BZPEERReading, trust and collaboration for research
Menu

Discover

GraphFollow connections around a paper, topic or saved view.

Workspaces

HomePick up where your research flow left off.InboxHandle requests, replies and notifications from one place.CollectionsCurate reading lists, evidence sets and shareable dossiers.ResearchSee your private provenance graph, trail and imports.CollaborationMove from discovery into focused discussion rooms.PublishDraft, preview and publish full-text research articles.

Network

ExpertsFind specialists worth following or asking for help.CommunitiesJoin research circles organized around shared work.PartnersSee external organizations active around the same topics.

Opportunities

ListingsBrowse roles, calls and collaborations with clearer fit signals.

Account

Log inReturn to your reading and collaboration workspace.Create accountStart saving work, following people and joining teams.
Log inCreate account

Researcher profile

Lei Wu

Lei Wu contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
hep-phMachine LearningCryptography and Securityhep-exphysics.comp-phphysics.flu-dynastro-ph.COmath.AGSoftware Engineeringmath.APmath.OCComputer Visioncond-mat.mes-halleess.IVmath.STStatistics Theoryastro-ph.HEcond-mat.str-elInformation Retrievalmath.ATmath.NAMultimediaNeural and Evolutionary ComputingNeurons and CognitionNumerical Analysisphysics.data-anquant-phSocial and Information Networks

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author
64works
0followers
28topics
4close collaborators

Actions

Decide how to stay connected

Follow researcher0

Identity and collaboration

How to connect with this researcher

Claiming links this public author record to a researcher profile and unlocks direct collaboration workflows.

Log in to claim

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

Research graph

See the researcher in context

Open full explorer

Inspect adjacent work, topics, institutions and collaborators without jumping out to a separate graph page.

Building this graph slice

BZPEER is loading the nearby papers, people, topics and institutions for this page.

Published work

64 published item(s)

preprint2026arXiv

Accelerated simulation of multiscale gas-radiation coupling flows via a general synthetic iterative scheme

Gas-radiation coupling critically influences hypersonic reentry flows, where extreme temperatures induce pronounced non-equilibrium gas and radiative heat transport. Accurate and efficient simulation of radiative gas dynamics is therefore indispensable for reliable design of thermal protection systems for atmospheric entry vehicles. In this study, a Boltzmann-type kinetic model for radiative gas flows is solved across a broad spectrum of flow and radiation transport regimes using the general synthetic iterative scheme (GSIS). The approach integrates an unstructured finite-volume discrete velocity method with a set of macroscopic synthetic equations. Within this framework, the kinetic model provides high-order closures for the constitutive relations in the synthetic equations. Simultaneously, the macroscopic synthetic equations drive the evolution of the mesoscopic kinetic system, significantly accelerating steady-state convergence in near-continuum regimes, as substantiated by linear Fourier stability analysis. Crucially, the algorithm is proven to be asymptotic-preserving, correctly recovering the continuum and optically thick limits, represented by the radiative Navier-Stokes-Fourier equations governing distinct translational, rotational, vibrational, and radiative temperatures, on coarse meshes independent of the mean free path. Numerical simulations of challenging benchmarks, including three-dimensional hypersonic flow over an Apollo reentry capsule, demonstrate that GSIS achieves orders-of-magnitude speedup over conventional iterative schemes in multiscale simulations of radiative gas flows while accurately capturing non-equilibrium effects and radiative heat transfer in hypersonic environments.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Dual-Diffusional Generative Fashion Recommendation

Personalized generative recommender systems have emerged as a promising solution for fashion recommendation. However, existing methods primarily rely on implicit visual embeddings from historical interactions, which often contain preference-irrelevant information and result in insufficient user behavior modeling. Moreover, these models typically generate only item images, providing limited interpretability. To address these limitations, we propose DualFashion, a Dual-Diffusional Generative Fashion Recommendation Architecture that jointly models image and text modalities for personalized and explainable recommendation. DualFashion adopts a dual-diffusion Transformer with image and text branches, where structured attribute-level captions and visual outfit information are jointly used as conditioning signals to model user behavior. The proposed architecture produces both fashion item images and textual descriptions, ensuring visual compatibility while providing explicit semantic interpretability. Furthermore, we introduce a text-augmented fine-tuning strategy that enhances generation diversity and enables effective cross-modal knowledge transfer without incurring heavy computational costs. Extensive experiments on iFashion and Polyvore-U across Personalized Fill-in-the-Blank and Generative Outfit Recommendation tasks demonstrate that DualFashion achieves strong performance in behavior modeling, interpretability, and efficiency compared to state-of-the-art methods. Our code and model checkpoints are available at https://github.com/LinkMingzhe/DualFashion.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Geomagnetic constraints on Millicharged Dark Matter

Millicharged particles are well-motivated dark matter candidates arising in many extensions of the Standard Model. We show that, despite their tiny coupling $e_m$ to photons, millicharged dark matter (mDM) in the Earth's geomagnetic field can generate a quasi-static, monochromatic magnetic signal with angular frequency twice the mDM mass. Using null results from the SuperMAG and SNIPE Hunt collaborations, we constrain the effective charge of bosonic mDM in the mass range $10^{-18}$--$10^{-14}\,\text{eV}$. The resulting upper bounds exceed stellar cooling constraints by over thirteen orders of magnitude, demonstrating the power of this method.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Embedding Inequalities for Barron-type Spaces

An important problem in machine learning theory is to understand the approximation and generalization properties of two-layer neural networks in high dimensions. To this end, researchers have introduced the Barron space $\mathcal{B}_s(Ω)$ and the spectral Barron space $\mathcal{F}_s(Ω)$, where the index $s\in [0,\infty)$ indicates the smoothness of functions within these spaces and $Ω\subset\mathbb{R}^d$ denotes the input domain. However, the precise relationship between the two types of Barron spaces remains unclear. In this paper, we establish a continuous embedding between them as implied by the following inequality: for any $δ\in (0,1), s\in \mathbb{N}^{+}$ and $f: Ω\mapsto\mathbb{R}$, it holds that \[ δ\|f\|_{\mathcal{F}_{s-δ}(Ω)}\lesssim_s \|f\|_{\mathcal{B}_s(Ω)}\lesssim_s \|f\|_{\mathcal{F}_{s+1}(Ω)}. \] Importantly, the constants do not depend on the input dimension $d$, suggesting that the embedding is effective in high dimensions. Moreover, we also show that the lower and upper bound are both tight.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Field-Induced Lifshitz Transition in the Magnetic Weyl Semimetal Candidate PrAlSi

