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We present a Multimodal Interlaced Transformer (MIT) that jointly considers 2D and 3D data for weakly supervised point cloud segmentation. Research studies have shown that 2D and 3D features are complementary for point cloud segmentation. However, existing methods require extra 2D annotations to achieve 2D-3D information fusion. Considering the high annotation cost of point clouds, effective 2D and 3D feature fusion based on weakly supervised learning is in great demand. To this end, we propose a transformer model with two encoders and one decoder for weakly supervised point cloud segmentation using only scene-level class tags. Specifically, the two encoders compute the self-attended features for 3D point clouds and 2D multi-view images, respectively. The decoder implements interlaced 2D-3D cross-attention and carries out implicit 2D and 3D feature fusion. We alternately switch the roles of queries and key-value pairs in the decoder layers. It turns out that the 2D and 3D features are iteratively enriched by each other. Experiments show that it performs favorably against existing weakly supervised point cloud segmentation methods by a large margin on the S3DIS and ScanNet benchmarks. The project page will be available at https://jimmy15923.github.io/mit_web/.
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https://arxiv.org/abs/2310.12817v2
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Large-scale point cloud generated from 3D sensors is more accurate than its
image-based counterpart. However, it is seldom used in visual pose estimation
due to the difficulty in obtaining 2D-3D image to point cloud correspondences.
In this paper, we propose the 2D3D-MatchNet - an end-to-end deep network
architecture to jointly learn the descriptors for 2D and 3D keypoint from image
and point cloud, respectively. As a result, we are able to directly match and
establish 2D-3D correspondences from the query image and 3D point cloud
reference map for visual pose estimation. We create our Oxford 2D-3D Patches
dataset from the Oxford Robotcar dataset with the ground truth camera poses and
2D-3D image to point cloud correspondences for training and testing the deep
network. Experimental results verify the feasibility of our approach.
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http://arxiv.org/abs/1904.09742v1
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The commonly adopted detect-then-match approach to registration finds difficulties in the cross-modality cases due to the incompatible keypoint detection and inconsistent feature description. We propose, 2D3D-MATR, a detection-free method for accurate and robust registration between images and point clouds. Our method adopts a coarse-to-fine pipeline where it first computes coarse correspondences between downsampled patches of the input image and the point cloud and then extends them to form dense correspondences between pixels and points within the patch region. The coarse-level patch matching is based on transformer which jointly learns global contextual constraints with self-attention and cross-modality correlations with cross-attention. To resolve the scale ambiguity in patch matching, we construct a multi-scale pyramid for each image patch and learn to find for each point patch the best matching image patch at a proper resolution level. Extensive experiments on two public benchmarks demonstrate that 2D3D-MATR outperforms the previous state-of-the-art P2-Net by around $20$ percentage points on inlier ratio and over $10$ points on registration recall. Our code and models are available at https://github.com/minhaolee/2D3DMATR.
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https://arxiv.org/abs/2308.05667v2
|
We presented a 2D/3D MV image registration method based on a Convolutional
Neural Network. Most of the traditional image registration method
intensity-based, which use optimization algorithms to maximize the similarity
between to images. Although these methods can achieve good results for
kilovoltage images, the same does not occur for megavoltage images due to the
lower image quality. Also, these methods most of the times do not present a
good capture range. To deal with this problem, we propose the use of
Convolutional Neural Network. The experiments were performed using a dataset of
50 brain images. The results showed to be promising compared to traditional
image registration methods.
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http://arxiv.org/abs/1811.11816v1
|
This study considers the 3D human pose estimation problem in a single RGB
image by proposing a conditional random field (CRF) model over 2D poses, in
which the 3D pose is obtained as a byproduct of the inference process. The
unary term of the proposed CRF model is defined based on a powerful heat-map
regression network, which has been proposed for 2D human pose estimation. This
study also presents a regression network for lifting the 2D pose to 3D pose and
proposes the prior term based on the consistency between the estimated 3D pose
and the 2D pose. To obtain the approximate solution of the proposed CRF model,
the N-best strategy is adopted. The proposed inference algorithm can be viewed
as sequential processes of bottom-up generation of 2D and 3D pose proposals
from the input 2D image based on deep networks and top-down verification of
such proposals by checking their consistencies. To evaluate the proposed
method, we use two large-scale datasets: Human3.6M and HumanEva. Experimental
results show that the proposed method achieves the state-of-the-art 3D human
pose estimation performance.
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http://arxiv.org/abs/1704.03986v2
|
Action recognition and human pose estimation are closely related but both
problems are generally handled as distinct tasks in the literature. In this
work, we propose a multitask framework for jointly 2D and 3D pose estimation
from still images and human action recognition from video sequences. We show
that a single architecture can be used to solve the two problems in an
efficient way and still achieves state-of-the-art results. Additionally, we
demonstrate that optimization from end-to-end leads to significantly higher
accuracy than separated learning. The proposed architecture can be trained with
data from different categories simultaneously in a seamlessly way. The reported
results on four datasets (MPII, Human3.6M, Penn Action and NTU) demonstrate the
effectiveness of our method on the targeted tasks.
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http://arxiv.org/abs/1802.09232v2
|
Camera localization in 3D LiDAR maps has gained increasing attention due to its promising ability to handle complex scenarios, surpassing the limitations of visual-only localization methods. However, existing methods mostly focus on addressing the cross-modal gaps, estimating camera poses frame by frame without considering the relationship between adjacent frames, which makes the pose tracking unstable. To alleviate this, we propose to couple the 2D-3D correspondences between adjacent frames using the 2D-2D feature matching, establishing the multi-view geometrical constraints for simultaneously estimating multiple camera poses. Specifically, we propose a new 2D-3D pose tracking framework, which consists: a front-end hybrid flow estimation network for consecutive frames and a back-end pose optimization module. We further design a cross-modal consistency-based loss to incorporate the multi-view constraints during the training and inference process. We evaluate our proposed framework on the KITTI and Argoverse datasets. Experimental results demonstrate its superior performance compared to existing frame-by-frame 2D-3D pose tracking methods and state-of-the-art vision-only pose tracking algorithms. More online pose tracking videos are available at \url{https://youtu.be/yfBRdg7gw5M}
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https://arxiv.org/abs/2309.11335v1
|
We study an active Brownian run-and-tumble particle (ABRTP) model, that consists of an active Brownian run state during which the active velocity of the particle diffuses on the unit circle, and a tumble state during which the active velocity is zero, both with exponentially distributed time. Additionally we add a harmonic trap as an external potential. In the appropriate limits the ABRTP model reduces either to the active Brownian particle model, or the run-and-tumble particle model. Using the method of direct integration the equation of motion, pioneered by Kac, we obtain exact moments for the Laplace transform of the time dependent ABRTP, in the presence or absence of a harmonic trap. In addition we estimate the distribution moments with the help of the Chebyshev polynomials. Our results are in excellent agreement with the experiments.
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https://arxiv.org/abs/2504.20352v1
|
In this work, we have employed Monte Carlo calculations to study the Ising model on a 2D additive small-world network with long-range interactions depending on the geometric distance between interacting sites. The network is initially defined by a regular square lattice and with probability $p$ each site is tested for the possibility of creating a long-range interaction with any other site that has not yet received one. Here, we used the specific case where $p=1$, meaning that every site in the network has one long-range interaction in addition to the short-range interactions of the regular lattice. These long-range interactions depend on a power-law form, $J_{ij}=r_{ij}^{-\alpha}$, with the geometric distance $r_{ij}$ between connected sites $i$ and $j$. In current two-dimensional model, we found that mean-field critical behavior is observed only at $\alpha=0$. As $\alpha$ increases, the network size influences the phase transition point of the system, i.e., indicating a crossover behavior. However, given the two-dimensional system, we found the critical behavior of the short-range interaction at $\alpha\approx2$. Thus, the limitation in the number of long-range interactions compared to the globally coupled model, as well as the form of the decay of these interactions, prevented us from finding a regime with finite phase transition points and continuously varying critical exponents in $0<\alpha<2$.
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https://arxiv.org/abs/2409.02033v1
|
As transistor footprint scales down to sub-10 nm regime, the process development for advancing to further technology nodes has encountered slowdowns. Achieving greater functionality within a single chip requires concurrent development at the device, circuit, and system levels. Reconfigurable transistors possess the capability to transform into both n-type and p-type transistors dynamically during operation. This transistor-level reconfigurability enables field-programmable logic circuits with fewer components compared to conventional circuits. However, the reconfigurability requires additional polarity control gates in the transistor and potentially impairs the gain from a smaller footprint. In this paper, vertical transistors with ambipolar MoTe2 channels are fabricated using the transfer-metal method. The efficient asymmetric electrostatic gating in source and drain contacts gives rise to different Schottky barriers at the two contacts. Consequently, the ambipolar conduction is reduced to unipolar conduction due to different Schottky barrier widths for electrons and holes. The current flow direction determines the preferred carrier type. Temperature-dependent measurements reveal the Schottky barrier-controlled conduction in the vertical transistors and confirm different Schottky barrier widths with and without electrostatic gating. Without the complexity overhead from polarity control gates, control-free vertical reconfigurable transistors promise higher logic density and lower cost in future integrated circuits.
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https://arxiv.org/abs/2309.08746v1
|
Amodal instance segmentation aims to predict the complete mask of the occluded instance, including both visible and invisible regions. Existing 2D AIS methods learn and predict the complete silhouettes of target instances in 2D space. However, masks in 2D space are only some observations and samples from the 3D model in different viewpoints and thus can not represent the real complete physical shape of the instances. With the 2D masks learned, 2D amodal methods are hard to generalize to new viewpoints not included in the training dataset. To tackle these problems, we are motivated by observations that (1) a 2D amodal mask is the projection of a 3D complete model, and (2) the 3D complete model can be recovered and reconstructed from the occluded 2D object instances. This paper builds a bridge to link the 2D occluded instances with the 3D complete models by 3D reconstruction and utilizes 3D shape prior for 2D AIS. To deal with the diversity of 3D shapes, our method is pretrained on large 3D reconstruction datasets for high-quality results. And we adopt the unsupervised 3D reconstruction method to avoid relying on 3D annotations. In this approach, our method can reconstruct 3D models from occluded 2D object instances and generalize to new unseen 2D viewpoints of the 3D object. Experiments demonstrate that our method outperforms all existing 2D AIS methods.
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https://link.springer.com/chapter/10.1007/978-3-031-19818-2_10
|
Since the appearance of Covid-19 in late 2019, Covid-19 has become an active research topic for the artificial intelligence (AI) community. One of the most interesting AI topics is Covid-19 analysis of medical imaging. CT-scan imaging is the most informative tool about this disease. This work is part of the 3nd COV19D competition for Covid-19 Severity Prediction. In order to deal with the big gap between the validation and test results that were shown in the previous version of this competition, we proposed to combine the prediction of 2D and 3D CNN predictions. For the 2D CNN approach, we propose 2B-InceptResnet architecture which consists of two paths for segmented lungs and infection of all slices of the input CT-scan, respectively. Each path consists of ConvLayer and Inception-ResNet pretrained model on ImageNet. For the 3D CNN approach, we propose hybrid-DeCoVNet architecture which consists of four blocks: Stem, four 3D-ResNet layers, Classification Head and Decision layer. Our proposed approaches outperformed the baseline approach in the validation data of the 3nd COV19D competition for Covid-19 Severity Prediction by 36%.
