arXiv subjects

Lu Liu

Publications and source records attributed to Lu Liu.

At least 163 records · Page 9Linked to original sources

arXiv2022

Aspects of Muchnik's paradox in restricted betting

Muchnik's paradox says that enumerable betting strategies are not always reducible to enumerable strategies whose bets are restricted to either even rounds or odd rounds. In other words, there are outcome sequences x where an effectively enumerable strategy succeeds, but no such parity-restricted effectively enumerable strategy does. We characterize the effective Hausdorff dimension of such $x$, showing that it can be as low as 1/2 but not less. We also show that such reals that are random with respect to parity-restricted effectively enumerable strategies with packing dimension as low as $\log\sqrt3$. Finally we exhibit Muchnik's paradox in the case of computable integer-valued strategies.

math.LO↗

arXiv2022

Tailoring topological transition of anisotropic polaritons by interface engineering in biaxial crystals

Polaritons in polar biaxial crystals with extreme anisotropy offer a promising route to manipulate nanoscale light-matter interactions. The dynamical modulation of their dispersion is great significance for future integrated nano-optics but remains challenging. Here, we report a momentum-directed strategy, a coupling between the modes with extra momentum supported by the interface and in-plane hyperbolic polaritons, to tailor topological transitions of anisotropic polaritons in biaxial crystals. We experimentally demonstrate such tailored polaritons at the interface of heterostructures between graphene and α-phase molybdenum trioxide (α-MoO3). The interlayer coupling can be electrically modulated by changing the Fermi level in graphene, enabling a dynamic topological transition. More interestingly, we found that the topological transition occurs at a constant Fermi level when tuning the thickness of α-MoO3. The momentum-directed strategy implemented by interface engineering offers new insights for optical topological transitions, which may shed new light for programmable polaritonics, energy transfer and neuromorphic photonics.

physics.optics↗

arXiv2021

Recognizing Vector Graphics without Rasterization

In this paper, we consider a different data format for images: vector graphics. In contrast to raster graphics which are widely used in image recognition, vector graphics can be scaled up or down into any resolution without aliasing or information loss, due to the analytic representation of the primitives in the document. Furthermore, vector graphics are able to give extra structural information on how low-level elements group together to form high level shapes or structures. These merits of graphic vectors have not been fully leveraged in existing methods. To explore this data format, we target on the fundamental recognition tasks: object localization and classification. We propose an efficient CNN-free pipeline that does not render the graphic into pixels (i.e. rasterization), and takes textual document of the vector graphics as input, called YOLaT (You Only Look at Text). YOLaT builds multi-graphs to model the structural and spatial information in vector graphics, and a dual-stream graph neural network is proposed to detect objects from the graph. Our experiments show that by directly operating on vector graphics, YOLaT out-performs raster-graphic based object detection baselines in terms of both average precision and efficiency.

cs.CV↗

arXiv2021

CrSbSe3: a pseudo one-dimensional ferromagnetic semiconductor

Low-dimensional magnetic materials have attracted much attention due to their novel properties and high potential for spintronic applications. In this work, we study the electronic structure and magnetic properties of the pseudo one-dimensional compound CrSbSe$_3$, using density functional calculations, superexchange model analyses, and Monte Carlo simulations. We find that CrSbSe$_3$ is a ferromagnetic (FM) semiconductor with a band gap of about 0.65 eV. The FM couplings within each zig-zag spin chain are due to the Cr-Se-Cr superexchange with the near-90$^\circ$ bonds, and the inter-chain FM couplings are one order of magnitude weaker. By inclusion of the spin-orbit coupling (SOC) effects, our calculations reproduce the experimental observation of the easy magnetization $a$ axis and the hard $b$ axis (the spin-chain direction), with the calculated moderate magnetic anisotropy of 0.19 meV/Cr. Moreover, we identify the nearly equal contributions from the single ion anisotropy of Cr, and from the exchange anisotropy due to the strong SOC of the heavy elements Sb and Se and their couplings with Cr. Using the parameters of the magnetic coupling and anisotropy from the above calculations, our Monte Carlo simulations yield the Curie temperature $T_{\rm{C}}$ of 108 K.

