arXiv subjects

Yuanyuan Yang

Publications and source records attributed to Yuanyuan Yang.

51 records · Page 3Linked to original sources

arXiv2022

Unusual band splitting and superconducting gap evolution with sulfur substitution in FeSe

High-resolution angle-resolved photoemission measurements were taken on FeSe$_{1-x}$S$_x$ (x=0, 0.04, and 0.08) superconductors. With an ultrahigh energy resolution of 0.4 meV, unusual two hole bands near the Brillouin-zone center, which was possibly a result of additional symmetry breaking, were identified in all the sulfur-substituted samples. In addition, in both of the hole bands highly anisotropic superconducting gaps with resolution limited nodes were evidenced. We find that the larger superconducting gap on the outer hole band is reduced linearly to the nematic transition temperature while the gap on the inner hole is nearly S-substitution independent. Our observations strongly suggest that the superconducting gap increases with enhanced nematicity although the superconducting transition temperature is not only governed by the pairing strength, demonstrating strong constraints on theories in the FeSe family.

cond-mat.supr-con↗

arXiv2021

Just Least Squares: Binary Compressive Sampling with Low Generative Intrinsic Dimension

In this paper, we consider recovering $n$ dimensional signals from $m$ binary measurements corrupted by noises and sign flips under the assumption that the target signals have low generative intrinsic dimension, i.e., the target signals can be approximately generated via an $L$-Lipschitz generator $G: \mathbb{R}^k\rightarrow\mathbb{R}^{n}, k\ll n$. Although the binary measurements model is highly nonlinear, we propose a least square decoder and prove that, up to a constant $c$, with high probability, the least square decoder achieves a sharp estimation error $\mathcal{O} (\sqrt{\frac{k\log (Ln)}{m}})$ as long as $m\geq \mathcal{O}( k\log (Ln))$. Extensive numerical simulations and comparisons with state-of-the-art methods demonstrated the least square decoder is robust to noise and sign flips, as indicated by our theory. By constructing a ReLU network with properly chosen depth and width, we verify the (approximately) deep generative prior, which is of independent interest.

cs.LG↗

arXiv2021

TODG: Distributed Task Offloading with Delay Guarantees for Edge Computing

Edge computing has been an efficient way to provide prompt and near-data computing services for resource-and-delay sensitive IoT applications via computation offloading. Effective computation offloading strategies need to comprehensively cope with several major issues, including the allocation of dynamic communication and computational resources, the deadline constraints of heterogeneous tasks, and the requirements for computationally inexpensive and distributed algorithms. However, most of the existing works mainly focus on part of these issues, which would not suffice to achieve expected performance in complex and practical scenarios. To tackle this challenge, in this paper, we systematically study a distributed computation offloading problem with hard delay constraints, where heterogeneous computational tasks require continually offloading to a set of edge servers via a limiting number of stochastic communication channels. The task offloading problem is then cast as a delay-constrained long-term stochastic optimization problem under unknown priori statistical knowledge. To resolve this problem, we first provide a technical path to transform and decompose it into several slot-level subproblems, then we develop a distributed online algorithm, namely TODG, to efficiently allocate the resources and schedule the offloading tasks with delay guarantees. Further, we present a comprehensive analysis for TODG, in terms of the optimality gap, the delay guarantees, and the impact of system parameters. Extensive simulation results demonstrate the effectiveness and efficiency of TODG.

cs.NI↗

arXiv2021

Optical manipulation of electronic dimensionality in a quantum material

Exotic phenomenon can be achieved in quantum materials by confining electronic states into two dimensions. For example, relativistic fermions are realised in a single layer of carbon atoms, the quantized Hall effect can result from two-dimensional (2D) systems, and the superconducting transition temperature can be enhanced significantly in a one-atomic-layer material. Ordinarily, 2D electronic system can be obtained by exfoliating the layered materials, growing monolayer materials on substrates, or establishing interfaces between different materials. Herein, we use femtosecond infrared laser pulses to invert the periodic lattice distortion sectionally in a three-dimensional (3D) charge density wave material, creating macroscopic domain walls of transient 2D ordered electronic states with exotic properties. The corresponding ultrafast electronic and lattice dynamics are captured by time- and angle-resolved photoemission spectroscopy and MeV ultrafast electron diffraction. Surprisingly, a novel energy gap state, which might be a signature of light-induced superconductivity, is identified in the photoinduced 2D domain wall near the surface. Such optical modulation of atomic motion is a new path to realise 2D electronic states and will be a new platform for creating novel phases in quantum materials.