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https://arxiv.org/abs/2303.08740v1
|
This paper presents new mappings of 2D and 3D geometrical transformation on
the MorphoSys (M1) reconfigurable computing (RC) prototype [2]. This improves
the system performance as a graphics accelerator [1-5]. Three algorithms are
mapped including two for calculating 2D transformations, and one for 3D
transformations. The results presented indicate an improved performance. The
speedup achieved is explained as well as the advantages in the mapping of the
application. The transformations on an 8x8 RC array were run, and numerical
examples were simulated to validate our results, using the MorphoSys mULATE
program, which simulates MorphoSys operations. Comparisons with other systems
are presented, namely, with Intel processing systems and Celoxica RC-1000 FPGA.
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http://arxiv.org/abs/1904.12609v1
|
The following convective Brinkman-Forchheimer (CBF) equations (or damped Navier-Stokes equations) with potential \begin{equation*} \frac{\partial \boldsymbol{y}}{\partial t}-\mu \Delta\boldsymbol{y}+(\boldsymbol{y}\cdot\nabla)\boldsymbol{y}+\alpha\boldsymbol{y}+\beta|\boldsymbol{y}|^{r-1}\boldsymbol{y}+\nabla p+\Psi(\boldsymbol{y})\ni\boldsymbol{g},\ \nabla\cdot\boldsymbol{y}=0, \end{equation*} in a $d$-dimensional torus is considered in this work, where $d\in\{2,3\}$, $\mu,\alpha,\beta>0$ and $r\in[1,\infty)$. For $d=2$ with $r\in[1,\infty)$ and $d=3$ with $r\in[3,\infty)$ ($2\beta\mu\geq 1$ for $d=r=3$), we establish the existence of \textsf{\emph{a unique global strong solution}} for the above multi-valued problem with the help of the \textsf{\emph{abstract theory of $m$-accretive operators}}. %for nonlinear differential equations of accretive type in Banach spaces. Moreover, we demonstrate that the same results hold \textsf{\emph{local in time}} for the case $d=3$ with $r\in[1,3)$ and $d=r=3$ with $2\beta\mu<1$. We explored the $m$-accretivity of the nonlinear as well as multi-valued operators, Yosida approximations and their properties, and several higher order energy estimates in the proofs. For $r\in[1,3]$, we {quantize (modify)} the Navier-Stokes nonlinearity $(\boldsymbol{y}\cdot\nabla)\boldsymbol{y}$ to establish the existence and uniqueness results, while for $r\in[3,\infty)$ ($2\beta\mu\geq1$ for $r=3$), we handle the Navier-Stokes nonlinearity by the nonlinear damping term $\beta|\boldsymbol{y}|^{r-1}\boldsymbol{y}$. Finally, we discuss the applications of the above developed theory in feedback control problems like flow invariance, time optimal control and stabilization.
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https://arxiv.org/abs/2301.01527v2
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Objective: Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' representation and discrimination capacity regarding GC, via three tasks. Methods: Four-center 539 GC patients were retrospectively enrolled and divided into the training and validation cohorts. From 2D or 3D regions of interest (ROIs) annotated by radiologists, radiomic features were extracted respectively. Feature selection and model construction procedures were customed for each combination of two modalities (2D or 3D) and three tasks. Subsequently, six machine learning models (Model_2D^LNM, Model_3D^LNM; Model_2D^LVI, Model_3D^LVI; Model_2D^pT, Model_3D^pT) were derived and evaluated to reflect modalities' performances in characterizing GC. Furthermore, we performed an auxiliary experiment to assess modalities' performances when resampling spacing is different. Results: Regarding three tasks, the yielded areas under the curve (AUCs) were: Model_2D^LNM's 0.712 (95% confidence interval, 0.613-0.811), Model_3D^LNM's 0.680 (0.584-0.775); Model_2D^LVI's 0.677 (0.595-0.761), Model_3D^LVI's 0.615 (0.528-0.703); Model_2D^pT's 0.840 (0.779-0.901), Model_3D^pT's 0.813 (0.747-0.879). Moreover, the auxiliary experiment indicated that Models_2D are statistically more advantageous than Models3D with different resampling spacings. Conclusion: Models constructed with 2D radiomic features revealed comparable performances with those constructed with 3D features in characterizing GC. Significance: Our work indicated that time-saving 2D annotation would be the better choice in GC, and provided a related reference to further radiomics-based researches.
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https://arxiv.org/abs/2210.16640v1
|
Parkinson's Disease (PD) diagnosis remains challenging. This study applies Convolutional Kolmogorov-Arnold Networks (ConvKANs), integrating learnable spline-based activation functions into convolutional layers, for PD classification using structural MRI. The first 3D implementation of ConvKANs for medical imaging is presented, comparing their performance to Convolutional Neural Networks (CNNs) and Graph Convolutional Networks (GCNs) across three open-source datasets. Isolated analyses assessed performance within individual datasets, using cross-validation techniques. Holdout analyses evaluated cross-dataset generalizability by training models on two datasets and testing on the third, mirroring real-world clinical scenarios. In isolated analyses, 2D ConvKANs achieved the highest AUC of 0.99 (95% CI: 0.98-0.99) on the PPMI dataset, outperforming 2D CNNs (AUC: 0.97, p = 0.0092). 3D models showed promise, with 3D CNN and 3D ConvKAN reaching an AUC of 0.85 on PPMI. In holdout analyses, 3D ConvKAN demonstrated superior generalization, achieving an AUC of 0.85 on early-stage PD data. GCNs underperformed in 2D but improved in 3D implementations. These findings highlight ConvKANs' potential for PD detection, emphasize the importance of 3D analysis in capturing subtle brain changes, and underscore cross-dataset generalization challenges. This study advances AI-assisted PD diagnosis using structural MRI and emphasizes the need for larger-scale validation.
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https://arxiv.org/abs/2407.17380v2
|
We present multidimensional modeling of convection and oscillations in
main-sequence stars somewhat more massive than the Sun, using three separate
approaches: 1) Using the 3-D planar StellarBox radiation hydrodynamics code to
model the envelope convection zone and part of the radiative zone. Our goals
are to examine the interaction of stellar pulsations with turbulent convection
in the envelope, excitation of acoustic modes, and the role of convective
overshooting; 2) Applying the spherical 3-D MHD ASH (Anelastic Spherical
Harmonics) code to simulate the core convection and radiative zone. Our goal is
to determine whether core convection can excite low-frequency gravity modes,
and thereby explain the presence of low frequencies for some hybrid gamma
Doradus/delta Scuti variables for which the envelope convection zone is too
shallow for the convective blocking mechanism to drive gravity modes; 3)
Applying the ROTORC 2-D stellar evolution and dynamics code to calculate
evolution with a variety of initial rotation rates and extents of core
convective overshooting. The nonradial adiabatic pulsation frequencies of these
nonspherical models are calculated using the 2-D pulsation code NRO. We present
new insights into pulsations for stars of one to two solar masses gained by
multidimensional modeling.
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http://arxiv.org/abs/1605.04455v1
|
Collagen fiber orientations in bones, visible with Second Harmonic Generation (SHG) microscopy, represent the inner structure and its alteration due to influences like cancer. While analyses of these orientations are valuable for medical research, it is not feasible to analyze the needed large amounts of local orientations manually. Since we have uncertain borders for these local orientations only rough regions can be segmented instead of a pixel-wise segmentation. We analyze the effect of these uncertain borders on human performance by a user study. Furthermore, we compare a variety of 2D and 3D methods such as classical approaches like Fourier analysis with state-of-the-art deep neural networks for the classification of local fiber orientations. We present a general way to use pretrained 2D weights in 3D neural networks, such as Inception-ResNet-3D a 3D extension of Inception-ResNet-v2. In a 10 fold cross-validation our two stage segmentation based on Inception-ResNet-3D and transferred 2D ImageNet weights achieves a human comparable accuracy.
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https://arxiv.org/abs/1907.12868v1
|
Recently, it was shown that quantum spin Hall insulator (QSHI) phase with a
gap wide enough for practical applications can be realized in the ultra thin
films constructed from two inversely stacked structural elements of trivial
band insulator BiTeI. Here, we study the edge states in the free-standing
Bi$_2$Te$_2$I$_2$ sextuple layer (SL) and the electronic structure of the
Bi$_2$Te$_2$I$_2$ SL on the natural BiTeI substrate. We show that the
topological properties of the Bi$_2$Te$_2$I$_2$ SL on this substrate keep
$\mathbb Z_2$ invariant. We also demonstrate that ultra thin centrosymmetric
films constructed in the similar manner but from related material BiTeBr are
trivial band insulators up to five-SL film thickness. In contrast to
Bi$_2$Te$_2$I$_2$ for which the stacking of nontrivial SLs in 3D limit gives a
strong topological insulator (TI) phase, strong TI is realized in 3D
Bi$_2$Te$_2$Br$_2$ in spite of the SL is trivial. For the last material of the
BiTe$X$ ($X$=I,Br,Cl) series, BiTeCl, both 2D and 3D centrosymmetric phases are
characterized by topologically trivial band structure.
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http://arxiv.org/abs/1706.08127v1
|
The detection of vascular structures from noisy images is a fundamental
process for extracting meaningful information in many applications. Most
well-known vascular enhancing techniques often rely on Hessian-based filters.
This paper investigates the feasibility and deficiencies of detecting
curve-like structures using a Hessian matrix. The main contribution is a novel
enhancement function, which overcomes the deficiencies of established methods.
Our approach has been evaluated quantitatively and qualitatively using
synthetic examples and a wide range of real 2D and 3D biomedical images.
Compared with other existing approaches, the experimental results prove that
our proposed approach achieves high-quality curvilinear structure enhancement.
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http://arxiv.org/abs/1902.00550v1
|
We challenge two foundational principles of localization physics by analyzing conductance fluctuations in two dimensions with unprecedented precision: (i) the Thouless criterion, which defines localization as insensitivity to boundary conditions, and (ii) that symmetry determines the universality class of Anderson localization. We reveal that the fluctuations of the conductance logarithm fall into distinct sub-universality classes inherited from Kardar-Parisi-Zhang (KPZ) physics, dictated by the lead configurations of the scattering system and unaffected by the presence of a magnetic field. Distinguishing between these probability distributions poses a significant challenge due to their striking similarity, requiring sampling beyond the usual threshold of $\sim 10^{-6}$ accessible through independent disorder realizations. To overcome this, we implement an importance sampling scheme - a Monte Carlo approach in disorder space - that enables us to probe rare disorder configurations and sample probability distribution tails down to $10^{-30}$. This unprecedented precision allows us to unambiguously differentiate between KPZ sub-universality classes of conductance fluctuations for different lead configurations, while demonstrating the insensitivity to magnetic fields.