Lifshitz transition (LT) refers to an abrupt change in the electronic structure and Fermi surface, and is associated to a variety of emergent quantum phenomena. Amongst the LTs observed in known materials, the field-induced LT has been rare and its origin remains elusive. To understand the origin of field-induced LT, it is important to extend the material basis beyond the usual setting of heavy fermion metals. Here, we report on a field-induced LT in PrAlSi, a magnetic Weyl semimetal candidate with localized 4$f$ electrons, through a study of magnetotransport up to 55 T. The quantum oscillation analysis reveals that across a threshold field $B^*\approx$14.5 T the oscillation frequency ($F_1$ = 43 T) is replaced by two new frequencies ($F_2$ = 62 T and $F_3$ = 103 T). Strikingly, the LT occurs well below quantum limit, with obvious temperature-dependent oscillation frequency and field-dependent cyclotron mass. Our work not only enriches the rare examples of field-induced LTs, but also paves the way for further investigation on the interplay among topology, magnetism and electronic correlation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A kinetic model for rarefied flows of molecular gas with vibrational modes

A kinetic model is proposed for rarefied flows of molecular gas with rotational and temperature-dependent vibrational degrees of freedom. The model reduces to the Boltzmann equation for monatomic gas when the energy exchange between the translational and internal modes is absent, thus the influence of intermolecular potential can be captured. Moreover, not only the transport coefficients but also their fundamental relaxation processes are recovered. The accuracy of our kinetic model is validated by the direct simulation Monte Carlo method in several rarefied gas flows, including the shock wave, Fourier flow, Couette flow, and the creep flow driven by Maxwell's demon. Then the kinetic model is adopted to investigate thermally-induced flows. By adjusting the viscosity index in the Boltzmann collision operator, we find that the intermolecular potential significantly influences the velocity and Knudsen force. Interestingly, in the transition flow regime, the Knudsen force exerting on a heated beam could reverse the direction when the viscosity index changes from 0.5 (hard-sphere gas) to 1 (Maxwell gas). This discovery is useful in the design of micro-electromechanical systems for microstructure actuation and gas sensing.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Logarithm Depth Quantum Converter: From One-hot Encoding to Binary Encoding

Within the quantum computing, there are two ways to encode a normalized vector $\{ α_i \}$. They are one-hot encoding and binary coding. The one-hot encoding state is denoted as $\left | ψ_O^{(N)} \right \rangle=\sum_{i=0}^{N-1} α_i \left |0 \right \rangle^{\otimes N-i-1} \left |1 \right \rangle \left |0 \right \rangle ^{\otimes i}$ and the binary encoding state is denoted as $\left | ψ_B^{(N)} \right \rangle=\sum_{i=0}^{N-1} α_i \left |b_i \right \rangle$, where $b_i$ is interpreted in binary of $i$ as the tensor product sequence of qubit states. In this paper, we present a method converting between the one-hot encoding state and the binary encoding state by taking the Edick state as the transition state, where the Edick state is defined as $\left | ψ_E^{(N)} \right \rangle=\sum_{i=0}^{N-1} α_i \left |0 \right \rangle^{\otimes N-i-1} \left |1 \right \rangle ^{\otimes i}$. Compared with the early work, our circuit achieves the exponential speedup with $O(\log^2 N)$ depth and $O(N)$ size.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A spectral-based analysis of the separation between two-layer neural networks and linear methods

We propose a spectral-based approach to analyze how two-layer neural networks separate from linear methods in terms of approximating high-dimensional functions. We show that quantifying this separation can be reduced to estimating the Kolmogorov width of two-layer neural networks, and the latter can be further characterized by using the spectrum of an associated kernel. Different from previous work, our approach allows obtaining upper bounds, lower bounds, and identifying explicit hard functions in a united manner. We provide a systematic study of how the choice of activation functions affects the separation, in particular the dependence on the input dimension. Specifically, for nonsmooth activation functions, we extend known results to more activation functions with sharper bounds. As concrete examples, we prove that any single neuron can instantiate the separation between neural networks and random feature models. For smooth activation functions, one surprising finding is that the separation is negligible unless the norms of inner-layer weights are polynomially large with respect to the input dimension. By contrast, the separation for nonsmooth activation functions is independent of the norms of inner-layer weights.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

A Survey on EOSIO Systems Security: Vulnerability, Attack, and Mitigation

EOSIO, as one of the most representative blockchain 3.0 platforms, involves lots of new features, e.g., delegated proof of stake consensus algorithm and updatable smart contracts, enabling a much higher transaction per second and the prosperous decentralized applications (DApps) ecosystem. According to the statistics, it has reached nearly 18 billion USD, taking the third place of the whole cryptocurrency market, following Bitcoin and Ethereum. Loopholes, however, are hiding in the shadows. EOSBet, a famous gambling DApp, was attacked twice within a month and lost more than 1 million USD. No existing work has surveyed the EOSIO from a security researcher perspective. To fill this gap, in this paper, we collected all occurred attack events against EOSIO, and systematically studied their root causes, i.e., vulnerabilities lurked in all relying components for EOSIO, as well as the corresponding attacks and mitigations. We also summarized some best practices for DApp developers, EOSIO official team, and security researchers for future directions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Axion Dark Radiation: Hubble Tension and Hyper-kamiokande Neutrino Experiment

In this work, we investigate the dark sector of a supersymmetric axion model, consisting of the late-decaying gravitino/axino dark matter and axion dark radiation. In the early universe, the decay of the scalar superpartner of the axion (saxion) will produce a large amount of entropy. The additional entropy can not only dilute the relic density of the gravitino/axino dark matter to avoid overclosing the universe but also relax the constraint on the reheating temperature $T_{R}$ after inflation. Meanwhile, the axion dark radiation from the saxion decay will increase the effective number of neutrino species $N_{\rm eff}$, which can help to reduce the cosmological Hubble tension. In the late universe, the decay of long-lived gravitino/axino dark matter produces the axions with MeV-scale kinetic energy. We study the potential of searching for such energetic axions through the inverse Primakoff process $a+A \to γ+ A$ in the neutrino experiments, such as Hyper-Kamiokande.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Beyond the Quadratic Approximation: the Multiscale Structure of Neural Network Loss Landscapes

A quadratic approximation of neural network loss landscapes has been extensively used to study the optimization process of these networks. Though, it usually holds in a very small neighborhood of the minimum, it cannot explain many phenomena observed during the optimization process. In this work, we study the structure of neural network loss functions and its implication on optimization in a region beyond the reach of a good quadratic approximation. Numerically, we observe that neural network loss functions possesses a multiscale structure, manifested in two ways: (1) in a neighborhood of minima, the loss mixes a continuum of scales and grows subquadratically, and (2) in a larger region, the loss shows several separate scales clearly. Using the subquadratic growth, we are able to explain the Edge of Stability phenomenon [5] observed for the gradient descent (GD) method. Using the separate scales, we explain the working mechanism of learning rate decay by simple examples. Finally, we study the origin of the multiscale structure and propose that the non-convexity of the models and the non-uniformity of training data is one of the causes. By constructing a two-layer neural network problem we show that training data with different magnitudes give rise to different scales of the loss function, producing subquadratic growth and multiple separate scales.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Deep Learning Jet Image as a Probe of Light Higgsino Dark Matter at the LHC