cond-mat.mtrl-sci↗

arXiv2021

The reverse mathematics of the Thin set and Erdős-Moser theorems

The thin set theorem for $n$-tuples and $k$ colors ($\mathsf{TS}^n_k$) states that every $k$-coloring of $[\mathbb{N}]^n$ admits an infinite set of integers $H$ such that $[H]^n$ avoids at least one color. In this paper, we study the combinatorial weakness of the thin set theorem in reverse mathematics by proving neither $\mathsf{TS}^n_k$, nor the free set theorem ($\mathsf{FS}^n$) imply the Erdős-Moser theorem ($\mathsf{EM}$) whenever $k$ is sufficiently large (answering a question of Patey and giving a partial result towards a question of Cholak Giusto, Hirst and Jockusch). Given a problem $\mathsf{P}$, a computable instance of $\mathsf{P}$ is universal iff its solution computes a solution of any other computable $\mathsf{P}$-instance. It has been established that most of Ramsey-type problems do not have a universal instance, but the case of Erdős-Moser theorem remained open so far. We prove that Erdős-Moser theorem does not admit a universal instance (answering a question of Patey).

math.LO↗

arXiv2021

Distributed Optimal Output Consensus of Uncertain Nonlinear Multi-Agent Systems over Unbalanced Directed Networks via Output Feedback

In this note, a novel observer-based output feedback control approach is proposed to address the distributed optimal output consensus problem of uncertain nonlinear multi-agent systems in the normal form over unbalanced directed graphs. The main challenges of the concerned problem lie in unbalanced directed graphs and nonlinearities of multi-agent systems with their agent states not available for feedback control. Based on a two-layer controller structure, a distributed optimal coordinator is first designed to convert the considered problem into a reference-tracking problem. Then a decentralized output feedback controller is developed to stabilize the resulting augmented system. A high-gain observer is exploited in controller design to estimate the agent states in the presence of uncertainties and disturbances so that the proposed controller relies only on agent outputs. The semi-global convergence of the agent outputs toward the optimal solution that minimizes the sum of all local cost functions is proved under standard assumptions. A key feature of the obtained results is that the nonlinear agents under consideration are only required to be locally Lipschitz and possess globally asymptotically stable and locally exponentially stable zero dynamics.

eess.SY↗

arXiv2021

Optimal Output Consensus of Heterogeneous Linear Multi-Agent Systems Over Weight-Unbalanced Directed Networks

This paper investigates the distributed optimal output consensus problem of heterogeneous linear multi-agent systems over weight-unbalanced directed networks. A novel distributed continuous-time state feedback controller is proposed to steer the outputs of all the agents to converge to the optimal solution of the global cost function. Under the standard condition that the unbalanced digraph is strongly connected and the local cost functions are strongly convex with global Lipschitz gradients, the exponential convergence of the closed-loop multi-agent system is established. Then, the proposed state feedback control law is extended to an observer-based output feedback setting. Two examples are finally provided to illustrate the effectiveness of the proposed control schemes.

math.OC↗

arXiv2021

The masking condition for quantum state in two-dimensional Hilbert space

This paper focuses on quantum information masking for quantum state in two-dimensional Hilbert space. We present a system of equations as the condition of quantum information masking. It is shown that quantum information contained in a single qubit state can be masked, if and only if the coefficients of quantum state satisfy the given system of equations. By observing the characteristics of non-orthogonal maskable quantum states, we obtain a related conclusion, namely, if two non-orthogonal two-qubit quantum states can mask a single qubit state, they have the same number of terms and the same basis. Finally, for maskable orthogonal quantum states, we analyze two special examples and give their images for an intuitive description.

quant-ph↗

arXiv2021

Triaxial magnetic anisotropy in the two-dimensional ferromagnetic semiconductor CrSBr

Two-dimensional (2D) ferromagnets have recently drawn extensive attention, and here we study the electronic structure and magnetic properties of the bulk and monolayer of CrSBr, using first-principles calculations and Monte Carlo simulations. Our results show that bulk CrSBr is a magnetic semiconductor and has the easy magnetization b-axis, hard c-axis, and intermediate a-axis. Thus, the experimental triaxial magnetic anisotropy (MA) is well reproduced here, and it is identified to be the joint effects of spin-orbit coupling (SOC) and magnetic dipole-dipole interaction. We find that bulk CrSBr has a strong ferromagnetic (FM) intralayer coupling but a marginal interlayer one. We also study CrSBr monolayer in detail and find that the intralayer FM exchange persists and the shape anisotropy has a more pronounced contribution to the MA. Using the parameters of the FM exchange and the triaxial MA, our Monte Carlo simulations show that CrSBr monolayer has Curie temperature Tc = 175 K. Moreover, we find that a uniaxial tensile (compressive) strain along the a (b) axis would further increase the Tc.

