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Quantum transient heat transport in the hyperparametric oscillator

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Physical Review A 2021年第5期104卷 052220-052220页

作者： JungYun Han Daniel Leykam Dimitris G. Angelakis Juzar Thingna Center for Theoretical Physics of Complex Systems Institute for Basic Science (IBS) Daejeon 34126 Republic of Korea Basic Science Program University of Science and Technology (UST) Daejeon 34113 Republic of Korea Centre for Quantum Technologies National University of Singapore 3 Science Drive 2 Singapore 117543 School of Electrical and Computer Engineering Technical University of Crete Chania Crete 73100 Greece

We explore nonequilibrium quantum heat transport in nonlinear bosonic systems in the presence of a non-Kerr-type interaction governed by hyperparametric oscillation due to two-photon hopping between the two cavities. We estimate the thermodynamic response analytically by constructing the su(2) algebra of the nonlinear Hamiltonian and predict that the system exhibits a negative excitation mode. Consequently, this specific form of interaction enables the cooling of the system by inducing a ground-state transition when the number of particles increases, even though the interaction strength is small. We demonstrate a transition of the heat current numerically in the presence of symmetric coupling between the system and the bath and show long relaxation times in the cooling phase. We compare with the Kerr-type Bose-Hubbard form of interaction induced via cross-phase modulation, which does not exhibit any such transition. We further compute the nonequilibrium heat current in the presence of two baths at different temperatures and observe that the cooling effect for the non-Kerr-type interaction persists. Our findings may help in the manipulation of quantum states using the system's interactions to induce cooling.

关键词： Quantum optics Quantum thermodynamics Quantum transport

Mine-to-client planning with Markov Decision Process 18

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Mine-to-client planning with Markov Decision Process

18th European Control Conference, ECC 2020

作者： Leal Gomes Leite, Joao Marcelo Arruda, Edilson F. Bahiense, Laura Marujo, Lino G. Federal University of Rio de Janeiro Industrial Engineering Program Alberto Luiz Coimbra Institute Graduate School and Research in Engineering Rio de Janeiro Brazil Federal University of Rio de Janeiro Systems Engineer and Computer Science Program Alberto Luiz Coimbra Institute Graduate School and Research in Engineering Rio de Janeiro Brazil

ISBN: (纸本)9783907144015

This works presents an innovative application of Markov Decision Process (MDP) to a medium-term mining logistics planning problem considering the mine-to-client supply chain. We implemented three distinct algorithms based on state-of-the-art approaches to solve large-scale problems, and compared their results. Furthermore, we combined all three variants in a single novel algorithm that attained fast convergence and may be an alternative to circumvent the curse of dimensionality underlying large scale problems. © 2020 EUCA.

关键词： Supply chains

SYNAPTIC SPINE HEAD MORPHODYNAMICS FROM GRAPH GRAMMAR RULES FOR ACTIN DYNAMICS

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arXiv 2025年

作者： Hur, Matthew Bartol, Thomas Rangamani, Padmini Sejnowski, Terrence Mjolsness, Eric Program in Mathematical Computational and Systems Biology Univeristy of California Irvine IrvineCA United States Computational Neurobiology Laboratory Salk Institute for Biological Studies La Jolla CA United States Departments of Pharmacology and Mechanical and Aerospace Engineering University of California San Diego La Jolla CA United States Deparment of Neurobiology University of California San Diego La Jolla CA United States Departments of Computer Science and Mathematics Univeristy of California Irvine IrvineCA United States

There is a morphodynamic component to synaptic learning by which changes in dendritic (postsynaptic) spine head size are associated with the strengthening or weakening of the synaptic connection between two neurons, in response to the temporal correlation of local presynaptic and postsynaptic signals. These morphological factors are in turn sculpted by the dynamics of the actin cytoskeleton. In this paper, we use Dynamical Graph Grammars (DGGs) implemented within a computer algebra system to model how networks of actin filaments can dynamically grow or shrink, reshaping the spine head. Dynamical Graph Grammars (DGGs) provide a well-defined way to accommodate dynamically changing system structure such as active cytoskeleton represented using dynamic graphs, within nonequilibrium statistical physics under the master equation. We show that DGGs can also incorporate biophysical forces between graph-connected objects at a finer time scale, with specialized DGG kinetic rules obeying biophysical constraints of Galilean invariance, conservation of momentum, and dissipation of conserved global energy. We use graph-local energy functions for cytoskeleton networks interacting with membranes, and derive DGG rules from the specialization of dissipative stochastic dynamics - separated into dissipative and thermal noise rule types - to a mutually exclusive and exhaustive collection of graph-local neighborhood types for the rule left hand sides. The dissipative rules comprise a stochastic version of gradient descent dynamics. The thermal noise rules use a Gaussian approximation of each position coordinate to sample jitter-like displacements. For the spine head model we designed and implemented DGG grammar mathematical sub-models including actin network growth, non-equilibrium statistical mechanics, and filament-membrane mechanical interaction to regulate the re-writing of graph objects. We simulate emergent biophysics of simplified networks of actin polymers and their interactions

