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Hui Xu

Publications and source records attributed to Hui Xu.

At least 127 records · Page 7Linked to original sources
arXiv2021

SafeDrop: Detecting Memory Deallocation Bugs of Rust Programs via Static Data-Flow Analysis

Rust is an emerging programming language that aims to prevent memory-safety bugs. However, the current design of Rust also brings side effects which may increase the risk of memory-safety issues. In particular, it employs OBRM (ownership-based resource management) and enforces automatic deallocation of unused resources without the garbage collector. It may therefore falsely deallocate reclaimed memory and lead to use-after-free or double-free issues. In this paper, we study the problem of invalid memory deallocation and propose SafeDrop, a static path-sensitive data-flow analysis approach to detect such bugs. Our approach analyzes each API of a Rust crate iteratively by traversing the control-flow graph and extracting all aliases of each data-flow. To guarantee precision and scalability, we leverage a modified Tarjan algorithm to achieve scalable path-sensitive analysis, and a cache-based strategy to achieve efficient inter-procedural analysis. Our experiment results show that our approach can successfully detect all existing CVEs of such issues with a limited number of false positives. The analysis overhead ranges from 1.0% to 110.7% in comparison with the original compilation time. We further apply our tool to several real-world Rust crates and find 8 Rust crates involved with invalid memory deallocation issues.

cs.PL↗
arXiv2021

A dynamical argument for a Ramsey property

We show by a dynamical argument that there is a positive integer valued function $q$ defined on positive integer set $\mathbb N$ such that $q([\log n]+1)$ is a super-polynomial with respect to positive $n$ and \[\liminf_{n\rightarrow\infty} r\left((2n+1)^2, q(n)\right)<\infty,\] where $r(\ ,\ )$ is the opposite-Ramsey number function.

math.DS↗
arXiv2021

Randomization Inference for Composite Experiments with Spillovers and Peer Effects

Group-formation experiments, in which experimental units are randomly assigned to groups, are a powerful tool for studying peer effects in the social sciences. Existing design and analysis approaches allow researchers to draw inference from such experiments without relying on parametric assumptions. In practice, however, group-formation experiments are often coupled with a second, external intervention, that is not accounted for by standard nonparametric approaches. This note shows how to construct Fisherian randomization tests and Neymanian asymptotic confidence intervals for such composite experiments, including in settings where the second intervention exhibits spillovers. We also propose an approach for designing optimal composite experiments.

stat.ME↗
arXiv2021

Memory-Safety Challenge Considered Solved? An In-Depth Study with All Rust CVEs

Rust is an emerging programing language that aims at preventing memory-safety bugs without sacrificing much efficiency. The claimed property is very attractive to developers, and many projects start using the language. However, can Rust achieve the memory-safety promise? This paper studies the question by surveying 186 real-world bug reports collected from several origins which contain all existing Rust CVEs (common vulnerability and exposures) of memory-safety issues by 2020-12-31. We manually analyze each bug and extract their culprit patterns. Our analysis result shows that Rust can keep its promise that all memory-safety bugs require unsafe code, and many memory-safety bugs in our dataset are mild soundness issues that only leave a possibility to write memory-safety bugs without unsafe code. Furthermore, we summarize three typical categories of memory-safety bugs, including automatic memory reclaim, unsound function, and unsound generic or trait. While automatic memory claim bugs are related to the side effect of Rust newly-adopted ownership-based resource management scheme, unsound function reveals the essential challenge of Rust development for avoiding unsound code, and unsound generic or trait intensifies the risk of introducing unsoundness. Based on these findings, we propose two promising directions towards improving the security of Rust development, including several best practices of using specific APIs and methods to detect particular bugs involving unsafe code. Our work intends to raise more discussions regarding the memory-safety issues of Rust and facilitate the maturity of the language.

cs.PL↗
arXiv2021

DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration

Reinforcement learning (RL) has demonstrated great success in the past several years. However, most of the scenarios focus on simulated environments. One of the main challenges of transferring the policy learned in a simulated environment to real world, is the discrepancy between the dynamics of the two environments. In prior works, Domain Randomization (DR) has been used to address the reality gap for both robotic locomotion and manipulation tasks. In this paper, we propose Domain Randomization Optimization IDentification (DROID), a novel framework to exploit single-shot human demonstration for identifying the simulator's distribution of dynamics parameters, and apply it to training a policy on a door opening task. Our results show that the proposed framework can identify the difference in dynamics between the simulated and the real worlds, and thus improve policy transfer by optimizing the simulator's randomization ranges. We further illustrate that based on these same identified parameters, our method can generalize the learned policy to different but related tasks.

cs.RO↗
arXiv2021

Sensitive group actions on regular curves of almost $\leq n$ order

Let $X$ be a regular curve and $n$ be a positive integer such that for every nonempty open set $U\subset X$, there is a nonempty connected open set $V\subset U$ with the cardinality $|\partial_X(V)|\leq n$. We show that if $X$ admits a sensitive action of a group $G$, then $G$ contains a free subsemigroup and the action has positive geometric entropy. As a corollary, $X$ admits no sensitive nilpotent group action.