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https://arxiv.org/abs/2504.17010v1
|
Poor tissue visualization and quantitative accuracy in CBCT is a major barrier in expanding the role of CBCT imaging from target localization to quantitative treatment monitoring and plan adaptations in radiation therapy sessions. To further improve image quality in CBCT, 2D antiscatter grid based scatter rejection was combined with a raw data processing pipeline and iterative image reconstruction. The culmination of these steps was referred as quantitative CBCT, qCBCT. qCBCT data processing steps include 2D antiscatter grid implementation, measurement based residual scatter, image lag, and beam hardening correction for offset detector geometry CBCT with a bow tie filter. Images were reconstructed with iterative image reconstruction to reduce image noise. To evaluate image quality, qCBCT acquisitions were performed using a variety of phantoms to investigate the effect of object size and its composition on image quality. qCBCT image quality was benchmarked against clinical CBCT and MDCT images. Addition of image lag and beam hardening correction to scatter suppression reduced HU degradation in qCBCT by 10 HU and 40 HU, respectively. When compared to gold standard MDCT, mean HU errors in qCBCT and clinical CBCT were 10 HU and 27 HU, respectively. HU inaccuracy due to change in phantom size was 22 HU and 85 HU in qCBCT and clinical CBCT images, respectively. With iterative reconstruction, contrast to noise ratio improved by a factor of 1.25 when compared to clinical CBCT protocols. Robust artifact and noise suppression in qCBCT images can reduce the image quality gap between CBCT and MDCT, improving the promise of qCBCT in fulfilling the tasks that demand high quantitative accuracy, such as CBCT based dose calculations and treatment response assessment in image guided radiation therapy.
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https://arxiv.org/abs/2308.09095v1
|
The freshness of sensor data is critical for all types of cyber-physical systems. An established measure for quantifying data freshness is the Age-of-Information (AoI), which has been the subject of extensive research. Recently, there has been increased interest in multi-sensor systems: redundant sensors producing samples of the same physical process, sensors such as cameras producing overlapping views, or distributed sensors producing correlated samples. When the information from a particular sensor is outdated, fresh samples from other correlated sensors can be helpful. To quantify the utility of distant but correlated samples, we put forth a two-dimensional (2D) model of AoI that takes into account the sensor distance in an age-equivalent representation. Since we define 2D-AoI as equivalent to AoI, it can be readily linked to existing AoI research, especially on parallel systems. We consider physical phenomena modeled as spatio-temporal processes and derive the 2D-AoI for different Gaussian correlation kernels. For a basic exponential product kernel, we find that spatial distance causes an additive offset of the AoI, while for other kernels the effects of spatial distance are more complex and vary with time. Using our methodology, we evaluate the 2D-AoI of different spatial topologies and sensor densities.
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https://arxiv.org/abs/2412.12789v1
|
Irregular scene text, which has complex layout in 2D space, is challenging to most previous scene text recognizers. Recently, some irregular scene text recognizers either rectify the irregular text to regular text image with approximate 1D layout or transform the 2D image feature map to 1D feature sequence. Though these methods have achieved good performance, the robustness and accuracy are still limited due to the loss of spatial information in the process of 2D to 1D transformation. Different from all of previous, we in this paper propose a framework which transforms the irregular text with 2D layout to character sequence directly via 2D attentional scheme. We utilize a relation attention module to capture the dependencies of feature maps and a parallel attention module to decode all characters in parallel, which make our method more effective and efficient. Extensive experiments on several public benchmarks as well as our collected multi-line text dataset show that our approach is effective to recognize regular and irregular scene text and outperforms previous methods both in accuracy and speed.
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https://arxiv.org/abs/1906.05708v1
|
In two-dimensional space, we consider a system of $N$ anyons interacts via a short range attractive two-body interaction. In the stable regime, we derive the average-field Pauli functional as the mean-field limit of many-body quantum mechanics. Furthermore, we investigate the collapse phenomenon in the collapse regime where the strength of attractions tends to a critical value (defined by the cubic NLS equation) while simultaneously considering the weak field regime where the strength of the self-generated magnetic field tends to zero.
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https://arxiv.org/abs/2409.00409v1
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In this paper we will show that one can summarize the major two particle
reaction plane azimuthal correlations for Au + Au mid-central collisions at
$\sqrt{s_{NN}} =$ 200 GeV by defining a 2D azimuthal space which is a summary
of the event by event average.
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http://arxiv.org/abs/1910.07597v1
|
In the era of tensions, when precision cosmology is blooming, numerous new theoretical models are emerging. However, it's crucial to pause and question the extent to which the observational data we rely on are model-dependent. In this work, we study the comoving position of the acoustic peak, a cornerstone standard ruler in cosmology. We considered BAO observational datasets from two distinct teams and calculated the product $hr_d$ with the help of each BAO data set along with SN I-a data from the Pantheon Plus sample. Our conclusion at present is that 2D and 3D BAO datasets are compatible with each other. Considering, no systematics in BAO, interpreting $\Omega_{m0}-hr_d$ plane may require physics beyond $\Lambda$CDM not just while using observational BAO data but also while observing it.
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https://arxiv.org/abs/2406.05453v1
|
Ordinary 3D Baryon Acoustic Oscillations (BAO) data are model-dependent, requiring the assumption of a cosmological model to calculate comoving distances during data reduction. Throughout the present-day literature, the assumed model is $\Lambda$CDM. However, it has been pointed out in several recent works that this assumption can be inadequate when analyzing alternative cosmologies, potentially biasing the Hubble constant ($H_0$) low, thus contributing to the Hubble tension. To address this issue, 3D BAO data can be replaced with 2D BAO data, which is only weakly model-dependent. The impact of using 2D BAO data, in combination with alternative cosmological models beyond $\Lambda$CDM, has been explored for several phenomenological models, showing a promising reduction in the Hubble tension. In this work, we accommodate these models in the theoretically robust framework of bimetric gravity. This is a modified theory of gravity that exhibits a transition from a (possibly) negative cosmological constant in the early universe to a positive one in the late universe. By combining 2D BAO data with cosmic microwave background and type Ia supernovae data, we find that the inverse distance ladder in this theory yields a Hubble constant of $H_0 = (71.0 \pm 0.9) \, \mathrm{km/s/Mpc}$, consistent with the SH0ES local distance ladder measurement of $H_0 = (73.0 \pm 1.0) \, \mathrm{km/s/Mpc}$. Replacing 2D BAO with 3D BAO results in $H_0 = (68.6 \pm 0.5) \, \mathrm{km/s/Mpc}$ from the inverse distance ladder. Thus, the choice of BAO data significantly impacts the Hubble tension, with ordinary 3D BAO data exacerbating the tension, while 2D BAO data provides results consistent with the local distance ladder.
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https://arxiv.org/abs/2407.04322v3
|
Basement relief gravimetry is crucial in geophysics, especially for oil exploration and mineral prospecting. It involves solving an inverse problem to infer geological model parameters from observed data. The model represents basement relief with constant-density prisms, and the data reflect gravitational anomalies from these prisms. Inverse problems are often ill-posed, meaning small data changes can lead to large solution variations. To mitigate this, regularization techniques like Tikhonov's are used to stabilize solutions. This study compares regularization methods applied to gravimetric inversion, including Smoothness Constraints, Total Variation, Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT) using Daubechies D4 wavelets. Optimization, particularly with Genetic Algorithms (GA), is used to find prism depths that best match observed anomalies. GA, inspired by natural selection, selects the best solutions to minimize the objective function. The results, evaluated through fit metrics and error analysis, show the effectiveness of all regularization methods and GA, with the Smoothness constraint performing best in synthetic models. For the real data model, all methods performed similarly.
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https://arxiv.org/abs/2410.14942v1
|
We present a novel algorithm based on a Bayesian method for 2D tilted-ring analysis of disk galaxy velocity fields. Compared to the conventional algorithms based on a chi-squared minimisation procedure, this new Bayesian-based algorithm suffers less from local minima of the model parameters even with highly multi-modal posterior distributions. Moreover, the Bayesian analysis, implemented via Markov Chain Monte Carlo (MCMC) sampling, only requires broad ranges of posterior distributions of the parameters, which makes the fitting procedure fully automated. This feature will be essential when performing kinematic analysis on the large number of resolved galaxies expected to be detected in neutral hydrogen (HI) surveys with the Square Kilometre Array (SKA) and its pathfinders. The so-called '2D Bayesian Automated Tilted-ring fitter' (2DBAT) implements Bayesian fits of 2D tilted-ring models in order to derive rotation curves of galaxies. We explore 2DBAT performance on (a) artificial HI data cubes built based on representative rotation curves of intermediate-mass and massive spiral galaxies, and (b) Australia Telescope Compact Array (ATCA) HI data from the Local Volume HI Survey (LVHIS). We find that 2DBAT works best for well-resolved galaxies with intermediate inclinations (20 deg < i < 70 deg), complementing three-dimensional techniques better suited to modelling inclined galaxies.
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https://arxiv.org/abs/1709.02049v1
|
In this paper, we investigate the beam domain statistical channel state information (CSI) estimation for the two dimensional (2D) beam based statistical channel model (BSCM) in massive MIMO systems.The problem is to estimate the beam domain channel power matrices (BDCPMs) based on multiple receive pilot signals. A receive model shows the relation between the statistical property of the receive pilot signals and the BDCPMs is derived from the 2D-BSCM. On the basis of the receive model,we formulate an optimization problem with the Kullback-Leibler (KL) divergence. By solving the optimization problem, a novel method to estimate the statistical CSI without involving instantaneous CSI is proposed. The proposed method has much lower complexity than the MMV focal underdetermined system solver (M-FOCUSS) algorithm. We further reduce the complexity of the proposed method by utilizing the circulant structures of particular matrices in the algorithm. We also showed the generality of the proposed method by introducing another application. Simulations results show that the proposed method works well and bring significant performance gain when used in channel estimation.
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https://arxiv.org/abs/2207.04695v1
|
Many emerging reconfigurable optical systems are limited by routing complexity when producing dynamic, two-dimensional (2D) electric fields. Using a gradient-based inverse designed, static phase-mask doublet, we propose an optical system to produce 2D intensity wavefronts using a one-dimensional (1D) intensity Spatial Light Modulator (SLM). We show the capability of mapping each point in a 49 element 1D array to a distinct 7x7 2D spatial distribution. Our proposed method will significantly relax the routing complexity of 2D sub-wavelength SLMs, possibly enabling next-generation SLMs to leverage novel pixel architectures and new materials.
|
https://arxiv.org/abs/2101.04085v1
|
We report a 2D Boundary Element Method (BEM) modeling of the thermal
diffusion-controlled growth of a vapor bubble attached to a heating surface
during saturated pool boiling. The transient heat conduction problem is solved
in a liquid that surrounds a bubble with a free boundary and in a semi-infinite
solid heater. The heat generated homogeneously in the heater causes
evaporation, i. e. the bubble growth. A singularity exists at the point of the
triple (liquid-vapor-solid) contact. At high system pressure the bubble is
assumed to grow slowly, its shape being defined by the surface tension and the
vapor recoil force, a force coming from the liquid evaporating into the bubble.
