Searcharxiv⌕ Search

arXiv subjects

Liang Ma

Publications and source records attributed to Liang Ma.

At least 91 records · Page 5Linked to original sources
arXiv2021

High-Tc superconductivity in clathrate calcium hydride CaH6

Recent discovery of superconductive rare earth/actinide superhydrides has ushered in a new era of superconductivity research at high pressures. This distinct type of clathrate metal hydrides was first proposed for alkaline-earth-metal hydride CaH6 that, however, has long eluded experimental synthesis, impeding an understanding of pertinent physics. Here, we report successful synthesis of CaH6 and its measured superconducting critical temperature Tc of 215 K at 172 GPa, which is evidenced by a sharp drop of resistivity to zero and a characteristic decrease of Tc under a magnetic field up to 9 T.An estimate based on the Werthamer-Helfand-Hohenberg model gives a giant zero-temperature upper critical magnetic field of 203 T. These remarkable benchmark superconducting properties place CaH6 among the most outstanding high-Tc superhydrides, marking it as the hitherto only clathrate metal hydride outside the family of rare earth/actinide hydrides. This exceptional case raises great prospects of expanding the extraordinary class of high-Tc superhydrides to a broader variety of compounds that possess more diverse material features and physics characteristics.

cond-mat.supr-con↗
arXiv2021

Design and Synthesis of Clathrate LaB8 with Superconductivity

Boron-based clathrate materials, typically with three-dimensional networks of B atoms, have tunable properties through substitution of guest atoms, but the tuning of B cages themselves has not yet been developed. By combining crystal structural search with the laser-heated diamond anvil cell technique, we successfully synthesized a new B-based clathrate boride, LaB8, at ~108 GPa and ~2100 K. The novel structure has a B-richest cage, with 26 B atoms encapsulating a single La atom. LaB8 demonstrates phonon-mediated superconductivity with an estimated transition temperature of 14 K at ambient pressure, mainly originating from electron-phonon coupling of B cage. This work creates a prototype platform for subsequent investigation on tunable electronic properties through the choice of captured atoms.

cond-mat.mtrl-sci↗
arXiv2021

Making artificial $\textit{p}_{x,y}$-orbital honeycomb electron lattice on metal surface

We theoretically demonstrate that the desired $p_{x,y}$-orbital honeycomb electron lattice can be readily realized by arranging CO molecules into a hexagonal lattice on Cu(111) surface with scanning tunneling microscopy (STM). The electronic structure of the Cu surface states in the presence of CO molecules is calculated with various methods, \textit{i.e.}~DFT simulations, muffin-tin potential model and tight-binding model. Our calculations indicate that, by measuring the LDOS pattern using STM, the $p$-orbital surface bands can be immediately identified in experiment. We also give an analytic interpretation of the $p$-orbital LDOS pattern with $k \cdot p$ method. Meanwhile, different from the case of graphene, the $p$-orbital honeycomb lattice has two kinds of edge states, which can also be directly observed in STM experiment. Our work points out a feasible way to construct a $p_{x,y}$-orbital honeycomb electron lattice in a real system, which may have exotic properties, such as Wigner crystal, ferromagnetism, $f$-wave superconductivity, quantum anomalous Hall (QAH) effect. Furthermore, we also propose a simple way to calculate and identify the modified Cu surface bands in the Cu/CO systems with the DFT simulations. Considering the recent works about $p$-orbital square lattice in similar systems [M. R. Slot, \textit{et al.} Nat. Phys. \textbf{13}, 672 (2017); Liang Ma, \textit{et al.} Phys. Rev. B \textbf{99}, 205403 (2019)], our work once again illustrates that the artificial electron lattice on metal surface is an ideal platform to study the orbital physics in a controllable way.

cond-mat.mes-hall↗
arXiv2021

Jointly-Learned State-Action Embedding for Efficient Reinforcement Learning

While reinforcement learning has achieved considerable successes in recent years, state-of-the-art models are often still limited by the size of state and action spaces. Model-free reinforcement learning approaches use some form of state representations and the latest work has explored embedding techniques for actions, both with the aim of achieving better generalization and applicability. However, these approaches consider only states or actions, ignoring the interaction between them when generating embedded representations. In this work, we establish the theoretical foundations for the validity of training a reinforcement learning agent using embedded states and actions. We then propose a new approach for jointly learning embeddings for states and actions that combines aspects of model-free and model-based reinforcement learning, which can be applied in both discrete and continuous domains. Specifically, we use a model of the environment to obtain embeddings for states and actions and present a generic architecture that leverages these to learn a policy. In this way, the embedded representations obtained via our approach enable better generalization over both states and actions by capturing similarities in the embedding spaces. Evaluations of our approach on several gaming, robotic control, and recommender systems show it significantly outperforms state-of-the-art models in both discrete/continuous domains with large state/action spaces, thus confirming its efficacy.