cond-mat.str-el↗

arXiv2021

CFLMEC: Cooperative Federated Learning for Mobile Edge Computing

We investigate a cooperative federated learning framework among devices for mobile edge computing, named CFLMEC, where devices co-exist in a shared spectrum with interference. Keeping in view the time-average network throughput of cooperative federated learning framework and spectrum scarcity, we focus on maximize the admission data to the edge server or the near devices, which fills the gap of communication resource allocation for devices with federated learning. In CFLMEC, devices can transmit local models to the corresponding devices or the edge server in a relay race manner, and we use a decomposition approach to solve the resource optimization problem by considering maximum data rate on sub-channel, channel reuse and wireless resource allocation in which establishes a primal-dual learning framework and batch gradient decent to learn the dynamic network with outdated information and predict the sub-channel condition. With aim at maximizing throughput of devices, we propose communication resource allocation algorithms with and without sufficient sub-channels for strong reliance on edge servers (SRs) in cellular link, and interference aware communication resource allocation algorithm for less reliance on edge servers (LRs) in D2D link. Extensive simulation results demonstrate the CFLMEC can achieve the highest throughput of local devices comparing with existing works, meanwhile limiting the number of the sub-channels.

cs.NI↗

arXiv2020

Towards Efficient Scheduling of Federated Mobile Devices under Computational and Statistical Heterogeneity

Originated from distributed learning, federated learning enables privacy-preserved collaboration on a new abstracted level by sharing the model parameters only. While the current research mainly focuses on optimizing learning algorithms and minimizing communication overhead left by distributed learning, there is still a considerable gap when it comes to the real implementation on mobile devices. In this paper, we start with an empirical experiment to demonstrate computation heterogeneity is a more pronounced bottleneck than communication on the current generation of battery-powered mobile devices, and the existing methods are haunted by mobile stragglers. Further, non-identically distributed data across the mobile users makes the selection of participants critical to the accuracy and convergence. To tackle the computational and statistical heterogeneity, we utilize data as a tuning knob and propose two efficient polynomial-time algorithms to schedule different workloads on various mobile devices, when data is identically or non-identically distributed. For identically distributed data, we combine partitioning and linear bottleneck assignment to achieve near-optimal training time without accuracy loss. For non-identically distributed data, we convert it into an average cost minimization problem and propose a greedy algorithm to find a reasonable balance between computation time and accuracy. We also establish an offline profiler to quantify the runtime behavior of different devices, which serves as the input to the scheduling algorithms. We conduct extensive experiments on a mobile testbed with two datasets and up to 20 devices. Compared with the common benchmarks, the proposed algorithms achieve 2-100x speedup epoch-wise, 2-7% accuracy gain and boost the convergence rate by more than 100% on CIFAR10.

cs.LG↗

arXiv2020

Laboratory study of the formation of fullerene (from smaller to larger, C$_{44}$ to C$_{70}$)/anthracene cluster cations in the gas phase

The formation and evolution mechanism of fullerenes in the planetary nebula or in the interstellar medium are still not understood. Here we present the study on the cluster formation and the relative reactivity of fullerene cations (from smaller to larger, C$_{44}$ to C$_{70}$) with anthracene molecule (C$_{14}$H$_{10}$). The experiment is performed in the apparatus that combines a quadrupole ion trap with a time-of-flight mass spectrometer. By using a 355 nm laser beam to irradiate the trapped fullerenes cations (C$_{60}$$^+$ or C$_{70}$$^+$), smaller fullerene cations C$_{(60-2n)}$$^+$, n=1-8 or C$_{(70-2m)}$$^+$, m=1-11 are generated, respectively. Then reacting with anthracene molecules, series of fullerene/anthracene cluster cations are newly formed (e.g., (C$_{14}$H$_{10}$)C$_{(60-2n)}$$^+$, n=1-8 and (C$_{14}$H$_{10}$)C$_{(70-2m)}$$^+$, m=1-11), and slight difference of the reactivity within the smaller fullerene cations are observed. Nevertheless, smaller fullerenes show obviously higher reactivity when comparing to fullerene C$_{60}$$^+$ and C$_{70}$$^+$. A successive loss of C$_2$ fragments mechanism is suggested to account for the formation of smaller fullerene cations, which then undergo addition reaction with anthracene molecules to form the fullerene-anthracene cluster cations. It is found that the higher laser energy and longer irradiation time are key factors that affect the formation of smaller fullerene cations. This may indicate that in the strong radiation field environment (such as photon-dominated regions) in space, fullerenes are expected to follow the top-down evolution route, and then form small grain dust (e.g., clusters) through collision reaction with co-existing molecules, here, smaller PAHs.