cond-mat.mtrl-sci↗

arXiv2021

New Era of Deeplearning-Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning

With the development of artificial intelligence algorithms like deep learning models and the successful applications in many different fields, further similar trails of deep learning technology have been made in cyber security area. It shows the preferable performance not only in academic security research but also in industry practices when dealing with part of cyber security issues by deep learning methods compared to those conventional rules. Especially for the malware detection and classification tasks, it saves generous time cost and promotes the accuracy for a total pipeline of malware detection system. In this paper, we construct special deep neural network, ie, MalDeepNet (TB-Malnet and IB-Malnet) for malware dynamic behavior classification tasks. Then we build the family clustering algorithm based on deep learning and fulfil related testing. Except that, we also design a novel malware prediction model which could detect the malware coming in future through the Mal Generative Adversarial Network (Mal-GAN) implementation. All those algorithms present fairly considerable value in related datasets afterwards.

cs.CR↗

arXiv2021

Full-Cycle Energy Consumption Benchmark for Low-Carbon Computer Vision

The energy consumption of deep learning models is increasing at a breathtaking rate, which raises concerns due to potential negative effects on carbon neutrality in the context of global warming and climate change. With the progress of efficient deep learning techniques, e.g., model compression, researchers can obtain efficient models with fewer parameters and smaller latency. However, most of the existing efficient deep learning methods do not explicitly consider energy consumption as a key performance indicator. Furthermore, existing methods mostly focus on the inference costs of the resulting efficient models, but neglect the notable energy consumption throughout the entire life cycle of the algorithm. In this paper, we present the first large-scale energy consumption benchmark for efficient computer vision models, where a new metric is proposed to explicitly evaluate the full-cycle energy consumption under different model usage intensity. The benchmark can provide insights for low carbon emission when selecting efficient deep learning algorithms in different model usage scenarios.

cs.CV↗

arXiv2021

Cloning one's voice using very limited data in the wild

With the increasing popularity of speech synthesis products, the industry has put forward more requirements for personalized speech synthesis: (1) How to use low-resource, easily accessible data to clone a person's voice. (2) How to clone a person's voice while controlling the style and prosody. To solve the above two problems, we proposed the Hieratron model framework in which the prosody and timbre are modeled separately using two modules, therefore, the independent control of timbre and the other characteristics of audio can be achieved while generating speech. The practice shows that, for very limited target speaker data in the wild, Hieratron has obvious advantages over the traditional method, in addition to controlling the style and language of the generated speech, the mean opinion score on speech quality of the generated speech has also been improved by more than 0.2 points.

eess.AS↗

arXiv2021

Characterizing Transaction-Reverting Statements in Ethereum Smart Contracts

Smart contracts are programs running on blockchain to execute transactions. When input constraints or security properties are violated at runtime, the transaction being executed by a smart contract needs to be reverted to avoid undesirable consequences. On Ethereum, the most popular blockchain that supports smart contracts, developers can choose among three transaction-reverting statements (i.e., require, if...revert, and if...throw) to handle anomalous transactions. While these transaction-reverting statements are vital for preventing smart contracts from exhibiting abnormal behaviors or suffering malicious attacks, there is limited understanding of how they are used in practice. In this work, we perform the first empirical study to characterize transaction-reverting statements in Ethereum smart contracts. We measured the prevalence of these statements in 3,866 verified smart contracts from popular dapps and built a taxonomy of their purposes via manually analyzing 557 transaction-reverting statements. We also compared template contracts and their corresponding custom contracts to understand how developers customize the use of transaction-reverting statements. Finally, we analyzed the security impact of transaction-reverting statements by removing them from smart contracts and comparing the mutated contracts against the original ones. Our study led to important findings, which can shed light on further research in the broad area of smart contract quality assurance and provide practical guidance to smart contract developers on the appropriate use of transaction-reverting statements.

