arXiv subjects

Chang Liu

Publications and source records attributed to Chang Liu.

At least 883 records · Page 49Linked to original sources

arXiv2021

Neutron Star$-$Neutron Star and Neutron Star$-$Black Hole Mergers: Multiband Observations and Early Warnings

The detections of gravitational waves (GWs) from binary neutron star (BNS) systems and neutron star--black hole (NSBH) systems provide new insights into dense matter properties in extreme conditions and associated high-energy astrophysical processes. However, currently information about NS equation of state (EoS) is extracted with very limited precision. Meanwhile, the fruitful results from the serendipitous discovery of the $γ$-ray burst alongside GW170817 show the necessity of early warning alerts. Accurate measurements of the matter effects and sky location could be achieved by joint GW detection from space and ground. In our work, based on two example cases, GW170817 and GW200105, we use the Fisher information matrix analysis to investigate the multiband synergy between the space-borne decihertz GW detectors and the ground-based Einstein Telescope (ET). We specially focus on the parameters pertaining to spin-induced quadrupole moment, tidal deformability, and sky localization. We demonstrate that, (i) only with the help of multiband observations can we constrain the quadrupole parameter; and (ii) with the inclusion of decihertz GW detectors, the errors of tidal deformability would be a few times smaller, indicating that many more EoSs could be excluded; (iii) with the inclusion of ET, the sky localization improves by about an order of magnitude. Furthermore, we have systematically compared the different limits from four planned decihertz detectors and adopting two widely used waveform models.

astro-ph.HE↗

arXiv2021

Towards Generating Real-World Time Series Data

Time series data generation has drawn increasing attention in recent years. Several generative adversarial network (GAN) based methods have been proposed to tackle the problem usually with the assumption that the targeted time series data are well-formatted and complete. However, real-world time series (RTS) data are far away from this utopia, e.g., long sequences with variable lengths and informative missing data raise intractable challenges for designing powerful generation algorithms. In this paper, we propose a novel generative framework for RTS data - RTSGAN to tackle the aforementioned challenges. RTSGAN first learns an encoder-decoder module which provides a mapping between a time series instance and a fixed-dimension latent vector and then learns a generation module to generate vectors in the same latent space. By combining the generator and the decoder, RTSGAN is able to generate RTS which respect the original feature distributions and the temporal dynamics. To generate time series with missing values, we further equip RTSGAN with an observation embedding layer and a decide-and-generate decoder to better utilize the informative missing patterns. Experiments on the four RTS datasets show that the proposed framework outperforms the previous generation methods in terms of synthetic data utility for downstream classification and prediction tasks.

cs.LG↗

arXiv2021

Signless Laplacian spectral radius and matching in graphs

The signless Laplacian matrix of a graph $G$ is given by $Q(G)=D(G)+A(G)$, where $D(G)$ is a diagonal matrix of vertex degrees and $A(G)$ is the adjacency matrix. The largest eigenvalue of $Q(G)$ is called the signless Laplacian spectral radius, denoted by $q_1=q_1(G)$. In this paper, some properties between the signless Laplacian spectral radius and perfect matching in graphs are establish. Let $r(n)$ be the largest root of equation $x^3-(3n-7)x^2+n(2n-7)x-2(n^2-7n+12)=0$. We show that $G$ has a perfect matching for $n=4$ or $n\geq10$, if $q_1(G)>r(n)$, and for $n=6$ or $n=8$, if $q_1(G)>4+2\sqrt{3}$ or $q_1(G)>6+2\sqrt{6}$ respectively, where $n$ is a positive even integer number. Moreover, there exists graphs $K_{n-3}\vee K_1 \vee \overline{K_2}$ such that $q_1(K_{n-3}\vee K_1 \vee \overline{K_2})=r(n)$ if $n\geq4$, a graph $K_2\vee\overline{K_4}$ such that $q_1(K_2\vee\overline{K_4})=4+2\sqrt{3}$ and a graph $K_3\vee\overline{K_5}$ such that $q_1(K_3\vee\overline{K_5})=6+2\sqrt{6}$. These graphs all have no prefect matching.

