclidean geometry, group theory, and set theory, I argue that a mathematical model in its final form is a junction of a set of axioms and an internal partial interpretation of the corresponding language. It follows from the analysis that (i) mathematical objects do not exist in the external world: they are imagined objects, some of which, at least approximately, exist in our internal world of activities or we can realize or represent them there; (ii) mathematical truths are not truths about the external world but specifications (formulations) of mathematical conceptions; (iii) mathematics is first and foremost our imagined tool by which, with certain assumptions about its applicability, we explore nature and synthesize our rational cognition of it.
On the Scaling of the Cubic-to-Tetragonal Phase Transformation with Displacement Boundary Conditions
Creators: name: Rüland, Angkana
name: Tribuzio, Antonio
Publisher: name: arXiv
Year: 2023
Subjects: subject: Analysis of PDEs (math.AP)
subject: FOS: Mathematics

description: We provide (upper and lower) scaling bounds for a singular perturbation model for the cubic-to-tetragonal phase transformation with (partial) displacement boundary data. We illustrate that the order of lamination of the affine displacement data determines the complexity of the microstructure. As in \cite{RT21} we heavily exploit careful Fourier space localization methods in distinguishing between the different lamination orders in the data.
description: 34 pages, 6 figures, comments welcome; added argument in Proposition 3.6, corrected typos
Psychosocial Predictors of Verbal Aggression towards Infants in a Canadian Perinatal Sample
Creators: name: Oloomi, Sarvenaz
Publisher: name: The University of British Columbia
Year: 2022

description: Child maltreatment remains a persistent problem with significant detrimental consequences for child development and psychosocial health (e.g., Sousa et al., 2011). Globally, it is estimated that 2.3% of children are physically abused, while 3.6% of children are emotionally abused at some point during their childhood (Stoltenborgh et al., 2015). Due to the explicit and often visible nature of physical abuse, it has been more extensively studied than less visible forms, such as verbal abuse, which also has lasting negative effects on children. Although abuse is estimated to occur more commonly among younger infants, research has mostly focused on older children (e.g., Black et al., 2001). Specifically, the abuse literature is largely missing research on potential predictors of verbal aggression, especially towards infants (i.e., children under the age of 12 months). In this study we investigated several potential predictors of maternal verbal aggression towards the infant. Specifically, we assessed parenting beliefs and attitudes associated with an increased risk of child maltreatment (prenatally), as well as postpartum depressed mood, sleep, and social support as predictors of verbal aggression towards the infant. Data was collected at 5- 7 weeks and at 6-9 months via questionnaire from 388 English-speaking people in British Columbia. Study hypotheses were tested using negative binomial regression. Consistent with our predictions, depressed mood showed a significant relationship with verbal aggression, B = 1.06, SEB = .02, p < .001, 95%CI [1.02, 1.11]. However, none of the other predictors showed a significant relationship. This research furthers our understanding of risk factors for verbal aggression towards infants with maternal depressed mood emerging as a significant predictor.
Opportunities for Large Language Models and Discourse in Engineering Design
Creators: name: Göpfert, Jan
name: Weinand, Jann M.
name: Kuckertz, Patrick
name: Stolten, Detlef
Publisher: name: arXiv
Year: 2023
Subjects: subject: Computation and Language (cs.CL)
subject: Artificial Intelligence (cs.AI)
subject: Computational Engineering, Finance, and Science (cs.CE)
subject: FOS: Computer and information sciences

description: In recent years, large language models have achieved breakthroughs on a wide range of benchmarks in natural language processing and continue to increase in performance. Recently, the advances of large language models have raised interest outside the natural language processing community and could have a large impact on daily life. In this paper, we pose the question: How will large language models and other foundation models shape the future product development process? We provide the reader with an overview of the subject by summarizing both recent advances in natural language processing and the use of information technology in the engineering design process. We argue that discourse should be regarded as the core of engineering design processes, and therefore should be represented in a digital artifact. On this basis, we describe how foundation models such as large language models could contribute to the design discourse by automating parts thereof that involve creativity and reasoning, and were previously reserved for humans. We describe how simulations, experiments, topology optimizations, and other process steps can be integrated into a machine-actionable, discourse-centric design process. Finally, we outline the future research that will be necessary for the implementation of the conceptualized framework.
Nagy type inequalities in metric measure spaces and some applications
Creators: name: Babenko, Vladyslav
name: Babenko, Vira
name: Kovalenko, Oleg
name: Parfinovych, Nataliia
Publisher: name: arXiv
Year: 2023
Subjects: subject: Functional Analysis (math.FA)
subject: FOS: Mathematics
subject: 26D10, 41A17, 41A44, 41A55