It is shown that at some typical time the dry spot under the bubble begins to
grow rapidly under the action of the vapor recoil. Such a bubble can eventually
spread into a vapor film that can separate the liquid from the heater, thus
triggering the boiling crisis (Critical Heat Flux phenomenon).
|
http://arxiv.org/abs/1601.07196v1
|
Abnormal behavior detection, action recognition, fight and violence detection in videos is an area that has attracted a lot of interest in recent years. In this work, we propose an architecture that combines a Bidirectional Gated Recurrent Unit (BiGRU) and a 2D Convolutional Neural Network (CNN) to detect violence in video sequences. A CNN is used to extract spatial characteristics from each frame, while the BiGRU extracts temporal and local motion characteristics using CNN extracted features from multiple frames. The proposed end-to-end deep learning network is tested in three public datasets with varying scene complexities. The proposed network achieves accuracies up to 98%. The obtained results are promising and show the performance of the proposed end-to-end approach.
|
https://arxiv.org/abs/2409.07588v1
|
We report two surprising results on $\alpha'$ corrections in string theory restricted to massless fields. First, for critical dimension Bianchi type I cosmologies with $q$ scale factors only $q-1$ of them have non-trivial $\alpha'$ corrections. In particular, for FRW backgrounds all $\alpha'$ corrections are trivial. Second, in non-critical dimensions, all terms in the spacetime action other than the cosmological term are field redefinition equivalent to terms with arbitrarily many derivatives, with the latter generally of the same order. Assuming an $\alpha'$ expansion with coefficients that fall off sufficiently fast, we consider field redefinitions consistent with this fall-off and classify the higher derivative terms for two-dimensional string theory with one timelike isometry. This most general duality-invariant theory permits black-hole solutions, and we provide perturbative and non-perturbative tools to explore them.
|
https://arxiv.org/abs/2304.06763v2
|
The allotropes of a new layered material, phosphorus carbide (PC), have been
predicted recently and a few of these predicted structures have already been
successfully fabricated. Herein, by using first-principles calculations we
investigated the effects of rippling a PC monolayer, one of the most stable
modifications of layered PC, under large compressive strains. Similar to
phosphorene, layered PC was found to have the extraordinary ability to bend and
form ripples with large curvatures under a sufficiently large strain applied
along its armchair direction. The band gap size, workfunction, and Young's
modulus of rippled PC monolayer are predicted to be highly tunable by strain
engineering. Moreover, a direct-indirect band gap transition is observed under
the compressive strains in a range from 6 to 11%. Another important feature of
PC monolayer rippled along the armchair direction is the possibility of its
rolling to a PC nanotube (PCNT) under extreme compressive strains. These tubes
of different sizes exhibit high thermal stability, possess a comparably high
Young's modulus, and a well tunable band gap which can vary from 0 to 0.95 eV.
In addition, for both structures, rippled PC and PCNTs, we have explained the
changes in their properties under compressive strain in terms of the
modification of their structural parameters.
|
http://arxiv.org/abs/2002.08093v1
|
We present a new geminal product wave function ansatz where the geminals are not constrained to be strongly orthogonal nor to be of seniority zero. Instead, we introduce weaker orthogonality constraints between geminals which significantly lower the computational effort, without sacrificing the indistinguishability of the electrons. That is to say, the electron pairs corresponding to the geminals are not fully distinguishable, and their product has still to be antisymmetrized according to the Pauli principle to form a \textit{bona fide} electronic wave function.Our geometrical constraints translate into simple equations involving the traces of products of our geminal matrices. In the simplest non-trivial model, a set of solutions is given by block-diagonal matrices where each block is of size 2x2 and consists of either a Pauli matrix or a normalized diagonal matrix, multiplied by a complex parameter to be optimized. With this simplified ansatz for geminals, the number of terms in the calculation of the matrix elements of quantum observables is considerably reduced. A proof of principle is reported and confirms that the ansatz is more accurate than strongly orthogonal geminal products while remaining computationally affordable.
|
https://arxiv.org/abs/2209.00834v4
|
Multiple dissipative self-assembly protocols designed to create novel structures or to reduce kinetic traps have recently emerged. Specifically, temporal oscillations of particle interactions have been shown effective at both aims, but investigations thus far have focused on systems of simple colloids or their binary mixtures. In this work, we expand our understanding of the effect of temporally oscillating interactions to a two-dimensional coarse-grained viral capsid-like model that undergoes a self-limited assembly. This model includes multiple intrinsic relaxation times due to the internal structure of the capsid subunits and, under certain interaction regimes, proceeds via a two-step nucleation mechanism. We find that oscillations much faster than the local intrinsic relaxation times can be described via a time averaged inter-particle potential across a wide range of interaction strengths, while oscillations much slower than these relaxation times result in structures that adapt to the attraction strength of the current half-cycle. Interestingly, oscillation periods similar to these relaxation times shift the interaction window over which orderly assembly occurs by enabling error correction during the half-cycles with weaker attractions. Our results provide fundamental insights to non-equilibrium self-assembly on temporally variant energy landscapes.
|
https://arxiv.org/abs/2404.11765v2
|
For many automated driving functions, a highly accurate perception of the vehicle environment is a crucial prerequisite. Modern high-resolution radar sensors generate multiple radar targets per object, which makes these sensors particularly suitable for the 2D object detection task. This work presents an approach to detect 2D objects solely depending on sparse radar data using PointNets. In literature, only methods are presented so far which perform either object classification or bounding box estimation for objects. In contrast, this method facilitates a classification together with a bounding box estimation of objects using a single radar sensor. To this end, PointNets are adjusted for radar data performing 2D object classification with segmentation, and 2D bounding box regression in order to estimate an amodal 2D bounding box. The algorithm is evaluated using an automatically created dataset which consist of various realistic driving maneuvers. The results show the great potential of object detection in high-resolution radar data using PointNets.
|
https://arxiv.org/abs/1904.08414v3
|
The Cardy-Rabinovici model is a toy model of the lattice $U(1)$ gauge theories to study various oblique confinement states associated with the nonzero $\theta$ angles. We reformulate the $2$d version of this model using the modified Villain lattice formalism, and we establish the exact $\theta$ periodicity for the Witten effect and the strong-weak duality at the finite lattice spacings. We then study the phase structure of this model based on the duality, symmetry and anomaly, and the perturbative renormalization group.
|
https://arxiv.org/abs/2505.19412v1
|
We study a 2D cellular automaton (CA) model for the evolution of coronal loop
plasmas. The model is based on the idea that coronal loops are made of
elementary magnetic strands that are tangled and stressed by the displacement
of their footpoints by photospheric motions. The magnetic stress accumulated
between neighbor strands is released in sudden reconnection events or
nanoflares that heat the plasma. We combine the CA model with the Enthalpy
Based Thermal Evolution of Loops (EBTEL) model to compute the response of the
plasma to the heating events. Using the known response of the XRT telescope on
board Hinode we also obtain synthetic data. The model obeys easy to understand
scaling laws relating the output (nanoflare energy, temperature, density,
intensity) to the input parameters (field strength, strand length, critical
misalignment angle). The nanoflares have a power-law distribution with a
universal slope of -2.5, independent of the input parameters. The repetition
frequency of nanoflares, expressed in terms of the plasma cooling time,
increases with strand length. We discuss the implications of our results for
the problem of heating and evolution of active region coronal plasmas.
|
http://arxiv.org/abs/1607.03883v1
|
We consider the late time behavior of the analytically continued partition
function $Z(\beta + it) Z(\beta - it)$ in holographic $2d$ CFTs. This is a
probe of information loss in such theories and in their holographic duals. We
show that each Virasoro character decays in time, and so information is not
restored at the level of individual characters. We identify a universal
decaying contribution at late times, and conjecture that it describes the
behavior of generic chaotic $2d$ CFTs out to times that are exponentially large
in the central charge. It was recently suggested that at sufficiently late
times one expects a crossover to random matrix behavior. We estimate an upper
bound on the crossover time, which suggests that the decay is followed by a
parametrically long period of late time growth. Finally, we discuss integrable
theories and show how information is restored at late times by a series of
characters. This hints at a possible bulk mechanism, where information is
restored by an infinite sum over non-perturbative saddles.
|
http://arxiv.org/abs/1611.04592v1
|
In this note we study two-dimensional CFTs at large global charge. Since the large-charge sector decouples from the dynamics, it does not control the dynamics and an EFT construction that works in higher-dimensional theories fails. It is however possible to use large charge in a double-scaling limit when another controlling parameter is present. We find some general features of the spectrum of models that admit an NLSM description in a WKB approximation and use the large-charge sector of the solvable $SU(2)_k$ WZW model to argue the regimes of applicability of both the large-Q expansion and the double-scaling limit.
|
https://arxiv.org/abs/2112.03286v2
|
According to observations and numerical simulations, the Milky Way could exhibit several spiral arm modes with multiple pattern speeds, wherein the slower patterns are located at larger Galactocentric distances. Our aim is to quantify the effects of the spiral arms on the azimuthal variations of the chemical abundances for oxygen, iron and for the first time for neutron-capture elements (europium and barium) in the Galactic disc. We assume a model based on multiple spiral arm modes with different pattern speeds. The resulting model represents an updated version of previous 2D chemical evolution models. We apply new analytical prescriptions for the spiral arms in a 2D Galactic disc chemical evolution model, exploring the possibility that the spiral structure is formed by the overlap of chunks with different pattern speeds and spatial extent. The predicted azimuthal variations in abundance gradients are dependent on the considered chemical element. Elements synthesised on short time scales (i.e., oxygen and europium in this study) exhibit larger abundance fluctuations. In fact, for progenitors with short lifetimes, the chemical elements restored into the ISM perfectly trace the star formation perturbed by the passage of the spiral arms. The map of the star formation rate predicted by our chemical evolution model with multiple patterns of spiral arms presents arcs and arms compatible with those revealed by multiple tracers (young upper main sequence stars, Cepheids, and distribution of stars with low radial actions). Finally, our model predictions are in good agreement with the azimuthal variations that emerged from the analysis of Gaia DR3 GSP-Spec [M/H] abundance ratios, if at most recent times the pattern speeds match the Galactic rotational curve at all radii.
|
https://arxiv.org/abs/2310.11504v1
|
Galactic disc chemical evolution models generally ignore azimuthal surface
density variation that can introduce chemical abundance azimuthal gradients.
Recent observations, however, have revealed chemical abundance changes with
azimuth in the gas and stellar components of both the Milky Way and external
galaxies. To quantify the effects of spiral arm density fluctuations on the
azimuthal variations of the oxygen and iron abundances in disc galaxies. We
develop a new 2D galactic disc chemical evolution model, capable of following
not just radial but also azimuthal inhomogeneities. The density fluctuations
resulting from a Milky Way-like N-body disc formation simulation produce
azimuthal variations in the oxygen abundance gradients of the order of 0.1 dex.
Moreover, in agreement with the most recent observations in external galaxies,
the azimuthal variations are more evident in the outer galactic regions. Using
a simple analytical model, we show that the largest fluctuations with azimuth
result near the spiral structure corotation resonance, where the relative speed
between spiral and gaseous disc is the slowest. In conclusion we provided a new
2D chemical evolution model capable of following azimuthal density variations.
