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Van der Waals heterogeneous interfaces are promising candidates for the scaling up of structural superlubricity to meet practical applications. Several factors, however, have been identified that may eliminate superlubricity. Elasticity is one such intrinsic factor, where shear induced lattice reconstruction leads to local interfacial pinning, even at clean pristine contacts. Here, through detailed atomistic simulations, we reveal that incomplete moir\'e tile pinning at the corners and edges of finite sliders dominates friction from the nano- to the microscales. We further demonstrate that slider shape tailoring and twisting allow to control energy dissipation and its scaling with contact size, thus opening the way to achieve large-scale superlubricity.

The mechanisms underlying chemifriction, i.e. the contribution of interfacial bonding to friction in defected twisted graphene interfaces are revealed using fully atomistic machine-learning molecular dynamics simulations. This involves stochastic events of consecutive bond formation and rupture, that are spatially separated but not necessarily independent. A unique shear-induced interlayer atomic transfer healing mechanism is discovered that can be harnessed to design a run-in procedure to restore superlubric sliding. This mechanism should be manifested as negative differential friction coefficients that are expected to emerge under moderate normal loads. A physically motivated phenomenological model is developed to predict the effects of chemifriction in experimentally relevant sliding velocity regimes. This allows us to identify a distinct transition between logarithmic increase and logarithmic decrease of frictional stress with increasing sliding velocity. While demonstrated for homogeneous graphene interfaces, a similar mechanism is expected to occur in other homogeneous or heterogeneous defected two-dimensional material interfaces., Comment: 14 pages, 5 figures

Large-area gratings play a crucial role in various engineering fields. However, traditional interference lithography is limited by the size of optical component apertures, making large-area fabrication a challenging task. Here, a method for fabricating laser interference lithography pattern arrays with a global alignment reference strategy is proposed. This approach enables alignment of each area of the laser interference lithography pattern arrays, including phase, period, and tilt angle. Two reference gratings are utilized: one is detached from the substrate, while the other remains fixed to it. To achieve global alignment, the exposure area is adjusted by alternating between moving the beam and the substrate. In our experiment, a 3 $\times$ 3 regions grating array was fabricated, and the $-1$st-order diffraction wavefront measured by the Fizeau interferometer exhibited good continuity. This technique enables effective and efficient alignment with high accuracy across any regions in an interference lithography pattern array on large substrates., Comment: 13 pages, 8 figures

We apply a universal two-zero texture (UTZT) to all mass matrices for matters in their flavour space in SO(10) GUT framework. This texture can be realised by assigning different charge for each family in a $Z_6$ symmetry. By fixing charged fermion masses at their best-fit values, we fit the rest 9 precisely measured observables (three angles and one CP-violating phase in the quark mixing, three angles in the lepton mixing, and two neutrino mass-squared differences) with 8 model parameters. The model fits all data of fermion masses and mixing very well and the leptonic CP-violating phase is predicted in the range $(90^\circ, 230^\circ)$. The model further predicts the right-handed neutrino masses, with the lightest and heaviest of order $10^9$ and $10^{12}$ GeV, respectively. Gauge unification and proton decay have been checked with the assumption of a breaking chain with two intermediate symmetries above the electroweak scale. It indicates that $\alpha_{\rm GUT}$ ranges in (0.022,0.032) as long as the assumption of economical choice of Higgs contents, and $M_{\rm GUT}$ should be bigger than $4.5\times 10^{15}$ GeV to meet the Super-K bound. We show effective mass $m_{ee}$ for neutrinoless double beta decay, which provides us with a possibility to test grand unification with neutrinoless double beta decay experiments., Comment: 21 pages, 3 figures