math.DS↗
arXiv2020

Deep Learning in the Era of Edge Computing: Challenges and Opportunities

The era of edge computing has arrived. Although the Internet is the backbone of edge computing, its true value lies at the intersection of gathering data from sensors and extracting meaningful information from the sensor data. We envision that in the near future, majority of edge devices will be equipped with machine intelligence powered by deep learning. However, deep learning-based approaches require a large volume of high-quality data to train and are very expensive in terms of computation, memory, and power consumption. In this chapter, we describe eight research challenges and promising opportunities at the intersection of computer systems, networking, and machine learning. Solving those challenges will enable resource-limited edge devices to leverage the amazing capability of deep learning. We hope this chapter could inspire new research that will eventually lead to the realization of the vision of intelligent edge.

cs.LG↗
arXiv2020

High-Order Relation Construction and Mining for Graph Matching

Graph matching pairs corresponding nodes across two or more graphs. The problem is difficult as it is hard to capture the structural similarity across graphs, especially on large graphs. We propose to incorporate high-order information for matching large-scale graphs. Iterated line graphs are introduced for the first time to describe such high-order information, based on which we present a new graph matching method, called High-order Graph Matching Network (HGMN), to learn not only the local structural correspondence, but also the hyperedge relations across graphs. We theoretically prove that iterated line graphs are more expressive than graph convolution networks in terms of aligning nodes. By imposing practical constraints, HGMN is made scalable to large-scale graphs. Experimental results on a variety of settings have shown that, HGMN acquires more accurate matching results than the state-of-the-art, verifying our method effectively captures the structural similarity across different graphs.

cs.AI↗
arXiv2020

Topological conjugation classes of tightly transitive subgroups of $\text{Homeo}_{+}(\mathbb{S}^1)$

Let $\text{Homeo}_{+}(\mathbb{S}^1)$ denote the group of orientation preserving homeomorphisms of the circle $\mathbb{S}^1$. A subgroup $G$ of $\text{Homeo}_{+}(\mathbb{S}^1)$ is tightly transitive if it is topologically transitive and no subgroup $H$ of $G$ with $[G: H]=\infty$ has this property; is almost minimal if it has at most countably many nontransitive points. In the paper, we determine all the topological conjugation classes of tightly transitive and almost minimal subgroups of $\text{Homeo}_{+}(\mathbb{S}^1)$ which are isomorphic to $\mathbb{Z}^n$ for any integer $n\geq 2$.

math.DS↗
arXiv2020

Invariant Radon measures and minimal sets for subgroups of $\text{Homeo}_+(\mathbb{R})$

Let $G$ be a subgroup of $\text{Homeo}_+(\mathbb{R})$ without crossed elements. We show the equivalence among three items: (1) existence of $G$-invariant Radon measures on $\mathbb R$; (2) existence of minimal closed subsets of $\mathbb R$; (3) nonexistence of infinite towers covering the whole line. For a nilpotent subgroup $G$ of $\text{Homeo}_+(\mathbb{R})$, we show that $G$ always has an invariant Radon measure and a minimal closed set if every element of $G$ is $C^{1+α} (α>0$); a counterexample of $C^1$ commutative subgroup of $\text{Homeo}_+(\mathbb{R})$ is constructed.

math.DS↗
arXiv2020

Deep Reinforcement Learning in Fluid Mechanics: a promising method for both Active Flow Control and Shape Optimization

In recent years, Artificial Neural Networks (ANNs) and Deep Learning have become increasingly popular across a wide range of scientific and technical fields, including Fluid Mechanics. While it will take time to fully grasp the potentialities as well as the limitations of these methods, evidence is starting to accumulate that point to their potential in helping solve problems for which no theoretically optimal solution method is known. This is particularly true in Fluid Mechanics, where problems involving optimal control and optimal design are involved. Indeed, such problems are famously difficult to solve effectively with traditional methods due to the combination of non linearity, non convexity, and high dimensionality they involve. By contrast, Deep Reinforcement Learning (DRL), a method of optimization based on teaching empirical strategies to an ANN through trial and error, is well adapted to solving such problems. In this short review, we offer an insight into the current state of the art of the use of DRL within fluid mechanics, focusing on control and optimal design problems.

physics.flu-dyn↗
arXiv2019

Nektar++: enhancing the capability and application of high-fidelity spectral/$hp$ element methods