Density fluctuations extracted from a Milky Way-like dynamical model lead to a
scatter in the azimuthal variations of the oxygen abundance gradient in
agreement with observations in external galaxies. We interpret the presence of
azimuthal scatter at all radii by the presence of multiple spiral modes moving
at different pattern speeds, as found in both observations and numerical
simulations.
|
http://arxiv.org/abs/1811.11196v3
|
Despite being invented in 1951 by R. Kikuchi, the 2-D Cluster Variation Method (CVM), has not yet received attention. Nevertheless, this method can usefully characterize 2-D topographies using just two parameters; the activation enthalpy and the interaction enthalpy. This Technical Report presents 2-D CVM details, including the dependence of the various configuration variables on the enthalpy parameters, as well as illustrations of various topographies (ranging from scale-free-like to rich club-like) that result from different parameter selection. The complete derivation for the analytic solution, originally presented simply as a result in Kikuchi and Brush (1967) is given here, along with careful comparison of the analytically-predicted configuration variables versus those obtained when performing computational free energy minimization on a 2-D grid. The 2-D CVM can potentially function as a secondary free energy minimization within the hidden layer of a neural network, providing a basis for extending node activations over time and allowing temporal correlation of patterns.
|
https://arxiv.org/abs/1909.09366v1
|
The coherence factor (CF) is defined as the ratio of coherent power to
incoherent power received by the radar aperture. The incoherent power is
computed by the multi-antenna receiver based on only the spatial variable. In
this respect, it is a one-dimensional (1-D) CF, and thereby the image sidelobes
in down-range cannot be effectively suppressed. We propose a two-dimensional
(2-D) CF by supplementing the 1-D CF by an incoherent sum dealing with the
frequency dimension. In essence, we employ both spatial diversity and frequency
diversity which, respectively, enhance imaging quality in cross range and
range. Simulations and experimental results are provided to demonstrate the
performance advantages of the proposed approach.
|
http://arxiv.org/abs/1903.10119v1
|
The compass model on a square lattice provides a natural template for
building subsystem stabilizer codes. The surface code and the Bacon-Shor code
represent two extremes of possible codes depending on how many gauge qubits are
fixed. We explore threshold behavior in this broad class of local codes by
trading locality for asymmetry and gauge degrees of freedom for stabilizer
syndrome information. We analyze these codes with asymmetric and spatially
inhomogeneous Pauli noise in the code capacity and phenomenological models. In
these idealized settings, we observe considerably higher thresholds against
asymmetric noise. At the circuit level, these codes inherit the bare-ancilla
fault-tolerance of the Bacon-Shor code.
|
http://arxiv.org/abs/1809.01193v2
|
The electron-hole liquid, which features a macroscopic population of
correlated electrons and holes, may offer a path to room temperature
semiconductor devices that harness collective electronic phenomena. We report
on the gas-to-liquid phase transition of electrons and holes in ultrathin
molybdenum ditelluride photocells revealed through multi-parameter dynamic
photoresponse microscopy (MPDPM). By combining rich visualization with
comprehensive analysis of very large data sets acquired through MPDPM, we find
that ultrafast laser excitation at a graphene-MoTe$_2$-graphene interface leads
to the abrupt formation of ring-like spatial patterns in the photocurrent
response as a function of increasing optical power at T = 297 K. These
patterns, together with extreme sublinear power dependence and picosecond-scale
photocurrent dynamics, provide strong evidence for the formation of a
two-dimensional electron-hole condensate.
|
http://arxiv.org/abs/1711.06917v1
|
We study 2D Navier-Stokes equations with a constraint on $L^2$ energy of the
solution. We prove the existence and uniqueness of a global solution for the
constrained Navier-Stokes equation on $\R^2$ and $\T$, by a fixed point
argument. We also show that the solution of constrained Navier-Stokes converges
to the solution of Euler equation as viscosity $\nu$ vanishes.
|
http://arxiv.org/abs/1606.08360v1
|
We present a complete analysis of the problem of convection-diffusion in low
Re, 2-dimensional flows with distributions of singularities, such as those
found in open-space microfluidics and in groundwater flows. Using Boussinesq
transformations and solving the problem in streamline coordinates, we obtain
concentration profiles in flows with complex arrangements of sources and sinks
for both high and low Pe. These yield the complete analytical concentration
profile at every point in applications that previously relied on material
surface tracking, local lump models or numerical analysis such as microfluidic
probes, groundwater heat pumps, or diffusive flows in porous media. Using
conformal transforms, we generate families of symmetrical solutions from simple
ones, and provide a general methodology that can be used to analyze any
arrangement of source and sinks. The solutions obtained that contain the
explicit dependence on the various parameters of the problems, such as Pe, the
spacing of the apertures and their relative injection and aspiration rates. In
particular, we show that the high Pe models can model problems with Pe as low
as 1 with a maximum error committed of under $10\%$, and that this error
decreases approximately as $Pe^{-1.5}$.
|
http://arxiv.org/abs/2003.05818v1
|
IceCube DeepCore is an extension of the IceCube Neutrino Observatory designed to measure GeV scale atmospheric neutrino interactions for the purpose of neutrino oscillation studies. Distinguishing muon neutrinos from other flavors and reconstructing inelasticity are especially difficult tasks at GeV scale energies in IceCube DeepCore due to sparse instrumentation. Convolutional neural networks (CNNs) have been found to have better success at neutrino event reconstruction than conventional likelihood-based methods. In this contribution, we present a new CNN model that exploits time and depth translational symmetry in IceCube DeepCore data and present the model's performance, specifically for flavor identification and inelasticity reconstruction.
|
https://arxiv.org/abs/2307.16373v1
|
Automated methods for breast cancer detection have focused on 2D mammography and have largely ignored 3D digital breast tomosynthesis (DBT), which is frequently used in clinical practice. The two key challenges in developing automated methods for DBT classification are handling the variable number of slices and retaining slice-to-slice changes. We propose a novel deep 2D convolutional neural network (CNN) architecture for DBT classification that simultaneously overcomes both challenges. Our approach operates on the full volume, regardless of the number of slices, and allows the use of pre-trained 2D CNNs for feature extraction, which is important given the limited amount of annotated training data. In an extensive evaluation on a real-world clinical dataset, our approach achieves 0.854 auROC, which is 28.80% higher than approaches based on 3D CNNs. We also find that these improvements are stable across a range of model configurations.
|
https://arxiv.org/abs/2002.12314v1
|
Broadening the knowledge and understanding on the magnetic correlations in
van der Waals layered magnets is critical in realizing their potential
next-generation applications. In this study, we employ high frequency (240 GHz)
electron spin resonance (ESR) spectroscopy on plate-like CrBr$_{3}$ to gain
insight into the magnetic interactions as a function of temperature (200 - 4 K)
and the angle of rotation ${\theta}$. We find that the temperature dependence
of the ESR linewidth is well described by the Ginzberg-Landau critical model as
well as Berezinskii-Kosterlitz-Thouless (BKT) transition model, indicative of
the presence of two-dimensional (2D) correlations. This suggests that the
three-dimensional ferromagnet CrBr$_{3}$, which has been described as an Ising
or Heisenberg ferromagnet, could present 2D magnetic correlations and BKT-like
behavior even in its bulk form; an observation that, to the best of our
knowledge, has not been reported in the literature. Furthermore, our findings
show that the resonance field follows a $(3cos^2{\theta} - 1)$-like angular
dependence, while the linewidth follows a $(3cos^2{\theta} - 1)^2$-like angular
dependence. This observed angular dependence of the resonance field and
linewidth further confirm an unanticipated 2D magnetic behavior in CrBr$_{3}$.
This behavior is likely due to the interaction of the external magnetic field
applied during the ESR experiment that allows for the mediation of long-range
vortex-like correlations between the spin clusters that may have formed due to
magnetic phase separation. This study demonstrates the significance of
employing spin sensitive techniques such as ESR to better understand the
magnetic correlations in similar van der Waals magnets.
|
http://arxiv.org/abs/2010.15342v1
|
Covariant affine integral quantization is studied and applied to the motion of a particle in a punctured plane R^2_\ast=R^2\{0}, for which the phase space is R^2_\ast=R^2\{0}X R^2. We examine the consequences of different quantizer operators built from weight functions on this phase space. To illustrate the procedure, we examine two examples of weights. The first one corresponds to 2-D coherent state families, while the second one corresponds to the affine inversion in the punctured plane. The later yields the usual canonical quantization and a quasi-probability distribution (2-D affine Wigner function) which is real, marginal in both position and momentum.
|
https://arxiv.org/abs/1911.00578v2
|
Scene text recognition has been an important, active research topic in computer vision for years. Previous approaches mainly consider text as 1D signals and cast scene text recognition as a sequence prediction problem, by feat of CTC or attention based encoder-decoder framework, which is originally designed for speech recognition. However, different from speech voices, which are 1D signals, text instances are essentially distributed in 2D image spaces. To adhere to and make use of the 2D nature of text for higher recognition accuracy, we extend the vanilla CTC model to a second dimension, thus creating 2D-CTC. 2D-CTC can adaptively concentrate on most relevant features while excluding the impact from clutters and noises in the background; It can also naturally handle text instances with various forms (horizontal, oriented and curved) while giving more interpretable intermediate predictions. The experiments on standard benchmarks for scene text recognition, such as IIIT-5K, ICDAR 2015, SVP-Perspective, and CUTE80, demonstrate that the proposed 2D-CTC model outperforms state-of-the-art methods on the text of both regular and irregular shapes. Moreover, 2D-CTC exhibits its superiority over prior art on training and testing speed. Our implementation and models of 2D-CTC will be made publicly available soon later.
|
https://arxiv.org/abs/1907.09705v1
|
Aligning large language models with human preferences is crucial for their safe deployment. While Direct Preference Optimization (DPO) offers an efficient alternative to reinforcement learning from human feedback, traditional DPO methods are limited by their reliance on single preference pairs. Recent work like Curriculum-DPO integrates multiple pairs using a one-dimensional difficulty curriculum based on pairwise distinguishability (PD), but overlooks the complexity of the input prompt itself. To address this, we propose 2D-Curri-DPO, a novel framework employing a two-dimensional curriculum that jointly models Prompt Complexity (PC) and Pairwise Distinguishability. This framework introduces dual difficulty metrics to quantify prompt semantic complexity and response preference clarity, defines a curriculum strategy space encompassing multiple selectable strategies for task adaptation, and incorporates a KL-divergence-based adaptive mechanism for dynamic reference model updates to enhance training stability. Comprehensive experiments demonstrate that 2D-Curri-DPO significantly outperforms standard DPO and prior curriculum methods across multiple benchmarks, including MT-Bench, Vicuna Bench, and WizardLM. Our approach achieves state-of-the-art performance on challenging test sets like UltraFeedback. Ablation studies confirm the benefits of the 2D structure and adaptive mechanisms, while analysis provides guidance for strategy selection. These findings demonstrate that effective alignment requires modeling both prompt complexity and pairwise distinguishability, establishing adaptive, multi-dimensional curriculum learning as a powerful and interpretable new paradigm for preference-based language model optimization.
|
https://arxiv.org/abs/2504.07856v2
|
Recently, multi-modality models have been introduced because of the complementary information from different sensors such as LiDAR and cameras. It requires paired data along with precise calibrations for all modalities, the complicated calibration among modalities hugely increases the cost of collecting such high-quality datasets, and hinder it from being applied to practical scenarios. Inherit from the previous works, we not only fuse the information from multi-modality without above issues, and also exhaust the information in the RGB modality. We introduced the 2D Detection Annotations Transmittable Aggregation(\textbf{2DDATA}), designing a data-specific branch, called \textbf{Local Object Branch}, which aims to deal with points in a certain bounding box, because of its easiness of acquiring 2D bounding box annotations. We demonstrate that our simple design can transmit bounding box prior information to the 3D encoder model, proving the feasibility of large multi-modality models fused with modality-specific data.