physics.atm-clus↗

arXiv2020

Non-Coulomb strong electron-hole binding in $Ta_2NiSe_5$ revealed by time- and angle-resolved photoemission spectroscopy

We reveal an ultrafast purely electronic phase transition in \TNS, which is a plausible excitonic insulator, after excited by an ultrafast infrared laser pulse. Specifically, the order parameter of the strong electron-hole binding shrinks with enhancing the pump pulse, and above a critical pump fluence, a photo-excited semimetallic state is experimentally identified with the absence of ultrafast structural transition. In addition, the bare valence and conduction bands and also the effective exciton binding energy in \TNS~are determined. These findings and detailed analysis suggest a bare nonequilibrium semimetallic phase in $Ta_2NiSe_5$ and the strong electron-hole binding cannot be exclusively driven by Coulomb interaction.

cond-mat.str-el↗

arXiv2020

CL-ADMM: A Cooperative Learning Based Optimization Framework for Resource Management in MEC

We consider the problem of intelligent and efficient resource management framework in mobile edge computing (MEC), which can reduce delay and energy consumption, featuring distributed optimization and efficient congestion avoidance mechanism. In this paper, we present a Cooperative Learning framework for resource management in MEC from an Alternating Direction Method of Multipliers (ADMM) perspective, called CL-ADMM framework. First, in order to caching task efficiently in a group, a novel task popularity estimating scheme is proposed, which is based on semi-Markov process model, then a greedy task cooperative caching mechanism has been established, which can effectively reduce delay and energy consumption. Secondly, for addressing group congestion, a dynamic task migration scheme based on cooperative improved Q-learning is proposed, which can effectively reduce delay and alleviate congestion. Thirdly, for minimizing delay and energy consumption for resources allocation in a group, we formulate it as an optimization problem with a large number of variables, and then exploit a novel ADMM based scheme to address this problem, which can reduce the complexity of problem with a new set of auxiliary variables, these sub-problems are all convex problems, and can be solved by using a primal-dual approach, guaranteeing its convergences. Then we prove that the convergence by using Lyapunov theory. Numerical results demonstrate the effectiveness of the CL-ADMM and it can effectively reduce delay and energy consumption for MEC.

cs.NI↗

arXiv2020

A Parallel Optimal Task Allocation Mechanism for Large-Scale Mobile Edge Computing

We consider the problem of intelligent and efficient task allocation mechanism in large-scale mobile edge computing (MEC), which can reduce delay and energy consumption in a parallel and distributed optimization. In this paper, we study the joint optimization model to consider cooperative task management mechanism among mobile terminals (MT), macro cell base station (MBS), and multiple small cell base station (SBS) for large-scale MEC applications. We propose a parallel multi-block Alternating Direction Method of Multipliers (ADMM) based method to model both requirements of low delay and low energy consumption in the MEC system which formulates the task allocation under those requirements as a nonlinear 0-1 integer programming problem. To solve the optimization problem, we develop an efficient combination of conjugate gradient, Newton and linear search techniques based algorithm with Logarithmic Smoothing (for global variables updating) and the Cyclic Block coordinate Gradient Projection (CBGP, for local variables updating) methods, which can guarantee convergence and reduce computational complexity with a good scalability. Numerical results demonstrate the effectiveness of the proposed mechanism and it can effectively reduce delay and energy consumption for a large-scale MEC system.

cs.NI↗

arXiv2020

A time- and angle-resolved photoemission spectroscopy with probe photon energy up to 6.7 eV