math.CO↗

arXiv2021

On the Generative Utility of Cyclic Conditionals

We study whether and how can we model a joint distribution $p(x,z)$ using two conditional models $p(x|z)$ and $q(z|x)$ that form a cycle. This is motivated by the observation that deep generative models, in addition to a likelihood model $p(x|z)$, often also use an inference model $q(z|x)$ for extracting representation, but they rely on a usually uninformative prior distribution $p(z)$ to define a joint distribution, which may render problems like posterior collapse and manifold mismatch. To explore the possibility to model a joint distribution using only $p(x|z)$ and $q(z|x)$, we study their compatibility and determinacy, corresponding to the existence and uniqueness of a joint distribution whose conditional distributions coincide with them. We develop a general theory for operable equivalence criteria for compatibility, and sufficient conditions for determinacy. Based on the theory, we propose a novel generative modeling framework CyGen that only uses the two cyclic conditional models. We develop methods to achieve compatibility and determinacy, and to use the conditional models to fit and generate data. With the prior constraint removed, CyGen better fits data and captures more representative features, supported by both synthetic and real-world experiments.

cs.LG↗

arXiv2021

Learning Causal Semantic Representation for Out-of-Distribution Prediction

Conventional supervised learning methods, especially deep ones, are found to be sensitive to out-of-distribution (OOD) examples, largely because the learned representation mixes the semantic factor with the variation factor due to their domain-specific correlation, while only the semantic factor causes the output. To address the problem, we propose a Causal Semantic Generative model (CSG) based on a causal reasoning so that the two factors are modeled separately, and develop methods for OOD prediction from a single training domain, which is common and challenging. The methods are based on the causal invariance principle, with a novel design in variational Bayes for both efficient learning and easy prediction. Theoretically, we prove that under certain conditions, CSG can identify the semantic factor by fitting training data, and this semantic-identification guarantees the boundedness of OOD generalization error and the success of adaptation. Empirical study shows improved OOD performance over prevailing baselines.

stat.ML↗

arXiv2021

Coo: Rethink Data Anomalies In Databases

Transaction processing technology has three important contents: data anomalies, isolation levels, and concurrent control algorithms. Concurrent control algorithms are used to eliminate some or all data anomalies at different isolation levels to ensure data consistency. Isolation levels in the current ANSI standard are defined by disallowing certain kinds of data anomalies. Yet, the definitions of data anomalies in the ANSI standard are controversial. On one hand, the definitions lack a mathematical formalization and cause ambiguous interpretations. On the other hand, the definitions are made in a case-by-case manner and lead to a situation that even a senior DBA could not have infallible knowledge of data anomalies, due to a lack of a full understanding of its nature. While revised definitions in existing literature propose various mathematical formalizations to correct the former argument, how to address the latter argument still remains an open problem. In this paper, we present a general framework called Coo with the capability to systematically define data anomalies. Under this framework, we show that existing reported data anomalies are only a small portion. While we theoretically prove that Coo is complete to mathematically formalize data anomalies, we employ a novel method to classify infinite data anomalies. In addition, we use this framework to define new isolation levels and quantitatively describe the concurrency and rollback rate of mainstream concurrency control algorithms. These works show that the C and I of ACID can be quantitatively analyzed based on all data anomalies.

cs.DB↗

arXiv2021

Decentralized On-Ramp Merging Control of Connected and Automated Vehicles in the Mixed Traffic Using Control Barrier Functions

The cooperative control of the connected and automated vehicle (CAV) is recognized as an effective approach to alleviate traffic congestion and improve traffic safety, especially for on-ramp bottlenecks. However, in the mixed traffic, the uncertainty of human-driven vehicles (HDVs) makes the on-ramp merging control for CAVs more challenging. This paper proposes a decentralized optimal control method to address the merging control problem of CAVs at highway on-ramps in the mixed traffic. We first formulate the optimal merging control problem, which includes the constraints of safety and vehicle dynamics, with the objectives of minimizing travel time and energy consumption. Then, a control framework, combining control barrier functions (CBFs) and control Lyapunov functions (CLFs) is proposed. CBFs render the system subject to safety-critical constraints, while CLFs stabilize the system to the objectives. In addition, to enable effective control of CAVs in the mixed traffic, a recursive merging control framework is proposed, where HDVs are regarded as disturbances, and CAVs collect surrounding vehicles' states repeatedly and update their trajectories recursively to satisfy strict merging requirements. Finally, the merging problem is reformulated as a quadratic programming problem, which allows for real-time application. Simulation results show that the proposed on-ramp merging control method is robust in resisting disturbance from the HDV with traffic efficiency and energy economy improvement.

