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Chi Zhang

Publications and source records attributed to Chi Zhang.

At least 613 records · Page 34Linked to original sources
arXiv2023

Leveraging Deep Learning and Digital Twins to Improve Energy Performance of Buildings

Digital transformation in buildings accumulates massive operational data, which calls for smart solutions to utilize these data to improve energy performance. This study has proposed a solution, namely Deep Energy Twin, for integrating deep learning and digital twins to better understand building energy use and identify the potential for improving energy efficiency. Ontology was adopted to create parametric digital twins to provide consistency of data format across different systems in a building. Based on created digital twins and collected data, deep learning methods were used for performing data analytics to identify patterns and provide insights for energy optimization. As a demonstration, a case study was conducted in a public historic building in Norrköping, Sweden, to compare the performance of state-of-the-art deep learning architectures in building energy forecasting.

cs.LG↗
arXiv2023

An Overview of Resource Allocation in Integrated Sensing and Communication

Integrated sensing and communication (ISAC) is considered as a promising solution for improving spectrum efficiency and relieving wireless spectrum congestion. This paper systematically introduces the evolutionary path of ISAC technologies, then sorts out and summarizes the current research status of ISAC resource allocation. From the perspective of different integrated levels of ISAC, we introduce and elaborate the research progress of resource allocation in different stages, namely, resource separated, orthogonal, converged, and collaborative stages. In addition, we give in-depth consideration to propose a new resource allocation framework from a multi-granularity perspective. Finally, we demonstrate the feasibility of our proposed framework with a case of full-duplex ISAC system.

eess.SP↗
arXiv2023

Observation of 1D Fermi arc states in Weyl semimetal TaAs

Fermi arcs on Weyl semimetals exhibit many exotic quantum phenomena. Usually considered on atomically-flat surfaces with approximate translation symmetry, Fermi arcs are rooted in peculiar topology of bulk Bloch bands of three-dimensional (3D) crystals. The fundamental question of whether a 1D Fermi arc can be probed remains unanswered. Such answer could significantly broaden potential applications of Weyl semimetals. Here, we report a direct observation of robust edge states on atomic-scale ledges in TaAs using low-temperature scanning tunneling microscopy/spectroscopy. Spectroscopic signatures and theoretical calculations reveal that the 1D Fermi arcs arise from the chiral Weyl points of bulk crystal. The crossover from 2D Fermi arcs to eventual complete localization on 1D edges was arrested experimentally on a sequence of surfaces. Our results demonstrate extreme robustness of the bulk-boundary correspondence, which offers topological protection for Fermi arcs, even in cases in which the boundaries are down to atomic-scale. The persistent 1D Fermi arcs can be profitably exploited in miniaturized quantum devices.

cond-mat.mes-hall↗
arXiv2023

A method for automated regression test in scientific computing libraries: illustration with SPHinXsys

Scientific computing libraries, either being in-house or open-source, have experienced enormous progress in both engineering and scientific research. It is therefore essential to ensure that the modifications in the source code aroused by bug fixing or new feature development wouldn't compromise the accuracy and functionality that has already been validated and verified. With this in mind, this paper introduces a method for developing and implementing an automatic regression test environment and takes the open-source multi-physics library SPHinXsys \cite{zhang2021sphinxsys} as an example. Firstly, the reference database for each benchmark test is generated from monitored data by multiple executions. This database contains the maximum variation range of metrics for different types of strategies, i.e., time-averaged method, ensemble-averaged method as well as the dynamic time warping method, covering the uncertainty arising from parallel computing, particle relaxation, physical instabilities, etc. Then, new results obtained after source code modification will be tested with them according to a curve-similarity based comparison. Whenever the source code is updated, the regression test will be carried out automatically for all test cases and used to report the validity of the current results. This regression test environment has already been implemented in all dynamics test cases released in SPHinXsys, including fluid dynamics, solid mechanics, fluid-structure interaction, thermal and mass diffusion, reaction-diffusion, and their multi-physics coupling, and shows good capability for testing various problems. It's worth noting that while the present test environment is built and implemented for a specific scientific computing library, its underlying principle is generic and can be applied to many others.

physics.comp-ph↗
arXiv2023

An efficient and generalized consistency correction method for weakly-compressible SPH

