Well-posedness of the viscous equations with strong stratification
This is the old version of this project. Please find the new version at 1906.12233.
math.AP↗
arXiv subjects
Xin Liu
Publications and source records attributed to Xin Liu.
On the resolvability of the dynamics of one fluid flow via a testing fluid in a two-fluids flow model
In this work, by considering an isentropic fluid-fluid interaction model with a large symmetric drag force, a commonly used simplified two-fluids flow model is justified as the asymptotic limit. Equations for each fluid component with an interaction term are identified in addition to the simplified two-fluids flow model, which can be used to resolve the density of one fluid specie based on information on density and the velocity of the other fluid specie, i.e., the testing flow.
physics.flu-dyn↗
An Efficient Subpopulation-based Membership Inference Attack
Membership inference attacks allow a malicious entity to predict whether a sample is used during training of a victim model or not. State-of-the-art membership inference attacks have shown to achieve good accuracy which poses a great privacy threat. However, majority of SOTA attacks require training dozens to hundreds of shadow models to accurately infer membership. This huge computation cost raises questions about practicality of these attacks on deep models. In this paper, we introduce a fundamentally different MI attack approach which obviates the need to train hundreds of shadow models. Simply put, we compare the victim model output on the target sample versus the samples from the same subpopulation (i.e., semantically similar samples), instead of comparing it with the output of hundreds of shadow models. The intuition is that the model response should not be significantly different between the target sample and its subpopulation if it was not a training sample. In cases where subpopulation samples are not available to the attacker, we show that training only a single generative model can fulfill the requirement. Hence, we achieve the state-of-the-art membership inference accuracy while significantly reducing the training computation cost.
cs.LG↗
Numerical study of PbTe-Pb hybrid nanowires for engineering Majorana zero modes
Epitaxial semiconductor-superconductor (SM-SC) hybrid nanowires are potential candidates for implementing Majorana qubits. Recent experimental and theoretical works show that charged impurities in SM remain a major problem in all existing hybrid nanowires, in which the SM is either InAs or InSb while the SC is mainly Al. Here, we theoretically validate the recently proposed PbTe-Pb hybrid nanowire as a potential candidate for Majorana devices. By studying the electrostatic and electronic properties of PbTe nanowires, we demonstrate that the huge dielectric constant of PbTe endows itself a high tolerance of charged impurity, which is a potential advantage over InAs and InSb nanowires. Moreover, we find that the effective axial Landé $g$ factor and Rashba spin-orbit coupling strength of PbTe nanowires are comparable to those of InAs nanowires. The conceivable merits of using Pb as the SC are (i) Pb has a larger superconducting gap, higher critical temperature, and higher parallel critical magnetic field than those of Al; (ii) a superconducting gap comparable with those of InAs-Al and InSb-Al can be induced in PbTe-Pb even by a weak coupling between Pb and PbTe, which simultaneously relieves the adverse renormalization and induced disorder effects on SM from SC; and (iii) Pb film can be grown on PbTe with a thin buffer CdTe layer in between, solving the lattice mismatch problem as an important source of disorder. In the presence of a parallel magnetic field, we show that the typical BdG energy spectrum and tunneling spectroscopy of PbTe-Pb resemble those of InAs and InSb based hybrid nanowires exposed to a tilting magnetic field, as a result of the highly anisotropic Landé $g$ factors of PbTe nanowires. The calculated topological phase diagrams of PbTe-Pb indicate that the multivalley character of PbTe makes it easier than InAs and InSb to access topological superconducting phases.
