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Lei Wang

Publications and source records attributed to Lei Wang.

At least 739 records · Page 41Linked to original sources
arXiv2023

DECLOAK: Enable Secure and Cheap Multi-Party Transactions on Legacy Blockchains by a Minimally Trusted TEE Network

As the confidentiality and scalability of smart contracts have become a crucial demand of blockchains, off-chain contract execution frameworks have been promising. Some have recently expanded off-chain contracts to Multi-Party Computation (MPC), which seek to transition the on-chain states by off-chain MPC. The most general problem among these solutions is MPT, since its off-chain MPC takes on- and off-chain inputs, delivers on- and off-chain outputs, and can be publicly verified by the blockchain, thus capable of covering more scenarios. However, existing Multi-Party Transaction (MPT) solutions lack at least one of data availability, financial fairness, delivery fairness, and delivery atomicity. These properties are crucially valued by communities, e.g., the Ethereum community, or users. Even worse, these solutions require high-cost interactions between the blockchain and off-chain systems. This paper proposes a novel MPT-enabled off-chain contract execution framework, DECLOAK. DECLOAK is the first to achieve data availability of MPT, and our method can apply to other fields that seek to persist user data on-chain. Moreover, DECLOAK solves all mentioned shortcomings with even lower gas costs and weaker assumptions. Specifically, DECLOAK tolerates all but one Byzantine party and TEE executors. Evaluating on 10 MPTs, DECLOAK reduces the gas cost of the SOTA, Cloak, by 65.6%. Consequently, we are the first to not only achieve such level secure MPT in practical assumption, but also demonstrate that evaluating MPT in the comparable gas cost to normal Ethereum transaction is possible. And the cost superiority of DECLOAK increases as the number of MPT parties grows.

cs.CR↗
arXiv2023

ReDirTrans: Latent-to-Latent Translation for Gaze and Head Redirection

Learning-based gaze estimation methods require large amounts of training data with accurate gaze annotations. Facing such demanding requirements of gaze data collection and annotation, several image synthesis methods were proposed, which successfully redirected gaze directions precisely given the assigned conditions. However, these methods focused on changing gaze directions of the images that only include eyes or restricted ranges of faces with low resolution (less than $128\times128$) to largely reduce interference from other attributes such as hairs, which limits application scenarios. To cope with this limitation, we proposed a portable network, called ReDirTrans, achieving latent-to-latent translation for redirecting gaze directions and head orientations in an interpretable manner. ReDirTrans projects input latent vectors into aimed-attribute embeddings only and redirects these embeddings with assigned pitch and yaw values. Then both the initial and edited embeddings are projected back (deprojected) to the initial latent space as residuals to modify the input latent vectors by subtraction and addition, representing old status removal and new status addition. The projection of aimed attributes only and subtraction-addition operations for status replacement essentially mitigate impacts on other attributes and the distribution of latent vectors. Thus, by combining ReDirTrans with a pretrained fixed e4e-StyleGAN pair, we created ReDirTrans-GAN, which enables accurately redirecting gaze in full-face images with $1024\times1024$ resolution while preserving other attributes such as identity, expression, and hairstyle. Furthermore, we presented improvements for the downstream learning-based gaze estimation task, using redirected samples as dataset augmentation.

cs.CV↗
arXiv2023

MALM: Mask Augmentation based Local Matching for Food-Recipe Retrieval

Image-to-recipe retrieval is a challenging vision-to-language task of significant practical value. The main challenge of the task lies in the ultra-high redundancy in the long recipe and the large variation reflected in both food item combination and food item appearance. A de-facto idea to address this task is to learn a shared feature embedding space in which a food image is aligned better to its paired recipe than other recipes. However, such supervised global matching is prone to supervision collapse, i.e., only partial information that is necessary for distinguishing training pairs can be identified, while other information that is potentially useful in generalization could be lost. To mitigate such a problem, we propose a mask-augmentation-based local matching network (MALM), where an image-text matching module and a masked self-distillation module benefit each other mutually to learn generalizable cross-modality representations. On one hand, we perform local matching between the tokenized representations of image and text to locate fine-grained cross-modality correspondence explicitly. We involve representations of masked image patches in this process to alleviate overfitting resulting from local matching especially when some food items are underrepresented. On the other hand, predicting the hidden representations of the masked patches through self-distillation helps to learn general-purpose image representations that are expected to generalize better. And the multi-task nature of the model enables the representations of masked patches to be text-aware and thus facilitates the lost information reconstruction. Experimental results on Recipe1M dataset show our method can clearly outperform state-of-the-art (SOTA) methods. Our code will be available at https://github.com/MyFoodChoice/MALM_Mask_Augmentation_based_Local_Matching-_for-_Food_Recipe_Retrieval