eess.SY↗

arXiv2021

Improving Location Recommendation with Urban Knowledge Graph

Location recommendation is defined as to recommend locations (POIs) to users in location-based services. The existing data-driving approaches of location recommendation suffer from the limitation of the implicit modeling of the geographical factor, which may lead to sub-optimal recommendation results. In this work, we address this problem by introducing knowledge-driven solutions. Specifically, we first construct the Urban Knowledge Graph (UrbanKG) with geographical information and functional information of POIs. On the other side, there exist a fact that the geographical factor not only characterizes POIs but also affects user-POI interactions. To address it, we propose a novel method named UKGC. We first conduct information propagation on two sub-graphs to learn the representations of POIs and users. We then fuse two parts of representations by counterfactual learning for the final prediction. Extensive experiments on two real-world datasets verify that our method can outperform the state-of-the-art methods.

cs.IR↗

arXiv2021

Tunable vertical ferroelectricity and domain walls by interlayer sliding in $β$-ZrI$_{2}$

Vertical ferroelectricity where a net dipole moment appears as a result of in-plane ionic displacements has gained enormous attention following its discovery in transition metal dichalcogenides. Based on first-principles calculations, we report on the evidence of robust vertical ferroelectricity upon interlayer sliding in layered semiconducting $β$-ZrI$_{2}$, a sister material of polar semimetals MoTe$_{2}$ and WTe$_{2}$. The microscopic origin of ferroelectricity in ZrI$_{2}$ is attributed to asymmetric shifts of electronic charges within a trilayer, revealing a subtle interplay of rigid sliding displacements and charge redistribution down to ultrathin thicknesses. We further investigate the variety of ferroelectric domain boundaries and predict a stable charged domain wall with a quasi-two-dimensional electron gas and a high built-in electric field that can increase electron mobility and electromechanical response in multifunctional devices. Semiconducting behaviour and a small switching barrier of ZrI$_{2}$ hold promise for novel ferroelectric applications, and our results provide important insights for further development of slidetronics ferroelectricity.

cond-mat.mtrl-sci↗

arXiv2021

Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning

Behavioral cloning has proven to be effective for learning sequential decision-making policies from expert demonstrations. However, behavioral cloning often suffers from the causal confusion problem where a policy relies on the noticeable effect of expert actions due to the strong correlation but not the cause we desire. This paper presents Object-aware REgularizatiOn (OREO), a simple technique that regularizes an imitation policy in an object-aware manner. Our main idea is to encourage a policy to uniformly attend to all semantic objects, in order to prevent the policy from exploiting nuisance variables strongly correlated with expert actions. To this end, we introduce a two-stage approach: (a) we extract semantic objects from images by utilizing discrete codes from a vector-quantized variational autoencoder, and (b) we randomly drop the units that share the same discrete code together, i.e., masking out semantic objects. Our experiments demonstrate that OREO significantly improves the performance of behavioral cloning, outperforming various other regularization and causality-based methods on a variety of Atari environments and a self-driving CARLA environment. We also show that our method even outperforms inverse reinforcement learning methods trained with a considerable amount of environment interaction.

cs.LG↗

arXiv2021

Unrestricted Adversarial Attacks on ImageNet Competition

Many works have investigated the adversarial attacks or defenses under the settings where a bounded and imperceptible perturbation can be added to the input. However in the real-world, the attacker does not need to comply with this restriction. In fact, more threats to the deep model come from unrestricted adversarial examples, that is, the attacker makes large and visible modifications on the image, which causes the model classifying mistakenly, but does not affect the normal observation in human perspective. Unrestricted adversarial attack is a popular and practical direction but has not been studied thoroughly. We organize this competition with the purpose of exploring more effective unrestricted adversarial attack algorithm, so as to accelerate the academical research on the model robustness under stronger unbounded attacks. The competition is held on the TianChi platform (\url{https://tianchi.aliyun.com/competition/entrance/531853/introduction}) as one of the series of AI Security Challengers Program.

