arXiv subjects

Ke Liu

Publications and source records attributed to Ke Liu.

At least 145 records · Page 8Linked to original sources

arXiv2021

$w$-Dominating Set Problem on Graphs of Bounded Treewidth

Let $G=(V,E)$ be a graph. Let $w$ be a positive integer. A $w$-dominating set is a vertex subset $S$ such that for all $v\in V$, either $v\in S$ or it has at least $w$ neighbors in $S$. The $w$-Dominating Set problem is to find the minimum $w$-dominating set. The $L$-Max $w$-Dominating Set problem is to find the vertex subset $S$ of cardinality at most $L$ that maximizes $|S|+|\{v\in V\setminus S~|~|N(v)\cap S|\geq w\}|$, where $N(v)=\{u|uv\in E\}$. In this paper, we give polynomial time algorithms to $w$-Dominating Set problem and $L$-Max $w$-Dominating Set problem on graphs of bounded treewidth.

math.CO↗

arXiv2020

The view of TK-SVM on the phase hierarchy in the classical kagome Heisenberg antiferromagnet

We illustrate how the tensorial kernel support vector machine (TK-SVM) can probe the hidden multipolar orders and emergent local constraint in the classical kagome Heisenberg antiferromagnet. We show that TK-SVM learns the finite-temperature phase diagram in an unsupervised way. Moreover, in virtue of its strong interpretability, it identifies the tensorial quadrupolar and octupolar orders, which define a biaxial $D_{3h}$ spin nematic, and the local constraint that underlies the selection of coplanar states. We then discuss the disorder hierarchy of the phases, which can be inferred from both the analytical order parameters and a SVM bias parameter. For completeness we mention that the machine also picks up the leading $\sqrt{3} \times \sqrt{3}$ correlations in the dipolar channel at very low temperature, which are however weak compared to the quadrupolar and octupolar orders. Our work shows how TK-SVM can facilitate and speed up the analysis of classical frustrated magnets.

cond-mat.str-el↗

arXiv2020

How Fast You Can Actually Fly: A Comparative Investigation of Flight Airborne Time in China and the U.S

Actual airborne time (AAT) is the time between wheels-off and wheels-on of a flight. Understanding the behavior of AAT is increasingly important given the ever growing demand for air travel and flight delays becoming more rampant. As no research on AAT exists, this paper performs the first empirical analysis of AAT behavior, comparatively for the U.S. and China. The focus is on how AAT is affected by scheduled block time (SBT), origin-destination (OD) distance, and the possible pressure to reduce AAT from other parts of flight operations. Multiple econometric models are developed. The estimation results show that in both countries AAT is highly correlated with SBT and OD distance. Flights in the U.S. are faster than in China. On the other hand, facing ground delay prior to takeoff, a flight has limited capability to speed up. The pressure from short turnaround time after landing to reduce AAT is immaterial. Sensitivity analysis of AAT to flight length and aircraft utilization is further conducted. Given the more abundant airspace, flexible routing networks, and efficient ATFM procedures, a counterfactual that the AAT behavior in the U.S. were adopted in China is examined. We find that by doing so significant efficiency gains could be achieved in the Chinese air traffic system. On average, 11.8 minutes of AAT per flight would be saved, coming from both reduction in SBT and reduction in AAT relative to the new SBT. Systemwide fuel saving would amount to over 300 million gallons with direct airline operating cost saving of nearly $1.3 billion nationwide in 2016.

stat.AP↗

arXiv2019

Identification of hidden order and emergent constraints in frustrated magnets using tensorial kernel methods