cs.CV↗
arXiv2023

Enhanced ferroelectric properties of low-annealed SrBi2(Ta,Nb)2O9 thin films for NvFeRAM applications

Micro-Raman spectroscopy and X-ray diffraction have been used to explore the lattice dynamics of Nb-substituted SrBi2(Ta1-xNbx)2O9 (SBTN) crystalline thin films annealed at low temperature, 700oC. It turned out that SrBi2(Ta1-xNbx)2O9 films consist of fine-grained spherical structures for x=0.1-0.4, while the formation of rod-like grains occurs for x=0.5 due to the stress-induced transformation of the thin film perovskite structure. Moreover, it was revealed that during Nb cationic substitution Aurivillius phase formation was enhanced and become dominated in SBTN thin films and fluorite/pyrochlore phase formation was highly suppressed. We assume that these changes are conditioned by the ferrodistortion occurring in ferroic perovskites, namely by the tilting distortion of (Ta,Nb)O6 octahedra for x=0.2-0.5. The octahedral tilting distortion can change the coordination environment of the A-cite cation, as well as it lowers the SBTN symmetry due to the differences in ionic radius and mass between Ta and Nb in the B-sites, that can lead to significant changes of the SBTN crystal structure. The same nonmonotonic trends were observed for the ferroelectric perovskite phase fraction and remanent polarization on Nb content in SBTN films. The substituting Nb atoms with a concentration of 10-20 % made it possible to increase the remanent polarization in 3 times and raise the perovskite phase fraction from 66% to 87%. Therefore, obtained results can be used for the production of lead-free thin films with a high remanent polarization under low annealing temperature, being promising for advanced nonvolatile random access ferroelectric memory (NvFeRAM) applications. Keywords: ferroelectric, sol-gel method, perovskite structure, crystallization annealing, defects, remanent polarization, SBTN films, Raman spectroscopy, Landau-Devonshire approach.

cond-mat.mtrl-sci↗
arXiv2023

Read, Diagnose and Chat: Towards Explainable and Interactive LLMs-Augmented Depression Detection in Social Media

This paper proposes a new depression detection system based on LLMs that is both interpretable and interactive. It not only provides a diagnosis, but also diagnostic evidence and personalized recommendations based on natural language dialogue with the user. We address challenges such as the processing of large amounts of text and integrate professional diagnostic criteria. Our system outperforms traditional methods across various settings and is demonstrated through case studies.

cs.CL↗
arXiv2023

CMLCompiler: A Unified Compiler for Classical Machine Learning

Classical machine learning (CML) occupies nearly half of machine learning pipelines in production applications. Unfortunately, it fails to utilize the state-of-the-practice devices fully and performs poorly. Without a unified framework, the hybrid deployments of deep learning (DL) and CML also suffer from severe performance and portability issues. This paper presents the design of a unified compiler, called CMLCompiler, for CML inference. We propose two unified abstractions: operator representations and extended computational graphs. The CMLCompiler framework performs the conversion and graph optimization based on two unified abstractions, then outputs an optimized computational graph to DL compilers or frameworks. We implement CMLCompiler on TVM. The evaluation shows CMLCompiler's portability and superior performance. It achieves up to 4.38$\times$ speedup on CPU, 3.31$\times$ speedup on GPU, and 5.09$\times$ speedup on IoT devices, compared to the state-of-the-art solutions -- scikit-learn, intel sklearn, and hummingbird. Our performance of CML and DL mixed pipelines achieves up to 3.04x speedup compared with cross-framework implementations. The project documents and source code are available at https://www.computercouncil.org/cmlcompiler.