description: We obtain a sharp Nagy type inequality in a metric space $(X,ρ)$ with measure $μ$ that estimates the uniform norm of a function using its $\|\cdot\|_{H^ω}$ -- norm determined by a modulus of continuity $ω$, and a seminorm that is defined on a space of locally integrable functions. We consider charges $ν$ that are defined on the set of $μ$-measurable subsets of $X$ and are absolutely continuous with respect to $μ$. Using the obtained Nagy type inequality, we prove a sharp Landau-Kolmogorov type inequality that estimates the uniform norm of a Radon-Nikodym derivative of a charge via a $\|\cdot\|_{H^ω}$-norm of this derivative, and a seminorm defined on the space of such charges. We also prove a sharp inequality for a hypersingular integral operator. In the case $X=\mathbb{R}_+^m\times \mathbb{R}^{d-m}$, $0\le m\le d$, we obtain inequalities that estimate the uniform norm of a mixed derivative of a function using the uniform norm of the function and the $\|\cdot\|_{H^ω}$-norm of its mixed derivative.
EMP-A_2025_OnSSET-Niger
Creators: name: BONKANEY, Abdou Latif
name: POIROT, Victoria
Publisher: name: Zenodo
Year: 2025
Subjects: Niger
GEP
OnSSET

description: This presentation constitutes the outcome of a three-weeks capacity building activity on GEP-OnSSET as part of the 2025 Energy Modelling Platform for Africa workshop. The author(s) collected input data, set parameters, calibrated the model, and ran electrification investment scenarios for NIGER in order to explore pathways leading to the achievement of electrification targets. The results of this analysis are not conclusive, but rather the output of experimentation with the model and thus only indicative. You may contact the author for more information and/or questions about the methodology, limitations, and follow-up activities of this study.
Existence and Uniqueness of Solutions of the Koopman--von Neumann Equation on Bounded Domains
Creators: name: Stengl, Marian
name: Gelß, Patrick
name: Klus, Stefan
name: Pokutta, Sebastian
Publisher: name: arXiv
Year: 2023
Subjects: subject: Analysis of PDEs (math.AP)
subject: Mathematical Physics (math-ph)
subject: Dynamical Systems (math.DS)
subject: Functional Analysis (math.FA)
subject: FOS: Mathematics
subject: FOS: Physical sciences
subject: 35A05, 35F10, 37C30, 46E35, 47D06

description: The Koopman--von Neumann equation describes the evolution of a complex-valued wavefunction corresponding to the probability distribution given by an associated classical Liouville equation. Typically, it is defined on the whole Euclidean space. The investigation of bounded domains, particularly in practical scenarios involving quantum-based simulations of dynamical systems, has received little attention so far. We consider the Koopman--von Neumann equation associated with an ordinary differential equation on a bounded domain whose trajectories are contained in the set's closure. Our main results are the construction of a strongly continuous semigroup together with the existence and uniqueness of solutions of the associated initial value problem. To this end, a functional-analytic framework connected to Sobolev spaces is proposed and analyzed. Moreover, the connection of the Koopman--von Neumann framework to transport equations is highlighted.
Towards Parallel Learned Sorting
Creators: name: Carvalho, Ivan
Publisher: name: The University of British Columbia
Year: 2022