cs.CV↗

arXiv2021

Beamforming Design for Intelligent Reflecting Surface-Enhanced Symbiotic Radio Systems

This paper investigates multiuser multi-input single-output downlink symbiotic radio communication systems assisted by an intelligent reflecting surface (IRS). Different from existing methods ideally assuming the secondary user (SU) can jointly decode information symbols from both the access point (AP) and the IRS via multiuser detection, we consider a more practical SU that only non-coherent detection is available. To characterize the non-coherent decoding performance, a practical upper bound of the average symbol error rate (SER) is derived. Subsequently, we jointly optimize the beamformer at the AP and the phase shifts at the IRS to maximize the average sum-rate of the primary system taking into account the maximum tolerable SER constraint for the SU. To circumvent the couplings of variables, we exploit the Schur complement that facilitates the design of a suboptimal beamforming algorithm based on successive convex approximation. Our simulation results show that compared with various benchmark algorithms, the proposed scheme significantly improves the average sum-rate of the primary system, while guaranteeing the decoding performance of the secondary system.

cs.IT↗

arXiv2021

Photoluminescence switching in a two-dimensional atomic crystal

Two-dimensional materials are an emerging class of new materials with a wide range of electrical and optical properties and potential applications. Single-layer structures of semiconducting transition metal dichalcogenides are gaining increasing attention for use in field-effect transistors. Here, we report a photoluminescence switching effect based on single-layer WSe2 transistors. Dual gates are used to tune the photoluminescence intensity. In particular, a side-gate is utilized to control the location of ions within a solid polymer electrolyte to form an electric double layer at the interface of electrolyte and WSe2 and induce a vertical electric field. Additionally, a back-gate is used to apply a 2nd vertical electric field. An on-off ratio of the light emission up to 90 was observed under constant pump light intensity. In addition, a blue shift of the photoluminescence line up to 36 meV was observed. We attribute this blue shift to the decrease of exciton binding energy due to the change of nonlinear in-plane dielectric constant and use it to determine the 3rd order off-diagonal susceptibility \c{hi}^((3) )=3.50*10^(-19)m2/V2.

cond-mat.mtrl-sci↗

arXiv2021

Descriptors of intrinsic hydrodynamic thermal transport: screening a phonon database in a machine learning approach

Machine learning techniques are used to explore the intrinsic origins of the hydrodynamic thermal transport and to find new materials interesting for science and engineering. The hydrodynamic thermal transport is governed intrinsically by the hydrodynamic scale and the thermal conductivity. The correlations between these intrinsic properties and harmonic and anharmonic properties, and a large number of compositional (290) and structural (1224) descriptors of 131 crystal compound materials are obtained, revealing some of the key descriptors that determines the magnitude of the intrinsic hydrodynamic effects, most of them related with the phonon relaxation times. Then, a trained black-box model is applied to screen more than 5000 materials. The results identify materials with potential technological applications. Understanding the properties correlated to hydrodynamic thermal transport can help to find new thermoelectric materials and on the design of new materials to ease the heat dissipation in electronic devices.

cond-mat.mtrl-sci↗

arXiv2021

Spatial-temporal water area monitoring of Miyun Reservoir using remote sensing imagery from 1984 to 2020