关键词： Stochastic models

Scalable and efficient neural speech coding: A hybrid design

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arXiv 2021年

作者： Zhen, Kai Sung, Jongmo Lee, Mi Suk Beack, Seungkwon Kim, Minje Department of Computer Science and Cognitive Science Program Indiana University BloomingtonIN47408 United States Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of Dept. of Intelligent Systems Engineering Indiana University United States

We present a scalable and efficient neural waveform coding system for speech compression. We formulate the speech coding problem as an autoencoding task, where a convolutional neural network (CNN) performs encoding and decoding as a neural waveform codec (NWC) during its feedforward routine. The proposed NWC also defines quantization and entropy coding as a trainable module, so the coding artifacts and bitrate control are handled during the optimization process. We achieve efficiency by introducing compact model components to NWC, such as gated residual networks and depthwise separable convolution. Furthermore, the proposed models are with a scalable architecture, cross-module residual learning (CMRL), to cover a wide range of bitrates. To this end, we employ the residual coding concept to concatenate multiple NWC autoencoding modules, where each NWC module performs residual coding to restore any reconstruction loss that its preceding modules have created. CMRL can scale down to cover lower bitrates as well, for which it employs linear predictive coding (LPC) module as its first autoencoder. The hybrid design integrates LPC and NWC by redefining LPC’s quantization as a differentiable process, making the system training an end-to-end manner. The decoder of proposed system is with either one NWC (0.12 million parameters) in low to medium bitrate ranges (12 to 20 kbps) or two NWCs in the high bitrate (32 kbps). Although the decoding complexity is not yet as low as that of conventional speech codecs, it is significantly reduced from that of other neural speech coders, such as a WaveNet-based vocoder. For wide-band speech coding quality, our system yields comparable or superior performance to AMR-WB and Opus on TIMIT test utterances at low and medium bitrates. The proposed system can scale up to higher bitrates to achieve near transparent performance. © 2021, CC BY.

关键词： Convolution

Fair Patient Model: Mitigating Bias in the Patient Representation Learned from the Electronic Health Records

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arXiv 2023年

作者： Sivarajkumar, Sonish Huang, Yufei Wang, Yanshan Intelligent Systems Program School of Computing and Information University of Pittsburgh PittsburghPA United States Department of Electrical and Computer Engineering University of Pittsburgh PittsburghPA United States Department of Health Information Management University of Pittsburgh PittsburghPA United States Department of Biomedical Informatics University of Pittsburgh PittsburghPA United States Clinical and Translational Science Institute University of Pittsburgh PittsburghPA United States

Objective: To pre-train fair and unbiased patient representations from Electronic Health Records (EHRs) using a novel weighted loss function that reduces bias and improves fairness in deep representation learning models. Methods: We defined a new loss function, called weighted loss function, in the deep representation learning model to balance the importance of different groups of patients and features. We applied the proposed model, called Fair Patient Model (FPM), to a sample of 34,739 patients from the MIMIC-III dataset and learned patient representations for four clinical outcome prediction tasks. Results: FPM outperformed the baseline models in terms of three fairness metrics: demographic parity, equality of opportunity difference, and equalized odds ratio. FPM also achieved comparable predictive performance with the baselines, with an average accuracy of 0.7912. Feature analysis revealed that FPM captured more information from clinical features than the baselines. Conclusion: FPM is a novel method to pre-train fair and unbiased patient representations from EHR data using a weighted loss function. The learned representations can be used for various downstream tasks in healthcare and can be extended to other domains where bias and fairness are important. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep learning