|
https://arxiv.org/abs/2309.11755v1
|
In this paper we propose a fully automatic 2-stage cascaded approach for
segmentation of liver and its tumors in CT (Computed Tomography) images using
densely connected fully convolutional neural network (DenseNet). We
independently train liver and tumor segmentation models and cascade them for a
combined segmentation of the liver and its tumor. The first stage involves
segmentation of liver and the second stage uses the first stage's segmentation
results for localization of liver and henceforth tumor segmentations inside
liver region. The liver model was trained on the down-sampled axial slices
$(256 \times 256)$, whereas for the tumor model no down-sampling of slices was
done, but instead it was trained on the CT axial slices windowed at three
different Hounsfield (HU) levels. On the test set our model achieved a global
dice score of 0.923 and 0.625 on liver and tumor respectively. The computed
tumor burden had an rmse of 0.044.
|
http://arxiv.org/abs/1802.02182v1
|
We address the problem of 2D particle density control. The particles are immersed in dielectric fluid and acted upon by manipulating an electric field. The electric field is controlled by an array of electrodes and used to bring the particle density to a desired pattern using dielectrophoretic forces. We use a lumped, 2D, capacitive-based, nonlinear model describing the motion of a particle. The spatial dependency of the capacitances is estimated using electrostatic COMSOL simulations. We formulate an optimal control problem, where the loss function is defined in terms of the error between the particle density at some final time and a target density. We use a kernel density estimator (KDE) as a proxy for the true particle density. The KDE is computed using the particle positions that are changed by varying the electrode potentials. We showcase our approach through numerical simulations, where we demonstrate how the particle positions and the electrode potentials vary when shaping the particle positions from a uniform to a Gaussian distribution.
|
https://arxiv.org/abs/2209.03550v1
|
We derive the Weil-Petersson measure on the moduli space of hyperbolic surfaces with defects of arbitrary opening angles and use this to compute its volume. We conjecture a matrix integral computing the corresponding volumes and confirm agreement in simple cases. We combine this mathematical result with the equivariant localization approach to Jackiw-Teitelboim gravity to justify a proposed exact solution of pure 2d dilaton gravity for a large class of dilaton potentials.
|
https://arxiv.org/abs/2304.14948v2
|
The thermodynamic and superfluid properties of the dilute two-dimensional
binary Bose mixture at low temperatures are discussed. We also considered the
problem of the emergence of the long-range order in these systems. All
calculations are performed by means of celebrated Popov's path-integral
approach for the Bose gas with a short-range interparticle potential.
|
http://arxiv.org/abs/1708.00432v2
|
BiSbTeSe$_2$ is a 3D topological insulator (3D-TI) with Dirac type surface states and low bulk carrier density, as donors and acceptors compensate each other. Dominating low temperature surface transport in this material is heralded by Shubnikov-de Haas oscillations and the quantum Hall effect. Here, we experimentally probe and model the electronic density of states (DOS) in thin layers of BiSbTeSe$_2$ by capacitance experiments both without and in quantizing magnetic fields. By probing the lowest Landau levels, we show that a large fraction of the electrons filled via field effect into the system ends up in (localized) bulk states and appears as a background DOS. The surprisingly strong temperature dependence of such background DOS can be traced back to Coulomb interactions. Our results point at the coexistence and intimate coupling of Dirac surface states with a bulk many-body phase (a Coulomb glass) in 3D-TIs.
|
https://arxiv.org/abs/1912.02725v3
|
This work proposes a novel 2-D formation control scheme for acyclic triangulated directed graphs (a class of minimally acyclic persistent graphs) based on bipolar coordinates with (almost) global convergence to the desired shape. Prescribed performance control is employed to devise a decentralized control law that avoids singularities and introduces robustness against external disturbances while ensuring predefined transient and steady-state performance for the closed-loop system. Furthermore, it is shown that the proposed formation control scheme can handle formation maneuvering, scaling, and orientation specifications simultaneously. Additionally, the proposed control law is implementable in agents' arbitrarily oriented local coordinate frames using only low-cost onboard vision sensors, which are favorable for practical applications. Finally, a formation maneuvering simulation study verifies the proposed approach.
|
https://arxiv.org/abs/2108.00916v3
|
We introduce a number of new theoretical approaches and improvements to the thermo-chemical disc modelling code ProDiMo to better predict and analyse the JWST line spectra of protoplanetary discs. We develop a new line escape probability method for disc geometries, a new scheme for dust settling, and discuss how to apply UV molecular shielding factors to photorates in 2D disc geometry. We show that these assumptions are crucial for the determination of the gas heating/cooling rates and discuss how they affect the predicted molecular concentrations and line emissions. We apply our revised 2D models to the protoplanetary disc around the T Tauri star EX Lupi in quiescent state. We calculate infrared line emission spectra between 5 and 20 mic by CO, H2O, OH, CO2, HCN, C2H2 and H2, including lines of atoms and ions, using our full 2D predictions of molecular abundances, dust opacities, gas and dust temperatures. We develop a disc model with a slowly increasing surface density structure around the inner rim that can simultaneously fit the spectral energy distribution, the overall shape of the JWST spectrum of EX Lupi, and the main observed molecular characteristics in terms of column densities, emitting areas and molecular emission temperatures, which all result from one consistent disc model. The spatial structure of the line emitting regions of the different molecules is discussed. High abundances of HCN and C2H2 are caused in the model by stellar X-ray irradiation of the gas around the inner rim.
|
https://arxiv.org/abs/2311.18321v1
|
Two-Dimensional (2D) Discrete Fourier Transform (DFT) is a basic and
computationally intensive algorithm, with a vast variety of applications. 2D
images are, in general, non-periodic, but are assumed to be periodic while
calculating their DFTs. This leads to cross-shaped artifacts in the frequency
domain due to spectral leakage. These artifacts can have critical consequences
if the DFTs are being used for further processing. In this paper we present a
novel FPGA-based design to calculate high-throughput 2D DFTs with simultaneous
edge artifact removal. Standard approaches for removing these artifacts using
apodization functions or mirroring, either involve removing critical
frequencies or a surge in computation by increasing image size. We use a
periodic-plus-smooth decomposition based artifact removal algorithm optimized
for FPGA implementation, while still achieving real-time ($\ge$23 frames per
second) performance for a 512$\times$512 size image stream. Our optimization
approach leads to a significant decrease in external memory utilization thereby
avoiding memory conflicts and simplifies the design. We have tested our design
on a PXIe based Xilinx Kintex 7 FPGA system communicating with a host PC which
gives us the advantage to further expand the design for industrial
applications.
|
http://arxiv.org/abs/1603.05154v1
|
We discuss a discretisation of the de Rham-Hodge theory in the two-dimensional case based on a discrete exterior calculus framework. We present discrete analogues of the Hodge-Dirac and Laplace operators in which key geometric aspects of the continuum counterpart are captured. We provide and prove a discrete version of the Hodge decomposition theorem. Special attention has been paid to discrete models on a combinatorial torus. In this particular case, we also define and calculate the cohomology groups.
|
https://arxiv.org/abs/2202.03923v1
|
In this paper, we introduce a discretization scheme for the Yang-Mills equations in the two-dimensional case using a framework based on discrete exterior calculus. Within this framework, we define discrete versions of the exterior covariant derivative operator and its adjoint, which capture essential geometric features similar to their continuous counterparts. Our focus is on discrete models defined on a combinatorial torus, where the discrete Yang-Mills equations are presented in the form of both a system of difference equations and a matrix form.
|
https://arxiv.org/abs/2405.15315v1
|
MXenes are rapidly emerging two-dimensional (2D) materials with thickness, composition, and functionalization-dependent outstanding properties having applications in diverse fields. To disclose nano-spintronic applications of 2D-double transition metal (DTM) carbide and nitride-based pristine and surface-functionalized MXenes (M'2M"X2Tx, M' and M" = Cr, Mo, W; X = C/N; T = -F/-OH/=O), a systematic investigation has been performed on structural stability, magnetic properties and electronic structure using spin-polarized first-principles calculations. 36 stables functionalized MXenes were screened from 144 explored DTM based MXenes. The explored materials exhibit striking properties, having wide range of magnetic ground states, from non-magnetic to ferromagnetic, and then to antiferromagnetic, accompanied by metallic to half-metallic or gapless half-metallic properties, depending on transition metal(s) and terminating group. Mo and W-based MXenes are found to be nonmagnetic and metallic, whereas Cr-Mo and Cr-W-based MXenes are magnetic with varying metallic behavior. W2CrN2O2 and Mo2CrN2O2 systems are found to be ferromagnetic half-metallic 2D materials with a direct band gap of 1.35 eV and 0.77 eV respectively, in the minority spin channel. The comprehensive study on DTM MXenes, provide intrinsic half-metallic properties along with robust ferromagnetism, opens up a class of promising new 2D materials with tunable magnetic and electronic properties for potential device applications in nano-spintronics and electronics.
|
https://arxiv.org/abs/2211.00846v1
|
Recent advancements in Direct Preference Optimization (DPO) have significantly enhanced the alignment of Large Language Models (LLMs) with human preferences, owing to its simplicity and effectiveness. However, existing methods typically optimize a scalar score or ranking reward, thereby overlooking the multi-dimensional nature of human preferences. In this work, we propose to extend the preference of DPO to two dimensions: segments and aspects. We first introduce a 2D supervision dataset called HelpSteer-2D. For the segment dimension, we divide the response into sentences and assign scores to each segment. For the aspect dimension, we meticulously design several criteria covering the response quality rubrics. With the 2-dimensional signals as feedback, we develop a 2D-DPO framework, decomposing the overall objective into multi-segment and multi-aspect objectives. Extensive experiments on popular benchmarks demonstrate that 2D-DPO performs better than methods that optimize for scalar or 1-dimensional preferences.
|
https://arxiv.org/abs/2410.19720v1
|
In this paper, we present a technique that places 3D bounding boxes around objects in an RGB-D scene. Our approach makes best use of the 2D information to quickly reduce the search space in 3D, benefiting from state-of-the-art 2D object detection techniques. We then use the 3D information to orient, place, and score bounding boxes around objects. We independently estimate the orientation for every object, using previous techniques that utilize normal information. Object locations and sizes in 3D are learned using a multilayer perceptron (MLP). In the final step, we refine our detections based on object class relations within a scene. When compared to state-of-the-art detection methods that operate almost entirely in the sparse 3D domain, extensive experiments on the well-known SUN RGB-D dataset show that our proposed method is much faster (4.1s per image) in detecting 3D objects in RGB-D images and performs better (3 mAP higher) than the state-of-the-art method that is 4.7 times slower and comparably to the method that is two orders of magnitude slower. This work hints at the idea that 2D-driven object detection in 3D should be further explored, especially in cases where the 3D input is sparse.
|
http://openaccess.thecvf.com/content_iccv_2017/html/Lahoud_2D-Driven_3D_Object_ICCV_2017_paper.html
|
We find new $\mathcal{N}=(2,2)$ and $\mathcal{N}=(0,2)$ dualities through the twisted compactifications of 4d supersymmetric theories on $S^2$. Our findings include dualities for both $\mathcal{N}=(2,2)$ and $\mathcal{N}=(0,2)$ non-Abelian gauge theories, as well as $\mathcal{N}=(0,2)$ Gauge/Landau-Ginzburg duality.