cs.CR↗

arXiv2021

Free Lunch for Few-shot Learning: Distribution Calibration

Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples, then an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. We assume every dimension in the feature representation follows a Gaussian distribution so that the mean and the variance of the distribution can borrow from that of similar classes whose statistics are better estimated with an adequate number of samples. Our method can be built on top of off-the-shelf pretrained feature extractors and classification models without extra parameters. We show that a simple logistic regression classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy on two datasets (~5% improvement on miniImageNet compared to the next best). The visualization of these generated features demonstrates that our calibrated distribution is an accurate estimation.

cs.LG↗

arXiv2021

Optimal Output Consensus of Second-Order Uncertain Nonlinear Systems on Weight-Unbalanced Directed Networks

This paper investigates the distributed optimal output consensus problem of second-order uncertain nonlinear multi-agent systems over weight-unbalanced directed networks. Under the standard assumption that local cost functions are strongly convex with globally Lipschitz gradients, a novel distributed dynamic state feedback controller is developed such that the outputs of all the agents reach the optimal solution to minimize the global cost function which is the sum of all the local cost functions. The controller design is based on a two-layer strategy, where a distributed optimal coordinator and a reference-tracking controller are proposed to address the challenges arising from unbalanced directed networks and uncertain nonlinear functions respectively. A key feature of the proposed controller is that the nonlinear functions containing the uncertainties and disturbances are not required to be globally Lipschitz. Furthermore, by exploiting adaptive control technique, no prior knowledge of the uncertainties or disturbances is required either. Two simulation examples are finally provided to illustrate the effectiveness of the proposed control scheme.

math.OC↗

arXiv2021

Towards robust and domain agnostic reinforcement learning competitions

Reinforcement learning competitions have formed the basis for standard research benchmarks, galvanized advances in the state-of-the-art, and shaped the direction of the field. Despite this, a majority of challenges suffer from the same fundamental problems: participant solutions to the posed challenge are usually domain-specific, biased to maximally exploit compute resources, and not guaranteed to be reproducible. In this paper, we present a new framework of competition design that promotes the development of algorithms that overcome these barriers. We propose four central mechanisms for achieving this end: submission retraining, domain randomization, desemantization through domain obfuscation, and the limitation of competition compute and environment-sample budget. To demonstrate the efficacy of this design, we proposed, organized, and ran the MineRL 2020 Competition on Sample-Efficient Reinforcement Learning. In this work, we describe the organizational outcomes of the competition and show that the resulting participant submissions are reproducible, non-specific to the competition environment, and sample/resource efficient, despite the difficult competition task.

cs.LG↗

arXiv2021

Certainty Driven Consistency Loss on Multi-Teacher Networks for Semi-Supervised Learning

One of the successful approaches in semi-supervised learning is based on the consistency regularization. Typically, a student model is trained to be consistent with teacher prediction for the inputs under different perturbations. To be successful, the prediction targets given by teacher should have good quality, otherwise the student can be misled by teacher. Unfortunately, existing methods do not assess the quality of the teacher targets. In this paper, we propose a novel Certainty-driven Consistency Loss (CCL) that exploits the predictive uncertainty in the consistency loss to let the student dynamically learn from reliable targets. Specifically, we propose two approaches, i.e. Filtering CCL and Temperature CCL to either filter out uncertain predictions or pay less attention on them in the consistency regularization. We further introduce a novel decoupled framework to encourage model difference. Experimental results on SVHN, CIFAR-10, and CIFAR-100 demonstrate the advantages of our method over a few existing methods.

cs.CV↗

arXiv2021

Human Object Interaction Detection using Two-Direction Spatial Enhancement and Exclusive Object Prior

Human-Object Interaction (HOI) detection aims to detect visual relations between human and objects in images. One significant problem of HOI detection is that non-interactive human-object pair can be easily mis-grouped and misclassified as an action, especially when humans are close and performing similar actions in the scene. To address the mis-grouping problem, we propose a spatial enhancement approach to enforce fine-level spatial constraints in two directions from human body parts to the object center, and from object parts to the human center. At inference, we propose a human-object regrouping approach by considering the object-exclusive property of an action, where the target object should not be shared by more than one human. By suppressing non-interactive pairs, our approach can decrease the false positives. Experiments on V-COCO and HICO-DET datasets demonstrate our approach is more robust compared to the existing methods under the presence of multiple humans and objects in the scene.

cs.CV↗