Deploying DL models on mobile Apps has become ever-more popular. However, existing studies show attackers can easily reverse-engineer mobile DL models in Apps to steal intellectual property or generate effective attacks. A recent approach, Model Obfuscation, has been proposed to defend against such reverse engineering by obfuscating DL model representations, such as weights and computational graphs, without affecting model performance. These existing model obfuscation methods use static methods to obfuscate the model representation, or they use half-dynamic methods but require users to restore the model information through additional input arguments. However, these static methods or half-dynamic methods cannot provide enough protection for on-device DL models. Attackers can use dynamic analysis to mine the sensitive information in the inference codes as the correct model information and intermediate results must be recovered at runtime for static and half-dynamic obfuscation methods. We assess the vulnerability of the existing obfuscation strategies using an instrumentation method and tool, DLModelExplorer, that dynamically extracts correct sensitive model information at runtime. Experiments show it achieves very high attack performance. To defend against such attacks based on dynamic instrumentation, we propose DynaMO, a Dynamic Model Obfuscation strategy similar to Homomorphic Encryption. The obfuscation and recovery process can be done through simple linear transformation for the weights of randomly coupled eligible operators, which is a fully dynamic obfuscation strategy. Experiments show that our proposed strategy can dramatically improve model security compared with the existing obfuscation strategies, with only negligible overheads for on-device models., Comment: Published on Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering (ASE'24)

Automated unit test generation has been widely studied, with Large Language Models (LLMs) recently showing significant potential. Moreover, in the context of unit test generation, these tools prioritize high code coverage, often at the expense of practical usability, correctness, and maintainability. In response, we propose Property-Based Retrieval Augmentation, a novel mechanism that extends LLM-based Retrieval-Augmented Generation (RAG) beyond basic vector, text similarity, and graph-based methods. Our approach considers task-specific context and introduces a tailored property retrieval mechanism. Specifically, in the unit test generation task, we account for the unique structure of unit tests by dividing the test generation process into Given, When, and Then phases. When generating tests for a focal method, we not only retrieve general context for the code under test but also consider task-specific context such as pre-existing tests of other methods, which can provide valuable insights for any of the Given, When, and Then phases. This forms property relationships between focal method and other methods, thereby expanding the scope of retrieval beyond traditional RAG. We implement this approach in a tool called APT, which sequentially performs preprocessing, property retrieval, and unit test generation, using an iterative strategy where newly generated tests guide the creation of subsequent ones. We evaluated APT on 12 open-source projects with 1515 methods, and the results demonstrate that APT consistently outperforms existing tools in terms of correctness, completeness, and maintainability of the generated tests. Moreover, we introduce a novel code-context-aware retrieval mechanism for LLMs beyond general context, offering valuable insights and potential applications for other code-related tasks.

Moir\'e superstructures arising at twisted 2D interfaces have recently attracted the attention of the scientific community due to exotic quantum states and unique mechanical and tribological behaviors that they exhibit. Here, we predict the emergence of chiral distortions in twisted layered interfaces of finite dimensions. This phenomenon originates in intricate interplay between interfacial interactions and contact boundary constraints. A metric termed the fractional chiral area, is introduced to quantify the overall chirality of the moir\'e superstructure and to characterize its spatial distribution. Despite the equilibrium nature of the discovered energetic and structural chirality effects they are shown to be manifested in the twisting dynamics of layered interfaces, which demonstrates a continuous transition from stick-slip to smooth rotation with no external trigger., Comment: Accepted in ACS Nano

In this work, we present a lattice QCD calculation of the Mellin moments of the twist-2 axial-vector generalized parton distribution (GPD), $\widetilde{H}(x,\xi,t)$, at zero skewness, $\xi$, with multiple values of the momentum transfer, $t$. Our analysis employs the short-distance factorization framework on ratio-scheme renormalized quasi-GPD matrix elements. The calculations are based on an $N_f=2+1+1$ twisted mass fermions ensemble with clover improvement, a lattice spacing of $a = 0.093$ fm, and a pion mass of $m_\pi = 260$ MeV. We consider both the iso-vector and iso-scalar cases, utilizing next-to-leading-order perturbative matching while ignoring the disconnected contributions and gluon mixing in the iso-scalar case. For the first time, we determine the Mellin moments of $\widetilde{H}$ up to the fifth order. From these moments, we discuss the quark helicity and orbital angular momentum contributions to the nucleon spin, as well as the spin-orbit correlations of the quarks. Additionally, we perform a Fourier transform over the momentum transfer, which allows us to explore the spin structure in the impact-parameter space., Comment: 17 pages, 13 figures