Higgsino in supersymmetric standard models can play the role of dark matter particle. In conjunction with the naturalness criterion, the higgsino mass parameter is expected to be around the electroweak scale. In this work, we explore the potential of probing the nearly degenerate light higgsinos with machine learning at the LHC. By analyzing jet images and other jet substructure information, we use the Convolutional Neural Network(CNN) to enhance the signal significance. We find that our deep learning jet image method can improve the previous result based on the conventional cut-flow by about a factor of two at the High-Luminosity LHC.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Electroweak Precision Fit and New Physics in light of $W$ Boson Mass

The $W$ boson mass is one of the most important electroweak precision observables for testing the Standard Model or its extensions. The very recent measured $W$ boson mass at CDF shows about $7σ$ deviations from the SM prediction, which may challenge the internal consistency of the SM. By performing the global electroweak fit with the new $W$-boson, we present the new values of the oblique parameters: $S = 0.06 \pm 0.10$, $T= 0.11 \pm 0.12$, $U=0.13 \pm 0.09$, or $S=0.14 \pm 0.08$, $T= 0.26 \pm 0.06$ with $U =0$ and the corresponding correlation matrices, which strongly indicates the need for the non-degenerate multiplets beyond the SM. As a proof-of-concept, we show that the new results can be accommodated in the two-Higgs doublet model, where the charged Higgs boson has to be either heavier or lighter than both two heavy neutral Higgs bosons. Therefore, searching for these non-SM Higgs bosons will provide a complementary way to test the new physics for the $W$ boson mass anomaly.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Exploiting the Potential of Datasets: A Data-Centric Approach for Model Robustness

Robustness of deep neural networks (DNNs) to malicious perturbations is a hot topic in trustworthy AI. Existing techniques obtain robust models given fixed datasets, either by modifying model structures, or by optimizing the process of inference or training. While significant improvements have been made, the possibility of constructing a high-quality dataset for model robustness remain unexplored. Follow the campaign of data-centric AI launched by Andrew Ng, we propose a novel algorithm for dataset enhancement that works well for many existing DNN models to improve robustness. Transferable adversarial examples and 14 kinds of common corruptions are included in our optimized dataset. In the data-centric robust learning competition hosted by Alibaba Group and Tsinghua University, our algorithm came third out of more than 3000 competitors in the first stage while we ranked fourth in the second stage. Our code is available at \url{https://github.com/hncszyq/tianchi_challenge}.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Global fits of SUSY at future Higgs factories

In this work, we study the impact of electroweak and Higgs precision measurements at future electron-positron colliders on several typical supersymmetric models, including the Constrained Minimal Supersymmetric Standard Model (CMSSM), Non-Universal Higgs Mass generalisations (NUHM1, NUHM2), and the 7-dimensional Minimal Supersymmetric Standard Model (MSSM7). Using publicly-available data from the \textsf{GAMBIT} community, we post-process previous SUSY global fits with additional likelihoods to explore the discovery potential of Higgs factories, such as the Circular Electron Positron Collider (CEPC), the Future Circular Collider (FCC) and the International Linear Collider (ILC). We show that the currently allowed parameter space of these models will be further tested by future precision measurements. In particular, dark matter annihilation mechanisms may be distinguished by precise measurements of Higgs observables.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Hilbert Expansion for the Relativistic Landau Equation

In this paper, we study the local-in-time validity of the Hilbert expansion for the relativistic Landau equation. We justify that solutions of the relativistic Landau equation converge to small classical solutions of the limiting relativistic Euler equations as the Knudsen number shrinks to zero in a weighted Sobolev space. The key difficulty comes from the temporal and spatial derivatives of the local Maxwellian, which produce momentum growth terms and are uncontrollable by the standard $L^2$-based energy and dissipation. We introduce novel time-dependent weight functions to generate additional dissipation terms to suppress the large momentum. The argument relies on a hierarchy of energy-dissipation structures with or without weights. As far as the authors are aware of, this is the first result of the Hilbert expansion for the Landau-type equation.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

HoVer-Trans: Anatomy-aware HoVer-Transformer for ROI-free Breast Cancer Diagnosis in Ultrasound Images

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. It would be a tremendous success if we can precisely diagnose breast cancer by breast ultrasound images (BUS). Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define ROI and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and two vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

iLibScope: Reliable Third-Party Library Detection for iOS Mobile Apps

Vetting security impacts introduced by third-party libraries in iOS apps requires a reliable library detection technique. Especially when a new vulnerability (or a privacy-invasive behavior) was discovered in a third-party library, there is a practical need to precisely identify the existence of libraries and their versions for iOS apps. However, few studies have been proposed to tackle this problem, and they all suffer from the code duplication problem in different libraries. In this paper, we focus on third-party library detection in iOS apps. Given an app, we aim to identify the integrated libraries and pinpoint their versions (or the version range).To this end, we first conduct an in-depth study on iOS third-party libraries to demystify the code duplication challenge. By doing so, we have two key observations: 1) even though two libraries can share classes, the shared classes cannot be integrated into an app simultaneously without causing a class name conflict; and 2) code duplication between multiple versions of two libraries can vary. Based on these findings, we propose a novel profile-based similarity comparison approach to perform the detection. Specifically, we build a library database consists of original library binaries with distinct versions. After extracting profiles for each library version and the target app, we conduct a similarity comparison to find the best matches. We implemented this approach in iLibScope. We built a benchmark consists of 5,807 apps with 10,495 library integrations and applied our tool to it. Our evaluation shows that iLibScope achieves a recall exceeds 99% and a precision exceeds 97% for library detection. We also applied iLibScope to detect the presence of well-known vulnerable third-party libraries in real-world iOS mobile apps to show the promising usage of our tool. It successfully identified 405 vulnerable library usage from 4,249 apps.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Inert Higgs Dark Matter for CDF-II W-boson Mass and Detection Prospects

The $W$-boson mass, which was recently measured at FermiLab with an unprecedented precision, suggests the presence of new multiplets beyond the Standard Model (SM). One of the minimal extensions of the SM is to introduce an additional scalar doublet, in which the non-SM scalars can enhance $W$-boson mass via the loop corrections. On the other hand, with a proper discrete symmetry, the lightest new scalar in the doublet can be stable and play the role of dark matter particle. We show that the inert two Higgs doublet model can naturally handle the new $W$-boson mass without violating other constraints, and the preferred dark matter mass is between $54$ and $74$ GeV. We identify three feasible parameter regions for the thermal relic density: the $SA$ co-annihilation, the Higgs resonance, and the $SS \to WW^*$ annihilation. We find that the first region can be fully tested by the HL-LHC, the second region will be tightly constrained by direct detection experiments, and the third region could yield detectable GeV gamma-ray and antiproton signals in the Galaxy that may have been observed by Fermi-LAT and AMS-02.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Learning a Single Neuron for Non-monotonic Activation Functions