cond-mat.mes-hall↗
Higher-order topological phases emerging from the Su-Schrieffer-Heeger stacking
In this work, we develop a systematical approach of constructing and classifying the model Hamiltonians for two-dimensional (2D) higher-order topological phase with corner zero energy states (CZESs). Our approach is based on the direct construction of analytical solution of the CZESs in a series of 2D systems that stack the 1D extended Su-Schrieffer-Heeger (SSH) model, two copies of the original SSH model, along two orthogonal directions. Fascinatingly, our approach not only gives the celebrated Benalcazar-Bernevig-Hughes and 2D SSH models but also reveals a novel model and we refer it to crossed 2D SSH model. Although these three models exhibit completely different bulk topology, we find that the CZESs can be universally characterized by edge winding number for 1D edge states, attributing to their unified Hamiltonian construction form and edge topology. Remarkably, our principle of obtaining CZESs can be readily generalized to arbitrary dimension and superconducting systems. Thus, our work sheds new light on the theoretical understanding of the higher-order topological phase and paves the way to looking for higher-order topological insulators and superconductors.
cond-mat.mes-hall↗
RGB Camera-based Physiological Sensing: Challenges and Future Directions
Numerous real-world applications have been driven by the recent algorithmic advancement of artificial intelligence (AI). Healthcare is no exception and AI technologies have great potential to revolutionize the industry. Non-contact camera-based physiological sensing, including remote photoplethysmography (rPPG), is a set of imaging methods that leverages ordinary RGB cameras (e.g., webcam or smartphone camera) to capture subtle changes in electromagnetic radiation (e.g., light) reflected by the body caused by physiological processes. RGB camera-based systems not only have the ability to measure the signals without contact with the body but also have the opportunity to capture multimodal information (e.g., facial expressions, activities and other context) from the same sensor. However, developing accessible, equitable and useful camera-based physiological sensing systems comes with various challenges. In this article, we identify four research challenges for the field of RGB camera-based physiological sensing and broader AI driven healthcare communities and suggest future directions to tackle these. We believe solving these challenges will help deliver accurate, equitable and generalizable AI systems for healthcare that are practical in real-world and clinical contexts.
cs.CV↗
Behaviour-Diverse Automatic Penetration Testing: A Curiosity-Driven Multi-Objective Deep Reinforcement Learning Approach
Penetration Testing plays a critical role in evaluating the security of a target network by emulating real active adversaries. Deep Reinforcement Learning (RL) is seen as a promising solution to automating the process of penetration tests by reducing human effort and improving reliability. Existing RL solutions focus on finding a specific attack path to impact the target hosts. However, in reality, a diverse range of attack variations are needed to provide comprehensive assessments of the target network's security level. Hence, the attack agents must consider multiple objectives when penetrating the network. Nevertheless, this challenge is not adequately addressed in the existing literature. To this end, we formulate the automatic penetration testing in the Multi-Objective Reinforcement Learning (MORL) framework and propose a Chebyshev decomposition critic to find diverse adversary strategies that balance different objectives in the penetration test. Additionally, the number of available actions increases with the agent consistently probing the target network, making the training process intractable in many practical situations. Thus, we introduce a coverage-based masking mechanism that reduces attention on previously selected actions to help the agent adapt to future exploration. Experimental evaluation on a range of scenarios demonstrates the superiority of our proposed approach when compared to adapted algorithms in terms of multi-objective learning and performance efficiency.
cs.LG↗
Perturbative QCD analysis of neutral $B$-meson decays into $σσ, σf_0$ and $f_0 f_0$
The decays of $B_{d,s}^0 \to σσ, σf_0, f_0 f_0$, with $σ$ and $f_0$ denoting the light scalar mesons $f_0(500)$ and $f_0(980)$ in the two-quark picture, are studied in the perturbative QCD approach based on $k_T$ factorization. With the referenced value of the mixing angle $|φ| \sim 25^\circ$ for the $σ-f_0$ mixing in the quark-flavor basis, it is of great interest to obtain that: (a) these neutral $B$-meson decays into $σσ, σf_0$, and $f_0 f_0$ have large branching ratios in the order of $10^{-6} \sim 10^{-4}$, which mean the possibly constructive interferences existed in the decays with different flavor states, and then are expected to be tested at the Large Hadron Collider beauty and/or Belle-II experiments in the (near) future; (b) the large direct CP violations could be easily found in the $B_d^0 \to σσ, f_0 f_0$ and $B_{d,s}^0 \to σf_0$ decays, which indicate the considerable interferences between the tree and the penguin decay amplitudes involved in these four modes, and would be confronted with the future measurements; (c) these neutral $B$-meson decays could be examined through the secondary decay chain $σ/f_0 \to π^+ π^-$, namely, the four-body decays of $B_{d,s}^0 \to (π^+ π^-)_{σ(f_0)} (π^+ π^-)_{σ(f_0)}$. On the other side, it seems that other 4 four-body decays of $B_d^0 \to (π^+ π^-)_σ (K^+ K^-)_{f_0}$, $B_s^0 \to (π^+ π^-)_{σ(f_0)} (K^+ K^-)_{f_0}$, and $B_s^0 \to (K^+ K^-)_{f_0} (K^+ K^-)_{f_0}$ could also be detected at the relevant experiments, if the $f_0 \to K^+ K^-$ could be identified from the $ϕ\to K^+ K^-$ clearly.