This paper selected eight totally eclipsing contact binaries for photometric and spectroscopic studies, spectral data were analyzed by ULySS, and photometric data were analyzed using PHOEBE through MCMC sampling. We used two methods to calculate the initial values for running MCMC: one method is a new approach proposed by ourselves to model light curves without spots, while the other method is the genetic algorithm (GA) which can determine physical parameters with spot. Due to the results, these eight targets are all small mass ratio contact binary stars with a mass ratio below 0.25. There are four systems exhibiting O'Connell effect. By adding a dark spot on the primary component, the ideal fitting can be obtained. Meanwhile, it was found that two systems are shallow contact binaries, while the remaining six are moderate contact binaries. An O-C analysis of the eight eclipsing binary stars revealed that seven of them exhibit long-term changes. Four of them display a long-term decreasing trend, while the other three show a long-term increasing trend, and two targets exhibit periodic variations. The decrease in period may be caused by the transfer of matter from the more massive component to the less massive component, while the increase in period may be caused by the transfer of matter from the less massive component to the more massive component. The absolute physical parameters, orbital angular momentum, initial masses, and ages of these eight systems were calculated. Additionally, their mass-luminosity and mass-radius distributions were analyzed., Comment: 20 pages, 9 figures, and 12 tables, accepted by ApJ

Recently, it has been proposed to compute parton distributions from boosted correlators fixed in the Coulomb gauge within the framework of Large-Momentum Effective Theory. This method does not involve Wilson lines and could greatly improve the efficiency and precision of lattice QCD calculations. However, there are concerns about whether the systematic uncertainties from Gribov copies, which correspond to the ambiguity in lattice gauge-fixing, are under control. This work gives an assessment of the Gribov copies' effect in the Coulomb-gauge-fixed quark correlators. We utilize different strategies for the Coulomb-gauge fixing, selecting two different groups of Gribov copies based on the lattice gauge configurations. We test the difference in the resulted spatial quark correlators in the vacuum and a pion state. Our findings indicate that the statistical errors of the matrix elements from both Gribov copies, regardless of the correlation range, decrease proportionally to the square root of the number of gauge configurations. The difference between the strategies does not show statistical significance compared to the gauge noise. This demonstrates that the effect of the Gribov copies can be neglected in the practical lattice calculation of the quark parton distributions.

Liver allograft failure occurs in approximately 20% of liver transplant recipients within five years post-transplant, leading to mortality or the need for retransplantation. Providing an accurate and interpretable model for individualized risk estimation of graft failure is essential for improving post-transplant care. To this end, we introduce the Model for Allograft Survival (MAS), a simple linear risk score that outperforms other advanced survival models. Using longitudinal patient follow-up data from the United States (U.S.), we develop our models on 82,959 liver transplant recipients and conduct multi-site evaluations on 11 regions. Additionally, by testing on a separate non-U.S. cohort, we explore the out-of-distribution generalization performance of various models without additional fine-tuning, a crucial property for clinical deployment. We find that the most complex models are also the ones most vulnerable to distribution shifts despite achieving the best in-distribution performance. Our findings not only provide a strong risk score for predicting long-term graft failure but also suggest that the routine machine learning pipeline with only in-distribution held-out validation could create harmful consequences for patients at deployment., Comment: Accepted at MLHC 2024

In a recent paper, "Non-local Nucleon Matrix Elements in the Rest Frame" (arXiv: 2407.16577), it was demonstrated that the next-to-leading order perturbative theory can describe, to a few percent accuracy, the lattice QCD static nucleon matrix elements of spatial correlators with separations up to 0.6~fm. We argue that perturbative QCD breaks down at such a distance scale after resumming the associated large logarithms, while the ansatz used in the analysis there did not account for resummation or the leading renormalon, both of which significantly affect the convergence of perturbation theory. Besides, we provide an explanation why the ansatz appears to describe the lattice data despite the breakdown of perturbation theory at large distances., Comment: 3 pages, 3 figures; comment on arXiv:2407.16577

Assume that $\alpha>1$ is an algebraic number and $\xi\neq0$ is a real number. We are concerned with the distribution of the fractional parts of the sequence $(\xi \alpha^{n})$. Under various Diophantine conditions on $\xi$ and $\alpha$, we obtain lower bounds on the number $n$ with $1\leq n\leq N $ for which the fractional part of the sequence $(\xi \alpha^{n})_{n\geq1}$ fall into a prescribed region $I\subset [0,1]$, extending several results in the literature. As an application, we show that the Fourier decay rate of some self-similar measures is logarithmic, generalizing a result of Varj\'{u} and Yu., Comment: 12 pages