description: We introduce a new sorting algorithm that is the combination of ML-enhanced sorting with the In-place Super Scalar Sample Sort (IPS4o). The main contribution of our work is to achieve parallel ML-enhanced sorting, as previous algorithms were limited to sequential implementations. We introduce the In-Place Parallel Learned Sort (IPLS) algorithm and compare it extensively against other sorting approaches. IPLS combines the IPS4o framework with linear models trained using the Fastest Minimum Conflict Degree algorithm to partition data. The experimental results do not crown IPLS as the fastest algorithm. However, they do show that IPLS is competitive among its peers and that using the IPS4o framework is a promising approach towards parallel learned sorting.
RCS-based Quasi-Deterministic Ray Tracing for Statistical Channel Modeling
Creators: name: Ebrahimizadeh, Javad
name: Vinogradov, Evgenii
name: Vandenbosch, Guy A. E.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Networking and Internet Architecture (cs.NI)
subject: Systems and Control (eess.SY)
subject: FOS: Computer and information sciences
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: This paper presents a quasi-deterministic ray tracing (QD-RT) method for analyzing the propagation of electromagnetic waves in street canyons. The method uses a statistical bistatic distribution to model the Radar Cross Section (RCS) of various irregular objects such as cars and pedestrians, instead of relying on exact values as in a deterministic propagation model. The performance of the QD-RT method is evaluated by comparing its generated path loss distributions to those of the deterministic ray tracing (D-RT) model using the Two-sample Cramer-von Mises test. The results indicate that the QD-RT method generates the same path loss distributions as the D-RT model while offering lower complexity. This study suggests that the QD-RT method has the potential to be used for analyzing complicated scenarios such as street canyon scenarios in mmWave wireless communication systems.
On some sharp Landau--Kolmogorov--Nagy type inequalities in Sobolev spaces of multivariate functions
Creators: name: Babenko, V. F.
name: Babenko, V. V.
name: Kovalenko, O. V.
name: Parfinovych, N. V.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Functional Analysis (math.FA)
subject: FOS: Mathematics
subject: 26D10, 41A17, 41A44

description: For a function $f$ from the Sobolev space $W^{1,p}(C)$ ($C\subset\mathbb{R}^d$ is an open convex cone), a sharp inequality that estimates $\| f\|_{L_{\infty}}$ via the $L_{p}$-norm of its gradient and a seminorm of the function is obtained. With the help of this inequality, a sharp inequality is proved, which estimates the ${L_{\infty}}$-norm of the Radon--Nikodym derivative of a charge defined on Lebesgue measurable subsets of $C$ via the $L_p$-norm of the gradient of this derivative and a seminorm of the charge. In the case, when $C=\mathbb{R}_+^m\times \mathbb{R}^{d-m}$, $0\le m\le d$, we obtain inequalities that estimate the ${L_{\infty}}$-norm of a mixed derivative of a function $f\colon C\to \mathbb{R}$ using its ${L_{\infty}}$-norm and the $L_p$-norm of the gradient of the function's mixed derivative.
Characterization of coherent errors in gate layers with robustness to Pauli noise
Creators: name: Kaufmann, Noah
name: Rojkov, Ivan
name: Reiter, Florentin
Publisher: name: arXiv
Year: 2023
Subjects: subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Characterization of quantum devices generates insights into their sources of disturbances. State-of-the-art characterization protocols often focus on incoherent noise and eliminate coherent errors when using Pauli or Clifford twirling techniques. This approach biases the structure of the effective noise and adds a circuit and sampling overhead. We motivate the extension of an incoherent local Pauli noise model to coherent errors and present a practical characterization protocol for an arbitrary gate layer. Notably, the coherent noise estimation is robust to Pauli noise. We demonstrate our protocol on a superconducting hardware platform and identify the leading coherent errors. To verify the characterized noise structure, we mitigate its coherent and incoherent components using a gate-level coherent noise mitigation scheme in conjunction with probabilistic error cancellation. The proposed characterization procedure opens up possibilities for device calibration, hardware development, and improvement of error mitigation and correction techniques.
description: 14 pages, 8 figures
Dehazing Ultrasound using Diffusion Models
Creators: name: Stevens, Tristan S. W.
name: Meral, Faik C.
name: Yu, Jason
name: Apostolakis, Iason Z.
name: Robert, Jean-Luc
name: van Sloun, Ruud J. G.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Signal Processing (eess.SP)
subject: FOS: Electrical engineering, electronic engineering, information engineering