Machine-learning techniques have proved successful in identifying ordered phases of matter. However, it remains an open question how far they can contribute to the understanding of phases without broken symmetry, such as spin liquids. Here we demonstrate how a machine learning approach can automatically learn the intricate phase diagram of a classical frustrated spin model. The method we employ is a support vector machine equipped with a tensorial kernel and a spectral graph analysis which admits its applicability in an effectively unsupervised context. Thanks to the interpretability of the machine we are able to infer, in closed form, both order parameter tensors of phases with broken symmetry, and the local constraints which signal an emergent gauge structure, and so characterize classical spin liquids. The method is applied to the classical XXZ model on the pyrochlore lattice where it distinguishes---among others---between a hidden biaxial spin nematic phase and several different classical spin liquids. The results are in full agreement with a previous analysis by Taillefumier \emph{et al.} [Phys. Rev. X 7, 041057 (2017)], but go further by providing a systematic hierarchy between disordered regimes, and establishing the physical relevance of the susceptibilities associated with the local constraints. Our work paves the way for the search of new orders and spin liquids in generic frustrated magnets.

cond-mat.str-el↗

arXiv2019

The Regularization of Small Sub-Constraint Satisfaction Problems

This paper describes a new approach on optimization of constraint satisfaction problems (CSPs) by means of substituting sub-CSPs with locally consistent regular membership constraints. The purpose of this approach is to reduce the number of fails in the resolution process, to improve the inferences made during search by the constraint solver by strengthening constraint propagation, and to maintain the level of propagation while reducing the cost of propagating the constraints. Our experimental results show improvements in terms of the resolution speed compared to the original CSPs and a competitiveness to the recent tabulation approach. Besides, our approach can be realized in a preprocessing step, and therefore wouldn't collide with redundancy constraints or parallel computing if implemented.

cs.AI↗

arXiv2019

Exploring Properties of Icosoku by Constraint Satisfaction Approach

Icosoku is a challenging and interesting puzzle that exhibits highly symmetrical and combinatorial nature. In this paper, we pose the questions derived from the puzzle, but with more difficulty and generality. In addition, we also present a constraint programming model for the proposed questions, which can provide the answers to our first two questions. The purpose of this paper is to share our preliminary result and problems to encourage researchers in both group theory and constraint communities to consider this topic further.

cs.AI↗

arXiv2019

Learning multiple order parameters with interpretable machines

Machine-learning techniques are evolving into a subsidiary tool for studying phase transitions in many-body systems. However, most studies are tied to situations involving only one phase transition and one order parameter. Systems that accommodate multiple phases of coexisting and competing orders, which are common in condensed matter physics, remain largely unexplored from a machine-learning perspective. In this paper, we investigate multiclassification of phases using support vector machines (SVMs) and apply a recently introduced kernel method for detecting hidden spin and orbital orders to learn multiple phases and their analytical order parameters. Our focus is on multipolar orders and their tensorial order parameters whose identification is difficult with traditional methods. The importance of interpretability is emphasized for physical applications of multiclassification. Furthermore, we discuss an intrinsic parameter of SVM, the bias, which allows for a special interpretation in the classification of phases, and its utility in diagnosing the existence of phase transitions. We show that it can be exploited as an efficient way to explore the topology of unknown phase diagrams where the supervision is entirely delegated to the machine.

cond-mat.stat-mech↗

arXiv2019

Probing hidden spin order with interpretable machine learning

The search of unconventional magnetic and nonmagnetic states is a major topic in the study of frustrated magnetism. Canonical examples of those states include various spin liquids and spin nematics. However, discerning their existence and the correct characterization is usually challenging. Here we introduce a machine-learning protocol that can identify general nematic order and their order parameter from seemingly featureless spin configurations, thus providing comprehensive insight on the presence or absence of hidden orders. We demonstrate the capabilities of our method by extracting the analytical form of nematic order parameter tensors up to rank 6. This may prove useful in the search for novel spin states and for ruling out spurious spin liquid candidates.