hep-ph↗
Large-System Insensitivity of Zero-Waiting Load Balancing Algorithms
This paper studies the sensitivity (or insensitivity) of a class of load balancing algorithms that achieve asymptotic zero-waiting in the sub-Halfin-Whitt regime, named LB-zero. Most existing results on zero-waiting load balancing algorithms assume the service time distribution is exponential. This paper establishes the {\em large-system insensitivity} of LB-zero for jobs whose service time follows a Coxian distribution with a finite number of phases. This result suggests that LB-zero achieves asymptotic zero-waiting for a large class of service time distributions, which is confirmed in our simulations. To prove this result, this paper develops a new technique, called "Iterative State-Space Peeling" (or ISSP for short). ISSP first identifies an iterative relation between the upper and lower bounds on the queue states and then proves that the system lives near the fixed point of the iterative bounds with a high probability. Based on ISSP, the steady-state distribution of the system is further analyzed by applying Stein's method in the neighborhood of the fixed point. ISSP, like state-space collapse in heavy-traffic analysis, is a general approach that may be used to study other complex stochastic systems.
math.PR↗
Spotting Anomalous Trades in NFT Markets: The Case of NBA Topshot
Non-Fungible Token (NFT) markets are one of the fastest growing digital markets today, with the sales during the third quarter of 2021 exceeding $10 billions! Nevertheless, these emerging markets - similar to traditional emerging marketplaces - can be seen as a great opportunity for illegal activities (e.g., money laundering, sale of illegal goods etc.). In this study we focus on a specific marketplace, namely NBA TopShot, that facilitates the purchase and (peer-to-peer) trading of sports collectibles. Our objective is to build a framework that is able to label peer-to-peer transactions on the platform as anomalous or not. To achieve our objective we begin by building a model for the profit to be made by selling a specific collectible on the platform. We then use RFCDE - a random forest model for the conditional density of the dependent variable - to model the errors from the profit models. This step allows us to estimate the probability of a transaction being anomalous. We finally label as anomalous any transaction whose aforementioned probability is less than 1%. Given the absence of ground truth for evaluating the model in terms of its classification of transactions, we analyze the trade networks formed from these anomalous transactions and compare it with the full trade network of the platform. Our results indicate that these two networks are statistically different when it comes to network metrics such as, edge density, closure, node centrality and node degree distribution. This network analysis provides additional evidence that these transactions do not follow the same patterns that the rest of the trades on the platform follow. However, we would like to emphasize here that this does not mean that these transactions are also illegal. These transactions will need to be further audited from the appropriate entities to verify whether or not they are illicit.
cs.SI↗
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning
The past decade witnesses a rapid development in the measurement and monitoring technologies for food science. Among these technologies, spectroscopy has been widely used for the analysis of food quality, safety, and nutritional properties. Due to the complexity of food systems and the lack of comprehensive predictive models, rapid and simple measurements to predict complex properties in food systems are largely missing. Machine Learning (ML) has shown great potential to improve classification and prediction of these properties. However, the barriers to collect large datasets for ML applications still persists. In this paper, we explore different approaches of data annotation and model training to improve data efficiency for ML applications. Specifically, we leverage Active Learning (AL) and Semi-Supervised Learning (SSL) and investigate four approaches: baseline passive learning, AL, SSL, and a hybrid of AL and SSL. To evaluate these approaches, we collect two spectroscopy datasets: predicting plasma dosage and detecting foodborne pathogen. Our experimental results show that, compared to the de facto passive learning approach, AL and SSL methods reduce the number of labeled samples by 50% and 25% for each ML application, respectively.
