Researcher profile

Wei Dai

Wei Dai contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate

Trust snapshot

Quick read

Trust 21 - EmergingVerification L1Unclaimed author

27works

0followers

28topics

4close collaborators

Actions

Decide how to stay connected

Follow researcher0

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

Direct collaboration

Open a focused conversation when the fit is right

Claim this author entity first to unlock direct invitations.

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.

preprint2026arXiv

A Control Theoretic Approach to Decentralized AI Economy Stabilization via Dynamic Buyback-and-Burn Mechanisms

The democratization of artificial intelligence through decentralized networks represents a paradigm shift in computational provisioning, yet the long-term viability of these ecosystems is critically endangered by the extreme volatility of their native economic layers. Current tokenomic models, which predominantly rely on static or threshold-based buyback heuristics, are ill-equipped to handle complex system dynamics and often function pro-cyclically, exacerbating instability during market downturns. To bridge this gap, we propose the Dynamic-Control Buyback Mechanism (DCBM), a formalized control-theoretic framework that utilizes a Proportional-Integral-Derivative (PID) controller with strict solvency constraints to regulate the token economy as a dynamical system. Extensive agent-based simulations utilizing Jump-Diffusion processes demonstrate that DCBM fundamentally outperforms static baselines, reducing token price volatility by approximately 66% and lowering operator churn from 19.5% to 8.1% in high-volatility regimes. These findings establish that converting tokenomics from static rules into continuous, structurally constrained control loops is a necessary condition for secure and sustainable decentralized intelligence networks.

preprint2026arXiv

CasualSynth: Generating Structurally Sound Synthetic Data

Large Language Models (LLMs) generate realistic synthetic data but offer no guarantee that their outputs respect the causal mechanisms governing the target domain. We introduce CausalSynth, a framework that decouples causal structure generation from semantic realization, yielding synthetic data that is both causally valid and linguistically rich. The framework operates in three phases. First, a Structural Causal Model (SCM) - a tuple of structural equations defined over a directed acyclic graph (DAG) generates causal skeletons, i.e., variable assignments that satisfy the Global Markov Property of the governing DAG, via ancestral sampling. Second, an LLM acts as a constrained \emph{realizer}, a conditional translator that maps each skeleton to a high-dimensional observation such as a clinical note or a transaction log. Third, an Iterative Consistency Verification module detects structural violations through deterministic extraction and feeds targeted corrections back to the LLM, forming a closed-loop refinement process. We identify the Semantic Backdoor problem the systematic tendency of LLMs to override imposed causal facts with pre-training priors -- and prove that our iterative mechanism reduces the resulting selection bias relative to standard rejection sampling. On three causal benchmarks (ASIA, ALARM, and MIMIC-Struct), CausalSynth preserved conditional independencies with false-positive rates near the nominal $α=0.05$ level and achieved realizability rates above 96% with 70B-parameter LLM backbones. The framework additionally supports principled interventional and counterfactual generation through noise retention and graph mutilation.

preprint2026arXiv

Differentially Private Motif-Preserving Multi-modal Hashing

Cross-modal hashing enables efficient retrieval by encoding images and text into compact binary codes. State-of-the-art methods rely on semantic similarity graphs derived from user interactions for supervision, yet these graphs encode sensitive behavioral patterns vulnerable to link reconstruction attacks. Existing privacy-preserving approaches fail on graph-structured data: Differentially Private SGD destroys relational motifs by treating samples independently, while graph synthesis methods suffer from unbounded local sensitivity in scale-free networks, hub nodes cause single-edge modifications to alter triangle counts by $\mathcal{O}(N)$, necessitating prohibitive noise injection. We term this phenomenon Hubness Explosion. We propose DMP-MH, a Sanitize-then-Distill framework that decouples privacy from representation learning. Our approach first bounds sensitivity by deterministically clipping node degrees, capping the $L_2$-sensitivity of triangle motifs independently of dataset size. A sanitized synthetic graph is then generated via Noisy Mirror Descent under $(ε,δ)$-Edge Differential Privacy. Finally, dual-stream hashing networks distill this topology using a holistic structural loss that enforces cross-modal alignment. Evaluated on MIRFlickr-25K and NUS-WIDE under a strict inductive protocol, DMP-MH outperforms private baselines by up to 11.4 mAP points while retaining up to 92.5% of non-private performance.