cond-mat.stat-mech↗

arXiv2018

Optimized Preference-Aware Multi-path Video Streaming with Scalable Video Coding

Most client hosts are equipped with multiple network interfaces (e.g., WiFi and cellular networks). Simultaneous access of multiple interfaces can significantly improve the users' quality of experience (QoE) in video streaming. An intuitive approach to achieve it is to use Multi-path TCP (MPTCP). However, the deployment of MPTCP, especially with link preference, requires OS kernel update at both the client and server side, and a vast amount of commercial content providers do not support MPTCP. Thus, in this paper, we realize a multi-path video streaming algorithm in the application layer instead, by considering Scalable Video Coding (SVC), where each layer of every chunk can be fetched from only one of the orthogonal paths. We formulate the quality decisions of video chunks subject to the available bandwidth of the different paths, chunk deadlines, and link preferences as an optimization problem. The objective is to to optimize a QoE metric that maintains a tradeoff between maximizing the playback rate of every chunk and ensuring fairness among chunks. The QoE is a weighted some of the following metrics: skip/stall duration, average playback rate, and quality switching rate. However, the weights are chosen such that pushing more chunks to the same quality level is more preferable over any other choice. Even though the formulation is a non-convex discrete optimization, we show that the problem can be solved optimally with a polynomial complexity in some special cases. We further propose an online algorithm where several challenges including bandwidth prediction errors, are addressed. Extensive emulated experiments in a real testbed with real traces of public dataset reveal the robustness of our scheme and demonstrate its significant performance improvement compared to other multi-path algorithms.

cs.NI↗

arXiv2018

Chemi-net: a graph convolutional network for accurate drug property prediction

Absorption, distribution, metabolism, and excretion (ADME) studies are critical for drug discovery. Conventionally, these tasks, together with other chemical property predictions, rely on domain-specific feature descriptors, or fingerprints. Following the recent success of neural networks, we developed Chemi-Net, a completely data-driven, domain knowledge-free, deep learning method for ADME property prediction. To compare the relative performance of Chemi-Net with Cubist, one of the popular machine learning programs used by Amgen, a large-scale ADME property prediction study was performed on-site at Amgen. The results showed that our deep neural network method improved current methods by a large margin. We foresee that the significantly increased accuracy of ADME prediction seen with Chemi-Net over Cubist will greatly accelerate drug discovery.

cs.LG↗

arXiv2018

Generic first-order phase transitions between isotropic and orientational phases with polyhedral symmetries

Polyhedral nematics are examples of exotic orientational phases that possess a complex internal symmetry, representing highly non-trivial ways of rotational symmetry breaking, and are subject to current experimental pursuits in colloidal and molecular systems. The classification of these phases has been known for a long time, however, their transitions to the disordered isotropic liquid phase remain largely unexplored, except for a few symmetries. In this work, we utilize a recently introduced non-Abelian gauge theory to explore the nature of the underlying nematic-isotropic transition for all three-dimensional polyhedral nematics. The gauge theory can readily be applied to nematic phases with an arbitrary point-group symmetry, including those where traditional Landau methods and the associated lattice models may become too involved to implement owing to a prohibitive order-parameter tensor of high rank or (the absence of) mirror symmetries. By means of exhaustive Monte Carlo simulations, we find that the nematic-isotropic transition is generically first-order for all polyhedral symmetries. Moreover, we show that this universal result is fully consistent with our expectation from a renormalization group approach, as well as with other lattice models for symmetries already studied in the literature. We argue that extreme fine tuning is required to promote those transitions to second order ones. We also comment on the nature of phase transitions breaking the $O(3)$ symmetry in general cases.

cond-mat.stat-mech↗

arXiv2018

Optimizing TCP Loss Recovery Performance Over Mobile Data Networks

Recent advances in high-speed mobile networks have revealed new bottlenecks in ubiquitous TCP protocol deployed in the Internet. In addition to differentiating non-congestive loss from congestive loss, our experiments revealed two significant performance bottlenecks during the loss recovery phase: flow control bottleneck and application stall, resulting in degradation in QoS performance. To tackle these two problems we firstly develop a novel opportunistic retransmission algorithm to eliminate the flow control bottleneck, which enables TCP sender to transmit new packets even if receiver's receiving window is exhausted. Secondly, application stall can be significantly alleviated by carefully monitoring and tuning the TCP sending buffer growth mechanism. We implemented and modularized the proposed algorithms in the Linux kernel thus they can plug-and-play with the existing TCP loss recovery algorithms easily. Using emulated experiments we showed that, compared to the existing TCP loss recovery algorithms, the proposed optimization algorithms improve the bandwidth efficiency by up to 133% and completely mitigate RTT spikes, i.e., over 50% RTT reduction, over the loss recovery phase.