cs.LG↗
arXiv2021

GTN-ED: Event Detection Using Graph Transformer Networks

Recent works show that the graph structure of sentences, generated from dependency parsers, has potential for improving event detection. However, they often only leverage the edges (dependencies) between words, and discard the dependency labels (e.g., nominal-subject), treating the underlying graph edges as homogeneous. In this work, we propose a novel framework for incorporating both dependencies and their labels using a recently proposed technique called Graph Transformer Networks (GTN). We integrate GTNs to leverage dependency relations on two existing homogeneous-graph-based models, and demonstrate an improvement in the F1 score on the ACE dataset.

cs.CL↗
arXiv2021

A Correspondence between Ricci-flat Kerr and Kaluza-Klein AdS Black Hole

We establish an explicit correspondence of Einstein gravity on the squashed spheres that are the $U(1)$ bundles over $\mathbb{CP}^m$ to the Kaluza-Klein AdS gravity on the tori. This allows us to map the Ricci-flat Kerr metrics in odd dimensions with all equal angular momenta to charged Kaluza-Klein AdS black holes that can be lifted to become singly rotating M-branes and D3-branes. Furthermore, we find maps between Ricci-flat gravitational instantons to the AdS domain walls. In particular the supersymmetric bolt instantons correspond to domain walls that can be interpreted as distributed M-branes and D3-branes, whilst the non-supersymmetric Taub-NUT solutions yield new domain walls that can be lifted to become solutions in M-theory or type IIB supergravity. The correspondence also inspires us to obtain a new superpotential in the Kaluza-Klein AdS gravity in four dimensions.

hep-th↗
arXiv2020

Failure Localization Capability: Theorem Proof and Evaluation

This is a technical report, containing all the theorem proofs and additional evaluations in paper "Network Capability in Localizing Node Failures via End-to-end Path Measurements" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IEEE/ACM Transactions on Networking, vol. 25, no. 1, pp. 434-450, 2017.

cs.NI↗
arXiv2020

Query Answering via Decentralized Search

Expert networks are formed by a group of expert-professionals with different specialties to collaboratively resolve specific queries posted to the network. In such networks, when a query reaches an expert who does not have sufficient expertise, this query needs to be routed to other experts for further processing until it is completely solved; therefore, query answering efficiency is sensitive to the underlying query routing mechanism being used. Among all possible query routing mechanisms, decentralized search, operating purely on each expert's local information without any knowledge of network global structure, represents the most basic and scalable routing mechanism, which is applicable to any network scenarios even in dynamic networks. However, there is still a lack of fundamental understanding of the efficiency of decentralized search in expert networks. In this regard, we investigate decentralized search by quantifying its performance under a variety of network settings. Our key findings reveal the existence of network conditions, under which decentralized search can achieve significantly short query routing paths (i.e., between $O(\log n)$ and $O(\log^2 n)$ hops, $n$: total number of experts in the network). Based on such theoretical foundation, we further study how the unique properties of decentralized search in expert networks is related to the anecdotal small-world phenomenon. In addition, we demonstrate that decentralized search is robust against estimation errors introduced by misinterpreting the required expertise levels. To the best of our knowledge, this is the first work studying fundamental behaviors of decentralized search in expert networks. The developed performance bounds, confirmed by real datasets, are able to assist in predicting network performance and designing complex expert networks.

cs.AI↗
arXiv2020

Identification of Additive Link Metrics: Proof of Selected Theorems

This is a technical report, containing all the theorem proofs in the following two papers: (1) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Identifiability of Link Metrics Based on End-to-end Path Measurements," in ACM IMC, 2013. (2) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Inferring Link Metrics from End-to-end Path Measurements: Identifiability and Monitor Placement," IEEE/ACM Transactions on Networking, vol. 22, no. 4, pp. 1351-1368, 2014.

cs.NI↗
arXiv2020

Influence Maximization Under Generic Threshold-based Non-submodular Model

As a widely observable social effect, influence diffusion refers to a process where innovations, trends, awareness, etc. spread across the network via the social impact among individuals. Motivated by such social effect, the concept of influence maximization is coined, where the goal is to select a bounded number of the most influential nodes (seed nodes) from a social network so that they can jointly trigger the maximal influence diffusion. A rich body of research in this area is performed under statistical diffusion models with provable submodularity, which essentially simplifies the problem as the optimal result can be approximated by the simple greedy search. When the diffusion models are non-submodular, however, the research community mostly focuses on how to bound/approximate them by tractable submodular functions so as to estimate the optimal result. In other words, there is still a lack of efficient methods that can directly resolve non-submodular influence maximization problems. In this regard, we fill the gap by proposing seed selection strategies using network graphical properties in a generalized threshold-based model, called influence barricade model, which is non-submodular. Specifically, under this model, we first establish theories to reveal graphical conditions that ensure the network generated by node removals has the same optimal seed set as that in the original network. We then exploit these theoretical conditions to develop efficient algorithms by strategically removing less-important nodes and selecting seeds only in the remaining network. To the best of our knowledge, this is the first graph-based approach that directly tackles non-submodular influence maximization.