preprint2026arXiv

MathDoc: Benchmarking Structured Extraction and Active Refusal on Noisy Mathematics Exam Papers

The automated extraction of structured questions from paper-based mathematics exams is fundamental to intelligent education, yet remains challenging in real-world settings due to severe visual noise. Existing benchmarks mainly focus on clean documents or generic layout analysis, overlooking both the structural integrity of mathematical problems and the ability of models to actively reject incomplete inputs. We introduce MathDoc, the first benchmark for document-level information extraction from authentic high school mathematics exam papers. MathDoc contains \textbf{3,609} carefully curated questions with real-world artifacts and explicitly includes unrecognizable samples to evaluate active refusal behavior. We propose a multi-dimensional evaluation framework covering stem accuracy, visual similarity, and refusal capability. Experiments on SOTA MLLMs, including Qwen3-VL and Gemini-2.5-Pro, show that although end-to-end models achieve strong extraction performance, they consistently fail to refuse illegible inputs, instead producing confident but invalid outputs. These results highlight a critical gap in current MLLMs and establish MathDoc as a benchmark for assessing model reliability under degraded document conditions. Our project repository is available at \href{https://github.com/winnk123/papers/tree/master}{GitHub repository}

preprint2025arXiv

Exploiting Scale-Variant Attention for Segmenting Small Medical Objects

Early detection and accurate diagnosis can predict the risk of malignant disease transformation, thereby increasing the probability of effective treatment. Identifying mild syndrome with small pathological regions serves as an ominous warning and is fundamental in the early diagnosis of diseases. While deep learning algorithms, particularly convolutional neural networks (CNNs), have shown promise in segmenting medical objects, analyzing small areas in medical images remains challenging. This difficulty arises due to information losses and compression defects from convolution and pooling operations in CNNs, which become more pronounced as the network deepens, especially for small medical objects. To address these challenges, we propose a novel scale-variant attention-based network (SvANet) for accurately segmenting small-scale objects in medical images. The SvANet consists of scale-variant attention, cross-scale guidance, Monte Carlo attention, and vision transformer, which incorporates cross-scale features and alleviates compression artifacts for enhancing the discrimination of small medical objects. Quantitative experimental results demonstrate the superior performance of SvANet, achieving 96.12%, 96.11%, 89.79%, 84.15%, 80.25%, 73.05%, and 72.58% in mean Dice coefficient for segmenting kidney tumors, skin lesions, hepatic tumors, polyps, surgical excision cells, retinal vasculatures, and sperms, which occupy less than 1% of the image areas in KiTS23, ISIC 2018, ATLAS, PolypGen, TissueNet, FIVES, and SpermHealth datasets, respectively.

preprint2022arXiv

A New Learning Paradigm for Stochastic Configuration Network: SCN+

Learning using privileged information (LUPI) paradigm, which pioneered teacher-student interaction mechanism, makes the learning models use additional information in training stage. This paper is the first to propose an incremental learning algorithm with LUPI paradigm for stochastic configuration network (SCN), named SCN+. This novel algorithm can leverage privileged information into SCN in the training stage, which provides a new method to train SCN. Moreover, the convergences have been studied in this paper. Finally, experimental results indicate that SCN+ indeed performs favorably.

preprint2022arXiv

Blind Two-Dimensional Super-Resolution and Its Performance Guarantee (Extended Version)

We study the problem of identifying the parameters of a linear system from its response to multiple unknown waveforms. We assume that the system response is a scaled superposition of time-delayed and frequency-shifted versions of the unknown waveforms. Such kind of problem is severely ill-posed and does not yield a unique solution without introducing further constraints. To fully characterize the system, we assume that the unknown waveforms lie in a common known low-dimensional subspace that satisfies certain properties. Then, we develop a blind two-dimensional (2D) super-resolution framework that applies to a large number of applications. In this framework, we show that under a minimum separation between the time-frequency shifts, all the unknowns that characterize the system can be recovered precisely and with high probability provided that a lower bound on the number of the observed samples is satisfied. The proposed framework is based on a 2D atomic norm minimization problem, which is shown to be reformulated and solved via semidefinite programming. Simulation results that confirm the theoretical findings of the paper are provided.