cs.NI↗

arXiv2017

Finite temperature physics of $1D$ topological Kondo insulator: Stable Haldane phase, Emergent energy scale and Beyond

We have studied the one-dimensional $p$-wave periodic Anderson model at finite temperature with the help of the numerically exact determinant quantum Monte Carlo simulation. It is found that the topological Haldane phase established for ground-state is still stable against small thermal fluctuation and its characteristic edge magnetization develops at low temperature. Moreover, the saturated low-$T$ spin structure factor and the $\frac{1}{T}$-law of susceptibility are useful to detect the free edge spin moment, which may be relevant for experimental explorations. We have also comparatively studied the conventional $s$-wave periodic Anderson model, which helps us identify an emergent energy scale $T_{cr}$. $T_{cr}$ signals a crossover into interesting low-$T$ regime and seems to be the expected Ruderman-Kittel-Kasuya-Yosida (RKKY) coupling. Finally, the collective Kondo screening effect has been examined and it is heavily reduced at boundary, which may give a fruitful playground for novel physics beyond the well-established Haldane state and topological band insulators.

cond-mat.str-el↗

arXiv2017

Low-loss Tunable 1-D ITO-slot Photonic Crystal Nanobeam Cavity

Tunable optical material properties enable novel applications in both versatile metamaterials and photonic components including optical sources and modulators. Transparent conductive oxides (TCOs) are able to highly tune their optical properties with applied bias via altering their free carrier concentration and hence plasma dispersion. The TCO material indium tin oxide (ITO) exhibits unity-strong index changes, and epsilon-near-zero behavior. However, with such tuning the corresponding high optical losses, originating from the fundamental Kramers-Kronig relations, result in low cavity finesse. However, achieving efficient tuning in ITO-cavities without using light matter interaction enhancement techniques such as polaritonic modes, which are inherently lossy, is a challenge. Here we discuss a novel one-dimensional photonic crystal nanobeam cavity to deliver a cavity system offering a wide range of resonance tuning range, while preserving physical compact footprints. We show that a vertical Silicon-slot waveguide incorporating an actively gated-ITO layer delivers ~3.4 nm of tuning. By deploying distributed feedback, we are able to keep the Q-factor moderately high with tuning. Combining this with the sub-diffraction limited mode volume (0.1 (λ/2n)3) from the photonic (non-plasmonic) slot waveguide, facilitates a high Purcell factor exceeding one thousand. This strong light-matter-interaction shows that reducing the mode volume of a cavity outweighs reducing the losses in diffraction limited modal cavities such as those from bulk Si3N4. These tunable cavities enable future modulators and optical sources such as tunable lasers.

physics.optics↗

arXiv2017

Prediction of amino acid side chain conformation using a deep neural network

A deep neural network based architecture was constructed to predict amino acid side chain conformation with unprecedented accuracy. Amino acid side chain conformation prediction is essential for protein homology modeling and protein design. Current widely-adopted methods use physics-based energy functions to evaluate side chain conformation. Here, using a deep neural network architecture without physics-based assumptions, we have demonstrated that side chain conformation prediction accuracy can be improved by more than 25%, especially for aromatic residues compared with current standard methods. More strikingly, the prediction method presented here is robust enough to identify individual conformational outliers from high resolution structures in a protein data bank without providing its structural factors. We envisage that our amino acid side chain predictor could be used as a quality check step for future protein structure model validation and many other potential applications such as side chain assignment in Cryo-electron microscopy, crystallography model auto-building, protein folding and small molecule ligand docking.