Development of Low-Temperature Doping Process in Cdse-Nanocrystal Thin Films for Flexible Electronic and Optoelectronic Devices

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SSRN

SSRN 2022年

作者： Lee, Yong Min Jung, Byung Ku Ahn, Junhyuk Park, Taesung Shin, Chanho Ng, Tse Nga Kim, In Soo Choi, Ji-Hyuk Oh, Soong Ju Department of Semiconductor Systems Engineering Korea University Seoul02841 Korea Republic of Department of Materials Science and Engineering Korea University Seoul02841 Korea Republic of Materials Science Engineering Program Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States Nanophotonics Research Center Korea Institute of Science and Technology Seoul02792 Korea Republic of Department of Converging Science and Technology KHU KIST Seoul02447 Korea Republic of Resources Utilization Research Center Korea Institute of Geoscience and Mineral Resources Daejeon34132 Korea Republic of

We introduce an effective surface-doping strategy for CdSe nanocrystals (NCs) by examining the size-dependency of the doping behavior. A CdSe NC thin-film transistor (TFT) is fabricated via a doping process with InCl3 treatment followed by an annealing process. Spectroscopic and electronic device characterization reveal that smaller NCs are more effectively doped than larger NCs at all annealing temperatures. This is attributed to the larger specific surface area, where the dopant can be more readily coordinated. The degenerate behavior of a 3.3-nm CdSe-NC TFT appears at a lower temperature than that of larger NC TFTs. We achieve a high electron mobility and on-to-off current ratio of 1.79 cm2/Vs and 5.2 × 105, respectively, in a low-temperature annealing process (150 °C) with 3.3-nm CdSe NCs. This facilitates the fabrication of a flexible CdSe-NC TFT, which is demonstrated on a polyethylene terephthalate substrate with an Al2O3 dielectric layer using atomic layer deposition. The flexible 3.3-nm CdSe-NC TFT is successfully fabricated even at 150 °C, with a high on-to-off current ratio and low hysteresis at a low operation voltage, which is impossible for larger CdSe-NC TFTs. © 2022, The Authors. All rights reserved.

关键词： Nanocrystals

A Two-stage Reconfiguration in Network Function Virtualization: Toward Service Function Chain Optimization

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IEEE Transactions on Network and Service Management 2025年

作者： Assis, Karcius D. R. Almeida, Raul C. Baghban, Hojjat Santos, Alex F. Boutaba, Raouf Electrical and Computer Engineering Department Salvador Brazil University of Waterloo David R. Cheriton School of Computer Science Canada Department of Electronics and Systems Recife Brazil Chang Gung University Department of Artificial Intelligence Taiwan Chang Gung University College of Intelligent Computing Taiwan Center of Science and Technology in Energy and Sustainability Brazil Postgraduate Program in Computer Science Bahia Brazil

Network Function Virtualization (NFV), as a promising paradigm, speeds up the service deployment by separating network functions from proprietary devices and deploying them on common servers in the form of software. Any service in NFV-enabled networks is achieved as a Service Function Chain (SFC) which consists of a series of ordered Virtual Network Functions (VNFs). However, migration of VNFs for more flexible services within a dynamic NFV-enabled network is a key challenge to be addressed. Current VNF migration studies mainly focus on single VNF migration decisions without considering the sharing and concurrent migration of VNF instances. In this paper, we assume that each deployed VNF is used by multiple SFCs and deal with the optimal placement for the contemporaneous migration of VNFs based on the actual network situation. We formalize the VNF migration and SFC reconfiguration problem as a mathematical model, which aims to minimize the VNF migration between nodes or the total number of core changes per node. The approach is a two-stage MILP based on optimal order to solve the reconfiguration. Extensive evaluation shows that the proposed approach can reduce the change in terms of location or number of cores per node in a 6-node and 14-node networks while ensuring network latency compared with the model without reconfiguration. © 2004-2012 IEEE.