preprint2022arXiv

Data-Efficient Modeling for Precise Power Consumption Estimation of Quadrotor Operations Using Ensemble Learning

Electric Take-Off and Landing (eVTOL) aircraft is considered as the major aircraft type in the emerging urban air mobility. Accurate power consumption estimation is crucial to eVTOL, supporting advanced power management strategies and improving the efficiency and safety performance of flight operations. In this study, a framework for power consumption modeling of eVTOL aircraft was established. We employed an ensemble learning method, namely stacking, to develop a data-driven model using flight records of three different types of quadrotors. Random forest and extreme gradient boosting, showing advantages in prediction, were chosen as base-models, and a linear regression model was used as the meta-model. The established stacking model can accurately estimate the power of a quadrotor. Error analysis shows that about 80% prediction errors fall within one standard deviation interval and less than 0.5% error in the prediction for an entire flight can be expected with a confidence of more than 80%. Our model outperforms the existing models in two aspects: firstly, our model has a better prediction performance, and secondly, our model is more data-efficient, requiring a much smaller dataset. Our model provides a powerful tool for operators of eVTOL aircraft in mission management and contributes to promoting safe and energy-efficient urban air traffic.

preprint2022arXiv

Digging into Primary Financial Market: Challenges and Opportunities of Adopting Blockchain

Since the emergence of blockchain technology, its application in the financial market has always been an area of focus and exploration by all parties. With the characteristics of anonymity, trust, tamper-proof, etc., blockchain technology can effectively solve some problems faced by the financial market, such as trust issues and information asymmetry issues. To deeply understand the application scenarios of blockchain in the financial market, the issue of securities issuance and trading in the primary market is a problem that must be studied clearly. We conducted an empirical study to investigate the main difficulties faced by primary market participants in their business practices and the potential challenges of the deepening application of blockchain technology in the primary market. We adopted a hybrid method combining interviews (qualitative methods) and surveys (quantitative methods) to conduct this research in two stages. In the first stage, we interview 15 major primary market participants with different backgrounds and expertise. In the second phase, we conducted a verification survey of 54 primary market practitioners to confirm various insights from the interviews, including challenges and desired improvements. Our interviews and survey results revealed several significant challenges facing blockchain applications in the primary market: complex due diligence, mismatch, and difficult monitoring. On this basis, we believe that our future research can focus on some aspects of these challenges.

preprint2022arXiv

Maximum principles and the method of moving planes for the uniformly elliptic nonlocal Bellman operator and applications

In this paper, we establish various maximum principles and develop the method of moving planes and the sliding method (on general unbounded domains) for equations involving the uniformly elliptic nonlocal Bellman operator. As a consequence, we derive multiple applications of these maximum principles and the moving planes method. For instance, we prove symmetry, monotonicity and uniqueness results and asymptotic properties for solutions to various equations involving the uniformly elliptic nonlocal Bellman operator in bounded domains, unbounded domains, epigraph or $\mathbb{R}^{n}$. In particular, the uniformly elliptic nonlocal Monge-Ampère operator introduced by Caffarelli and Charro in \cite{CC} is a typical example of the uniformly elliptic nonlocal Bellman operator.

preprint2022arXiv

Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics

As a randomized learner model, SCNs are remarkable that the random weights and biases are assigned employing a supervisory mechanism to ensure universal approximation and fast learning. However, the randomness makes SCNs more likely to generate approximate linear correlative nodes that are redundant and low quality, thereby resulting in non-compact network structure. In the light of a fundamental principle in machine learning, that is, a model with fewer parameters holds improved generalization. This paper proposes orthogonal SCN, termed OSCN, to filtrate out the low-quality hidden nodes for network structure reduction by incorporating Gram-Schmidt orthogonalization technology. The universal approximation property of OSCN and an adaptive setting for the key construction parameters have been presented in details. In addition, an incremental updating scheme is developed to dynamically determine the output weights, contributing to improved computational efficiency. Finally, experimental results on two numerical examples and several real-world regression and classification datasets substantiate the effectiveness and feasibility of the proposed approach.