description: Echocardiography has been a prominent tool for the diagnosis of cardiac disease. However, these diagnoses can be heavily impeded by poor image quality. Acoustic clutter emerges due to multipath reflections imposed by layers of skin, subcutaneous fat, and intercostal muscle between the transducer and heart. As a result, haze and other noise artifacts pose a real challenge to cardiac ultrasound imaging. In many cases, especially with difficult-to-image patients such as patients with obesity, a diagnosis from B-Mode ultrasound imaging is effectively rendered unusable, forcing sonographers to resort to contrast-enhanced ultrasound examinations or refer patients to other imaging modalities. Tissue harmonic imaging has been a popular approach to combat haze, but in severe cases is still heavily impacted by haze. Alternatively, denoising algorithms are typically unable to remove highly structured and correlated noise, such as haze. It remains a challenge to accurately describe the statistical properties of structured haze, and develop an inference method to subsequently remove it. Diffusion models have emerged as powerful generative models and have shown their effectiveness in a variety of inverse problems. In this work, we present a joint posterior sampling framework that combines two separate diffusion models to model the distribution of both clean ultrasound and haze in an unsupervised manner. Furthermore, we demonstrate techniques for effectively training diffusion models on radio-frequency ultrasound data and highlight the advantages over image data. Experiments on both in-vitro and in-vivo cardiac datasets show that the proposed dehazing method effectively removes haze while preserving signals from weakly reflected tissue.
description: 12pages, 15 figures, Transactions on Medical Imaging
A power-assisted robotic base for C-arms with closed-loop control for improved, intraoperative fluoroscopic imaging
Creators: name: Sepehri, Anoush
Publisher: name: The University of British Columbia
Year: 2022

description: C-arms are mobile X-ray machines frequently used during orthopedic surgeries. The current standard for operating these machines is for radiology technologists to maneuver them into position, often intraoperatively around a patient, to capture an X-ray of a region of interest. Because these machines can be over 350 kg, they can be difficult to maneuver, thus leading to higher risks of musculoskeletal injury. Furthermore, their size and mass result in limited positioning accuracy. Often multiple images, referred to as scouting images, are required to get the exact X-ray necessary for the surgeons. This can result in unnecessary radiation exposure which is harmful over the long term. The work in this paper combines two previously developed technologies, the Easy-C and the OPTIX to develop an autonomous, closed-loop controlled, positioning system for C-arms. The OPTIX is an optical tracking system that can be used to track the position of the C-arm base relative to the operating room. The Easy-C is a power-assisted robotic base that can be used to eliminate the need to manually position and move the C-arm. The system developed and presented in this work utilizes the OPTIX tracking system for position feedback to the Easy-C to enable autonomous positioning using closed-loop control. Preliminary experiments were conducted with this system by adding 40 kg to the Easy-C and moving the robotic base to target positions. Translational and rotational errors were in most cases less than 1 cm and 1 degree. While several limitations were discovered, the results and conclusions drawn from this work demonstrate that the robotic base could be an effective tool in clinical applications.
BO'LAJAK INFORMATIKA FANI O'QITUVCHILARINI RAQAMLI TEXNOLOGIYALARDAN FOYDALANISH KOMPETENTLIGINI TAKOMILLASHTIRISHNING ASOSIY KOMPONENTLARI
Creators: name: Ashurov, Jamshid
Publisher: name: Zenodo
Year: 2025

description: Mazkur ishda differensial yondashuv asosida talabalarni raqamli texnologiyalardan foydalanish kompetentligini takomillashtirish uchun zarur bo‘lgan pedagogik shart-sharoitlar tahlil qilinadi. Ilg‘or xorijiy tajribalar o‘rganilib, ularning mahalliy ta’lim tizimiga moslashuvi bo‘yicha tavsiyalar ishlab chiqilgan. Tadqiqotda zamonaviy ta’lim texnologiyalari, masofaviy o‘qitish usullari, va innovatsion yondashuvlar asosida talabalar kompetentligini oshirish imkoniyatlari yoritilgan.
Classification of unitary operators by local generatability
Creators: name: Liu, Xu
name: Culver, Adrian B.
name: Harper, Fenner
name: Roy, Rahul
Publisher: name: arXiv
Year: 2023
Subjects: subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: Quantum Gases (cond-mat.quant-gas)
subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: Quantum Physics (quant-ph)
subject: FOS: Physical sciences