关键词： Network function virtualization

Emergent dimer-model topological order and quasi-particle excitations in liquid crystals: combinatorial vortex lattices

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arXiv 2025年

作者： Meng, Cuiling Wu, Jin-Sheng Kos, Žiga Dunkel, Jörn Nisoli, Cristiano Smalyukh, Ivan I. Department of Physics and Chemical Physics Program University of Colorado BoulderCO80309 United States Hiroshima University Hiroshima Higashihiroshima Japan Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States Faculty of Mathematics and Physics University of Ljubljana Ljubljana Slovenia Department of Condensed Matter Physics Jožef Stefan Institute Ljubljana Slovenia Theoretical Division Physics of Condensed Matter and Complex Systems Center For Nonlinear Studies Los Alamos National Laboratory Los AlamosNM87545 United States Department of Electrical Computer and Energy Engineering Materials Science and Engineering Program University of Colorado BoulderCO80309 United States Renewable and Sustainable Energy Institute National Renewable Energy Laboratory University of Colorado BoulderCO80309 United States

Liquid crystals have proven to provide a versatile experimental and theoretical platform for studying topological objects such as vortices, skyrmions, and hopfions. In parallel, in hard condensed matter physics, the concept of topological phases and topological order has been introduced in the context of spin liquids to investigate emergent phenomena like quantum Hall effects and high-temperature superconductivity. Here, we bridge these two seemingly disparate perspectives on topology in physics. Combining experiments and simulations, we show how topological defects in liquid crystals can be used as versatile building blocks to create complex, highly degenerate topological phases, which we refer to as ‘Combinatorial Vortex Lattices’ (CVLs). CVLs exhibit extensive residual entropy and support locally stable quasi-particle excitations in the form of charge-conserving topological monopoles, which can act as mobile information carriers and be linked via Dirac strings. CLVs can be rewritten and reconfigured on demand, endowed with various symmetries, and modified through laser-induced topological surgery — an essential capability for information storage and retrieval. We demonstrate experimentally the realization, stability, and precise optical manipulation of CVLs, thus opening new avenues for understanding and technologically exploiting higher-hierarchy topology in liquid crystals and other ordered media. Copyright © 2025, The Authors. All rights reserved.

关键词： Laser surgery

Valley-polarized Exitonic Mott Insulator in WS2/WSe2 Moiré Superlattice

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arXiv 2023年

作者： Lian, Zhen Meng, Yuze Ma, Lei Maity, Indrajit Yan, Li Wu, Qiran Huang, Xiong Chen, Dongxue Chen, Xiaotong Chen, Xinyue Blei, Mark Taniguchi, Takashi Watanabe, Kenji Tongay, Sefaattin Lischner, Johannes Cui, Yong-Tao Shi, Su-Fei Department of Chemical and Biological Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Departments of Materials The Thomas Young Centre for Theory and Simulation of Materials Imperial College London United Kingdom Department of Physics and Astronomy University of California RiversideCA92521 United States Department of Materials Science and Engineering University of California RiversideCA92521 United States Materials Science and Engineering Program School for Engineering of Matter Transport and Energy Arizona State University TempeAZ85287 United States International Center for Materials Nanoarchitectonics National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Research Center for Functional Materials National Institute for Materials Science 1-1 Namiki Tsukuba305-0044 Japan Department of Electrical Computer & Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

Strongly enhanced electron-electron interaction in semiconducting moiré superlattices formed by transition metal dichalcogenides (TMDCs) heterobilayers has led to a plethora of intriguing fermionic correlated states1–7. Meanwhile, interlayer excitons in a type-II aligned TMDC heterobilayer moiré superlattice, with electrons and holes separated in different layers8,9, inherit this enhanced interaction and strongly interact with each other, promising for realizing tunable correlated bosonic quasiparticles10,11 with valley degree of freedom. We employ photoluminescence spectroscopy to investigate the strong repulsion between interlayer excitons and correlated electrons in a WS2/WSe2 moiré superlattice and combine with theoretical calculations to reveal the spatial extent of interlayer excitons and the band hierarchy of correlated states. We further find that an excitonic Mott insulator state emerges when one interlayer exciton occupies one moiré cell, evidenced by emerging photoluminescence peaks under increased optical excitation power. Double occupancy of excitons in one unit cell requires overcoming the energy cost of exciton-exciton repulsion of about 30-40 meV, depending on the stacking configuration of the WS2/WSe2 heterobilayer. Further, the valley polarization of the excitonic Mott insulator state is enhanced by nearly one order of magnitude. Our study demonstrates the WS2/WSe2 moiré superlattice as a promising platform for engineering and exploring new correlated states of fermion, bosons, and a mixture of both12 © 2023, CC BY.

关键词： Excitons