preprint2022arXiv

Solving DC Power Flow Problems Using Quantum and Hybrid algorithms

Power flow calculation plays an important role in planning, operation, and control of the power system. The quantum HHL algorithm can achieve theoretical exponential speedup over classical algorithms on DC power flow calculation. Since the qubit resources in the Noisy Intermediate-scale Quantum (NISQ) era are limited, it is important to discuss the performance considering this limitation. The coefficient matrix of the linear systems of equations in DC power flow problems cannot be represented perfectly by finite binary number strings, which leads to imperfect phase estimation. This work is carried out under the assumption of imperfect phase estimation. The performance of the HHL algorithm is systematically investigated with different accuracy and redundant qubits. In order to further reduce the required qubit resources, a hybrid quantum-classical algorithm is proposed. By comparing errors of the HHL and hybrid algorithms in the DC power flow calculation of the IEEE 5-bus test system, it is found that the hybrid algorithm can achieve comparable precision with fewer qubits than HHL by increasing the number of phase estimation modules, which may make the hybrid algorithm a feasible route in the NISQ era.

preprint2021arXiv

Improved ACD-based financial trade durations prediction leveraging LSTM networks and Attention Mechanism

The liquidity risk factor of security market plays an important role in the formulation of trading strategies. A more liquid stock market means that the securities can be bought or sold more easily. As a sound indicator of market liquidity, the transaction duration is the focus of this study. We concentrate on estimating the probability density function p(Δt_(i+1) |G_i) where Δt_(i+1) represents the duration of the (i+1)-th transaction, G_i represents the historical information at the time when the (i+1)-th transaction occurs. In this paper, we propose a new ultra-high-frequency (UHF) duration modelling framework by utilizing long short-term memory (LSTM) networks to extend the conditional mean equation of classic autoregressive conditional duration (ACD) model while retaining the probabilistic inference ability. And then the attention mechanism is leveraged to unveil the internal mechanism of the constructed model. In order to minimize the impact of manual parameter tuning, we adopt fixed hyperparameters during the training process. The experiments applied to a large-scale dataset prove the superiority of the proposed hybrid models. In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer.

preprint2021arXiv

Liouville type theorems for fractional and higher order Hénon-Hardy type equations via the method of scaling spheres

In this paper, we are concerned with the fractional and higher order Hénon-Hardy type equations \begin{equation*} (-Δ)^{\fracα{2}}u(x)=f(x,u(x)) \,\,\,\,\,\,\,\,\,\,\,\, \text{in} \,\,\, \mathbb{R}^{n}, \,\,\, \mathbb{R}^{n}_{+} \,\,\, \text{or} \,\,\, Ω\end{equation*} with $n>α$, $0<α<2$ or $α=2m$ with $1\leq m<\frac{n}{2}$. We first consider the typical case $f(x,u)=|x|^{a}u^{p}$ with $a\in(-α,\infty)$ and $0<p<p_{c}(a):=\frac{n+α+2a}{n-α}$. By using the method of scaling spheres, we prove Liouville theorems for the above Hénon-Hardy equations and equivalent integral equations in $\mathbb{R}^{n}$ and $\mathbb{R}^{n}_{+}$. Our results improve the known Liouville theorems for some especially admissible subranges of $a$ and $1<p<\min\left\{\frac{n+α+a}{n-α},p_{c}(a)\right\}$ to the full range $a\in(-α,\infty)$ and $p\in(0,p_{c}(a))$. When $a>0$, we covered the gap $p\in\big[\frac{n+α+a}{n-α},p_{c}(a)\big)$. In particular, when $α=2$, our results give an affirmative answer to the conjecture posed by Phan and Souplet \cite{PS}. As a consequence, we derive a priori estimates and existence of positive solutions to higher order Lane-Emden equations in bounded domains for all $1<p<\frac{n+2m}{n-2m}$. Our theorems improve the results in \cite{CFL,DPQ} remarkably to the maximal range of $p$. For bounded domains $Ω$, we also apply the method of scaling spheres to derive Liouville theorems for super-critical problems. Extensions to PDEs and IEs with general nonlinearities $f(x,u)$ are also included. We believe the method of scaling spheres developed here can be applied conveniently to various fractional or higher order problems with singularities or without translation invariance or in the cases the method of moving planes in conjunction with Kelvin transforms do not work.

