arXiv subjects

Ao Liu

Publications and source records attributed to Ao Liu.

69 records · Page 4Linked to original sources

arXiv2021

Truthful Information Elicitation from Hybrid Crowds

Suppose a decision maker wants to predict weather tomorrow by eliciting and aggregating information from crowd. How can the decision maker incentivize the crowds to report their information truthfully? Many truthful peer prediction mechanisms have been proposed for homogeneous agents, whose types are drawn from the same distribution. However, in many situations, the population is a hybrid crowd of different types of agents with different forms of information, and the decision maker has neither the identity of any individual nor the proportion of each types of agents in the crowd. Ignoring the heterogeneity among the agent may lead to inefficient of biased information, which would in turn lead to suboptimal decisions. In this paper, we propose the first framework for information elicitation from hybrid crowds, and two mechanisms to motivate agents to report their information truthfully. The first mechanism combines two mechanisms via linear transformations and the second is based on mutual information. With two mechanisms, the decision maker can collect high quality information from hybrid crowds, and learns the expertise of agents.

cs.GT↗

arXiv2021

Certifiably Robust Interpretation via Renyi Differential Privacy

Motivated by the recent discovery that the interpretation maps of CNNs could easily be manipulated by adversarial attacks against network interpretability, we study the problem of interpretation robustness from a new perspective of \Renyi differential privacy (RDP). The advantages of our Renyi-Robust-Smooth (RDP-based interpretation method) are three-folds. First, it can offer provable and certifiable top-$k$ robustness. That is, the top-$k$ important attributions of the interpretation map are provably robust under any input perturbation with bounded $\ell_d$-norm (for any $d\geq 1$, including $d = \infty$). Second, our proposed method offers $\sim10\%$ better experimental robustness than existing approaches in terms of the top-$k$ attributions. Remarkably, the accuracy of Renyi-Robust-Smooth also outperforms existing approaches. Third, our method can provide a smooth tradeoff between robustness and computational efficiency. Experimentally, its top-$k$ attributions are {\em twice} more robust than existing approaches when the computational resources are highly constrained.

cs.LG↗

arXiv2021

AN-GCN: An Anonymous Graph Convolutional Network Defense Against Edge-Perturbing Attack

Recent studies have revealed the vulnerability of graph convolutional networks (GCNs) to edge-perturbing attacks, such as maliciously inserting or deleting graph edges. However, a theoretical proof of such vulnerability remains a big challenge, and effective defense schemes are still open issues. In this paper, we first generalize the formulation of edge-perturbing attacks and strictly prove the vulnerability of GCNs to such attacks in node classification tasks. Following this, an anonymous graph convolutional network, named AN-GCN, is proposed to counter against edge-perturbing attacks. Specifically, we present a node localization theorem to demonstrate how the GCN locates nodes during its training phase. In addition, we design a staggered Gaussian noise based node position generator, and devise a spectral graph convolution based discriminator in detecting the generated node positions. Further, we give the optimization of the above generator and discriminator. AN-GCN can classify nodes without taking their position as input. It is demonstrated that the AN-GCN is secure against edge-perturbing attacks in node classification tasks, as AN-GCN classifies nodes without the edge information and thus makes it impossible for attackers to perturb edges anymore. Extensive evaluations demonstrated the effectiveness of the general edge-perturbing attack model in manipulating the classification results of the target nodes. More importantly, the proposed AN-GCN can achieve 82.7% in node classification accuracy without the edge-reading permission, which outperforms the state-of-the-art GCN.

cs.LG↗

arXiv2021

The Smoothed Likelihood of Doctrinal Paradox

When aggregating logically interconnected judgments from $n$ agents, the result might be inconsistent with the logical connection. This inconsistency is known as the doctrinal paradox, which plays a central role in the field of judgment aggregation. Despite a large body of literature on the worst-case analysis of the doctrinal paradox, little is known about its likelihood under natural statistical models, except for a few i.i.d. distributions [List, 2005]. In this paper, we characterize the likelihood of the doctrinal paradox under a much more general and realistic model called the smoothed social choice framework [Xia, 2020b], where agents' ground truth judgments are arbitrarily correlated while the noises are independent. Our main theorem states that under mild conditions, the smoothed likelihood of the doctrinal paradox is either $0$, $\exp(-Θ(n))$, $Θ(n^{-1/2})$ or $Θ(1)$. This not only answers open questions by List [2005] for i.i.d. distributions but also draws clear lines between situations with frequent and with vanishing paradoxes.

cs.CY↗

arXiv2021

How Private Are Commonly-Used Voting Rules?