Nektar++ is an open-source framework that provides a flexible, high-performance and scalable platform for the development of solvers for partial differential equations using the high-order spectral/$hp$ element method. In particular, Nektar++ aims to overcome the complex implementation challenges that are often associated with high-order methods, thereby allowing them to be more readily used in a wide range of application areas. In this paper, we present the algorithmic, implementation and application developments associated with our Nektar++ version 5.0 release. We describe some of the key software and performance developments, including our strategies on parallel I/O, on in situ processing, the use of collective operations for exploiting current and emerging hardware, and interfaces to enable multi-solver coupling. Furthermore, we provide details on a newly developed Python interface that enables a more rapid introduction for new users unfamiliar with spectral/$hp$ element methods, C++ and/or Nektar++. This release also incorporates a number of numerical method developments - in particular: the method of moving frames, which provides an additional approach for the simulation of equations on embedded curvilinear manifolds and domains; a means of handling spatially variable polynomial order; and a novel technique for quasi-3D simulations to permit spatially-varying perturbations to the geometry in the homogeneous direction. Finally, we demonstrate the new application-level features provided in this release, namely: a facility for generating high-order curvilinear meshes called NekMesh; a novel new AcousticSolver for aeroacoustic problems; our development of a 'thick' strip model for the modelling of fluid-structure interaction problems in the context of vortex-induced vibrations. We conclude by commenting some directions for future code development and expansion.

cs.MS↗
arXiv2019

Regression via Arbitrary Quantile Modeling

In the regression problem, L1 and L2 are the most commonly used loss functions, which produce mean predictions with different biases. However, the predictions are neither robust nor adequate enough since they only capture a few conditional distributions instead of the whole distribution, especially for small datasets. To address this problem, we proposed arbitrary quantile modeling to regulate the prediction, which achieved better performance compared to traditional loss functions. More specifically, a new distribution regression method, Deep Distribution Regression (DDR), is proposed to estimate arbitrary quantiles of the response variable. Our DDR method consists of two models: a Q model, which predicts the corresponding value for arbitrary quantile, and an F model, which predicts the corresponding quantile for arbitrary value. Furthermore, the duality between Q and F models enables us to design a novel loss function for joint training and perform a dual inference mechanism. Our experiments demonstrate that our DDR-joint and DDR-disjoint methods outperform previous methods such as AdaBoost, random forest, LightGBM, and neural networks both in terms of mean and quantile prediction.

cs.LG↗
arXiv2019

Detecting Deep Neural Network Defects with Data Flow Analysis

Deep neural networks (DNNs) are shown to be promising solutions in many challenging artificial intelligence tasks. However, it is very hard to figure out whether the low precision of a DNN model is an inevitable result, or caused by defects. This paper aims at addressing this challenging problem. We find that the internal data flow footprints of a DNN model can provide insights to locate the root cause effectively. We develop DeepMorph (DNN Tomography) to analyze the root cause, which can guide a DNN developer to improve the model.

cs.LG↗
arXiv2019

HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking

The use of automatic methods, often referred to as Neural Architecture Search (NAS), in designing neural network architectures has recently drawn considerable attention. In this work, we present an efficient NAS approach, named HM- NAS, that generalizes existing weight sharing based NAS approaches. Existing weight sharing based NAS approaches still adopt hand-designed heuristics to generate architecture candidates. As a consequence, the space of architecture candidates is constrained in a subset of all possible architectures, making the architecture search results sub-optimal. HM-NAS addresses this limitation via two innovations. First, HM-NAS incorporates a multi-level architecture encoding scheme to enable searching for more flexible network architectures. Second, it discards the hand-designed heuristics and incorporates a hierarchical masking scheme that automatically learns and determines the optimal architecture. Compared to state-of-the-art weight sharing based approaches, HM-NAS is able to achieve better architecture search performance and competitive model evaluation accuracy. Without the constraint imposed by the hand-designed heuristics, our searched networks contain more flexible and meaningful architectures that existing weight sharing based NAS approaches are not able to discover.

cs.LG↗
arXiv2019

Oil drop deposition on solid surfaces in mixed polymer-surfactant solutions in relation to hair- and skin-care applications

The deposition of oil drops on solid substrates from mixed solutions of surfactants and cationic polymer is investigated. The used anionic surfactants are sodium laurylethersulfate (SLES) and sulfonated methyl esters (SME); the zwitterionic surfactant is cocamidopropyl betaine (CAPB). A new method, called the pressed drop method (PDM), was proposed to study the drop adhesion to substrates of different hydrophobicity. The PDM allows one to detect the presence or absence of drop adhesion at different degrees of dilution of the initial solution and, thus, to determine the threshold concentration of drop adhesion. The results show that the increase of the fraction of CAPB in the mixture with the anionic surfactant suppresses the oil-drop deposition; SME provides easier drop adhesion than SLES; the addition of NaCl enhances, whereas coco-fatty-acid-monoethanolamide (CMEA) suppresses the drop deposition; no drop adhesion is observed in the absence of polymer. The drop-to-substrate adhesion is interpreted in terms of the acting surface forces: polymer bridging attraction; hydrophobic attraction between segments of adsorbed polymer brushes and electrostatic forces. From viewpoint of applications, the PDM experiments enable one to compare the performance of various components in personal care formulations and to optimize their composition with respect to the oil-drop deposition.

physics.chem-ph↗