We study the problem of learning a single neuron $\mathbf{x}\mapsto σ(\mathbf{w}^T\mathbf{x})$ with gradient descent (GD). All the existing positive results are limited to the case where $σ$ is monotonic. However, it is recently observed that non-monotonic activation functions outperform the traditional monotonic ones in many applications. To fill this gap, we establish learnability without assuming monotonicity. Specifically, when the input distribution is the standard Gaussian, we show that mild conditions on $σ$ (e.g., $σ$ has a dominating linear part) are sufficient to guarantee the learnability in polynomial time and polynomial samples. Moreover, with a stronger assumption on the activation function, the condition of input distribution can be relaxed to a non-degeneracy of the marginal distribution. We remark that our conditions on $σ$ are satisfied by practical non-monotonic activation functions, such as SiLU/Swish and GELU. We also discuss how our positive results are related to existing negative results on training two-layer neural networks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Logarithmic cotangent bundles, Chern-Mather classes, and the Huh-Sturmfels Involution conjecture

Using compactifications in the logarithmic cotangent bundle, we obtain a formula for the Chern classes of the pushforward of Lagrangian cycles under an open embedding with normal crossing complement. This generalizes earlier results of Aluffi and Wu-Zhou. The first application of our formula is a geometric description of Chern-Mather classes of an arbitrary very affine variety, generalizing earlier results of Huh which held under the smooth and schon assumptions. As the second application, we confirm an involution formula relating sectional maximum likelihood (ML) degrees and ML bidegrees, which was conjectured by Huh and Sturmfels in 2013.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Non-monotonic heat dissipation phenomenon in close-packed quasi-2D and 3D hotspot system

Transient heat dissipation in close-packed quasi-2D nanoline and 3D nanocuboid hotspot systems is studied based on phonon Boltzmann transport equation. It is found that, counter-intuitively, the heat dissipation efficiency is not a monotonic function of the distance between adjacent nanoscale heat sources: the heat dissipation efficiency reaches the highest value when this distance is comparable to the phonon mean free path. This is due to the competition of two thermal transport processes: quasiballistic transport when phonons escape from the nanoscale heat source and the scattering among phonons originating from adjacent nanoscale heat source.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

On self-supervised multi-modal representation learning: An application to Alzheimer's disease

Introspection of deep supervised predictive models trained on functional and structural brain imaging may uncover novel markers of Alzheimer's disease (AD). However, supervised training is prone to learning from spurious features (shortcut learning) impairing its value in the discovery process. Deep unsupervised and, recently, contrastive self-supervised approaches, not biased to classification, are better candidates for the task. Their multimodal options specifically offer additional regularization via modality interactions. In this paper, we introduce a way to exhaustively consider multimodal architectures for contrastive self-supervised fusion of fMRI and MRI of AD patients and controls. We show that this multimodal fusion results in representations that improve the results of the downstream classification for both modalities. We investigate the fused self-supervised features projected into the brain space and introduce a numerically stable way to do so.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

On the emergence of heat waves in the transient thermal grating geometry

The propagation of heat in the transient thermal grating geometry is studied based on phonon Boltzmann transport equation (BTE) in different phonon transport regimes. Our analytical and numerical results show that the phonon dispersion relation and temperature play a significant role in the emergence of heat wave. For the frequency-independent BTE, the heat wave appears as long as the phonon resistive scattering is not sufficient, while for the frequency-dependent BTE, the heat wave could disappear in the ballistic regime, depending on the grating period and temperature. We predict that the heat wave could appear in the suspended graphene and silicon in extremely low temperature but disappear at room temperature.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Penny Wise and Pound Foolish: Quantifying the Risk of Unlimited Approval of ERC20 Tokens on Ethereum

The prosperity of decentralized finance motivates many investors to profit via trading their crypto assets on decentralized applications (DApps for short) of the Ethereum ecosystem. Apart from Ether (the native cryptocurrency of Ethereum), many ERC20 (a widely used token standard on Ethereum) tokens obtain vast market value in the ecosystem. Specifically, the approval mechanism is used to delegate the privilege of spending users' tokens to DApps. By doing so, the DApps can transfer these tokens to arbitrary receivers on behalf of the users. To increase the usability, unlimited approval is commonly adopted by DApps to reduce the required interaction between them and their users. However, as shown in existing security incidents, this mechanism can be abused to steal users' tokens. In this paper, we present the first systematic study to quantify the risk of unlimited approval of ERC20 tokens on Ethereum. Specifically, by evaluating existing transactions up to 31st July 2021, we find that unlimited approval is prevalent (60%, 15.2M/25.4M) in the ecosystem, and 22% of users have a high risk of their approved tokens for stealing. After that, we investigate the security issues that are involved in interacting with the UIs of 22 representative DApps and 9 famous wallets to prepare the approval transactions. The result reveals the worrisome fact that all DApps request unlimited approval from the front-end users and only 10% (3/31) of UIs provide explanatory information for the approval mechanism. Meanwhile, only 16% (5/31) of UIs allow users to modify their approval amounts. Finally, we take a further step to characterize the user behavior into five modes and formalize the good practice, i.e., on-demand approval and timely spending, towards securely spending approved tokens. However, the evaluation result suggests that only 0.2% of users follow the good practice to mitigate the risk.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Probing ultra-light dark photon from inverse Compton-like scattering

Dark photon not only provides a portal linking dark sector particles and ordinary matter but also is a well-motivated dark matter candidate. We propose to detect the dark photon dark matter through the inverse Compton-like scattering process $p+γ^\prime \to p+γ$. Thanks to the ultra-high energy primary cosmic rays, we find that such a method is able to probe the dark photon mass from $10^{-2}$ eV down to $10^{-19}$ eV with the expected sensitivity of eROSITA $X$-ray telescope, which can extend the current lower limit of dark photon mass from Jupiter's magnetic fields experiment by about three orders of magnitude.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Rarefaction effects in head-on collision of two identical droplets

The head-on collision of two identical droplets is investigated based on the BGK-Boltzmann equation. Gauss-Hermite quadratures with different degree of precision are used to solve the kinetic equation, so that the continuum (solution truncated at the Navier-Stokes order) and non-continuum (rarefied gas dynamics) solutions can be compared. When the kinetic equation is solved with adequate accuracy, prominent variations of the vertical velocity (the collision is in the horizontal direction), the viscous stress components, and droplet morphology are observed during the formation of liquid bridge, which demonstrates the importance of the rarefaction effects and the failure of the Navier-Stokes equation. The rarefaction effects change the topology of streamlines near the droplet surface, suppress the high-magnitude vorticity concentration inside the interdroplet region, and promote the vorticity diffusion around outer droplet surface. Two physical mechanisms responsible for the local energy conversion between the free and kinetic energies are identified, namely, the total pressure-dilatation coupling effect and the interaction between the density gradient and strain rate tensor. An energy conversion analysis is performed to show that the rarefaction effects can enhance the conversion from free energy to kinetic energy and facilitate the discharge of interdroplet gas film along the vertical direction, thereby boosting droplet coalescence.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Self-supervised multimodal neuroimaging yields predictive representations for a spectrum of Alzheimer's phenotypes