Large-scale text-to-image diffusion models have been a revolutionary milestone in the evolution of generative AI and multimodal technology, allowing wonderful image generation with natural-language text prompt. However, the issue of lacking controllability of such models restricts their practical applicability for real-life content creation. Thus, attention has been focused on leveraging a reference image to control text-to-image synthesis, which is also regarded as manipulating (or editing) a reference image as per a text prompt, namely, text-driven image-to-image translation. This paper contributes a novel, concise, and efficient approach that adapts pre-trained large-scale text-to-image (T2I) diffusion model to the image-to-image (I2I) paradigm in a plug-and-play manner, realizing high-quality and versatile text-driven I2I translation without any model training, model fine-tuning, or online optimization process. To guide T2I generation with a reference image, we propose to decompose diverse guiding factors with different frequency bands of diffusion features in the DCT spectral space, and accordingly devise a novel frequency band substitution layer which realizes dynamic control of the reference image to the T2I generation result in a plug-and-play manner. We demonstrate that our method allows flexible control over both guiding factor and guiding intensity of the reference image simply by tuning the type and bandwidth of the substituted frequency band, respectively. Extensive qualitative and quantitative experiments verify superiority of our approach over related methods in I2I translation visual quality, versatility, and controllability. The code is publicly available at: https://github.com/XiangGao1102/FBSDiff., Comment: Accepted conference paper of ACM MM 2024

Machine learning interatomic potentials (MLIPs) have been widely used to facilitate large scale molecular simulations with ab initio level accuracy. However, MLIP-based molecular simulations frequently encounter the issue of collapse due to decreased prediction accuracy for out-of-distribution (OOD) data. To mitigate this issue, it is crucial to enrich the training set with active learning, where uncertainty estimation serves as an effective method for identifying and collecting OOD data. Therefore, a feasible method for uncertainty estimation in MLIPs is desired. The existing methods either require expensive computations or compromise prediction accuracy. In this work, we introduce evidential deep learning for interatomic potentials (eIP) with a physics-inspired design. Our experiments demonstrate that eIP consistently generates reliable uncertainties without incurring notable additional computational costs, while the prediction accuracy remains unchanged. Furthermore, we present an eIP-based active learning workflow, where eIP is used not only to estimate the uncertainty of molecular data but also to perform uncertainty-driven dynamics simulations. Our findings show that eIP enables efficient sampling for a more diverse dataset, thereby advancing the feasibility of MLIP-based molecular simulations.

Reconstructing geometry and topology structures from raw unstructured data has always been an important research topic in indoor mapping research. In this paper, we aim to reconstruct the floorplan with a vectorized representation from point clouds. Despite significant advancements achieved in recent years, current methods still encounter several challenges, such as missing corners or edges, inaccuracies in corner positions or angles, self-intersecting or overlapping polygons, and potentially implausible topology. To tackle these challenges, we present PolyRoom, a room-aware Transformer that leverages uniform sampling representation, room-aware query initialization, and room-aware self-attention for floorplan reconstruction. Specifically, we adopt a uniform sampling floorplan representation to enable dense supervision during training and effective utilization of angle information. Additionally, we propose a room-aware query initialization scheme to prevent non-polygonal sequences and introduce room-aware self-attention to enhance memory efficiency and model performance. Experimental results on two widely used datasets demonstrate that PolyRoom surpasses current state-of-the-art methods both quantitatively and qualitatively. Our code is available at: https://github.com/3dv-casia/PolyRoom/., Comment: Accepted by ECCV2024

We present the first lattice QCD calculation of the rapidity anomalous dimension of transverse-momentum-dependent distributions (TMDs), i.e. the Collins-Soper (CS) kernel, employing the recently proposed Coulomb-gauge-fixed quasi-TMD formalism as well as a chiral-symmetry preserving lattice discretization. This unitary lattice calculation is conducted using the domain wall fermion discretization scheme, a fine lattice spacing of approximately 0.08 fm, and physical values for light and strange quark masses. The CS kernel is determined analyzing the ratios of pion quasi-TMD wave functions (quasi-TMDWFs) at next-to-leading logarithmic (NLL) perturbative accuracy. Thanks to the absence of Wilson-lines, the Coulomb-gauge-fixed quasi-TMDWF demonstrates a remarkably slower decay of signals with increasing quark separations. This allows us to access the non-perturbative CS kernel up to transverse separations of 1 fm. For small transverse separations, our results agree well with perturbative predictions. At larger transverse separations, our non-perturbative CS kernel clearly favors certain global fits., Comment: 7 pages, 5 figures, contribution to the 31st International Workshop on Deep Inelastic Scattering and Related Subjects (DIS2024)