In this paper, a new efficient and generalized consistency correction method for weakly-compressible smoothed particle hydrodynamics is proposed and successfully implemented in the simulation of violent free-surface flow exhibiting breaking and impact events for the first time. It's well known that the original kernel gradient correction (KGC) encounters numerical instability resulting from matrix inversion. The present method remedies this issue by introducing a weighted average of the KGC matrix and the identity matrix, other than directly applying KGC matrix, to achieve numerical stability meanwhile decrease numerical dissipation. To ensure momentum conservation, the correction is implemented in a particle-average pattern by rewriting the the pressure term of the Riemann solution. Furthermore, the proposed weighted KGC scheme is incorporated into the dual-criteria time-stepping framework developed by Zhang et al. (2020) \cite{22} to achieve optimized computational efficiency. A set of numerical examples in both two- and three-dimensions are investigated to demonstrate that the present method can significantly reduce numerical dissipation meanwhile exhibit a smooth pressure field for general free-surface flows.

physics.flu-dyn↗
arXiv2023

New characterizations of a normal subgroup to be hypercyclically embedded

A normal subgroup $E$ of a group $G$ is said to be hypercyclically embedded in $G$ if either $E=1$ or $E\neq 1$ and every chief factor of $G$ below $E$ is cyclic. In this article, we present some new characterizations of a normal subgroup to be hypercyclically embedded. Some recent results in this literature are generalized and unified.

math.GR↗
arXiv2023

Deep Learning Based Joint Beamforming Design in IRS-Assisted Secure Communications

In this article, physical layer security (PLS) in an intelligent reflecting surface (IRS) assisted multiple-input multiple-output multiple antenna eavesdropper (MIMOME) system is studied. In particular, we consider a practical scenario without instantaneous channel state information (CSI) of the eavesdropper and assume that the eavesdropping channel is a Rayleigh channel. To reduce the complexity of currently available IRS-assisted PLS schemes, we propose a low-complexity deep learning (DL) based approach to design transmitter beamforming and IRS jointly, where the precoding vector and phase shift matrix are designed to minimize the secrecy outage probability. Simulation results demonstrate that the proposed DL-based approach can achieve a similar performance of that with conventional alternating optimization (AO) algorithms for a significant reduction in the computational complexity.

cs.IT↗
arXiv2023

Model-Agnostic Reachability Analysis on Deep Neural Networks

Verification plays an essential role in the formal analysis of safety-critical systems. Most current verification methods have specific requirements when working on Deep Neural Networks (DNNs). They either target one particular network category, e.g., Feedforward Neural Networks (FNNs), or networks with specific activation functions, e.g., RdLU. In this paper, we develop a model-agnostic verification framework, called DeepAgn, and show that it can be applied to FNNs, Recurrent Neural Networks (RNNs), or a mixture of both. Under the assumption of Lipschitz continuity, DeepAgn analyses the reachability of DNNs based on a novel optimisation scheme with a global convergence guarantee. It does not require access to the network's internal structures, such as layers and parameters. Through reachability analysis, DeepAgn can tackle several well-known robustness problems, including computing the maximum safe radius for a given input, and generating the ground-truth adversarial examples. We also empirically demonstrate DeepAgn's superior capability and efficiency in handling a broader class of deep neural networks, including both FNNs, and RNNs with very deep layers and millions of neurons, than other state-of-the-art verification approaches.

cs.LG↗
arXiv2023

DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning

In this work, we develop methods for few-shot image classification from a new perspective of optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations to determine image relevance. The EMD generates the optimal matching flows between structural elements that have the minimum matching cost, which is used to calculate the image distance for classification. To generate the important weights of elements in the EMD formulation, we design a cross-reference mechanism, which can effectively alleviate the adverse impact caused by the cluttered background and large intra-class appearance variations. To implement k-shot classification, we propose to learn a structured fully connected layer that can directly classify dense image representations with the EMD. Based on the implicit function theorem, the EMD can be inserted as a layer into the network for end-to-end training. Our extensive experiments validate the effectiveness of our algorithm which outperforms state-of-the-art methods by a significant margin on five widely used few-shot classification benchmarks, namely, miniImageNet, tieredImageNet, Fewshot-CIFAR100 (FC100), Caltech-UCSD Birds-200-2011 (CUB), and CIFAR-FewShot (CIFAR-FS). We also demonstrate the effectiveness of our method on the image retrieval task in our experiments.

cs.CV↗
arXiv2023

Experimental Twin-Field Quantum Key Distribution Over 1000 km Fiber Distance

Quantum key distribution (QKD) aims to generate secure private keys shared by two remote parties. With its security being protected by principles of quantum mechanics, some technology challenges remain towards practical application of QKD. The major one is the distance limit, which is caused by the fact that a quantum signal cannot be amplified while the channel loss is exponential with the distance for photon transmission in optical fiber. Here using the 3-intensity sending-or-not-sending protocol with the actively-odd-parity-pairing method, we demonstrate a fiber-based twin-field QKD over 1002 km. In our experiment, we developed a dual-band phase estimation and ultra-low noise superconducting nanowire single-photon detectors to suppress the system noise to around 0.02 Hz. The secure key rate is $9.53\times10^{-12}$ per pulse through 1002 km fiber in the asymptotic regime, and $8.75\times10^{-12}$ per pulse at 952 km considering the finite size effect. Our work constitutes a critical step towards the future large-scale quantum network.