preprint2020arXiv

Demonstration of Controlled-Phase Gates between Two Error-Correctable Photonic Qubits

To realize fault-tolerant quantum computing, it is necessary to store quantum information in logical qubits with error correction functions, realized by distributing a logical state among multiple physical qubits or by encoding it in the Hilbert space of a high-dimensional system. Quantum gate operations between these error-correctable logical qubits, which are essential for implementation of any practical quantum computational task, have not been experimentally demonstrated yet. Here we demonstrate a geometric method for realizing controlled-phase gates between two logical qubits encoded in photonic fields stored in cavities. The gates are realized by dispersively coupling an ancillary superconducting qubit to these cavities and driving it to make a cyclic evolution depending on the joint photonic state of the cavities, which produces a conditional geometric phase. We first realize phase gates for photonic qubits with the logical basis states encoded in two quasiorthogonal coherent states, which have important implications for continuous-variable-based quantum computation. Then we use this geometric method to implement a controlled-phase gate between two binomially encoded logical qubits, which have an error-correctable function.

preprint2020arXiv

Dictionary Learning with BLOTLESS Update

Algorithms for learning a dictionary to sparsely represent a given dataset typically alternate between sparse coding and dictionary update stages. Methods for dictionary update aim to minimise expansion error by updating dictionary vectors and expansion coefficients given patterns of non-zero coefficients obtained in the sparse coding stage. We propose a block total least squares (BLOTLESS) algorithm for dictionary update. BLOTLESS updates a block of dictionary elements and the corresponding sparse coefficients simultaneously. In the error free case, three necessary conditions for exact recovery are identified. Lower bounds on the number of training data are established so that the necessary conditions hold with high probability. Numerical simulations show that the bounds approximate well the number of training data needed for exact dictionary recovery. Numerical experiments further demonstrate several benefits of dictionary learning with BLOTLESS update compared with state-of-the-art algorithms especially when the amount of training data is small.

preprint2020arXiv

Direct methods for pseudo-relativistic Schrödinger operators

In this paper, we establish various maximal principles and develop the direct moving planes and sliding methods for equations involving the physically interesting (nonlocal) pseudo-relativistic Schrödinger operators $(-Δ+m^{2})^{s}$ with $s\in(0,1)$ and mass $m>0$. As a consequence, we also derive multiple applications of these direct methods. For instance, we prove monotonicity, symmetry and uniqueness results for solutions to various equations involving the operators $(-Δ+m^{2})^{s}$ in bounded domains, epigraph or $\mathbb{R}^{N}$, including pseudo-relativistic Schrödinger equations, 3D boson star equations and the equations with De Giorgi type nonlinearities.

preprint2020arXiv

EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation

Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations have made it all the more attractive. At the same time, FHE is notoriously hard to use with a very constrained programming model, a very unusual performance profile, and many cryptographic constraints. Existing compilers for FHE either target simpler but less efficient FHE schemes or only support specific domains where they can rely on expert-provided high-level runtimes to hide complications. This paper presents a new FHE language called Encrypted Vector Arithmetic (EVA), which includes an optimizing compiler that generates correct and secure FHE programs, while hiding all the complexities of the target FHE scheme. Bolstered by our optimizing compiler, programmers can develop efficient general-purpose FHE applications directly in EVA. For example, we have developed image processing applications using EVA, with a very few lines of code. EVA is designed to also work as an intermediate representation that can be a target for compiling higher-level domain-specific languages. To demonstrate this, we have re-targeted CHET, an existing domain-specific compiler for neural network inference, onto EVA. Due to the novel optimizations in EVA, its programs are on average 5.3x faster than those generated by CHET. We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers.