Recently, large-scale text-to-image (T2I) diffusion models have emerged as a powerful tool for image-to-image translation (I2I), allowing open-domain image translation via user-provided text prompts. This paper proposes frequency-controlled diffusion model (FCDiffusion), an end-to-end diffusion-based framework that contributes a novel solution to text-guided I2I from a frequency-domain perspective. At the heart of our framework is a feature-space frequency-domain filtering module based on Discrete Cosine Transform, which filters the latent features of the source image in the DCT domain, yielding filtered image features bearing different DCT spectral bands as different control signals to the pre-trained Latent Diffusion Model. We reveal that control signals of different DCT spectral bands bridge the source image and the T2I generated image in different correlations (e.g., style, structure, layout, contour, etc.), and thus enable versatile I2I applications emphasizing different I2I correlations, including style-guided content creation, image semantic manipulation, image scene translation, and image style translation. Different from related approaches, FCDiffusion establishes a unified text-guided I2I framework suitable for diverse image translation tasks simply by switching among different frequency control branches at inference time. The effectiveness and superiority of our method for text-guided I2I are demonstrated with extensive experiments both qualitatively and quantitatively. The code is publicly available at: https://github.com/XiangGao1102/FCDiffusion., Comment: Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI 2024)

In this work, we report a lattice calculation of $x$-dependent valence pion generalized parton distributions (GPDs) at zero skewness with multiple values of the momentum transfer $-t$. The calculations are based on an $N_f=2+1$ gauge ensemble of highly improved staggered quarks with Wilson-Clover valence fermion. The lattice spacing is 0.04 fm, and the pion valence mass is tuned to be 300 MeV. We determine the Lorentz-invariant amplitudes of the quasi-GPD matrix elements for both symmetric and asymmetric momenta transfers with similar values and show the equivalence of both frames. Then, focusing on the asymmetric frame, we utilize a hybrid scheme to renormalize the quasi-GPD matrix elements obtained from the lattice calculations. After the Fourier transforms, the quasi-GPDs are then matched to the light-cone GPDs within the framework of large momentum effective theory with improved matching, including the next-to-next-to-leading order perturbative corrections, and leading renormalon and renormalization group resummations. We also present the 3-dimensional image of the pion in impact-parameter space through the Fourier transform of the momentum transfer $-t$., Comment: 33 pages, 14 figures

We present a lattice QCD calculation of the $x$-dependent pion and kaon distribution amplitudes (DA) in the framework of large momentum effective theory. This calculation is performed on a fine lattice of $a=0.076$~fm at physical pion mass, with the pion boosted to $1.8$~GeV and kaon boosted to $2.3$~GeV. We renormalize the matrix elements in the hybrid scheme and match to $\overline{\rm MS}$ with a subtraction of the leading renormalon in the Wilson-line mass. The perturbative matching is improved by resumming the large logarithms related to the small quark and gluon momenta in the soft-gluon limit. After resummation, we demonstrate that we are able to calculate a range of $x\in[x_0,1-x_0]$ with $x_0=0.25$ for pion and $x_0=0.2$ for kaon with systematics under control. The kaon DA is shown to be slighted skewed, and narrower than pion DA. Although the $x$-dependence cannot be direct calculated beyond these ranges, we estimate higher moments of the pion and kaon DAs {by complementing} our calculation with short-distance factorization.