cs.SI↗
arXiv2020

Additive Link Metrics Identification: Proof of Selected Lemmas and Propositions

This is a technical report, containing all the lemma and proposition proofs in paper "Topological Constraints on Identifying Additive Link Metrics via End-to-end Paths Measurements" by Liang Ma, Ting He, Kin K. Leung, Don Towsley, and Ananthram Swami, published in Annual Conference of The International Technology Alliance (ACITA), 2012.

cs.NI↗
arXiv2020

Node Failure Localization: Theorem Proof

This is a technical report, containing all the theorem proofs in paper "On Optimal Monitor Placement for Localizing Node Failures via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IFIP WG 7.3 Performance, 2015.

cs.NI↗
arXiv2020

Fundamental Theories in Node Failure Localization

This is a technical report, containing all the theorem proofs in paper "Node Failure Localization in Communication Networks via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, Kin K. Leung, and Jessica Lowe, published in ITA Annual Fall Meeting, 2014.

cs.NI↗
arXiv2020

Partial Network Identifiability: Theorem Proof and Evaluation

This is a technical report, containing all the theorem proofs and additional evaluations in paper "Monitor Placement for Maximal Identifiability in Network Tomography" by Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, Don Towsley, published in IEEE INFOCOM, 2014.

cs.NI↗
arXiv2020

State Action Separable Reinforcement Learning

Reinforcement Learning (RL) based methods have seen their paramount successes in solving serial decision-making and control problems in recent years. For conventional RL formulations, Markov Decision Process (MDP) and state-action-value function are the basis for the problem modeling and policy evaluation. However, several challenging issues still remain. Among most cited issues, the enormity of state/action space is an important factor that causes inefficiency in accurately approximating the state-action-value function. We observe that although actions directly define the agents' behaviors, for many problems the next state after a state transition matters more than the action taken, in determining the return of such a state transition. In this regard, we propose a new learning paradigm, State Action Separable Reinforcement Learning (sasRL), wherein the action space is decoupled from the value function learning process for higher efficiency. Then, a light-weight transition model is learned to assist the agent to determine the action that triggers the associated state transition. In addition, our convergence analysis reveals that under certain conditions, the convergence time of sasRL is $O(T^{1/k})$, where $T$ is the convergence time for updating the value function in the MDP-based formulation and $k$ is a weighting factor. Experiments on several gaming scenarios show that sasRL outperforms state-of-the-art MDP-based RL algorithms by up to $75\%$.

cs.LG↗
arXiv2020

Synetgy: Algorithm-hardware Co-design for ConvNet Accelerators on Embedded FPGAs

Using FPGAs to accelerate ConvNets has attracted significant attention in recent years. However, FPGA accelerator design has not leveraged the latest progress of ConvNets. As a result, the key application characteristics such as frames-per-second (FPS) are ignored in favor of simply counting GOPs, and results on accuracy, which is critical to application success, are often not even reported. In this work, we adopt an algorithm-hardware co-design approach to develop a ConvNet accelerator called Synetgy and a novel ConvNet model called DiracDeltaNet$^{\dagger}$. Both the accelerator and ConvNet are tailored to FPGA requirements. DiracDeltaNet, as the name suggests, is a ConvNet with only $1\times 1$ convolutions while spatial convolutions are replaced by more efficient shift operations. DiracDeltaNet achieves competitive accuracy on ImageNet (88.7\% top-5), but with 42$\times$ fewer parameters and 48$\times$ fewer OPs than VGG16. We further quantize DiracDeltaNet's weights to 4-bit and activations to 4-bits, with less than 1\% accuracy loss. These quantizations exploit well the nature of FPGA hardware. In short, DiracDeltaNet's small model size, low computational OP count, low precision and simplified operators allow us to co-design a highly customized computing unit for an FPGA. We implement the computing units for DiracDeltaNet on an Ultra96 SoC system through high-level synthesis. Our accelerator's final top-5 accuracy of 88.1\% on ImageNet, is higher than all the previously reported embedded FPGA accelerators. In addition, the accelerator reaches an inference speed of 66.3 FPS on the ImageNet classification task, surpassing prior works with similar accuracy by at least 11.6$\times$.

cs.CV↗