description: Periodically driven (Floquet) systems can exhibit possibilities beyond what can be obtained in equilibrium. Both in Floquet systems and in the related problems of discrete-time quantum walks and quantum cellular automata, a basic distinction arises among unitary time evolution operators: while all physical operators are local, not all are locally generated (i.e., generated by some local Hamiltonian). In this paper, we define the notion of equivalence up to a locally generated unitary in all Altland-Zirnbauer symmetry classes. We then classify noninteracting unitaries in all dimensions on this basis by showing that equivalence up to a locally generated unitary is identical to homotopy equivalence.
description: 15 + 9 pages, 2 figures; added Appendices A.2, B.1, and E; further discussion of composition operation; published version
TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution
Creators: name: Liu, Baolin
name: Yang, Zongyuan
name: Wang, Pengfei
name: Zhou, Junjie
name: Liu, Ziqi
name: Song, Ziyi
name: Liu, Yan
name: Xiong, Yongping
Publisher: name: arXiv
Year: 2023
Subjects: subject: Computer Vision and Pattern Recognition (cs.CV)
subject: FOS: Computer and information sciences

description: The goal of scene text image super-resolution is to reconstruct high-resolution text-line images from unrecognizable low-resolution inputs. The existing methods relying on the optimization of pixel-level loss tend to yield text edges that exhibit a notable degree of blurring, thereby exerting a substantial impact on both the readability and recognizability of the text. To address these issues, we propose TextDiff, the first diffusion-based framework tailored for scene text image super-resolution. It contains two modules: the Text Enhancement Module (TEM) and the Mask-Guided Residual Diffusion Module (MRD). The TEM generates an initial deblurred text image and a mask that encodes the spatial location of the text. The MRD is responsible for effectively sharpening the text edge by modeling the residuals between the ground-truth images and the initial deblurred images. Extensive experiments demonstrate that our TextDiff achieves state-of-the-art (SOTA) performance on public benchmark datasets and can improve the readability of scene text images. Moreover, our proposed MRD module is plug-and-play that effectively sharpens the text edges produced by SOTA methods. This enhancement not only improves the readability and recognizability of the results generated by SOTA methods but also does not require any additional joint training. Available Codes:https://github.com/Lenubolim/TextDiff.
description: Correct and update some data
Scalable inference with Autoregressive Neural Ratio Estimation
Creators: name: Montel, Noemi Anau
name: Alvey, James
name: Weniger, Christoph
Publisher: name: arXiv
Year: 2023
Subjects: subject: Instrumentation and Methods for Astrophysics (astro-ph.IM)
subject: Cosmology and Nongalactic Astrophysics (astro-ph.CO)
subject: Astrophysics of Galaxies (astro-ph.GA)
subject: FOS: Physical sciences

description: In recent years, there has been a remarkable development of simulation-based inference (SBI) algorithms, and they have now been applied across a wide range of astrophysical and cosmological analyses. There are a number of key advantages to these methods, centred around the ability to perform scalable statistical inference without an explicit likelihood. In this work, we propose two technical building blocks to a specific sequential SBI algorithm, truncated marginal neural ratio estimation (TMNRE). In particular, first we develop autoregressive ratio estimation with the aim to robustly estimate correlated high-dimensional posteriors. Secondly, we propose a slice-based nested sampling algorithm to efficiently draw both posterior samples and constrained prior samples from ratio estimators, the latter being instrumental for sequential inference. To validate our implementation, we carry out inference tasks on three concrete examples: a toy model of a multi-dimensional Gaussian, the analysis of a stellar stream mock observation, and finally, a proof-of-concept application to substructure searches in strong gravitational lensing. In addition, we publicly release the code for both the autoregressive ratio estimator and the slice sampler.
description: 12 pages. 6 figures. Codes: swyft is available at https://github.com/undark-lab/swyft , torchns is available at https://github.com/undark-lab/torchns - v2: version accepted by MNRAS
GAIDE: A Framework for Using Generative AI to Assist in Course Content Development
Creators: name: Dickey, Ethan
name: Bejarano, Andres
Publisher: name: arXiv
Year: 2023
Subjects: subject: Computers and Society (cs.CY)
subject: FOS: Computer and information sciences
subject: K.3.2