Role-playing has wide-ranging applications in customer support, embodied agents, and computational social science. The influence of parametric world knowledge of large language models (LLMs) often causes role-playing characters to act out of character and to hallucinate about things outside the scope of their knowledge. In this work, we focus on the evaluation and mitigation of hallucination in fictional character role-play. We introduce a dataset with over 2,000 characters and 72,000 interviews, including 18,000 adversarial questions. We propose RoleFact, a role-playing method that mitigates hallucination by modulating the influence of parametric knowledge using a pre-calibrated confidence threshold. Experiments show that the proposed method improves the factual precision of generated responses by 18% for adversarial questions with a 44% reduction in temporal hallucination for time-sensitive interviews. The code and the dataset are available at https://github.com/NafisSadeq/rolefact.git., Comment: EMNLP 2024 Camera Ready

Detecting defects and vulnerabilities in the early stage has long been a challenge in software engineering. Static analysis, a technique that inspects code without execution, has emerged as a key strategy to address this challenge. Among recent advancements, the use of graph-based representations, particularly Code Property Graph (CPG), has gained traction due to its comprehensive depiction of code structure and semantics. Despite the progress, existing graph-based analysis tools still face performance and scalability issues. The main bottleneck lies in the size and complexity of CPG, which makes analyzing large codebases inefficient and memory-consuming. Also, query rules used by the current tools can be over-specific. Hence, we introduce QVoG, a graph-based static analysis platform for detecting defects and vulnerabilities. It employs a compressed CPG representation to maintain a reasonable graph size, thereby enhancing the overall query efficiency. Based on the CPG, it also offers a declarative query language to simplify the queries. Furthermore, it takes a step forward to integrate machine learning to enhance the generality of vulnerability detection. For projects consisting of 1,000,000+ lines of code, QVoG can complete analysis in approximately 15 minutes, as opposed to 19 minutes with CodeQL.

This study presents a novel fault diagnosis model for urban rail transit systems based on Wavelet Transform Residual Neural Network (WT-ResNet). The model integrates the advantages of wavelet transform for feature extraction and ResNet for pattern recognition, offering enhanced diagnostic accuracy and robustness. Experimental results demonstrate the effectiveness of the proposed model in identifying faults in urban rail trains, paving the way for improved maintenance strategies and reduced downtime., Comment: 12 pages,10 figures

We discuss the potential of future proton decay experiments on the exploration of the flavour space of grand unification. We focus on an economical SU(5) grand unified model (GUT) with the fermion sector extended by including only one copy of 24-plet. Neutrino masses are generated via type-(I+III) seesaw mechanism with the lightest neutrino massless. Gauge unification requires masses of fermions in the 24-plet to be hierarchical, in particular, the electroweak singlet and triplet heavy leptons to be around the canonical seesaw scale and TeV scale, respectively. We address how extra parameters in the flavour space which cannot be touched in flavour measurements can be tested by a multi-channel analysis in future proton decay measurements., Comment: 21 pages, 8 figures

We present a direct lattice QCD calculation of the $x$-dependence of the pion distribution amplitude (DA), which is performed using the quasi-DA in large momentum effective theory on a domain-wall fermion ensemble at physical quark masses and spacing $a\approx 0.084$ fm. The bare quais-DA matrix elements are renormalized in the hybrid scheme and matched to $\overline{\rm MS}$ with a subtraction of the leading renormalon in the Wilson-line mass. For the first time, we include threshold resummation in the perturbative matching onto the light-cone DA, which resums the large logarithms in the soft gluon limit at next-to-next-to-leading log. The resummed results show controlled scale-variation uncertainty within the range of momentum fraction $x\in[0.25,0.75]$ at the largest pion momentum $P_z\approx 1.85$~GeV. In addition, we apply the same analysis to quasi-DAs from a highly-improved-staggered-quark ensemble at physical pion mass and $a=0.076$ fm. By comparison we find with $2\sigma$ confidence level that the DA obtained from chiral fermions is flatter and lower near $x=0.5$., Comment: update to match the version published in journal

This research employs the Kraus representation and Sz.-Nagy dilation theorem to model a three-level quantum heat on quantum circuits, investigating its dynamic evolution and thermodynamic performance. The feasibility of the dynamic model is validated by tracking the changes of population. On the basis of reinforcement learning algorithm, the optimal cycle of the quantum heat engine for maximal average power is proposed and verified by the thermodynamic model. The stability of quantum circuit simulations is scrutinized through a comparative analysis of theoretical and simulated results, predicated on an orthogonal test. These results affirm the practicality of simulating quantum heat engines on quantum circuits, offering potential for substantially curtailing the experimental expenses associated with the construction of such engines.