preprint2020arXiv

HEAX: An Architecture for Computing on Encrypted Data

With the rapid increase in cloud computing, concerns surrounding data privacy, security, and confidentiality also have been increased significantly. Not only cloud providers are susceptible to internal and external hacks, but also in some scenarios, data owners cannot outsource the computation due to privacy laws such as GDPR, HIPAA, or CCPA. Fully Homomorphic Encryption (FHE) is a groundbreaking invention in cryptography that, unlike traditional cryptosystems, enables computation on encrypted data without ever decrypting it. However, the most critical obstacle in deploying FHE at large-scale is the enormous computation overhead. In this paper, we present HEAX, a novel hardware architecture for FHE that achieves unprecedented performance improvement. HEAX leverages multiple levels of parallelism, ranging from ciphertext-level to fine-grained modular arithmetic level. Our first contribution is a new highly-parallelizable architecture for number-theoretic transform (NTT) which can be of independent interest as NTT is frequently used in many lattice-based cryptography systems. Building on top of NTT engine, we design a novel architecture for computation on homomorphically encrypted data. We also introduce several techniques to enable an end-to-end, fully pipelined design as well as reducing on-chip memory consumption. Our implementation on reconfigurable hardware demonstrates 164-268x performance improvement for a wide range of FHE parameters.

preprint2020arXiv

Learning Optimal Tree Models Under Beam Search

Retrieving relevant targets from an extremely large target set under computational limits is a common challenge for information retrieval and recommendation systems. Tree models, which formulate targets as leaves of a tree with trainable node-wise scorers, have attracted a lot of interests in tackling this challenge due to their logarithmic computational complexity in both training and testing. Tree-based deep models (TDMs) and probabilistic label trees (PLTs) are two representative kinds of them. Though achieving many practical successes, existing tree models suffer from the training-testing discrepancy, where the retrieval performance deterioration caused by beam search in testing is not considered in training. This leads to an intrinsic gap between the most relevant targets and those retrieved by beam search with even the optimally trained node-wise scorers. We take a first step towards understanding and analyzing this problem theoretically, and develop the concept of Bayes optimality under beam search and calibration under beam search as general analyzing tools for this purpose. Moreover, to eliminate the discrepancy, we propose a novel algorithm for learning optimal tree models under beam search. Experiments on both synthetic and real data verify the rationality of our theoretical analysis and demonstrate the superiority of our algorithm compared to state-of-the-art methods.

preprint2020arXiv

Radial distribution of charm quarks in jets in high-energy heavy-ion collisions

Heavy flavor physics in high-energy heavy-ion collisions is a promising and active area to study the mass dependence of the "jet quenching" effects both at the RHIC and the LHC. In this talk, we present the first theoretical study on the $D^0$ meson radial distributions relative to the jet axis both in p+p and Pb+Pb collisions at $\sqrt{s_{NN}}=5.02$ TeV, where a nice agreement of our results with experimental data is observed. The in-medium parton propagations are described by a Monte Carlo transport model which uses the next-to-leading order (NLO) plus parton shower (PS) event generator SHERPA as input and includes elastic (collisional) and inelastic (radiative) in-medium interaction of heavy flavor jet. We find that, at low $D^0$ meson $p_T$, the radial distribution significantly shifts to larger radius indicating a strong diffusion effect, and the diffusion effects decrease quickly with $p_T$ ,which is consistent with the recent CMS measurements. We demonstrate that the angular deviation of charm quarks is sensitive to $D_s$ but not $\hat{q}$, which may provide new constrains on the collisional and radiative heavy quark energy loss.

preprint2020arXiv

Radial profile of heavy quarks in jets in high-energy nuclear collisions

In high energy nuclear collisions, heavy flavor tagged jets are useful hard probes to study the properties of the quark-gluon plasma (QGP). In this talk, we present the first theoretical prediction of the $D^0$ meson radial distributions in jets relative to the jet axis both in p+p and Pb+Pb collisions at $5.02$ TeV, it shows a nice agreement with the available experimental data. The in-medium jet evolution in the study is described by a Monte Carlo transport model which has been incorporated with the initial events as input provided by the next-to-leading order (NLO) plus parton shower (PS) event generator SHERPA. In such evolution process, both elastic and inelastic parton energy loss in the hot and dense medium are taken into account. Within this same simulation framework, we predict different modification patterns of the radial profile of charm and bottom quarks in jets in Pb+Pb collisions: jet quenching effect will lead the charm quarks diffuse to lager radius while lead the bottom quarks distributed closer to jet axis.