description: This paper introduces "GAIDE: Generative AI for Instructional Development and Education," a novel framework for using Generative AI (GenAI) to enhance educational content creation. GAIDE stands out by offering a practical approach for educators to produce diverse, engaging, and academically rigorous materials. It integrates GenAI into curriculum design, easing the workload of instructors and elevating material quality. With GAIDE, we present a distinct, adaptable model that harnesses technological progress in education, marking a step towards more efficient instructional development. Motivated by the demand for innovative educational content and the rise of GenAI use among students, this research tackles the challenge of adapting and integrating technology into teaching. GAIDE aims to streamline content development, encourage the creation of dynamic materials, and demonstrate GenAI's utility in instructional design. The framework is grounded in constructivist learning theory and TPCK, emphasizing the importance of integrating technology in a manner that complements pedagogical goals and content knowledge. Our approach aids educators in crafting effective GenAI prompts and guides them through interactions with GenAI tools, both of which are critical for generating high-quality, contextually appropriate content. Initial evaluations indicate GAIDE reduces time and effort in content creation, without compromising on the breadth or depth of the content. Moreover, the use of GenAI has shown promise in deterring conventional cheating methods, suggesting a positive impact on academic integrity and student engagement.
description: 6 pages plus references. v4: Accepted for publication in Frontiers in Education 2024, includes referenced supplementary material. v3: updated title, some expanded discussion, added process flowchart. v2: fix 1 arXiv reference number
Self-Supervised Metric Learning in Multi-View Data: A Downstream Task Perspective
Creators: name: Wang, Shulei
Publisher: name: Taylor & Francis
Year: 2022
Subjects: subject: Neuroscience
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences

description: Self-supervised metric learning has been a successful approach for learning a distance from an unlabeled dataset. The resulting distance is broadly useful for improving various distance-based downstream tasks, even when no information from downstream tasks is utilized in the metric learning stage. To gain insights into this approach, we develop a statistical framework to theoretically study how self-supervised metric learning can benefit downstream tasks in the context of multi-view data. Under this framework, we show that the target distance of metric learning satisfies several desired properties for the downstream tasks. On the other hand, our investigation suggests the target distance can be further improved by moderating each direction’s weights. In addition, our analysis precisely characterizes the improvement by self-supervised metric learning on four commonly used downstream tasks: sample identification, two-sample testing, k-means clustering, and k-nearest neighbor classification. When the distance is estimated from an unlabeled dataset, we establish the upper bound on distance estimation’s accuracy and the number of samples sufficient for downstream task improvement. Finally, numerical experiments are presented to support the theoretical results in the paper.
Transcendence of certain sequences of algebraic numbers
Creators: name: Laursen, Mathias L.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Number Theory (math.NT)
subject: FOS: Mathematics
subject: 11J81, 11J87

description: Using Schmidt's Subspace Theorem, this paper improves and extends an existing transcendence result for sequences of algebraic numbers. The theorems thus produced correspond to a central theorem on the irrationality of sequences due to Erdős.
description: 25 pages
CLASS Report Card 2021
Creators: name: Hatton, Angela D.
name: Holliday, N. Penny
name: Sands, Carla M.
name: CLASS Project Team
Publisher: name: Zenodo
Year: 2021
Subjects: Atlantic Ocean
Climate change
Ocean observation
Model development
Ocean management

description: Report edited by the listed authors. Design by Simon Beasley (NOC)

Content provided by the whole CLASS project team.

CLASS project partners are: National Oceanography Centre (NOC), Plymouth Marine Laboratory (PML), Scottish Association for Marine Science (SAMS), Marine Biological Association (MBA), Sea Mammal Research Unit (SMRU).

See https://projects.noc.ac.uk/class-project/ for more details

Funded by the Natural Environment Research Council under grant NE/R015953/1
Load Sharing Mechanism of Combined Pile-Raft Foundation (CPRF) under Seismic Loads
Creators: name: Kumar, Ashutosh
Publisher: name: Southeast Asian Geotechnical Society
Year: 2017
Subjects: Pile-raft
Pseudo-static
Seismic
Settlement
Load sharing
Lateral displacement

description: The Geotechnical Engineering Journal of the SEAGS & AGSSEA, 48, 3, 95-101
Isotopic Abundance Ratio Analysis of 1,2,3-Trimethoxybenzene (TMB) After Biofield Energy Treatment Using Gas Chromatography-Mass Spectrometry
Creators: name: Trivedi, Mahendra
Publisher: name: SMN - The ScienceMedia Network
Year: 2016
Subjects: Natural Sciences
Chemistry