Recent neuroimaging studies that focus on predicting brain disorders via modern machine learning approaches commonly include a single modality and rely on supervised over-parameterized models.However, a single modality provides only a limited view of the highly complex brain. Critically, supervised models in clinical settings lack accurate diagnostic labels for training. Coarse labels do not capture the long-tailed spectrum of brain disorder phenotypes, which leads to a loss of generalizability of the model that makes them less useful in diagnostic settings. This work presents a novel multi-scale coordinated framework for learning multiple representations from multimodal neuroimaging data. We propose a general taxonomy of informative inductive biases to capture unique and joint information in multimodal self-supervised fusion. The taxonomy forms a family of decoder-free models with reduced computational complexity and a propensity to capture multi-scale relationships between local and global representations of the multimodal inputs. We conduct a comprehensive evaluation of the taxonomy using functional and structural magnetic resonance imaging (MRI) data across a spectrum of Alzheimer's disease phenotypes and show that self-supervised models reveal disorder-relevant brain regions and multimodal links without access to the labels during pre-training. The proposed multimodal self-supervised learning yields representations with improved classification performance for both modalities. The concomitant rich and flexible unsupervised deep learning framework captures complex multimodal relationships and provides predictive performance that meets or exceeds that of a more narrow supervised classification analysis. We present elaborate quantitative evidence of how this framework can significantly advance our search for missing links in complex brain disorders.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Simple and statistically sound recommendations for analysing physical theories

Physical theories that depend on many parameters or are tested against data from many different experiments pose unique challenges to statistical inference. Many models in particle physics, astrophysics and cosmology fall into one or both of these categories. These issues are often sidestepped with statistically unsound ad hoc methods, involving intersection of parameter intervals estimated by multiple experiments, and random or grid sampling of model parameters. Whilst these methods are easy to apply, they exhibit pathologies even in low-dimensional parameter spaces, and quickly become problematic to use and interpret in higher dimensions. In this article we give clear guidance for going beyond these procedures, suggesting where possible simple methods for performing statistically sound inference, and recommendations of readily-available software tools and standards that can assist in doing so. Our aim is to provide any physicists lacking comprehensive statistical training with recommendations for reaching correct scientific conclusions, with only a modest increase in analysis burden. Our examples can be reproduced with the code publicly available at https://doi.org/10.5281/zenodo.4322283.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Tasting the cake: evaluating self-supervised generalization on out-of-distribution multimodal MRI data

Self-supervised learning has enabled significant improvements on natural image benchmarks. However, there is less work in the medical imaging domain in this area. The optimal models have not yet been determined among the various options. Moreover, little work has evaluated the current applicability limits of novel self-supervised methods. In this paper, we evaluate a range of current contrastive self-supervised methods on out-of-distribution generalization in order to evaluate their applicability to medical imaging. We show that self-supervised models are not as robust as expected based on their results in natural imaging benchmarks and can be outperformed by supervised learning with dropout. We also show that this behavior can be countered with extensive augmentation. Our results highlight the need for out-of-distribution generalization standards and benchmarks to adopt the self-supervised methods in the medical imaging community.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A fast-converging scheme for the Phonon Boltzmann equation with dual relaxation times

Callaway's dual relaxation times model, which takes into account the normal and resistive scatterings of phonon, is used to describe the heat conduction in materials like graphene. For steady-state problems, the Callaway model is usually solved by the conventional iterative scheme (CIS), which is efficient in the ballistic regime, but inefficient in the diffusive/hydrodynamic regime. In this paper, a general synthetic iterative scheme (GSIS) is proposed to expedite the convergence to steady-state solutions. First, macroscopic synthetic equations are designed to guide the evolution of equilibrium distribution functions for normal and resistive scatterings, so that fast convergence can be achieved even in the diffusive/hydrodynamic regime. Second, the Fourier stability analysis is conducted to find the convergence rate for both CIS and GSIS, which rigorously proves the efficiency of GSIS over CIS. Finally, several numerical simulations are carried out to demonstrate the accuracy and efficiency of GSIS, where up to three orders of magnitude of convergence acceleration is achieved.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Direct Detection of Spin-Dependent Sub-GeV Dark Matter via Migdal Effect

Motivated by the current strong constraints on the spin-independent dark matter (DM)-nucleus scattering, we investigate the spin-dependent (SD) interactions of the light Majorana DM with the nucleus mediated by an axial-vector boson. Due to the small nucleus recoil energy, the ionization signals have now been used to probe the light dark matter particles in direct detection experiments. With the existing ionization data, we derive the exclusion limits on the SD DM-nucleus scattering through Migdal effect in the MeV-GeV DM mass range. It is found that the lower limit of the DM mass can reach about several MeVs. Due to the momentum transfer correction induced by the light mediator, the bounds on the SD DM-nucleus scattering cross sections can be weakened in comparison with the heavy mediator.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

On the Quantum Boltzmann Equation near Maxwellian and Vacuum

We consider the non-relativistic quantum Boltzmann equation for fermions and bosons. Using the nonlinear energy method and mild formulation, we justify the global well-posedness when the density function is near the global Maxwellian and vacuum. This work is a generalization and adaptation of the classical Boltzmann theory. Our main contribution is a detailed analysis of the nonlinear operator $Q$ in the quantum context. This is the first piece of a long-term project on the quantum kinetic equations.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

The Generalization Error of the Minimum-norm Solutions for Over-parameterized Neural Networks

We study the generalization properties of minimum-norm solutions for three over-parametrized machine learning models including the random feature model, the two-layer neural network model and the residual network model. We proved that for all three models, the generalization error for the minimum-norm solution is comparable to the Monte Carlo rate, up to some logarithmic terms, as long as the models are sufficiently over-parametrized.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Towards Understanding and Demystifying Bitcoin Mixing Services

One reason for the popularity of Bitcoin is due to its anonymity. Although several heuristics have been used to break the anonymity, new approaches are proposed to enhance its anonymity at the same time. One of them is the mixing service. Unfortunately, mixing services have been abused to facilitate criminal activities, e.g., money laundering. As such, there is an urgent need to systematically understand Bitcoin mixing services. In this paper, we take the first step to understand state-of-the-art Bitcoin mixing services. Specifically, we propose a generic abstraction model for mixing services and observe that there are two mixing mechanisms in the wild, i.e. {swapping} and {obfuscating}. Based on this model, we conduct a transaction-based analysis and successfully reveal the mixing mechanisms of four representative services. Besides, we propose a method to identify mixing transactions that leverage the obfuscating mechanism. The proposed approach is able to identify over $92$\% of the mixing transactions. Based on identified transactions, we then estimate the profit of mixing services and provide a case study of tracing the money flow of stolen Bitcoins.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

VM Matters: A Comparison of WASM VMs and EVMs in the Performance of Blockchain Smart Contracts

WebAssemly is an emerging runtime for Web applications and has been supported in almost all browsers. Recently, WebAssembly is further regarded to be a the next-generation environment for blockchain applications, and has been adopted by Ethereum, namely eWASM, to replace the state-of-the-art EVM. However, whether and how well current eWASM outperforms EVM on blockchain clients is still unknown. This paper conducts the first measurement study, to measure the performance on WASM VM and EVM for executing smart contracts on blockchain. To our surprise, the current WASM VM does not perform in expected performance. The overhead introduced by WASM is really non-trivial. Our results highlight the challenges when deploying WASM in practice, and provide insightful implications for improvement space.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Comparative Analysis of the Optimization and Generalization Property of Two-layer Neural Network and Random Feature Models Under Gradient Descent Dynamics