|
https://arxiv.org/abs/2407.17350v3
|
Understanding the internal rotation of low mass stars all along their
evolution is of primary interest when studying their rotational dynamics,
internal mixing and magnetic field generation. In this context, helio- and
asteroseismology probe angular velocity gradients deep within solar type stars
at different evolutionary stages. Still the rotation close to the center of
such stars on the main sequence is hardly detectable and the dynamical
interaction of the radiative core with the surface convective envelope is not
well understood. For instance, the influence of the differential rotation
profile sustained by convection and applied as a boundary condition to the
radiation zone is very important in the formation of tachoclines. In this work,
we study a 2D hydrodynamical model of a radiative core when an imposed, solar
or anti-solar, differential rotation is applied at the upper boundary. This
model uses the Boussinesq approximation and we find that the shear induces a
cylindrical differential rotation associated with a unique cell of meridional
circulation in each hemisphere (counterclockwise when the shear is solar-like
and clockwise when it is anti-solar). The results are discussed in the
framework of seismic observables (internal rotation rate, core-to-surface
rotation ratio) while perspectives to improve our modeling by including
magnetic field or transport by internal gravity waves will be discussed.
|
http://arxiv.org/abs/1610.08798v1
|
Power cables have complex geometries in order to reduce their AC resistance. The cross-section of a cable consists of several conductors that are electrically insulated from each other to counteract the current displacement caused by the skin effect. Furthermore, the individual conductors are twisted over the cable's length. This geometry has a non-standard symmetry - a combination of translation and rotation. Exploiting this property allows formulating a dimensionally reduced boundary value problem. Dimension reduction is desirable, otherwise the electromagnetic modeling of these cables becomes impracticable due to tremendous computational efforts. We investigate 2D eddy current boundary value problems which still allow the analysis of 3D effects, such as the twisting of conductor layers.
|
https://arxiv.org/abs/2301.03370v1
|
The interest in single-chip mmWave Radar is driven by their compact form factor, cost-effectiveness, and robustness under harsh environmental conditions. Despite its promising attributes, the principal limitation of mmWave radar lies in its capacity for autonomous yaw rate estimation. Conventional solutions have often resorted to integrating inertial measurement unit (IMU) or deploying multiple radar units to circumvent this shortcoming. This paper introduces an innovative methodology for two-dimensional ego-motion estimation, focusing on yaw rate deduction, utilizing solely mmWave radar sensors. By applying a weighted Iterated Closest Point (ICP) algorithm to register processed points derived from heatmap data, our method facilitates 2D ego-motion estimation devoid of prior information. Through experimental validation, we verified the effectiveness and promise of our technique for ego-motion estimation using exclusively radar data.
|
https://arxiv.org/abs/2404.00830v1
|
A two dimensional electron gas on a cylindrical surface with a screw
dislocation is considered. More precisely, we investigate how both the geometry
and the deformed potential due to a lattice distortion affect the Landau levels
of such system. The case showing the deformed potential can be thought in the
context of 3D common semiconductors where the electrons are confined on a
cylindrical shell. We will show that important quantitative differences exist
due to this lattice distortion. For instance, the effective cyclotron frequency
is diminished by the deformed potential, which in turn enhances the Hall
conductivity.
|
http://arxiv.org/abs/1504.02968v1
|
A two dimensional eigenvalue problem (2DEVP) of a Hermitian matrix pair $(A, C)$ is introduced in this paper. The 2DEVP can be viewed as a linear algebraic formulation of the well-known eigenvalue optimization problem of the parameter matrix $H(\mu) = A - \mu C$. We present fundamental properties of the 2DEVP such as the existence, the necessary and sufficient condition for the finite number of 2D-eigenvalues and variational characterizations. We use eigenvalue optimization problems from the minmax of two Rayleigh quotients and the computation of distance to instability to show their connections with the 2DEVP and new insights of these problems derived from the properties of the 2DEVP.
|
https://arxiv.org/abs/1911.08109v3
|
In Part I of this paper, we introduced a two dimensional eigenvalue problem (2DEVP) of a matrix pair and investigated its fundamental theory such as existence, variational characterization and number of 2D-eigenvalues. In Part II, we proposed a Rayleigh quotient iteration (RQI)-like algorithm (2DRQI) for computing a 2D-eigentriplet of the 2DEVP near a prescribed point, and discussed applications of 2DEVP and 2DRQI for solving the minimax problem of Rayleigh quotients, and computing the distance to instability. In this third part, we present convergence analysis of the 2DRQI. We show that under some mild conditions, the 2DRQI is locally quadratically convergent for computing a nonsingular 2D-eigentriplet.
|
https://arxiv.org/abs/2303.05357v1
|
In Part I of this paper, we introduced a 2D eigenvalue problem (2DEVP) and presented theoretical results of the 2DEVP and its intrinsic connetion with the eigenvalue optimizations. In this part, we devise a Rayleigh quotient iteration (RQI)-like algorithm, 2DRQI in short, for computing a 2D-eigentriplet of the 2DEVP. The 2DRQI performs $2\times$ to $8\times$ faster than the existing algorithms for large scale eigenvalue optimizations arising from the minmax of Rayleigh quotients and the distance to instability of a stable matrix.
|
https://arxiv.org/abs/2209.12040v1
|
SPIDER is the prototype ion source of MITICA, the full-size neutral beam heating system conceived for the ITER tokamak. It includes eight drivers to heat and sustain the inductively coupled plasma (ICP). Owing to their near cylindrical symmetry, the coupling between the radio-frequency (RF) active currents and the source plasma is studied using a 2D electromagnetic approach with simplified expressions for the plasma electrical conductivity taken from the literature. The power absorbed by the plasma and the effect of the induced plasma currents in lowering the inductance of the driver are based on data from the dedicated S16 experimental campaign (y.~2020) of SPIDER: plasma electron densities on the order of $10^{18}$ m$^{-3}$, electron temperatures $\sim 10$ eV; neutral gas pressure $\sim 0.3$ Pa and up to $50$ kW of net power per driver. It is found that the plasma conductivity cannot be explained by the friction forces associated to local collisional processes alone. The inclusion of an effective collisionality associated to non-local processes seems also insufficient to explain the experimental information. Only when the electrical conductivity is reduced where the RF magnetic field is more intense, can the heating power and driver inductance be acceptably reproduced. We present the first 2D electromagnetic ICP calculations in SPIDER for two types of plasma, without and with the addition of a static magnetic field. The power transfer efficiency to the plasma of the first drivers of SPIDER, in view of these models, is around 50%
|
https://arxiv.org/abs/2305.09395v1
|
2D electron density profiles obtained from coherence imaging spectroscopy in different MAST-U divertor conditions are compared. The data includes variations of strike point position, core electron density, and heating power. The improved performance of the long-legged divertors results in a lower electron density and particle flux at the target compared to configurations with smaller strike point major radius, while also being characterized by lower temperatures and deeper detachment. Comparisons against SOLPS simulations generally show good agreement in profile shape along and across the separatrix. The peaking of the electron density downstream of the detachment front is associated with significant neutral drag acting on the plasma flow.
|
https://arxiv.org/abs/2410.00818v1
|
Semiconductor interfaces, such as these existing in multilayer structures
(e.g., quantum wells (QWs)), are interesting because of their ability to form
2D electron gases (2DEGs), in which charge carriers behave completely
differently than they do in the bulk. As an example, in the presence of a
strong magnetic field, the Landau quantization of electronic levels in the 2DEG
results in the quantum Hall effect (QHE), in which Hall conductance is
quantized. This chapter is devoted to the properties of such 2DEGs in
multilayer structures made of compound semiconductors belonging to the class of
Se- and Te-based chalcogenides. In particular, we will also discuss the
interesting question of how the QHE phenomenon is affected by the giant Zeeman
splitting characteristic of II-VI-based diluted magnetic semiconductors (DMSs),
especially when the Zeeman splitting and Landau splitting become comparable. We
will also shortly discuss novel topological phases in chalcogenide multilayers.
|
http://arxiv.org/abs/1905.08703v1
|
The 2D electrons trapped in vacuum near the atomically thin dielectric (ATD, mono- or $N$-layer film of $h$-BN or transition metal dichalcogenide) are considered. ATD is suspended above the back gate and forms the capacitor which is controlled by the biased voltage determining 2D concentration, $n_{2D}$. It is found that the leakage current through ATD is negligible and effect of the polarizability of ATD is weak if $N\leq 5$. At temperatures $T=0.1\div$15 K and $n_{2D}=5\times 10^8\div 10^{10}$ cm$^{-2}$, one deals with the Boltzmann liquid of the macroscopic thickness $\sim$100 A. Due to bending of ATD the quadratic dispersion law of the flexural vibrations is transformed into the linear one at small wave vectors. The scattering processes of the electrons caused by these phonons or the monolayer islands on ATD are examined and the momentum and energy relaxation rates are analyzed based on the corresponding balance equations. The momentum relaxation times varies over orders of magnitude in the above region ($T$, $n_{2D}$) and $N$. The response may changed from the polaron transport, for a perfect single-layer ATD at low $T$ and high $n_{2D}$, to the high-mobility ($\geq 10^7$ cm$^2$/Vs) regime at high $T$ and low $n_{2D}$. The quasi-elastic energy relaxation due to the phonon-induced scattering is considered and the conditions for heating of electrons by a weak in-plane electric field are found.
|
https://arxiv.org/abs/2103.10424v2
|
In the advent of big data era, interactive visualization of large data sets
consisting of M*10^5+ high-dimensional feature vectors of length N (N ~ 10^3+),
is an indispensable tool for data exploratory analysis. The state-of-the-art
data embedding (DE) methods of N-D data into 2-D (3-D) visually perceptible
space (e.g., based on t-SNE concept) are too demanding computationally to be
efficiently employed for interactive data analytics of large and
high-dimensional datasets. Herein we present a simple method, ivhd (interactive
visualization of high-dimensional data tool), which radically outperforms the
modern data-embedding algorithms in both computational and memory loads, while
retaining high quality of N-D data embedding in 2-D (3-D). We show that DE
problem is equivalent to the nearest neighbor nn-graph visualization, where
only indices of a few nearest neighbors of each data sample has to be known,
and binary distance between data samples -- 0 to the nearest and 1 to the other
samples -- is defined. These improvements reduce the time-complexity and memory
load from O(M log M) to O(M), and ensure minimal O(M) proportionality
coefficient as well. We demonstrate high efficiency, quality and robustness of
ivhd on popular benchmark datasets such as MNIST, 20NG, NORB and RCV1.
|
http://arxiv.org/abs/1902.01108v1
|
Partitionings (or segmentations) divide a given domain into disjoint connected regions whose union forms again the entire domain. Multi-dimensional partitionings occur, for example, when analyzing parameter spaces of simulation models, where each segment of the partitioning represents a region of similar model behavior. Having computed a partitioning, one is commonly interested in understanding how large the segments are and which segments lie next to each other. While visual representations of 2D domain partitionings that reveal sizes and neighborhoods are straightforward, this is no longer the case when considering multi-dimensional domains of three or more dimensions. We propose an algorithm for computing 2D embeddings of multi-dimensional partitionings. The embedding shall have the following properties: It shall maintain the topology of the partitioning and optimize the area sizes and joint boundary lengths of the embedded segments to match the respective sizes and lengths in the multi-dimensional domain. We demonstrate the effectiveness of our approach by applying it to different use cases, including the visual exploration of 3D spatial domain segmentations and multi-dimensional parameter space partitionings of simulation ensembles. We numerically evaluate our algorithm with respect to how well sizes and lengths are preserved depending on the dimensionality of the domain and the number of segments.