description: 1,2,3-Trimethoxybenzene (TMB) is one of the most versatile chemical used for the synthesis of several pharmaceuticals, dyes, polymers, organic compounds, etc. The stable isotope ratio analysis has increased attention day-by-days in several fields such as agricultural, food authenticity, biochemistry, medical research, etc. The current study was aimed to evaluate the effect of the biofield energy treatment on the isotopic abundance ratios of 13C/ 12C or 2H/ 1H or 17O/ 16O (PM+1 /PM ) and 18O/16O (PM+2 /PM ) in TMB using Gas chromatography - mass spectrometry (GC-MS) technique. TMB was divided into two parts - one part was denoted as control and another part was referred as biofield energy treated sample that was received through Mr. Trivediꞌs unique biofield energy (The Trivedi Effect ® ). The GC-MS of the biofield treated TMB was characterized at different time intervals considered as T1, T2, T3, and T4 to examine the impact of the biofield energy treatment on isotopic abundance ratio with respect to the time. The GC-MS spectra of the both control and biofield treated TMB exhibited the presence of molecular ion peak [M+ ] at m/z 168 (calculated 168.08 for C9H12O3 ) along with similar pattern of fragmentation. The relative peak intensities of the fragmented ions in the biofield treated TMB, particularly at T2 and T3 was altered from the control sample. The isotopic abundance ratio analysis in the biofield treated TMB exhibited that the isotopic abundance ratio of PM+1 /PM in the biofield treated TMB at T2 and T3 was significantly enhanced by 128.13 and 117.99%, respectively with respect to the control sample. Consequently, the percentage change in isotopic abundance ratio of PM+2 /PM was significantly increased in the biofield treated TMB at T2 and T3 by 125.93 and 116.67%, respectively as compared with the control TMB. The isotopic abundance ratios (PM+1 /PM and PM+2 /PM ) in the biofield treated TMB at T1 and T4 was altered with respect to the control TMB. In summary, 13C, 2H, and 17O contributions from (C9H12O3 )+ to m/z 169 and 18O contribution from (C9H12O3 )+ to m/z 170 for the biofield treated TMB, particularly at T2 and T3 were significantly improved and biofield treated TMB might exhibit changed isotope effects as compared to the control sample. The biofield treated TMB might assist to develop new chemicals and pharmaceuticals through using its kinetic isotope effects like understanding the reaction mechanism, the enzymatic transition state and all aspects of enzyme mechanisms.
Supplementary document for 80-μs pulsewidth from a circulation-free diode-pumped liquid organic laser limited by thermal blooming - 6197620.pdf
Creators: name: Longo, Antonio Valerio
name: Hamja, Amir
name: forget, sebastien
name: Chenais, Sebastien
Publisher: name: Optica Publishing Group
Year: 2025
Subjects: Uncategorized

description: Additional figures
Controller Synthesis of Collaborative Signal Temporal Logic Tasks for Multi-Agent Systems via Assume-Guarantee Contracts
Creators: name: Liu, Siyuan
name: Saoud, Adnane
name: Dimarogonas, Dimos V.
Publisher: name: arXiv
Year: 2023
Subjects: subject: Systems and Control (eess.SY)
subject: Multiagent Systems (cs.MA)
subject: FOS: Electrical engineering, electronic engineering, information engineering
subject: FOS: Computer and information sciences

description: This paper considers the problem of controller synthesis of signal temporal logic (STL) specifications for large-scale multi-agent systems, where the agents are dynamically coupled and subject to collaborative tasks. A compositional framework based on continuous-time assume-guarantee contracts is developed to break the complex and large synthesis problem into subproblems of manageable sizes. We first show how to formulate the collaborative STL tasks as assume-guarantee contracts by leveraging the idea of funnel-based control. The concept of contracts is used to establish our compositionality result, which allows us to guarantee the satisfaction of a global contract by the multi-agent system when all agents satisfy their local contracts. Then, a closed-form continuous-time feedback controller is designed to enforce local contracts over the agents in a distributed manner, which further guarantees the global task satisfaction based on the compositionality result. Finally, the effectiveness of our results is demonstrated by two numerical examples.
description: arXiv admin note: substantial text overlap with arXiv:2203.10041
The Governance of Decentralized Autonomous Organizations: A Study of Contributors' Influence, Networks, and Shifts in Voting Power
Creators: name: Kitzler, Stefan
name: Balietti, Stefano
name: Saggese, Pietro
name: Haslhofer, Bernhard
name: Strohmaier, Markus
Publisher: name: arXiv
Year: 2023
Subjects: subject: Social and Information Networks (cs.SI)
subject: Cryptography and Security (cs.CR)
subject: FOS: Computer and information sciences