quant-ph↗
arXiv2023

Wide-range continuous tuning of the thermal conductivity of $\rm La_{0.5}Sr_{0.5}CoO_{3-δ}$ films via room-temperature ion-gel gating

Solid-state control of the thermal conductivity of materials is of exceptional interest for novel devices such as thermal diodes and switches. Here, we demonstrate the ability to continuously tune the thermal conductivity of nanoscale films of $\rm La_{0.5}Sr_{0.5}CoO_{3-δ}$ (LSCO) by a factor of over 5, via a room-temperature electrolyte-gate-induced non-volatile topotactic phase transformation from perovskite (with $δ\approx 0.1$) to an oxygen-vacancy-ordered brownmillerite phase (with $δ=0.5$), accompanied by a metal-insulator transition. Combining time-domain thermoreflectance and electronic transport measurements, model analyses based on molecular dynamics and Boltzmann transport, and structural characterization by X-ray diffraction, we uncover and deconvolve the effects of these transitions on heat carriers, including electrons and lattice vibrations. The wide-range continuous tunability of LSCO thermal conductivity enabled by low-voltage (below 4 V) room-temperature electrolyte gating opens the door to non-volatile dynamic control of thermal transport in perovskite-based functional materials, for thermal regulation and management in device applications.

cond-mat.mtrl-sci↗
arXiv2023

Experimental Implementation of Short-Path Non-adiabatic Geometric Gates in a Superconducting Circuit

The non-adiabatic geometric quantum computation (NGQC) has attracted a lot of attention for noise-resilient quantum control. However, previous implementations of NGQC require long evolution paths that make them more vulnerable to incoherent errors than their dynamical counterparts.In this work, we experimentally realize a universal short-path non-adiabatic geometric gate set (SPNGQC) with a 2-times shorter evolution path on a superconducting quantum processor. Characterizing with both quantum process tomography and randomized benchmarking methods, we report an average single-qubit gate fidelity of 99.86% and a two-qubit gate fidelity of 97.9%. Additionally, we demonstrate superior robustness of single-qubit SP-NGQC gate to Rabi frequency error in some certain parameter space by comparing their performance to those of the dynamical gates and the former NGQC gates.

quant-ph↗
arXiv2023

Denoising and Prompt-Tuning for Multi-Behavior Recommendation

In practical recommendation scenarios, users often interact with items under multi-typed behaviors (e.g., click, add-to-cart, and purchase). Traditional collaborative filtering techniques typically assume that users only have a single type of behavior with items, making it insufficient to utilize complex collaborative signals to learn informative representations and infer actual user preferences. Consequently, some pioneer studies explore modeling multi-behavior heterogeneity to learn better representations and boost the performance of recommendations for a target behavior. However, a large number of auxiliary behaviors (i.e., click and add-to-cart) could introduce irrelevant information to recommenders, which could mislead the target behavior (i.e., purchase) recommendation, rendering two critical challenges: (i) denoising auxiliary behaviors and (ii) bridging the semantic gap between auxiliary and target behaviors. Motivated by the above observation, we propose a novel framework-Denoising and Prompt-Tuning (DPT) with a three-stage learning paradigm to solve the aforementioned challenges. In particular, DPT is equipped with a pattern-enhanced graph encoder in the first stage to learn complex patterns as prior knowledge in a data-driven manner to guide learning informative representation and pinpointing reliable noise for subsequent stages. Accordingly, we adopt different lightweight tuning approaches with effectiveness and efficiency in the following stages to further attenuate the influence of noise and alleviate the semantic gap among multi-typed behaviors. Extensive experiments on two real-world datasets demonstrate the superiority of DPT over a wide range of state-of-the-art methods. The implementation code is available online at https://github.com/zc-97/DPT.

cs.IR↗
arXiv2023

XFL: A High Performace, Lightweighted Federated Learning Framework

This paper introduces XFL, an industrial-grade federated learning project. XFL supports training AI models collaboratively on multiple devices, while utilizes homomorphic encryption, differential privacy, secure multi-party computation and other security technologies ensuring no leakage of data. XFL provides an abundant algorithms library, integrating a large number of pre-built, secure and outstanding federated learning algorithms, covering both the horizontally and vertically federated learning scenarios. Numerical experiments have shown the prominent performace of these algorithms. XFL builds a concise configuration interfaces with presettings for all federation algorithms, and supports the rapid deployment via docker containers.Therefore, we believe XFL is the most user-friendly and easy-to-develop federated learning framework. XFL is open-sourced, and both the code and documents are available at https://github.com/paritybit-ai/XFL.