This paper handles the problem of converting real pictures into traditional Chinese ink-wash paintings, i.e., Chinese ink-wash painting style transfer. Though this problem could be realized by a wide range of image-to-image translation models, a notable issue with all these methods is that the original image content details could be easily erased or corrupted due to transfer of ink-wash style elements. To solve or ameliorate this issue, we propose to incorporate saliency detection into the unpaired image-to-image translation framework to regularize content information of the generated paintings. The saliency map is utilized for content regularization from two aspects, both explicitly and implicitly: (\romannumeral1) we propose saliency IOU (SIOU) loss to explicitly regularize saliency consistency before and after stylization; (\romannumeral2) we propose saliency adaptive normalization (SANorm) which implicitly enhances content integrity of the generated paintings by injecting saliency information to the generator network to guide painting generation. Besides, we also propose saliency attended discriminator network which harnesses saliency mask to focus generative adversarial attention onto salient image regions, it contributes to producing finer ink-wash stylization effect for salient objects of images. Qualitative and quantitative experiments consistently demonstrate superiority of our model over related advanced methods for Chinese ink-wash painting style transfer., Comment: 25 pages, 14 figures

We report the first lattice QCD computation of pion and kaon electromagnetic form factors, $F_M(Q^2)$, at large momentum transfer up to 10 and 28 $\mathrm{GeV}^2$, respectively. Utilizing physical masses and two fine lattices, we achieve good agreement with JLab experimental results at $Q^2 \lesssim 4~\mathrm{GeV}^2$. For $Q^2 \gtrsim 4~\mathrm{GeV}^2$, our results provide $\textit{ab-initio}$ QCD benchmarks for the forthcoming experiments at JLab 12 GeV and future electron-ion colliders. We also test the QCD collinear factorization framework utilizing our high-$Q^2$ form factors at next-to-next-to-leading order in perturbation theory, which relates the form factors to the leading Fock-state meson distribution amplitudes. Comparisons with independent lattice QCD calculations using the same framework demonstrate, within estimated uncertainties, the universality of these nonperturbative quantities., Comment: Phys. Rev. Lett. 133, 181902; 15 pages, 9 figures

Recent studies show that deployed deep learning (DL) models such as those of Tensor Flow Lite (TFLite) can be easily extracted from real-world applications and devices by attackers to generate many kinds of attacks like adversarial attacks. Although securing deployed on-device DL models has gained increasing attention, no existing methods can fully prevent the aforementioned threats. Traditional software protection techniques have been widely explored, if on-device models can be implemented using pure code, such as C++, it will open the possibility of reusing existing software protection techniques. However, due to the complexity of DL models, there is no automatic method that can translate the DL models to pure code. To fill this gap, we propose a novel method, CustomDLCoder, to automatically extract the on-device model information and synthesize a customized executable program for a wide range of DL models. CustomDLCoder first parses the DL model, extracts its backend computing units, configures the computing units to a graph, and then generates customized code to implement and deploy the ML solution without explicit model representation. The synthesized program hides model information for DL deployment environments since it does not need to retain explicit model representation, preventing many attacks on the DL model. In addition, it improves ML performance because the customized code removes model parsing and preprocessing steps and only retains the data computing process. Our experimental results show that CustomDLCoder improves model security by disabling on-device model sniffing. Compared with the original on-device platform (i.e., TFLite), our method can accelerate model inference by 21.8% and 24.3% on x86-64 and ARM64 platforms, respectively. Most importantly, it can significantly reduce memory consumption by 68.8% and 36.0% on x86-64 and ARM64 platforms, respectively., Comment: Accepted by the ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA2024)

The accurate construction of tokamak equilibria, which is critical for the effective control and optimization of plasma configurations, depends on the precise distribution of magnetic fields and magnetic fluxes. Equilibrium fitting codes, such as EFIT relying on traditional equilibrium algorithms, require solving the GS equation by iterations based on the least square method constrained with measured magnetic signals. The iterative methods face numerous challenges and complexities in the pursuit of equilibrium optimization. Furthermore, these methodologies heavily depend on the expertise and practical experience, demanding substantial resource allocation in personnel and time. This paper reconstructs magnetic equilibria for the EAST tokamak based on artificial neural networks through a supervised learning method. We use a fully connected neural network to replace the GS equation and reconstruct the poloidal magnetic flux distribution by training the model based on EAST datasets. The training set, validation set, and testing set are partitioned randomly from the dataset of poloidal magnetic flux distributions of the EAST experiments in 2016 and 2017 years. The feasibility of the neural network model is verified by comparing it to the offline EFIT results. It is found that the neural network algorithm based on the supervised machine learning method can accurately predict the location of different closed magnetic flux surfaces at a high efficiency. The similarities of the predicted X-point position and last closed magnetic surface are both 98%. The Pearson coherence of the predicted q profiles is 92%. Compared with the target value, the model results show the potential of the neural network model for practical use in plasma modeling and real-time control of tokamak operations., Comment: 24 pages, 10 figures