cs.LG↗
Admission Control for Double-ended Queues
We consider a controlled double-ended queue consisting of two classes of customers, labeled sellers and buyers. The sellers and buyers arrive in a trading market according to two independent renewal processes. Whenever there is a seller and buyer pair, they are matched and leave the system instantaneously. The matching follows first-come-first-match service discipline. Those customers who cannot be matched immediately need to wait in the designated queue, and they are assumed to be impatient with generally distributed patience times. The control problem is concerned with the trade-off between blocking and abandonment for each class and the interplay of statistical behaviors of the two classes, and its objective is to choose optimal queue-capacities (buffer lengths) for sellers and buyers to minimize an infinite horizon discounted linear cost functional which consists of holding costs and penalty costs for blocking and abandonment. When the arrival intensities of both customer classes tend to infinity in concert, we use a heavy traffic approximation to formulate an approximate diffusion control problem (DCP), and develop an optimal threshold policy for the DCP. Finally, we employ the DCP solution to establish an easy-to-implement asymptotically optimal threshold policy for the original queueing control problem.
math.OC↗
ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities
Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect commonsense knowledge. In this paper, we propose to develop principles for collecting commonsense knowledge based on selectional preference. We generalize the definition of selectional preference from one-hop linguistic syntactic relations to higher-order relations over linguistic graphs. Unlike previous commonsense knowledge definition (e.g., ConceptNet), selectional preference (SP) knowledge only relies on statistical distribution over linguistic graphs, which can be efficiently and accurately acquired from the unlabeled corpus with modern tools. Following this principle, we develop a large-scale eventuality (a linguistic term covering activity, state, and event)-based knowledge graph ASER, where each eventuality is represented as a dependency graph, and the relation between them is a discourse relation defined in shallow discourse parsing. The higher-order selectional preference over collected linguistic graphs reflects various kinds of commonsense knowledge. Moreover, motivated by the observation that humans understand events by abstracting the observed events to a higher level and can thus transfer their knowledge to new events, we propose a conceptualization module to significantly boost the coverage of ASER. In total, ASER contains 648 million edges between 438 million eventualities. After conceptualization with Probase, a selectional preference based concept-instance relational knowledge base, our concept graph contains 15 million conceptualized eventualities and 224 million edges between them. Detailed analysis is provided to demonstrate its quality. All the collected data, APIs, and tools are available at https://github.com/HKUST-KnowComp/ASER.
cs.AI↗
Decentralized Optimization Over the Stiefel Manifold by an Approximate Augmented Lagrangian Function
In this paper, we focus on the decentralized optimization problem over the Stiefel manifold, which is defined on a connected network of $d$ agents. The objective is an average of $d$ local functions, and each function is privately held by an agent and encodes its data. The agents can only communicate with their neighbors in a collaborative effort to solve this problem. In existing methods, multiple rounds of communications are required to guarantee the convergence, giving rise to high communication costs. In contrast, this paper proposes a decentralized algorithm, called DESTINY, which only invokes a single round of communications per iteration. DESTINY combines gradient tracking techniques with a novel approximate augmented Lagrangian function. The global convergence to stationary points is rigorously established. Comprehensive numerical experiments demonstrate that DESTINY has a strong potential to deliver a cutting-edge performance in solving a variety of testing problems.