A fairly comprehensive analysis is presented for the gradient descent dynamics for training two-layer neural network models in the situation when the parameters in both layers are updated. General initialization schemes as well as general regimes for the network width and training data size are considered. In the over-parametrized regime, it is shown that gradient descent dynamics can achieve zero training loss exponentially fast regardless of the quality of the labels. In addition, it is proved that throughout the training process the functions represented by the neural network model are uniformly close to that of a kernel method. For general values of the network width and training data size, sharp estimates of the generalization error is established for target functions in the appropriate reproducing kernel Hilbert space.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Priori Estimates of the Population Risk for Two-layer Neural Networks

New estimates for the population risk are established for two-layer neural networks. These estimates are nearly optimal in the sense that the error rates scale in the same way as the Monte Carlo error rates. They are equally effective in the over-parametrized regime when the network size is much larger than the size of the dataset. These new estimates are a priori in nature in the sense that the bounds depend only on some norms of the underlying functions to be fitted, not the parameters in the model, in contrast with most existing results which are a posteriori in nature. Using these a priori estimates, we provide a perspective for understanding why two-layer neural networks perform better than the related kernel methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Atmospheric Dark Matter and Xenon1T Excess

Very recently, the Xenon1T collaboration has reported an intriguing electron recoil excess, which may imply for light dark matter. In order to interpret this anomaly, we propose the atmospheric dark matter (ADM) from the inelastic collision of cosmic rays (CRs) with the atmosphere. Due to the boost effect of high energy CRs, we show that the light ADM can be fast-moving and successfully fit the observed electron recoil spectrum through the ADM-electron scattering process. Meanwhile, our ADM predicts the scattering cross section $σ_e \sim {\cal O}(10^{-38}- 10^{-39}$) cm$^{2}$, and thus can evade other direct detection constraints. The search for light meson rare decays, such as $η\to π+ \slashed E_T$, would provide a complementary probe of our ADM in the future.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Automated Deobfuscation of Android Native Binary Code

With the popularity of Android apps, different techniques have been proposed to enhance app protection. As an effective approach to prevent reverse engineering, obfuscation can be used to serve both benign and malicious purposes. In recent years, more and more sensitive logic or data have been implemented as obfuscated native code because of the limitations of Java bytecode. As a result, native code obfuscation becomes a great obstacle for security analysis to understand the complicated logic. In this paper, we propose DiANa, an automated system to facilitate the deobfuscation of native binary code in Android apps. Specifically, given a binary obfuscated by Obfuscator-LLVM (the most popular native code obfuscator), DiANa is capable of recovering the original Control Flow Graph. To the best of our knowledge, DiANa is the first system that aims to tackle the problem of Android native binary deobfuscation. We have applied DiANa in different scenarios, and the experimental results demonstrate the effectiveness of DiANa based on generic similarity comparison metrics.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Bernstein-Sato ideals and hyperplane arrangements

We study the relation between zero loci of Bernstein-Sato ideals and roots of b-functions and obtain a criterion to guarantee that roots of b-functions of a reducible polynomial are determined by the zero locus of the associated Bernstein-Sato ideal. Applying the criterion together with a result of Maisonobe we prove that the set of roots of the b-function of a free hyperplane arrangement is determined by its intersection lattice. We also study the zero loci of Bernstein-Sato ideals and the associated relative characteristic cycles for arbitrary central hyperplane arrangements. We prove the multivariable n/d conjecture of Budur for complete factorizations of arbitrary hyperplane arrangements, which in turn proves the strong monodromy conjecture for the associated multivariable topological zeta functions.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Calibrating the dynamic Huff model for business analysis using location big data

The Huff model has been widely used in location-based business analysis for delineating a trading area containing potential customers to a store. Calibrating the Huff model and its extensions requires empirical location visit data. Many studies rely on labor-intensive surveys. With the increasing availability of mobile devices, users in location-based platforms share rich multimedia information about their locations in a fine spatiotemporal resolution, which offers opportunities for business intelligence. In this research, we present a time-aware dynamic Huff model (T-Huff) for location-based market share analysis and calibrate this model using large-scale store visit patterns based on mobile phone location data across ten most populated U.S. cities. By comparing the hourly visit patterns of two types of stores, we demonstrate that the calibrated T-Huff model is more accurate than the original Huff model in predicting the market share of different types of business (e.g., supermarkets vs. department stores) over time. We also identify the regional variability where people in large metropolitan areas with a well-developed transit system show less sensitivity to long-distance visits. In addition, several socioeconomic and demographic factors (e.g., median household income) that potentially affect people's visit decisions are examined and summarized.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Characterizing Cryptocurrency Exchange Scams

As the indispensable trading platforms of the ecosystem, hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets. While, it also attracts the attentions of attackers. A number of scam attacks were reported targeting cryptocurrency exchanges, leading to a huge mount of financial loss. However, no previous work in our research community has systematically studied this problem. In this paper, we make the first effort to identify and characterize the cryptocurrency exchange scams. We first identify over 1,500 scam domains and over 300 fake apps, by collecting existing reports and using typosquatting generation techniques. Then we investigate the relationship between them, and identify 94 scam domain families and 30 fake app families. We further characterize the impacts of such scams, and reveal that these scams have incurred financial loss of 520k US dollars at least. We further observe that the fake apps have been sneaked to major app markets (including Google Play) to infect unsuspicious users. Our findings demonstrate the urgency to identify and prevent cryptocurrency exchange scams. To facilitate future research, we have publicly released all the identified scam domains and fake apps to the community.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Characterizing EOSIO Blockchain

EOSIO has become one of the most popular blockchain platforms since its mainnet launch in June 2018. In contrast to the traditional PoW-based systems (e.g., Bitcoin and Ethereum), which are limited by low throughput, EOSIO is the first high throughput Delegated Proof of Stake system that has been widely adopted by many applications. Although EOSIO has millions of accounts and billions of transactions, little is known about its ecosystem, especially related to security and fraud. In this paper, we perform a large-scale measurement study of the EOSIO blockchain and its associated DApps. We gather a large-scale dataset of EOSIO and characterize activities including money transfers, account creation and contract invocation. Using our insights, we then develop techniques to automatically detect bots and fraudulent activity. We discover thousands of bot accounts (over 30\% of the accounts in the platform) and a number of real-world attacks (301 attack accounts). By the time of our study, 80 attack accounts we identified have been confirmed by DApp teams, causing 828,824 EOS tokens losses (roughly 2.6 million US\$) in total.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Complexity Measures for Neural Networks with General Activation Functions Using Path-based Norms

A simple approach is proposed to obtain complexity controls for neural networks with general activation functions. The approach is motivated by approximating the general activation functions with one-dimensional ReLU networks, which reduces the problem to the complexity controls of ReLU networks. Specifically, we consider two-layer networks and deep residual networks, for which path-based norms are derived to control complexities. We also provide preliminary analyses of the function spaces induced by these norms and a priori estimates of the corresponding regularized estimators.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