Differential privacy has been widely applied to provide privacy guarantees by adding random noise to the function output. However, it inevitably fails in many high-stakes voting scenarios, where voting rules are required to be deterministic. In this work, we present the first framework for answering the question: "How private are commonly-used voting rules?" Our answers are two-fold. First, we show that deterministic voting rules provide sufficient privacy in the sense of distributional differential privacy (DDP). We show that assuming the adversarial observer has uncertainty about individual votes, even publishing the histogram of votes achieves good DDP. Second, we introduce the notion of exact privacy to compare the privacy preserved in various commonly-studied voting rules, and obtain dichotomy theorems of exact DDP within a large subset of voting rules called generalized scoring rules.

cs.CR↗

arXiv2021

SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space

In the encrypted network traffic intrusion detection, deep learning based schemes have attracted lots of attention. However, in real-world scenarios, data is often insufficient (few-shot), which leads to various deviations between the models prediction and the ground truth. Consequently, downstream tasks such as unknown attack detection based on few-shot will be limited by insufficient data. In this paper, we propose a novel unknown attack detection method based on Intra Categories Generation in Embedding Space, namely SFE-GACN, which might be the solution of few-shot problem. Concretely, we first proposed Session Feature Embedding (SFE) to summarize the context of sessions (session is the basic granularity of network traffic), bring the insufficient data to the pre-trained embedding space. In this way, we achieve the goal of preliminary information extension in the few-shot case. Second, we further propose the Generative Adversarial Cooperative Network (GACN), which improves the conventional Generative Adversarial Network by supervising the generated sample to avoid falling into similar categories, and thus enables samples to generate intra categories. Our proposed SFE-GACN can accurately generate session samples in the case of few-shot, and ensure the difference between categories during data augmentation. The detection results show that, compared to the state-of-the-art method, the average TPR is 8.38% higher, and the average FPR is 12.77% lower. In addition, we evaluated the graphics generation capabilities of GACN on the graphics dataset, the result shows our proposed GACN can be popularized for generating easy-confused multi-categories graphics.

cs.CR↗

arXiv2020

Categorizing Online Shopping Behavior from Cosmetics to Electronics: An Analytical Framework

A success factor for modern companies in the age of Digital Marketing is to understand how customers think and behave based on their online shopping patterns. While the conventional method of gathering consumer insights through questionnaires and surveys still form the bases of descriptive analytics for market intelligence units, we propose a machine learning framework to automate this process. In this paper we present a modular consumer data analysis platform that processes session level interaction records between users and products to predict session level, user journey level and customer behavior specific patterns leading towards purchase events. We explore the computational framework and provide test results on two Big data sets-cosmetics and consumer electronics of size 2GB and 15GB, respectively. The proposed system achieves 97-99% classification accuracy and recall for user-journey level purchase predictions and categorizes buying behavior into 5 clusters with increasing purchase ratios for both data sets. Thus, the proposed framework is extendable to other large e-commerce data sets to obtain automated purchase predictions and descriptive consumer insights.

cs.LG↗

arXiv2020

Fast dynamic aperture optimization with reversal integration

A fast method for dynamic aperture (DA) optimization of storage rings has been developed through the use of reversal integration. While chaotic dynamical systems have exact time-reversal symmetry, numerical forward integration differs from its reversal due to scaled cumulative round-off errors. The difference, intrinsically associated with the Lyapunov exponent, is a generic indicator of chaos because it represents the sensitivity of chaotic motion to an initial condition. A chaos indicator of the charged particle motion is then obtained by comparing the forward integrations of particle trajectories with corresponding reversals, a.k.a. "backward integrations." The indicator was confirmed to be observable through short-term particle tracking simulations. Therefore, adopting it as an objective function could speed up optimization. The DA of the National Synchrotron Light Source II storage ring, and another test diffraction-limited light source ring, were optimized using this method for the purpose of demonstration.

physics.acc-ph↗

arXiv2020

Direct visualization of electromagnetic wave dynamics by laser-free ultrafast electron microscopy

Integrating femtosecond (fs) lasers to electron microscopies has enabled direct imaging of transient structures and morphologies of materials in real time and space, namely, ultrafast electron microscopy (UEM). Here we report the development of a laser-free UEM offering the same capability of real-time imaging with high spatiotemporal resolutions but without requiring expensive fs lasers and intricate instrumental modifications. We create picosecond electron pulses for probing dynamic events by chopping a continuous beam with a radiofrequency (RF)-driven pulser, where the repetition rate of the electron pulses is tunable from 100 MHz to 12 GHz. A same broadband of electromagnetic wave is enabled for sample excitation. As a first application, we studied the GHz electromagnetic wave propagation dynamics in an interdigitated comb structure which is one of the basic building blocks for RF micro-electromechanical systems. A series of pump-probe images reveals, on nanometer space and picosecond time scales, the transient oscillating electromagnetic field around the tines of the combs, and time-resolved polarization, amplitude, and nonlinear local field enhancement. The success of this study demonstrates the feasibility of the low-cost laser-free UEM in real-space visualizing of dynamics for many research fields, especially the electrodynamics in devices associated with information processing technology.