preprint2020arXiv

Selective Confidence Intervals for Martingale Regression Model

In this paper we consider the problem of constructing confidence intervals for coefficients of martingale regression models (in particular, time series models) after variable selection. Although constructing confidence intervals are common practice in statistical analysis, it is challenging in our framework due to the data-dependence of the selected model and the correlation among the variables being selected and not selected. We first introduce estimators for the selected coefficients and show that it is consistent under martingale regression model, in which the observations can be dependent and the errors can be heteroskedastic. Then we use the estimators together with a resampling approach to construct confidence intervals. Our simulation results show that our approach outperforms other existing approaches in various data structures.

preprint2020arXiv

Self-similar solutions of energy-supercritical focusing wave equations in all dimensions

In this paper, we prove the existence of a countable family of regular spherically symmetric self-similar solutions to focusing energy super-critical semi-linear wave equations \begin{equation*} \partial_{tt}u-Δu=|u|^{p-1}u \qquad \text{in} \,\, \mathbb{R}^{N}, \end{equation*} where $N\geq 3$, $1+\frac{4}{N-2}<p$, and, if $N\geq 4$, $p \leq 1+\frac{4}{N-3}$. This was previously known only in the case $N=3$, for integer $p$ (see Bizoń, Maison and Wasserman \cite{BMW}). We also study the asymptotics of these solutions.

preprint2020arXiv

Sharp reversed Hardy-Littlewood-Sobolev inequality with extended kernel

In this paper, we prove the following reversed Hardy-Littlewood-Sobolev inequality with extended kernel \begin{equation*} \int_{\mathbb{R}_+^n}\int_{\partial\mathbb{R}^n_+} \frac{x_n^β}{|x-y|^{n-α}}f(y)g(x) dydx\geq C_{n,α,β,p}\|f\|_{L^{p}(\partial\mathbb{R}_+^n)} \|g\|_{L^{q'}(\mathbb{R}_+^n)} \end{equation*} for any nonnegative functions $f\in L^{p}(\partial\mathbb{R}_+^n)$ and $g\in L^{q'}(\mathbb{R}_+^n)$, where $n\geq2$, $p,\ q'\in (0,1)$, $α>n$, $0\leqβ<\frac{α-n}{n-1}$, $p>\frac{n-1}{α-1-(n-1)β}$ such that $\frac{n-1}{n}\frac{1}{p}+\frac{1}{q'}-\frac{α+β-1}{n}=1$. We prove the existence of extremal functions for the above inequality. Moreover, in the conformal invariant case, we classify all the extremal functions and hence derive the best constant via a variant method of moving spheres, which can be carried out \emph{without lifting the regularity of Lebesgue measurable solutions}. Finally, we derive the sufficient and necessary conditions for existence of positive solutions to the Euler-Lagrange equations by using Pohozaev identities. Our results are inspired by Hang, Wang and Yan \cite{HWY}, Dou, Guo and Zhu \cite{DGZ} for $α<n$ and $β=1$, and Gluck \cite{Gl} for $α<n$ and $β\geq0$.

preprint2020arXiv

Transverse Momentum Balance and Angular Distribution of $b\bar{b}$ Dijets in Pb+Pb collisions