DEPOSafe: Demystifying the Fake Deposit Vulnerability in Ethereum Smart Contracts

Cryptocurrency has seen an explosive growth in recent years, thanks to the evolvement of blockchain technology and its economic ecosystem. Besides Bitcoin, thousands of cryptocurrencies have been distributed on blockchains, while hundreds of cryptocurrency exchanges are emerging to facilitate the trading of digital assets. At the same time, it also attracts the attentions of attackers. Fake deposit, as one of the most representative attacks (vulnerabilities) related to exchanges and tokens, has been frequently observed in the blockchain ecosystem, causing large financial losses. However, besides a few security reports, our community lacks of the understanding of this vulnerability, for example its scale and the impacts. In this paper, we take the first step to demystify the fake deposit vulnerability. Based on the essential patterns we have summarized, we implement DEPOSafe, an automated tool to detect and verify (exploit) the fake deposit vulnerability in ERC-20 smart contracts. DEPOSafe incorporates several key techniques including symbolic execution based static analysis and behavior modeling based dynamic verification. By applying DEPOSafe to 176,000 ERC-20 smart contracts, we have identified over 7,000 vulnerable contracts that may suffer from two types of attacks. Our findings demonstrate the urgency to identify and prevent the fake deposit vulnerability.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Fast convergence and asymptotic preserving of the General Synthetic Iterative Scheme

Recently the general synthetic iteration scheme (GSIS) is proposed to find the steady-state solution of the Boltzmann equation~\cite{SuArXiv2019}, where various numerical simulations have shown that (i) the steady-state solution can be found within dozens of iterations at any Knudsen number $K$, and (ii) the solution is accurate even when the spatial cell size in the bulk region is much larger than the molecular mean free path, i.e. Navier-Stokes solutions are recovered at coarse grids. The first property indicates that the error decay rate between two consecutive iterations decreases to zero with $K$, while the second one implies that the GSIS is asymptotically preserving the Navier-Stokes limit. This paper is dedicated to the rigorous proof of both properties.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

General synthetic iteration scheme for non-linear gas kinetic simulation of multi-scale rarefied gas flows

The general synthetic iteration scheme (GSIS) is extended to find the steady-state solution of nonlinear gas kinetic equation, removing the long-standing problems of slow convergence and requirement of ultra-fine grids in near-continuum flows. The key ingredients of GSIS are that the gas kinetic equation and macroscopic synthetic equations are tightly coupled, and the constitutive relations in macroscopic synthetic equations explicitly contain Newton's law of shear stress and Fourier's law of heat conduction. The higher-order constitutive relations describing rarefaction effects are calculated from the velocity distribution function, however, their constructions are simpler than our previous work (Su et al. Journal of Computational Physics 407 (2020) 109245) for linearized gas kinetic equations. On the other hand, solutions of macroscopic synthetic equations are used to inform the evolution of gas kinetic equation at the next iteration step. A rigorous linear Fourier stability analysis in periodic system shows that the error decay rate of GSIS can be smaller than 0.5, which means that the deviation to steady-state solution can be reduced by 3 orders of magnitude in 10 iterations. Other important advantages of the GSIS are (i) it does not rely on the specific form of Boltzmann collision operator and (ii) it can be solved by sophisticated techniques in computational fluid dynamics, making it amenable to large scale engineering applications. In this paper, the efficiency and accuracy of GSIS is demonstrated by a number of canonical test cases in rarefied gas dynamics.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Isotriviality of smooth families of varieties of general type

In this paper, we proved that a log smooth family of log general type klt pairs with a special (in the sense of Campana) quasi-projective base is isotrivial. As a consequence, we proved the generalized Kebekus-Kovács conjecture \cite[Conjecture 1.1]{WW19}, for smooth families of general type varieties as well as log smooth families of log canonical pairs of log general type, assuming the existence of relative good minimal models.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

LFV and g-2 in non-universal SUSY models with light higgsinos

We consider a supersymmetric type-I seesaw framework with non-universal scalar masses at the GUT scale to explain the long-standing discrepancy of the anomalous magnetic moment of the muon. We find that it is difficult to accommodate the muon g-2 while keeping charged-lepton flavor violating processes under control for the conventional SO(10)-based relation between the up sector and neutrino sector. However, such tension can be relaxed by adding a Georgi-Jarlskog factor for the Yukawa matrices, which requires a non-trivial GUT-based model. In this model, we find that both observables are compatible for small mixings, CKM-like, in the neutrino Dirac Yukawa matrix.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the coverage of neutralino dark matter in coannihilations at the upgraded LHC

In the supersymmetric models, the coannihilation of the neutralino DM with a lighter supersymmetric particle provides a feasible way to accommodate the observed cosmological DM relic density. Such a mechanism predicts a compressed spectrum of the neutralino DM and its coannihilating partner, which results in the soft final states and makes the searches for sparticles challenging at colliders. On the other hand, the abundance of the freeze-out neutralino DM usually increases as the DM mass becomes heavier. This implies an upper bound on the mass of the neutralino DM. Given these observations, we explore the HE-LHC coverage of the neutralino DM for the coannihilations. By analyzing the events of the multijet with the missing transverse energy ($E^{miss}_T$), the monojet, the soft lepton pair plus $E^{miss}_T$, and the monojet plus a hadronic tau, we find that the neutralino DM mass can be excluded up to 2.6, 1.7 and 0.8 TeV in the gluino, stop and wino coannihilations at the $2σ$ level, respectively. However, there is still no sensitivity of the neutralino DM in stau coannihilation at the HE-LHC, due to the small cross section of the direct stau pair production and the low tagging efficiency of soft tau from the stau decay.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2

We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum. We detail current experimental offerings in direct searches for new particles, measurements, technical implementations and Open Data, and provide a set of recommendations for further improving the presentation of LHC results in order to better enable reinterpretation in the future. We also provide a brief description of existing software reinterpretation frameworks and recent global analyses of new physics that make use of the current data.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Security Analysis of EOSIO Smart Contracts

The EOSIO blockchain, one of the representative Delegated Proof-of-Stake (DPoS) blockchain platforms, has grown rapidly recently. Meanwhile, a number of vulnerabilities and high-profile attacks against top EOSIO DApps and their smart contracts have also been discovered and observed in the wild, resulting in serious financial damages. Most of EOSIO's smart contracts are not open-sourced and they are typically compiled to WebAssembly (Wasm) bytecode, thus making it challenging to analyze and detect the presence of possible vulnerabilities. In this paper, we propose EOSAFE, the first static analysis framework that can be used to automatically detect vulnerabilities in EOSIO smart contracts at the bytecode level. Our framework includes a practical symbolic execution engine for Wasm, a customized library emulator for EOSIO smart contracts, and four heuristics-driven detectors to identify the presence of four most popular vulnerabilities in EOSIO smart contracts. Experiment results suggest that EOSAFE achieves promising results in detecting vulnerabilities, with an F1-measure of 98%. We have applied EOSAFE to all active 53,666 smart contracts in the ecosystem (as of November 15, 2019). Our results show that over 25% of the smart contracts are vulnerable. We further analyze possible exploitation attempts against these vulnerable smart contracts and identify 48 in-the-wild attacks (25 of them have been confirmed by DApp developers), resulting in financial loss of at least 1.7 million USD.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The Quenching-Activation Behavior of the Gradient Descent Dynamics for Two-layer Neural Network Models