q-bio.BM↗

arXiv2017

Dual gauge field theory of quantum liquid crystals in two dimensions

We present a self-contained review of the theory of dislocation-mediated quantum melting at zero temperature in two spatial dimensions. The theory describes the liquid-crystalline phases with spatial symmetries in between a quantum crystalline solid and an isotropic superfluid: quantum nematics and smectics. It is based on an Abelian-Higgs-type duality mapping of phonons onto gauge bosons ("stress photons"), which encode for the capacity of the crystal to propagate stresses. Dislocations and disclinations, the topological defects of the crystal, are sources for the gauge fields and the melting of the crystal can be understood as the proliferation (condensation) of these defects, giving rise to the Anderson-Higgs mechanism on the dual side. For the liquid crystal phases, the shear sector of the gauge bosons becomes massive signaling that shear rigidity is lost. Resting on symmetry principles, we derive the phenomenological imaginary time actions of quantum nematics and smectics and analyze the full spectrum of collective modes. The quantum nematic is a superfluid having a true rotational Goldstone mode due to rotational symmetry breaking, and the origin of this 'deconfined' mode is traced back to the crystalline phase. The two-dimensional quantum smectic turns out to be a dizzyingly anisotropic phase with the collective modes interpolating between the solid and nematic in a non-trivial way. We also consider electrically charged bosonic crystals and liquid crystals, and carefully analyze the electromagnetic response of the quantum liquid crystal phases. In particular, the quantum nematic is a real superconductor and shows the Meissner effect. Their special properties inherited from spatial symmetry breaking show up mostly at finite momentum, and should be accessible by momentum-sensitive spectroscopy.

cond-mat.str-el↗

arXiv2017

Analysis of the semileptonic decay Λ_c->ne^+ν_e

The semileptonic weak decay process of the $Λ_c$ baryon to the neutron $Λ_c\rightarrow ne^+ν_e$ is examined. The transition form factors are investigated with light-cone QCD sum rules. The differential decay width is obtained in the dynamical region by fitting the sum rules-allowed results with the dipole formula. The total decay width and the branching ratio are estimated to be $Γ(Λ_c\rightarrow ne^+ν_e)=(8.57\pm0.41)\times10^{-15}\,\mbox{GeV}$ and $\mbox{Br}(Λ_c\rightarrow ne^+ν_e)=0.26\pm0.01\%$, respectively.

hep-ph↗

arXiv2017

Hierarchy of orientational phases and axial anisotropies in the gauge theoretical description of generalized nematics

The paradigm of spontaneous symmetry breaking encompasses the breaking of the rotational symmetries $O(3)$ of isotropic space to a discrete subgroup, i.e. a three-dimensional point group. The subgroups form a rich hierarchy and allow for many different phases of matter with orientational order. Such spontaneous symmetry breaking occurs in nematic liquid crystals and a highlight of such anisotropic liquids are the uniaxial and biaxial nematics. Generalizing the familiar uniaxial and biaxial nematics to phases characterized by an arbitrary point group symmetry, referred to as \emph{generalized nematics}, leads to a large hierarchy of phases and possible orientational phase transitions. We discuss how a particular class of nematic phase transitions related to axial point groups can be efficiently captured within a recently proposed gauge theoretical formulation of generalized nematics [K. Liu, J. Nissinen, R.-J. Slager, K. Wu, J. Zaanen, Phys. Rev. X {\bf 6}, 041025 (2016)]. These transitions can be introduced in the model by considering anisotropic couplings that do not break any additional symmetries. By and large this generalizes the well-known uniaxial-biaxial nematic phase transition to any arbitrary axial point group in three dimensions. We find in particular that the generalized axial transitions are distinguished by two types of phase diagrams with intermediate vestigial orientational phases and that the window of the vestigial phase is intimately related to the amount of symmetry of the defining point group due to inherently growing fluctuations of the order parameter. This might explain the stability of the observed uniaxial-biaxial phases as compared to the yet to be observed other possible forms of generalized nematic order with higher point group symmetries.

cond-mat.stat-mech↗