cs.LG↗
arXiv2023

Learning Partial Correlation based Deep Visual Representation for Image Classification

Visual representation based on covariance matrix has demonstrates its efficacy for image classification by characterising the pairwise correlation of different channels in convolutional feature maps. However, pairwise correlation will become misleading once there is another channel correlating with both channels of interest, resulting in the ``confounding'' effect. For this case, ``partial correlation'' which removes the confounding effect shall be estimated instead. Nevertheless, reliably estimating partial correlation requires to solve a symmetric positive definite matrix optimisation, known as sparse inverse covariance estimation (SICE). How to incorporate this process into CNN remains an open issue. In this work, we formulate SICE as a novel structured layer of CNN. To ensure end-to-end trainability, we develop an iterative method to solve the above matrix optimisation during forward and backward propagation steps. Our work obtains a partial correlation based deep visual representation and mitigates the small sample problem often encountered by covariance matrix estimation in CNN. Computationally, our model can be effectively trained with GPU and works well with a large number of channels of advanced CNNs. Experiments show the efficacy and superior classification performance of our deep visual representation compared to covariance matrix based counterparts.

cs.CV↗
arXiv2023

Physical cool-core condensation radius in massive galaxy clusters

We investigate the properties of cool cores in an optimally selected sample of 37 massive and X-ray-bright galaxy clusters, with regular morphologies, observed with Chandra. We measured the density, temperature, and abundance radial profiles of their intracluster medium (ICM). From these independent quantities, we computed the cooling (tcool) free-fall (tff), and turbulence (teddy) timescales as a function of radius. By requiring the profile-crossing condition, tcool=teddy=1, we measured the cool-core condensation radius Rccc, within which the balancing feeding and feedback processes generate the turbulent condensation rain and related chaotic cold accretion (CCA). We also constrained the complementary (quenched) cooling flow radius Rqcf, obtained via the condition tcool=25Xtff, that encompasses the region of thermally unstable cooling. We find that in our cluster sample and in the limited redshift range considered (1.3E14<M500<16.6E14 Msun, 0.03<z<0.29), the distribution of Rccc peaks at 0.01r500 and the entire range remains below 0.07r500, with a very weak increase with redshift and no dependence on the cluster mass. We find that Rqcf is typically 3 times larger than Rccc, with a wider distribution, and growing more slowly along Rccc, according to an average relation Rqcf~Rccc^(0.46), with a large intrinsic scatter. We suggest that this sublinear relation can be understood as an effect of the micro rain of pockets of cooled gas flickering in the turbulent ICM, whose dynamical and thermodynamical properties are referred to as "macro weather". Substituting the classical cool-core radius R(7.7Gyr), we propose that Rqcf is an indicator of the size of the global cores tied to the long-term macro weather, with the inner Rccc closely tracing the effective condensation rain and chaotic cold accretion (CCA) zone that feeds the central supermassive black hole.

astro-ph.GA↗
arXiv2023

Universal scaling of Klein bottle entropy near conformal critical points

We show that the Klein bottle entropy [Phys. Rev. Lett. 119, 261603 (2017)] for conformal field theories (CFTs) perturbed by a relevant operator is a universal function of the dimensionless coupling constant. The universal scaling of the Klein bottle entropy near criticality provides an efficient approach to extract the scaling dimension of lattice operators via data collapse. As paradigmatic examples, we validate the universal scaling of the Klein bottle entropy for Ising and Z3 parafermion CFTs with various perturbations using numerical simulation with continuous matrix product operator approach.

cond-mat.str-el↗
arXiv2023

A concise review on some Higgs-related new physics models in light of current experiments

Higgs boson may serve as a portal to new physics beyond the standard model (BSM) which is implied by theoretical naturalness or experimental anomalies. In this note we briefly survey some Higgs-related BSM physics models, including the low energy SUSY (focusing on the minimal SUSY model), the little Higgs models, the two-Higgs-doublet models and the simplest singlet extensions of the Higgs sector. For each illustrated BSM model, we emphatically elucidate its phenomenological power in explaining current measurements of the muon g-2, the W-boson mass and the dark matter. For the singlet extensions like the xSM and 2HDM+S, we discuss the induced cosmic phase transition and the dark matter relic density as well as the vacuum stability. Finally, we give an outlook.