A numerical and phenomenological study of the gradient descent (GD) algorithm for training two-layer neural network models is carried out for different parameter regimes when the target function can be accurately approximated by a relatively small number of neurons. It is found that for Xavier-like initialization, there are two distinctive phases in the dynamic behavior of GD in the under-parametrized regime: An early phase in which the GD dynamics follows closely that of the corresponding random feature model and the neurons are effectively quenched, followed by a late phase in which the neurons are divided into two groups: a group of a few "activated" neurons that dominate the dynamics and a group of background (or "quenched") neurons that support the continued activation and deactivation process. This neural network-like behavior is continued into the mildly over-parametrized regime, where it undergoes a transition to a random feature-like behavior. The quenching-activation process seems to provide a clear mechanism for "implicit regularization". This is qualitatively different from the dynamics associated with the "mean-field" scaling where all neurons participate equally and there does not appear to be qualitative changes when the network parameters are changed.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The Slow Deterioration of the Generalization Error of the Random Feature Model

The random feature model exhibits a kind of resonance behavior when the number of parameters is close to the training sample size. This behavior is characterized by the appearance of large generalization gap, and is due to the occurrence of very small eigenvalues for the associated Gram matrix. In this paper, we examine the dynamic behavior of the gradient descent algorithm in this regime. We show, both theoretically and experimentally, that there is a dynamic self-correction mechanism at work: The larger the eventual generalization gap, the slower it develops, both because of the small eigenvalues. This gives us ample time to stop the training process and obtain solutions with good generalization property.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Top quark as a probe of heavy Majorana neutrino at the LHC and future collider

Right-handed (RH) Majorana neutrinos play a crucial role in understanding the origin of neutrino mass, the nature of dark matter and the mechanism of matter-antimatter asymmetry. In this work, we investigate the observability of heavy Majorana neutrino through the top quark neutrinoless double beta decay process $t \to b \ell^+ \ell^+ j j$ at hadron colliders. By performing detector level simulation, we demonstrate that our method can give stronger limits on the light-heavy neutrino mixing parameters $|V_{eN, μN}|$ in the mass range of 15 GeV $< m_N <$ 80 GeV than other existing collider bounds.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Can we find steady-state solutions to multiscale rarefied gas flows within dozens of iterations?

One of the central problems in the study of rarefied gas dynamics is to find the steady-state solution of the Boltzmann equation quickly. When the Knudsen number is large, i.e. the system is highly rarefied, the conventional iteration scheme can lead to convergence within a few iterations. However, when the Knudsen number is small, i.e. the flow falls in the near-continuum regime, hundreds of thousands iterations are needed, and yet the &#34;converged&#34; solutions are prone to be contaminated by accumulated error and large numerical dissipation. Recently, based on the gas kinetic models, the implicit unified gas kinetic scheme (UGKS) and its variants have significantly reduced the iterations in the near-continuum flow regime, but still much higher than that of the highly rarefied gas flows. In this paper, we put forward a general synthetic iteration scheme (GSIS) to find the steady-state solutions of general rarefied gas flows within dozens of iterations at any Knudsen number. As the GSIS does not rely on the specific kinetic model/collision operator, it can be naturally extended to quickly find converged solutions for mixture flows and even flows involving chemical reactions. These two superior advantages are also expected to accelerate the slow convergence in simulation of near-continuum flows via the direct simulation Monte Carlo method and its low-variance version.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Testing electroweak SUSY for muon $g-2$ and dark matter at the LHC and beyond

Given that the LHC experiment has produced strong constraints on the colored supersymmetric particles (sparticles), testing the electroweak supersymmetry (EWSUSY) will be the next crucial task at the LHC. On the other hand, the light electroweakinos and sleptons in the EWSUSY can also contribute to the dark matter (DM) and low energy lepton observables. The precision measurements of them will provide the indirect evidence of SUSY. In this work, we confront the EWSUSY with the muon $g-2$ anomaly, the DM relic density, the direct detection limits and the latest LHC Run-2 data. We find that the sneutrino DM or the neutralino DM with sizable higgsino component has been excluded by the direct detections. Then two viable scenarios are pinned down: one has the light compressed bino and sleptons but heavy higgsinos, and the other has the light compressed bino, winos and sleptons. In the former case, the LSP and slepton masses have to be smaller than about 350 GeV. While in the latter case, the LSP and slepton masses have to be smaller than about 700 GeV and 800 GeV, respectively. From investigating the observability of these sparticles in both scenarios at future colliders, it turns out that the HE-LHC with a luminosity of 15 ab$^{-1}$ can exclude the whole BHL and most part of BWL scenarios at $2σ$ level. The precision measurement of the Higgs couplings at the lepton colliders could play a complementary role of probing the BWL scenario.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Unveiling CP property of top-Higgs coupling with graph neural networks at the LHC

The top-Higgs coupling plays an important role in particle physics and cosmology. The precision measurements of this coupling can provide an insight to new physics beyond the Standard Model. In this paper, we propose to use Message Passing Neural Network (MPNN) to reveal the CP nature of top-Higgs interaction through semi-leptonic channel $pp \to t(\to b\ell^-ν_\ell)\bar{t}(\to \bar{b}jj)h(\to b\bar{b})$. Using the test statistics constructed from the event classification probabilities given by the MPNN, we find that the pure CP-even and CP-odd components can be well distinguished at the LHC, with at most 300 fb$^{-1}$ experimental data.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

Boundary Layer of Transport Equation with In-Flow Boundary

Consider the steady neutron transport equation in 2D convex domains with in-flow boundary condition. In this paper, we establish the diffusive limit while the boundary layers are present. Our contribution relies on a delicate decomposition of boundary data to separate the regular and singular boundary layers, novel weighted $W^{1,\infty}$ estimates for the Milne problem with geometric correction in convex domains, as well as an $L^{2m}-L^{\infty}$ framework which yields stronger remainder estimates.

0 citations0 reviewsNo code linkedOpen access
preprint2018arXiv

High-Order Implicit Hybridizable Discontinuous Galerkin Method for the Boltzmann Equation

The high-order hybridizable discontinuous Galerkin (HDG) method combining with an implicit iterative scheme is used to find the steady-state solution of the Boltzmann equation with full collision integral on two-dimensional triangular meshes. The velocity distribution function and its trace are approximated in the piecewise polynomial space of degree up to 4. The fast spectral method (FSM) is incorporated into the DG discretization to evaluate the collision operator. Specific polynomial approximation is proposed for the collision term to reduce the computational cost. The proposed scheme is proved to be accurate and efficient.

0 citations0 reviewsNo code linkedOpen access