cs.LG↗
arXiv2023

Semantics-Preserving Sketch Embedding for Face Generation

With recent advances in image-to-image translation tasks, remarkable progress has been witnessed in generating face images from sketches. However, existing methods frequently fail to generate images with details that are semantically and geometrically consistent with the input sketch, especially when various decoration strokes are drawn. To address this issue, we introduce a novel W-W+ encoder architecture to take advantage of the high expressive power of W+ space and semantic controllability of W space. We introduce an explicit intermediate representation for sketch semantic embedding. With a semantic feature matching loss for effective semantic supervision, our sketch embedding precisely conveys the semantics in the input sketches to the synthesized images. Moreover, a novel sketch semantic interpretation approach is designed to automatically extract semantics from vectorized sketches. We conduct extensive experiments on both synthesized sketches and hand-drawn sketches, and the results demonstrate the superiority of our method over existing approaches on both semantics-preserving and generalization ability.

cs.CV↗
arXiv2023

Symbology for elliptic multiple polylogarithms and the symbol prime

Elliptic multiple polylogarithms occur in Feynman integrals and in particular in scattering amplitudes. They can be characterized by their symbol, a tensor product in the so-called symbol letters. In contrast to the non-elliptic case, the elliptic letters themselves satisfy highly non-trivial identities, which we discuss in this paper. Moreover, we introduce the symbol prime, an analog of the symbol for elliptic symbol letters, which makes these identities manifest. We demonstrate its use in two concrete examples at two-loop order: the unequal-mass sunrise integral in two dimensions and the ten-point double-box integral in four dimensions. Finally, we also report the result of the polylogarithmic nine-point double-box integral, which arises as the soft limit of the ten-point integral.

hep-th↗
arXiv2023

Reachability Analysis of Neural Network Control Systems

Neural network controllers (NNCs) have shown great promise in autonomous and cyber-physical systems. Despite the various verification approaches for neural networks, the safety analysis of NNCs remains an open problem. Existing verification approaches for neural network control systems (NNCSs) either can only work on a limited type of activation functions, or result in non-trivial over-approximation errors with time evolving. This paper proposes a verification framework for NNCS based on Lipschitzian optimisation, called DeepNNC. We first prove the Lipschitz continuity of closed-loop NNCSs by unrolling and eliminating the loops. We then reveal the working principles of applying Lipschitzian optimisation on NNCS verification and illustrate it by verifying an adaptive cruise control model. Compared to state-of-the-art verification approaches, DeepNNC shows superior performance in terms of efficiency and accuracy over a wide range of NNCs. We also provide a case study to demonstrate the capability of DeepNNC to handle a real-world, practical, and complex system. Our tool \textbf{DeepNNC} is available at \url{https://github.com/TrustAI/DeepNNC}.

cs.LG↗
arXiv2023

Characterizing the Fundamental Bending Vibration of a Linear Polyatomic Molecule for Symmetry Violation Searches

Polyatomic molecules have been identified as sensitive probes of charge-parity violating and parity-violating physics beyond the Standard Model (BSM). For example, many linear triatomic molecules are both laser-coolable and have parity doublets in the ground electronic $\tilde{X} {}^2Σ^+ (010)$ state arising from the bending vibration, both features that can greatly aid BSM searches. Understanding the $\tilde{X} {}^2Σ^+ (010)$ state is a crucial prerequisite to precision measurements with linear polyatomic molecules. Here, we characterize fundamental bending vibration of ${}^{174}$YbOH using high-resolution optical spectroscopy on the nominally forbidden $\tilde{X} {}^2Σ^+ (010) \rightarrow \tilde{A} {}^2Π_{1/2} (000)$ transition at 588 nm. We assign 39 transitions originating from the lowest rotational levels of the $\tilde{X} {}^2Σ^+ (010)$ state, and accurately model the state's structure with an effective Hamiltonian using best-fit parameters. Additionally, we perform Stark and Zeeman spectroscopy on the $\tilde{X} {}^2Σ^+ (010)$ state and fit the molecule-frame dipole moment to $D_\mathrm{mol}=2.16(1)$ D and the effective electron $g$-factor to $g_S=2.07(2)$. Further, we use an empirical model to explain observed anomalous line intensities in terms of interference from spin-orbit and vibronic perturbations in the excited $\tilde{A} {}^2Π_{1/2} (000)$ state. Our work is an essential step toward searches for BSM physics in YbOH and other linear polyatomic molecules.

physics.atom-ph↗