We present the development of a time- and angle-resolved photoemission spectroscopy based on a Yb-based femtosecond laser and a hemispherical electron analyzer. The energy of the pump photon is tunable between 1.4 and 1.9 eV, and the pulse duration is around 30 fs. We use a KBe$_2$BO$_3$F$_2$ non-linear optical crystal to generate probe pulses, of which the photon energy is up to 6.7 eV, and obtain an overall time resolution of 1 ps and energy resolution of 18 meV. In addition, $β$-BaB$_2$O$_4$ crystals are used to generate alternative probe pulses at 6.05 eV, giving an overall time resolution of 130 fs and energy resolution of 19 meV. We illustrate the performance of the system with representative data on several samples (Bi$_2$Se$_3$, YbCd$_2$Sb$_2$, FeSe).

cond-mat.str-el↗

arXiv2019

Identification of Interaction Clusters Using a Semi-supervised Hierarchical Clustering Method

Motivation: Identifying interaction clusters of large gene regulatory networks (GRNs) is critical for its further investigation, while this task is very challenging, attributed to data noise in experiment data, large scale of GRNs, and inconsistency between gene expression profiles and function modules, etc. It is promising to semi-supervise this process by prior information, but shortage of prior information sometimes make it very challenging. Meanwhile, it is also annoying, and sometimes impossible to discovery gold standard for evaluation of clustering results.\\ Results: With assistance of an online enrichment tool, this research proposes a semi-supervised hierarchical clustering method via deconvolved correlation matrix~(SHC-DC) to discover interaction clusters of large-scale GRNs. Three benchmark networks including a \emph{Ecoli} network and two \emph{Yeast} networks are employed to test semi-supervision scheme of the proposed method. Then, SHC-DC is utilized to cluster genes in sleep study. Results demonstrates it can find interaction modules that are generally enriched in various signal pathways. Besides the significant influence on blood level of interleukins, impact of sleep on important pathways mediated by them is also validated by the discovered interaction modules.

cs.LG↗

arXiv2019

Cryo-CMOS Band-gap Reference Circuits for Quantum Computing

The control interface of a large-scale quantum computer will likely require electronic sub-systems that operate in close proximity to the qubits, at deep cryogenic temperatures. Here, we report the low-temperature performance of custom cryo-CMOS band-gap reference circuits designed to provide stable voltages and currents on-chip, independent of local temperature fluctuations. Our circuits are fabricated in 0.35 um silicon Germanium (SiGe) BiCMOS and 28 nm Fully Depleted Silicon On Insulator (FDSOI) CMOS processes, and we compare the performance of each. Beyond their specific application as low-power references, these circuits are ideal test-vehicles for developing design approaches that mitigate the adverse effects of cryogenic temperatures on circuit performance.

physics.ins-det↗

arXiv2015

Joint Channel Assignment and Opportunistic Routing for Maximizing Throughput in Cognitive Radio Networks

In this paper, we consider the joint opportunistic routing and channel assignment problem in multi-channel multi-radio (MCMR) cognitive radio networks (CRNs) for improving aggregate throughput of the secondary users. We first present the nonlinear programming optimization model for this joint problem, taking into account the feature of CRNs-channel uncertainty. Then considering the queue state of a node, we propose a new scheme to select proper forwarding candidates for opportunistic routing. Furthermore, a new algorithm for calculating the forwarding probability of any packet at a node is proposed, which is used to calculate how many packets a forwarder should send, so that the duplicate transmission can be reduced compared with MAC-independent opportunistic routing & encoding (MORE) [11]. Our numerical results show that the proposed scheme performs significantly better that traditional routing and opportunistic routing in which channel assignment strategy is employed.

cs.NI↗

arXiv2015

CROR: Coding-Aware Opportunistic Routing in Multi-Channel Cognitive Radio Networks

Cognitive radio (CR) is a promising technology to improve spectrum utilization. However, spectrum availability is uncertain which mainly depends on primary user's (PU's) behaviors. This makes it more difficult for most existing CR routing protocols to achieve high throughput in multi-channel cognitive radio networks (CRNs). Inter-session network coding and opportunistic routing can leverage the broadcast nature of the wireless channel to improve the performance for CRNs. In this paper we present a coding aware opportunistic routing protocol for multi-channel CRNs, cognitive radio opportunistic routing (CROR) protocol, which jointly considers the probability of successful spectrum utilization, packet loss rate, and coding opportunities. We evaluate and compare the proposed scheme against three other opportunistic routing protocols with multichannel. It is shown that the CROR, by integrating opportunistic routing with network coding, can obtain much better results, with respect to throughput, the probability of PU-SU packet collision and spectrum utilization efficiency.

cs.NI↗