math.OC↗
Leaping Through Time with Gradient-based Adaptation for Recommendation
Modern recommender systems are required to adapt to the change in user preferences and item popularity. Such a problem is known as the temporal dynamics problem, and it is one of the main challenges in recommender system modeling. Different from the popular recurrent modeling approach, we propose a new solution named LeapRec to the temporal dynamic problem by using trajectory-based meta-learning to model time dependencies. LeapRec characterizes temporal dynamics by two complement components named global time leap (GTL) and ordered time leap (OTL). By design, GTL learns long-term patterns by finding the shortest learning path across unordered temporal data. Cooperatively, OTL learns short-term patterns by considering the sequential nature of the temporal data. Our experimental results show that LeapRec consistently outperforms the state-of-the-art methods on several datasets and recommendation metrics. Furthermore, we provide an empirical study of the interaction between GTL and OTL, showing the effects of long- and short-term modeling.
cs.IR↗
Enabling Real-time On-chip Audio Super Resolution for Bone Conduction Microphones
Voice communication using the air conduction microphone in noisy environments suffers from the degradation of speech audibility. Bone conduction microphones (BCM) are robust against ambient noises but suffer from limited effective bandwidth due to their sensing mechanism. Although existing audio super resolution algorithms can recover the high frequency loss to achieve high-fidelity audio, they require considerably more computational resources than available in low-power hearable devices. This paper proposes the first-ever real-time on-chip speech audio super resolution system for BCM. To accomplish this, we built and compared a series of lightweight audio super resolution deep learning models. Among all these models, ATS-UNet is the most cost-efficient because the proposed novel Audio Temporal Shift Module (ATSM) reduces the network's dimensionality while maintaining sufficient temporal features from speech audios. Then we quantized and deployed the ATS-UNet to low-end ARM micro-controller units for real-time embedded prototypes. Evaluation results show that our system achieved real-time inference speed on Cortex-M7 and higher quality than the baseline audio super resolution method. Finally, we conducted a user study with ten experts and ten amateur listeners to evaluate our method's effectiveness to human ears. Both groups perceived a significantly higher speech quality with our method when compared to the solutions with the original BCM or air conduction microphone with cutting-edge noise reduction algorithms.
cs.SD↗
Diaformer: Automatic Diagnosis via Symptoms Sequence Generation
Automatic diagnosis has attracted increasing attention but remains challenging due to multi-step reasoning. Recent works usually address it by reinforcement learning methods. However, these methods show low efficiency and require taskspecific reward functions. Considering the conversation between doctor and patient allows doctors to probe for symptoms and make diagnoses, the diagnosis process can be naturally seen as the generation of a sequence including symptoms and diagnoses. Inspired by this, we reformulate automatic diagnosis as a symptoms Sequence Generation (SG) task and propose a simple but effective automatic Diagnosis model based on Transformer (Diaformer). We firstly design the symptom attention framework to learn the generation of symptom inquiry and the disease diagnosis. To alleviate the discrepancy between sequential generation and disorder of implicit symptoms, we further design three orderless training mechanisms. Experiments on three public datasets show that our model outperforms baselines on disease diagnosis by 1%, 6% and 11.5% with the highest training efficiency. Detailed analysis on symptom inquiry prediction demonstrates that the potential of applying symptoms sequence generation for automatic diagnosis.
cs.AI↗
Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching
Graph neural networks (GNNs) and message passing neural networks (MPNNs) have been proven to be expressive for subgraph structures in many applications. Some applications in heterogeneous graphs require explicit edge modeling, such as subgraph isomorphism counting and matching. However, existing message passing mechanisms are not designed well in theory. In this paper, we start from a particular edge-to-vertex transform and exploit the isomorphism property in the edge-to-vertex dual graphs. We prove that searching isomorphisms on the original graph is equivalent to searching on its dual graph. Based on this observation, we propose dual message passing neural networks (DMPNNs) to enhance the substructure representation learning in an asynchronous way for subgraph isomorphism counting and matching as well as unsupervised node classification. Extensive experiments demonstrate the robust performance of DMPNNs by combining both node and edge representation learning in synthetic and real heterogeneous graphs. Code is available at https://github.com/HKUST-KnowComp/DualMessagePassing.
cs.LG↗