Miyun Reservoir has produced huge benefits in flood control, agricultural irrigation, power generation, aquaculture, tourism, and urban water supply. Accurately water mapping is of great significance to the ecological environment monitoring of the Miyun Reservoir and the management of the South-to-North Water Diversion Project. On the 60th anniversary of the completion of the Miyun Reservoir, we took the Miyun Reservoir as the study area and collected all the Landsat-5 and Landsat-8 remote sensing images from 1984 to 2020 for water mapping. Based on the spectral, topographical and temporal-spatial characteristics of water, we proposed an automated method for long-term researvoir mapping, which can solve the problems caused by cloud, shadow, ice and snow pixels. Moreover, it can also deal with 'the same objects with different spectra' and spectral mixed problems. The overall accuracy is as high as 98.2% for the case with no cloud or snow/ice cover. The landscape division index is introduced to analyze the morphological changes of Miyun Reservoir. Based on the mapping results, we analyzed the changes of Miyun Reservoir from 1984 to 2020 and the driving factors of them.

eess.IV↗

arXiv2021

Gate-tunable magnetoresistance in six-septuple-layer MnBi$_2$Te$_4$

The recently discovered antiferromagnetic topological insulator MnBi$_2$Te$_4$ hosts a variety of exotic topological quantum phases such as the axion insulator and Chern insulator states. Here we report systematic gate voltage dependent magneto transport studies in six septuple-layer MnBi$_2$Te$_4$. In p-type carrier regime, we observe positive linear magnetoresistance when MnBi$_2$Te$_4$ is polarized in the ferromagnetic state by an out-of-plane magnetic field. Whereas in n-type regime, distinct negative magnetoresistance behaviors are observed. The magnetoresistance in both regimes is highly robust against temperature even up to the Néel temperature. Within the antiferromagnetic state, the behavior of magnetoresistance exhibits a transition from negative to positive as applying a gate voltage. The boundaries between different magnetoresistance behaviors in the experimental phase diagram can be explicitly characterized by the gate-voltage-independent magnetic fields that denotes the processes of the spin-flop transition. The rich transport phenomena demonstrate the intricate interplay between topology, magnetism and dimensionality in MnBi$_2$Te$_4$.

cond-mat.mes-hall↗

arXiv2021

Formation of a stable surface oxide in MnBi$_2$Te$_4$ thin films

Understanding the air-stability of MnBi$_2$Te$_4$ thin films is crucial for the development and long-term operation of electronic devices based around magnetic topological insulators. In the present work, we study MnBi$_2$Te$_4$ thin films upon exposure to atmosphere using a combination of synchrotron-based photoelectron spectroscopy, room temperature electrical transport and atomic force microscopy to determine the oxidation process. After 2 days air exposure a 2 nm thick oxide passivates the surface, corresponding to oxidation of only the top two surface layers, with the underlying layers preserved. This protective oxide layer results in samples that still exhibit metallic conduction even after several days air exposure. Furthermore, the work function decreases from 4.4 eV for pristine MnBi$_2$Te$_4$ to 4.0 eV after the formation of the oxide, along with only a small shift in the core levels indicating minimal doping as a result of air exposure. With the oxide confined to the top surface layers, and the underlying layers preserved, it may be possible to explore new avenues in how to handle, prepare and passivate future MnBi$_2$Te$_4$ devices.

cond-mat.mtrl-sci↗

arXiv2021

Building an Efficient and Effective Retrieval-based Dialogue System via Mutual Learning

Establishing retrieval-based dialogue systems that can select appropriate responses from the pre-built index has gained increasing attention from researchers. For this task, the adoption of pre-trained language models (such as BERT) has led to remarkable progress in a number of benchmarks. There exist two common approaches, including cross-encoders which perform full attention over the inputs, and bi-encoders that encode the context and response separately. The former gives considerable improvements in accuracy but is often inapplicable in practice for large-scale retrieval given the cost of the full attention required for each sample at test time. The latter is efficient for billions of indexes but suffers from sub-optimal performance. In this work, we propose to combine the best of both worlds to build a retrieval system. Specifically, we employ a fast bi-encoder to replace the traditional feature-based pre-retrieval model (such as BM25) and set the response re-ranking model as a more complicated architecture (such as cross-encoder). To further improve the effectiveness of our framework, we train the pre-retrieval model and the re-ranking model at the same time via mutual learning, which enables two models to learn from each other throughout the training process. We conduct experiments on two benchmarks and evaluation results demonstrate the efficiency and effectiveness of our proposed framework.

cs.CL↗