First lattice QCD calculations of $x$-dependent GPD have been performed in the (symmetric) Breit frame, where the momentum transfer is evenly divided between the initial and final hadron states. However, employing the asymmetric frame, we are able to obtain proton GPDs for multiple momentum transfers in a computationally efficient setup. In these proceedings, we focus on the helicity twist-2 GPD at zero skewness that gives access to the $\widetilde{H}$ GPD. We will cover the implementation of the asymmetric frame, its comparison to the Breit frame, and the dependence of the GPD on the squared four-momentum transfer, $-t$. The calculation is performed on an $N_f = 2+1+1$ ensemble of twisted mass fermions with a clover improvement. The mass of the pion for this ensemble is roughly 260 MeV., Comment: 9 pages, 4 figures, Contribution to The 40th International Symposium on Lattice Field Theory (Lattice 2023)

In recent years, large language models (LLMs) have become increasingly prevalent, offering remarkable text generation capabilities. However, a pressing challenge is their tendency to make confidently wrong predictions, highlighting the critical need for uncertainty quantification (UQ) in LLMs. While previous works have mainly focused on addressing aleatoric uncertainty, the full spectrum of uncertainties, including epistemic, remains inadequately explored. Motivated by this gap, we introduce a novel UQ method, sampling with perturbation for UQ (SPUQ), designed to tackle both aleatoric and epistemic uncertainties. The method entails generating a set of perturbations for LLM inputs, sampling outputs for each perturbation, and incorporating an aggregation module that generalizes the sampling uncertainty approach for text generation tasks. Through extensive experiments on various datasets, we investigated different perturbation and aggregation techniques. Our findings show a substantial improvement in model uncertainty calibration, with a reduction in Expected Calibration Error (ECE) by 50\% on average. Our findings suggest that our proposed UQ method offers promising steps toward enhancing the reliability and trustworthiness of LLMs., Comment: Accepted to appear at EACL 2024

This work reports a novel Bundle Adjustment (BA) formulation using a Reproducing Kernel Hilbert Space (RKHS) representation called RKHS-BA. The proposed formulation is correspondence-free, enables the BA to use RGB-D/LiDAR and semantic labels in the optimization directly, and provides a generalization for the photometric loss function commonly used in direct methods. RKHS-BA can incorporate appearance and semantic labels within a continuous spatial-semantic functional representation that does not require optimization via image pyramids. We demonstrate its applications in sliding-window odometry and global LiDAR mapping, which show highly robust performance in extremely challenging scenes and the best trade-off of generalization and accuracy., Comment: 16 pages, 12 figures, technical report under review

We present a lattice QCD calculation of the rapidity anomalous dimension of quark transverse-momentum-dependent distributions, i.e., the Collins-Soper (CS) kernel, up to transverse separations of about 1 fm. This unitary lattice calculation is conducted, for the first time, employing the chiral-symmetry-preserving domain wall fermion discretization and physical values of light and strange quark masses. The CS kernel is extracted from the ratios of pion quasi-transverse-momentum-dependent wave functions (quasi-TMDWFs) at next-to-leading logarithmic perturbative accuracy. Also for the first time, we utilize the recently proposed Coulomb-gauge-fixed quasi-TMDWF correlator without a Wilson line. We observe significantly slower signal decay with increasing quark separations compared to the established gauge-invariant method with a staple-shaped Wilson line. This enables us to determine the CS kernel at large nonperturbative transverse separations and find its near-linear dependence on the latter. Our result is consistent with the recent lattice calculation using gauge-invariant quasi-TMDWFs, and agrees with various recent phenomenological parametrizations of experimental data., Comment: 7 pages, 4 figures; published version