physics.app-ph↗

arXiv2019

Differential Privacy for Eye-Tracking Data

As large eye-tracking datasets are created, data privacy is a pressing concern for the eye-tracking community. De-identifying data does not guarantee privacy because multiple datasets can be linked for inferences. A common belief is that aggregating individuals' data into composite representations such as heatmaps protects the individual. However, we analytically examine the privacy of (noise-free) heatmaps and show that they do not guarantee privacy. We further propose two noise mechanisms that guarantee privacy and analyze their privacy-utility tradeoff. Analysis reveals that our Gaussian noise mechanism is an elegant solution to preserve privacy for heatmaps. Our results have implications for interdisciplinary research to create differentially private mechanisms for eye tracking.

cs.CR↗

arXiv2018

Operation of normal-conducting RF cavities in multi-tesla magnetic fields for muon ionization cooling: a feasibility demonstration

Ionization cooling is the preferred method for producing bright muon beams. This cooling technique requires the operation of normal conducting, radio-frequency (RF) accelerating cavities within the multi-tesla fields of DC solenoid magnets. Under these conditions, cavities exhibit increased susceptibility to RF breakdown, which can damage channel components and imposes limits on channel length and transmission efficiency. We present a solution to the problem of breakdown in strong magnetic fields. We report, for the first time, stable high-vacuum, copper cavity operation at gradients above 50 MV/m and in an external magnetic field of three tesla. This eliminates a significant technical risk that has previously been inherent in ionization cooling channel designs.

physics.acc-ph↗

arXiv2018

Towards Non-Parametric Learning to Rank

This paper studies a stylized, yet natural, learning-to-rank problem and points out the critical incorrectness of a widely used nearest neighbor algorithm. We consider a model with $n$ agents (users) $\{x_i\}_{i \in [n]}$ and $m$ alternatives (items) $\{y_j\}_{j \in [m]}$, each of which is associated with a latent feature vector. Agents rank items nondeterministically according to the Plackett-Luce model, where the higher the utility of an item to the agent, the more likely this item will be ranked high by the agent. Our goal is to find neighbors of an arbitrary agent or alternative in the latent space. We first show that the Kendall-tau distance based kNN produces incorrect results in our model. Next, we fix the problem by introducing a new algorithm with features constructed from "global information" of the data matrix. Our approach is in sharp contrast to most existing feature engineering methods. Finally, we design another new algorithm identifying similar alternatives. The construction of alternative features can be done using "local information," highlighting the algorithmic difference between finding similar agents and similar alternatives.

cs.LG↗

arXiv2017

Automatically Extracting Action Graphs from Materials Science Synthesis Procedures

Computational synthesis planning approaches have achieved recent success in organic chemistry, where tabulated synthesis procedures are readily available for supervised learning. The syntheses of inorganic materials, however, exist primarily as natural language narratives contained within scientific journal articles. This synthesis information must first be extracted from the text in order to enable analogous synthesis planning methods for inorganic materials. In this work, we present a system for automatically extracting structured representations of synthesis procedures from the texts of materials science journal articles that describe explicit, experimental syntheses of inorganic compounds. We define the structured representation as a set of linked events made up of extracted scientific entities and evaluate two unsupervised approaches for extracting these structures on expert-annotated articles: a strong heuristic baseline and a generative model of procedural text. We also evaluate a variety of supervised models for extracting scientific entities. Our results provide insight into the nature of the data and directions for further work in this exciting new area of research.

cs.CL↗

arXiv2017

A FODO racetrack ring design and optimization for nuSTORM

The goal of nuSTORM is to provide well-defined neutrino beams for precise measurements of neutrino cross-sections and oscillations. The nuSTORM decay ring is a compact racetrack storage ring with a circumference of ~480 m that incorporates large aperture (60 cm diameter) magnets. There are many challenges in the design. In order to incorporate the Orbit Combination Section (OCS), used for injecting the pion beam into the ring, a dispersion suppressor is needed adjacent to the OCS. Concurrently, in order to maximize the number of useful muon decays, strong bending dipoles are needed in the arcs to minimize the arc length. These dipoles create strong chromatic effects, which need to be corrected by nonlinear sextupole elements in the ring. In this paper, a FODO racetrack ring design and its optimization using sextupolar fields via both a Genetic Algorithm (GA) and a Simulated Annealing (SA) algorithm will be discussed.

physics.acc-ph↗

arXiv2015

NuSTORM μ Ring -- Design and Injection Optimization

The design of the NuSTORM muon production beam line and storage ring is discussed. The facility includes a pion production target station with a pion collection horn and transport into a straight section of a storage ring. π decay within that straight section provides μ's that are stored within the ring for subsequent decay providing precision electron and muon neutrino beams. The ring and transport designs are described and optimized. Genetic Algorithm optimization of the horn and transport into the ring has been performed, providing a significant increase in intensity

physics.acc-ph↗