hep-ph↗
arXiv2023

Inflation, electroweak phase transition, and Higgs searches at the LHC in the two-Higgs-doublet model

Combining the electroweak phase transition and the Higgs searches at the LHC, we study the Higgs inflation in the type-I and type-II two-Higgs-doublet models with non-minimally couplings to gravity. Considering relevant theoretical and experimental constraints, we find that the Higgs inflation imposes stringent constraints on the mass splitting between $A$, $H^\pm$ and $H$, and they tend to be nearly degenerate in mass with increasing of their masses. The direct searches for Higgs at the LHC can exclude many points achieving Higgs inflation in the region of $m_H~(m_A)<$ 450 GeV in the type-I model, and impose a lower bound on $\tanβ$ for the type-II model. The Higgs inflation disfavors the wrong sign Yukawa coupling region of type-II model. In the parameter space achieving the Higgs inflation, the type-I and type-II models can produce a first order electroweak phase transition, but $v_c/T_c$ is much smaller than 1.0.

hep-ph↗
arXiv2023

Two-Higgs-doublet models in light of current experiments: a brief review

We briefly survey several typical CP-conserving two-Higgs-doublet models (2HDMs) in light of current experiments. First we derive the masses and couplings of the mass eigenstates from the Lagrangians. Then we analyze the constraints from theory and oblique electroweak parameters. Finally, we delineate the status of 2HDM in light of the LHC searches, the dark matter detections and the muon $g-2$ measurement.

hep-ph↗
arXiv2023

Zero-Shot Next-Item Recommendation using Large Pretrained Language Models

Large language models (LLMs) have achieved impressive zero-shot performance in various natural language processing (NLP) tasks, demonstrating their capabilities for inference without training examples. Despite their success, no research has yet explored the potential of LLMs to perform next-item recommendations in the zero-shot setting. We have identified two major challenges that must be addressed to enable LLMs to act effectively as recommenders. First, the recommendation space can be extremely large for LLMs, and LLMs do not know about the target user's past interacted items and preferences. To address this gap, we propose a prompting strategy called Zero-Shot Next-Item Recommendation (NIR) prompting that directs LLMs to make next-item recommendations. Specifically, the NIR-based strategy involves using an external module to generate candidate items based on user-filtering or item-filtering. Our strategy incorporates a 3-step prompting that guides GPT-3 to carry subtasks that capture the user's preferences, select representative previously watched movies, and recommend a ranked list of 10 movies. We evaluate the proposed approach using GPT-3 on MovieLens 100K dataset and show that it achieves strong zero-shot performance, even outperforming some strong sequential recommendation models trained on the entire training dataset. These promising results highlight the ample research opportunities to use LLMs as recommenders. The code can be found at https://github.com/AGI-Edgerunners/LLM-Next-Item-Rec.

cs.IR↗
arXiv2023

METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens

In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a "multi-expert joint diagnosis" mechanism to upgrade the existing "single expert" framework commonly seen in the current literature. To this end, we propose METransformer, a method to realize this idea with a transformer-based backbone. The key design of our method is the introduction of multiple learnable "expert" tokens into both the transformer encoder and decoder. In the encoder, each expert token interacts with both vision tokens and other expert tokens to learn to attend different image regions for image representation. These expert tokens are encouraged to capture complementary information by an orthogonal loss that minimizes their overlap. In the decoder, each attended expert token guides the cross-attention between input words and visual tokens, thus influencing the generated report. A metrics-based expert voting strategy is further developed to generate the final report. By the multi-experts concept, our model enjoys the merits of an ensemble-based approach but through a manner that is computationally more efficient and supports more sophisticated interactions among experts. Experimental results demonstrate the promising performance of our proposed model on two widely used benchmarks. Last but not least, the framework-level innovation makes our work ready to incorporate advances on existing "single-expert" models to further improve its performance.