|
https://arxiv.org/abs/2408.03641v2
|
A recently developed new approach, called ``Empirical Wavelet Transform'', aims to build 1D adaptive wavelet frames accordingly to the analyzed signal. In this paper, we present several extensions of this approach to 2D signals (images). We revisit some well-known transforms (tensor wavelets, Littlewood-Paley wavelets, ridgelets and curvelets) and show that it is possible to build their empirical counterpart. We prove that such constructions lead to different adaptive frames which show some promising properties for image analysis and processing.
|
https://arxiv.org/abs/2410.23533v1
|
Form factors of the energy-momentum tensor (EMT) can be interpreted in certain frames in terms of spatial distributions of energy, stress, linear and angular momentum, based on 2D or 3D Fourier transforms. This interpretation is in general subject to "relativistic recoil corrections", except when the nucleon moves at the speed of light like e.g. in the infinite-momentum frame. We show that it is possible to formulate a large-$N_c$ limit in which the probabilistic interpretation of the nucleon EMT distributions holds also in other frames. We use the bag model formulated in the large-$N_c$ limit as an internally consistent quark model framework to visualize the information content associated with the 2D EMT distributions. In order to provide more intuition, we present results in the physical situation and in three different limits: by considering a heavy-quark limit, a large system-size limit and a constituent-quark limit. The visualizations of the distributions in these extreme limits will help to interpret the results from experiments, lattice QCD, and other models or effective theories.
|
https://arxiv.org/abs/2202.01192v2
|
Introduced independently by Grothendieck and Heller in the 1980s, derivators provide a formal way to study homotopy theories by working in some quotient category such as the homotopy category of a model category. In 2015 Riehl and Verity introduced $\infty$-cosmoi, which are particular $(\infty,2)$-categories where one can develop $(\infty,1)$-category theory in a synthetic way. They noticed that much of the theory of $\infty$-cosmoi can be developed inside a quotient, the homotopy $2$-category. In the following, we begin a program that aims to formalise the $\infty$-cosmological approach to $\infty$-category theory in a derivator-like framework. In this paper we introduce some axioms and demonstrate they hold in a variety of models, including common models related to $\infty$-category theory. We also prove that these axioms are stable under a particular shift operation.
|
https://arxiv.org/abs/2309.05216v1
|
This paper introduces explicit Galois cohomological methods for determining the ranks of Bloch--Kato Selmer groups associated to the Tate twists of the 2-adic second \'etale cohomology of the Jacobian of a hyperelliptic curve with a rational Weierstrass point. In particular, this can give a method to determine the rational points on such curves via the Chabauty--Coleman--Kim method. This is applied to answer a question of Bugeaud, Mignotte, Siksek, Stoll and Tengely.
|
https://arxiv.org/abs/2312.04996v2
|
We give refined methods for proving finiteness of the Chabauty--Coleman--Kim set $X(\mathbb{Q}_2 )_2 $, when $X$ is a hyperelliptic curve with a rational Weierstrass point. The main developments are methods for computing Selmer conditions at $2$ and $\infty$ for the mod 2 Bloch--Kato Selmer group associated to the higher Chow group $\mathrm{CH}^2 (\mathrm{Jac}(X),1)$. As a result we show that most genus 2 curves in the LMFDB of Mordell--Weil rank 2 with exactly one rational Weierstrass point satsify $\# X(\mathbb{Q}_2 )_2 <\infty $. We also obtain a field-theoretic description of second descent on the Jacobian of a hyperelliptic curve (under some conditions).
|
https://arxiv.org/abs/2403.07476v1
|
Imperfect measurement can degrade a quantum error correction scheme. A
solution that restores fault tolerance is to add redundancy to the process of
syndrome extraction. In this work, we show how to optimize this process for an
arbitrary ratio of data qubit error probability to measurement error
probability. The key is to design the measurements so that syndromes that
correspond to different errors are separated by the maximum distance in the
signal space, in close analogy to classical error correction codes. We find
that the mathematical theory of 2-designs, appropriately modified, is the right
tool for this. Analytical and simulation results for the bit-flip code, the
5-qubit code, and the Steane code are presented. The results show that
design-based redundancy protocols show improvement in both cost and performance
relative to conventional fault-tolerant error-correction schemes in situations,
quite important in practice, where measure errors are common. In the near term,
the construction of a fault-tolerant logical qubit with a small number of noisy
physical qubits will benefit from targeted redundancy in syndrome extraction.
|
http://arxiv.org/abs/1907.04497v2
|
Recent research in computational imaging largely focuses on developing machine learning (ML) techniques for image reconstruction, which requires large-scale training datasets consisting of measurement data and ground-truth images. However, suitable experimental datasets for X-ray Computed Tomography (CT) are scarce, and methods are often developed and evaluated only on simulated data. We fill this gap by providing the community with a versatile, open 2D fan-beam CT dataset suitable for developing ML techniques for a range of image reconstruction tasks. To acquire it, we designed a sophisticated, semi-automatic scan procedure that utilizes a highly-flexible laboratory X-ray CT setup. A diverse mix of samples with high natural variability in shape and density was scanned slice-by-slice (5000 slices in total) with high angular and spatial resolution and three different beam characteristics: A high-fidelity, a low-dose and a beam-hardening-inflicted mode. In addition, 750 out-of-distribution slices were scanned with sample and beam variations to accommodate robustness and segmentation tasks. We provide raw projection data, reference reconstructions and segmentations based on an open-source data processing pipeline.
|
https://arxiv.org/abs/2306.05907v1
|
The limit from an Euler type system to the 2D Euler equations with Stratonovich transport noise is investigated. A weak convergence result for the vorticity field and a strong convergence result for the velocity field are proved. Our results aim to provide a stochastic reduction of fluid-dynamics models with three different time scales.
|
https://arxiv.org/abs/2101.03096v1
|
Understanding the excitation spectrum in two-dimensional quantum many-body systems has long been a challenging task. We present an approach by introducing an excitation ansatz based on an infinite matrix product state (MPS) on a helix structure. With the canonical form of MPS states, we can accurately extract key properties such as energy, degeneracy, spectrum weight, and scaling behavior of low-energy excited states simultaneously. To validate the effectiveness of this method, we begin by applying it to the critical point of the transverse-field Ising model. The extracted scaling exponent of the energy gap closely aligns with the conformal bootstrap results. Subsequently, we apply this approach to the $J_1$-$J_2$ Heisenberg model on a square lattice. We discover that the degeneracy of lowest-energy excitations serves as a reliable metric for distinguishing different phases. The phase boundary identified by our method is consistent with some of the previous findings. The present method provides a promising avenue for studying the excitation spectrum of two-dimensional quantum many-body systems.
|
https://arxiv.org/abs/2310.15759v2
|
We present spectral classifications from optical spectroscopy of 263 massive
stars in the north-eastern region of the Large Magellanic Cloud. The observed
two-degree field includes the massive 30 Doradus star-forming region, the
environs of SN1987A, and a number of star-forming complexes to the south of 30
Dor. These are the first classifications for the majority (203) of the stars
and include eleven double-lined spectroscopic binaries. The sample also
includes the first examples of early OC-type spectra (AAOmega 30 Dor 248 and
280), distinguished by the weakness of their nitrogen spectra and by C IV 4658
emission. We propose that these stars have relatively unprocessed CNO
abundances compared to morphologically normal O-type stars, indicative of an
earlier evolutionary phase. From analysis of observations obtained on two
consecutive nights, we present radial-velocity estimates for 233 stars, finding
one apparent single-lined binary and nine (>3sigma) outliers compared to the
systemic velocity; the latter objects could be runaway stars or large-amplitude
binary systems and further spectroscopy is required to investigate their
nature.
|
http://arxiv.org/abs/1508.03490v1
|
We present a new approach for face recognition system. The method is based on
2D face image features using subset of non-correlated and Orthogonal Gabor
Filters instead of using the whole Gabor Filter Bank, then compressing the
output feature vector using Linear Discriminant Analysis (LDA). The face image
has been enhanced using multi stage image processing technique to normalize it
and compensate for illumination variation. Experimental results show that the
proposed system is effective for both dimension reduction and good recognition
performance when compared to the complete Gabor filter bank. The system has
been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and
achieved average recognition rate of 98.9 %.
|
http://arxiv.org/abs/1503.03741v1
|
As 3D perception problems grow in popularity and the need for large-scale labeled datasets for LiDAR semantic segmentation increase, new methods arise that aim to reduce the necessity for dense annotations by employing weakly-supervised training. However these methods continue to show weak boundary estimation and high false negative rates for small objects and distant sparse regions. We argue that such weaknesses can be compensated by using RGB images which provide a denser representation of the scene. We propose an image-guidance network (IGNet) which builds upon the idea of distilling high level feature information from a domain adapted synthetically trained 2D semantic segmentation network. We further utilize a one-way contrastive learning scheme alongside a novel mixing strategy called FOVMix, to combat the horizontal field-of-view mismatch between the two sensors and enhance the effects of image guidance. IGNet achieves state-of-the-art results for weakly-supervised LiDAR semantic segmentation on ScribbleKITTI, boasting up to 98% relative performance to fully supervised training with only 8% labeled points, while introducing no additional annotation burden or computational/memory cost during inference. Furthermore, we show that our contributions also prove effective for semi-supervised training, where IGNet claims state-of-the-art results on both ScribbleKITTI and SemanticKITTI.
|
https://arxiv.org/abs/2311.15605v1
|
An invariant of SPT-phases with on-site finite group $G$ symmetry for two-dimensional Fermion systems was derived in [O]. This invariant is doubled compared to the conjectured one from the invertible quantum field theory. We show that if we require CRT-symmetry (which holds automatically in quantum field theory) in addition, then our invariant reduces to the conjectured one.
|
https://arxiv.org/abs/2212.09038v2
|
We consider a two-dimensional fermion on the strip in the presence of an
arbitrary number of zero-dimensional boundary changing defects. We show that
the theory is still conformal with time dependent stress-energy tensor and that
the allowed defects can be understood as excited spin fields. Finally we
compute correlation functions involving these excited spin fields without using
bosonization.
|
http://arxiv.org/abs/1912.07617v1
|
In the limit of the lattice spacing going to zero, we consider the dimer model on isoradial graphs in the presence of singular $SL(N,\mathbb{C})$ gauge fields flat away from a set of punctures. We consider the cluster expansion of this twisted dimer partition function show it matches an analogous cluster expansion of the 2D Dirac partition function in the presence of this gauge field. The latter is often referred to as a tau function. This reproduces and generalizes various computations of Dub\'edat (J. Eur. Math. Soc. 21 (2019), no. 1, pp. 1-54). In particular, both sides' cluster expansion are matched up term-by-term and each term is shown to equal a sum of a particular holomorphic integral and its conjugate. On the dimer side, we evaluate the terms in the expansion using various exact lattice-level identities of discrete exponential functions and the inverse Kasteleyn matrix. On the fermion side, the cluster expansion leads us to two novel series expansions of tau functions, one involving the Fuschian representation and one involving the monodromy representation.
|
https://arxiv.org/abs/2208.10640v2
|
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