The productions of inclusive b-jet and $b\bar{b}$ dijets in Pb+Pb collisions have been investigated by considering the heavy quark and the light quark in-medium evolution simultaneously. The initial hard processes of inclusive b-jet and $b\bar{b}$ dijets productions are described by a next-to-leading order (NLO) plus parton shower Monte Carlo (MC) event generator SHERPA which can be well-matched with the experimental data in p+p collisions. The framework combines the Langevin transport model to describe the evolution of the bottom quark also its collisional energy loss and the higher-twist description to consider the radiative energy loss of both the bottom and light quarks. We compare the theoretical simulation of inclusive jet and inclusive b-jet $R_{\rm AA}$ in Pb+Pb collisions at $\sqrt{s_{\rm NN}}=2.76$ TeV with the experimental data, and then present the theoretical simulation of the momentum balance of the $b\bar{b}$ dijet in Pb+Pb collisions at $5.02$ TeV with the recent CMS data for the first time. A similar trend as that in inclusive dijets has been observed in $b\bar{b}$ dijets, the production distribution is shifted to smaller $x_J$ due to the jet quenching effect. At last, the prediction of the normalized azimuthal angle distribution of the $b\bar{b}$ dijet in Pb+Pb collisions at $5.02$ TeV has been reported. The medium induced energy loss effect of the $b\bar{b}$ dijets will overall suppress its production, but the same side ($Δϕ\to 0$ region) suffers more energy loss than away side ($Δϕ\to π$ region), therefore lead to the suppression on the same side and the enhancement on the away side in the normalized azimuthal angle distribution in A+A collisions.

preprint2019arXiv

Benchmarking Contemporary Deep Learning Hardware and Frameworks:A Survey of Qualitative Metrics

This paper surveys benchmarking principles, machine learning devices including GPUs, FPGAs, and ASICs, and deep learning software frameworks. It also reviews these technologies with respect to benchmarking from the perspectives of a 6-metric approach to frameworks and an 11-metric approach to hardware platforms. Because MLPerf is a benchmark organization working with industry and academia, and offering deep learning benchmarks that evaluate training and inference on deep learning hardware devices, the survey also mentions MLPerf benchmark results, benchmark metrics, datasets, deep learning frameworks and algorithms. We summarize seven benchmarking principles, differential characteristics of mainstream AI devices, and qualitative comparison of deep learning hardware and frameworks.

Wei Dai

Quick read

Decide how to stay connected

How to connect with this researcher

Open a focused conversation when the fit is right

See the researcher in context

Building this graph slice

27 published item(s)

A Control Theoretic Approach to Decentralized AI Economy Stabilization via Dynamic Buyback-and-Burn Mechanisms

CasualSynth: Generating Structurally Sound Synthetic Data

Differentially Private Motif-Preserving Multi-modal Hashing

MathDoc: Benchmarking Structured Extraction and Active Refusal on Noisy Mathematics Exam Papers

Exploiting Scale-Variant Attention for Segmenting Small Medical Objects

A New Learning Paradigm for Stochastic Configuration Network: SCN+

Blind Two-Dimensional Super-Resolution and Its Performance Guarantee (Extended Version)

Data-Efficient Modeling for Precise Power Consumption Estimation of Quadrotor Operations Using Ensemble Learning

Digging into Primary Financial Market: Challenges and Opportunities of Adopting Blockchain

Maximum principles and the method of moving planes for the uniformly elliptic nonlocal Bellman operator and applications

Orthogonal Stochastic Configuration Networks with Adaptive Construction Parameter for Data Analytics

Solving DC Power Flow Problems Using Quantum and Hybrid algorithms

Improved ACD-based financial trade durations prediction leveraging LSTM networks and Attention Mechanism

Liouville type theorems for fractional and higher order Hénon-Hardy type equations via the method of scaling spheres

Demonstration of Controlled-Phase Gates between Two Error-Correctable Photonic Qubits

Dictionary Learning with BLOTLESS Update

Direct methods for pseudo-relativistic Schrödinger operators

EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation

HEAX: An Architecture for Computing on Encrypted Data

Learning Optimal Tree Models Under Beam Search

Radial distribution of charm quarks in jets in high-energy heavy-ion collisions

Radial profile of heavy quarks in jets in high-energy nuclear collisions

Selective Confidence Intervals for Martingale Regression Model

Self-similar solutions of energy-supercritical focusing wave equations in all dimensions

Sharp reversed Hardy-Littlewood-Sobolev inequality with extended kernel

Transverse Momentum Balance and Angular Distribution of $b\bar{b}$ Dijets in Pb+Pb collisions

Benchmarking Contemporary Deep Learning Hardware and Frameworks:A Survey of Qualitative Metrics