cs.CV↗
arXiv2023

Phase Field Model for Non-equilibrium Interface Conditions

This article presents a new phase-field formulation for non-equilibrium interface conditions in rapid phase transformations. With a particular way of defining concentration fields, the classical sharp and diffuse (thick) interface theories are unified into the present phase-field model. Each point inside the diffuse interface is comparable to the classical sharp interface model, governed by phase boundary migration and short-range atomic exchanges and connected by long-range diffusion. Their thermodynamic forces and fluxes are following the Onsager reciprocal relation and consistent with the classical irreversible thermodynamics. Furthermore, we establish the balance of driving forces and energy dissipations for both the diffuse interface and the single point inside it. The model is then validated by rapid solidification of Al-Cu alloys. With an effective mobility of considering non-equilibrium long-range diffusion, the model represents the main characteristics of non-equilibrium interface kinetics, i.e., solute trapping and solute drag. Especially, we reproduce the complete trapping at the high-velocity regime and provide a thermodynamic consistent description of the partial drag phenomena at the low-velocity region. Moreover, the dissipation analysis indicates that even the far-from-equilibrium diffuse interface is composed of numerous representative volume elements near the equilibrium, providing a new understanding of non-equilibrium interfaces.

cond-mat.mtrl-sci↗
arXiv2023

Smoothing Gradient Tracking for Decentralized Optimization over the Stiefel Manifold with Non-smooth Regularizers

Recently, decentralized optimization over the Stiefel manifold has attacked tremendous attentions due to its wide range of applications in various fields. Existing methods rely on the gradients to update variables, which are not applicable to the objective functions with non-smooth regularizers, such as sparse PCA. In this paper, to the best of our knowledge, we propose the first decentralized algorithm for non-smooth optimization over Stiefel manifolds. Our algorithm approximates the non-smooth part of objective function by its Moreau envelope, and then existing algorithms for smooth optimization can be deployed. We establish the convergence guarantee with the iteration complexity of $\mathcal{O} (ε^{-4})$. Numerical experiments conducted under the decentralized setting demonstrate the effectiveness and efficiency of our algorithm.

math.OC↗
arXiv2023

3Mformer: Multi-order Multi-mode Transformer for Skeletal Action Recognition

Many skeletal action recognition models use GCNs to represent the human body by 3D body joints connected body parts. GCNs aggregate one- or few-hop graph neighbourhoods, and ignore the dependency between not linked body joints. We propose to form hypergraph to model hyper-edges between graph nodes (e.g., third- and fourth-order hyper-edges capture three and four nodes) which help capture higher-order motion patterns of groups of body joints. We split action sequences into temporal blocks, Higher-order Transformer (HoT) produces embeddings of each temporal block based on (i) the body joints, (ii) pairwise links of body joints and (iii) higher-order hyper-edges of skeleton body joints. We combine such HoT embeddings of hyper-edges of orders 1, ..., r by a novel Multi-order Multi-mode Transformer (3Mformer) with two modules whose order can be exchanged to achieve coupled-mode attention on coupled-mode tokens based on 'channel-temporal block', 'order-channel-body joint', 'channel-hyper-edge (any order)' and 'channel-only' pairs. The first module, called Multi-order Pooling (MP), additionally learns weighted aggregation along the hyper-edge mode, whereas the second module, Temporal block Pooling (TP), aggregates along the temporal block mode. Our end-to-end trainable network yields state-of-the-art results compared to GCN-, transformer- and hypergraph-based counterparts.

cs.CV↗
arXiv2023

Projected d-wave superconducting state: a fermionic projected entangled pair state study

We investigate the physics of projected d-wave pairing states using their fermionic projected entangled pair state (fPEPS) representation. First, we approximate a d-wave Bardeen-Cooper-Schrieffer state using the Gaussian fPEPS. Next, we translate the resulting state into fPEPS tensors and implement the Gutzwiller projection which removes double occupancy by modifying the local tensor elements. The tensor network representation of the projected d-wave pairing state allows us to evaluate physical quantities in the thermodynamic limit without employing the Gutzwiller approximation. Despite having very few variational parameters, such physically motivated tensor network states are shown to exhibit competitive energies for the doped t-J model. We expect that such construction offers useful initial states and guidance for variational tensor network calculations.

cond-mat.str-el↗