description: We present a study analyzing the voting behavior of contributors, or vested users, in Decentralized Autonomous Organizations (DAOs). We evaluate their involvement in decision-making processes, discovering that in at least 7.54% of all DAOs, contributors, on average, held the necessary majority to control governance decisions. Furthermore, contributors have singularly decided at least one proposal in 20.41% of DAOs. Notably, contributors tend to be centrally positioned within the DAO governance ecosystem, suggesting the presence of inner power circles. Additionally, we observed a tendency for shifts in governance token ownership shortly before governance polls take place in 1202 (14.81%) of 8116 evaluated proposals. Our findings highlight the central role of contributors across a spectrum of DAOs, including Decentralized Finance protocols. Our research also offers important empirical insights pertinent to ongoing regulatory activities aimed at increasing transparency to DAO governance frameworks.
Interacting nodal semimetals with non-linear bands
Creators: name: Poli, Arianna
name: Wagner, Niklas
name: Fischer, Max
name: Toschi, Alessandro
name: Sangiovanni, Giorgio
name: Ciuchi, Sergio
Publisher: name: arXiv
Year: 2023
Subjects: subject: Strongly Correlated Electrons (cond-mat.str-el)
subject: Mesoscale and Nanoscale Physics (cond-mat.mes-hall)
subject: FOS: Physical sciences

description: We investigate the quasi-particle and transport properties of a model describing interacting Dirac and Weyl semimetals in the presence of local Hubbard repulsion $U$, where we explicitly include a deviation from the linearity of the energy-momentum dispersion through an intermediate-energy scale $Λ$. Our focus lies on the correlated phase of the semimetal. At the nodal point, the renormalization of spectral weight at a fixed temperature $T$ exhibits a weak dependence on $Λ$ but is sensitive to the proximity to the Mott transition. Conversely, the scattering rate of quasi-particles and the resistivity display high-temperature exponents that crucially rely on $Λ$, leading to a crossover towards a conventional Fermi-liquid behaviour at finite T. Finally, by employing the Nernst-Einstein relation for conductivity, we identify a corresponding density crossover as a function of the chemical potential.
description: 18 pages, 19 figures, including appendices
A combined quantum-classical method applied to material design: optimization and discovery of photochromic materials for photopharmacology applications
Creators: name: Gao, Qi
name: Sugawara, Michihiko
name: Nation, Paul D.
name: Kobayashi, Takao
name: Ohnishi, Yu-ya
name: Tezuka, Hiroyuki
name: Yamamoto, Naoki
Publisher: name: arXiv
Year: 2023
Subjects: subject: Quantum Physics (quant-ph)
subject: Applied Physics (physics.app-ph)
subject: FOS: Physical sciences

description: Integration of quantum chemistry simulations, machine learning techniques, and optimization calculations is expected to accelerate material discovery by making large chemical spaces amenable to computational study; a challenging task for classical computers. In this work, we develop a combined quantum-classical computing scheme involving the computational-basis Variational Quantum Deflation (cVQD) method for calculating excited states of a general classical Hamiltonian, such as Ising Hamiltonian. We apply this scheme to the practical use case of generating photochromic diarylethene (DAE) derivatives for photopharmacology applications. Using a data set of 384 DAE derivatives quantum chemistry calculation results, we show that a factorization-machine-based model can construct an Ising Hamiltonian to accurately predict the wavelength of maximum absorbance of the derivatives, $λ_{\rm max}$, for a larger set of 4096 DAE derivatives. A 12-qubit cVQD calculation for the constructed Ising Hamiltonian provides the ground and first four excited states corresponding to five DAE candidates possessing large $λ_{\rm max}$. On a quantum simulator, results are found to be in excellent agreement with those obtained by an exact eigensolver. Utilizing error suppression and mitigation techniques, cVQD on a real quantum device produces results with accuracy comparable to the ideal calculations on a simulator. Finally, we show that quantum chemistry calculations for the five DAE candidates provides a path to achieving large $λ_{\rm max}$ and oscillator strengths by molecular engineering of DAE derivatives. These findings pave the way for future work on applying hybrid quantum-classical approaches to large system optimization and the discovery of novel materials.
description: 13pages, 9 figures