arXiv subjects

Chao Zhou

Publications and source records attributed to Chao Zhou.

At least 127 records · Page 7Linked to original sources

arXiv2024

Dynamic Coalition Portfolio Selection with Recursive Utility

In this paper, we consider a dynamic coalition portfolio selection problem, with each agent's objective given by an Epstein--Zin recursive utility. To find a Pareto optimum, the coalition's problem is formulated as an optimization problem evolved by a multi-dimensional forward-backward SDE. Since the evolution system has a forward-backward structure, the problem is intrinsically time-inconsistent. With the dynamic-game point of view, we rigorously develop an approach to finding the equilibrium Pareto investment-consumption strategy. We find that the relationship between risk aversion and EIS has more influence on the coalition's problem than that on the one-agent problem. More interestingly, we show that the equilibrium Pareto consumption strategy associated with the recursive utility is much more effective than that associated with the CRRA expected utility, which highlights the feature of recursive utilities that the marginal benefit of consumption can depend on the future consumption.

math.OC↗

arXiv2024

KS-Net: Multi-band joint speech restoration and enhancement network for 2024 ICASSP SSI Challenge

This paper presents the speech restoration and enhancement system created by the 1024K team for the ICASSP 2024 Speech Signal Improvement (SSI) Challenge. Our system consists of a generative adversarial network (GAN) in complex-domain for speech restoration and a fine-grained multi-band fusion module for speech enhancement. In the blind test set of SSI, the proposed system achieves an overall mean opinion score (MOS) of 3.49 based on ITU-T P.804 and a Word Accuracy Rate (WAcc) of 0.78 for the real-time track, as well as an overall P.804 MOS of 3.43 and a WAcc of 0.78 for the non-real-time track, ranking 1st in both tracks.

cs.SD↗

arXiv2024

Interpersonal Relationship Analysis with Dyadic EEG Signals via Learning Spatial-Temporal Patterns

Interpersonal relationship quality is pivotal in social and occupational contexts. Existing analysis of interpersonal relationships mostly rely on subjective self-reports, whereas objective quantification remains challenging. In this paper, we propose a novel social relationship analysis framework using spatio-temporal patterns derived from dyadic EEG signals, which can be applied to quantitatively measure team cooperation in corporate team building, and evaluate interpersonal dynamics between therapists and patients in psychiatric therapy. First, we constructed a dyadic-EEG dataset from 72 pairs of participants with two relationships (stranger or friend) when watching emotional videos simultaneously. Then we proposed a deep neural network on dyadic-subject EEG signals, in which we combine the dynamic graph convolutional neural network for characterizing the interpersonal relationships among the EEG channels and 1-dimension convolution for extracting the information from the time sequence. To obtain the feature vectors from two EEG recordings that well represent the relationship of two subjects, we integrate deep canonical correlation analysis and triplet loss for training the network. Experimental results show that the social relationship type (stranger or friend) between two individuals can be effectively identified through their EEG data.

cs.CY↗

arXiv2023

Decoding Mean Field Games from Population and Environment Observations By Gaussian Processes

This paper presents a Gaussian Process (GP) framework, a non-parametric technique widely acknowledged for regression and classification tasks, to address inverse problems in mean field games (MFGs). By leveraging GPs, we aim to recover agents' strategic actions and the environment's configurations from partial and noisy observations of the population of agents and the setup of the environment. Our method is a probabilistic tool to infer the behaviors of agents in MFGs from data in scenarios where the comprehensive dataset is either inaccessible or contaminated by noises.

cs.GT↗

arXiv2023

BAE-Net: A Low complexity and high fidelity Bandwidth-Adaptive neural network for speech super-resolution

Speech bandwidth extension (BWE) has demonstrated promising performance in enhancing the perceptual speech quality in real communication systems. Most existing BWE researches primarily focus on fixed upsampling ratios, disregarding the fact that the effective bandwidth of captured audio may fluctuate frequently due to various capturing devices and transmission conditions. In this paper, we propose a novel streaming adaptive bandwidth extension solution dubbed BAE-Net, which is suitable to handle the low-resolution speech with unknown and varying effective bandwidth. To address the challenges of recovering both the high-frequency magnitude and phase speech content blindly, we devise a dual-stream architecture that incorporates the magnitude inpainting and phase refinement. For potential applications on edge devices, this paper also introduces BAE-NET-lite, which is a lightweight, streaming and efficient framework. Quantitative results demonstrate the superiority of BAE-Net in terms of both performance and computational efficiency when compared with existing state-of-the-art BWE methods.

cs.SD↗

arXiv2023

Experimental advances with the QICK (Quantum Instrumentation Control Kit) for superconducting quantum hardware

The QICK is a standalone open source qubit controller that was first introduced in 2022. In this follow-up work, we present recent experimental use cases that the QICK uniquely enabled for superconducting qubit systems. These include multiplexed signal generation and readout, mixer-free readout, pre-distorted fast flux pulses, and phase-coherent pulses for parametric operations, including high-fidelity parametric entangling gates. We explain in detail how the QICK was used to enable these experiments.

quant-ph↗

arXiv2023

Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering

Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based models with implicit feedback. In this paper, we propose a novel perspective based on the sampling area to revisit existing sampling methods. We point out that current sampling methods mainly focus on Point-wise or Line-wise sampling, lacking flexibility and leaving a significant portion of the hard sampling area un-explored. To address this limitation, we propose Dimension Independent Mixup for Hard Negative Sampling (DINS), which is the first Area-wise sampling method for training CF-based models. DINS comprises three modules: Hard Boundary Definition, Dimension Independent Mixup, and Multi-hop Pooling. Experiments with real-world datasets on both matrix factorization and graph-based models demonstrate that DINS outperforms other negative sampling methods, establishing its effectiveness and superiority. Our work contributes a new perspective, introduces Area-wise sampling, and presents DINS as a novel approach that achieves state-of-the-art performance for negative sampling. Our implementations are available in PyTorch.

cs.IR↗

arXiv2023

Realizing all-to-all couplings among detachable quantum modules using a microwave quantum state router

One of the primary challenges in realizing large-scale quantum processors is the realization of qubit couplings that balance interaction strength, connectivity, and mode confinement. Moreover, it is very desirable for the device elements to be detachable, allowing components to be built, tested, and replaced independently. In this work, we present a microwave quantum state router, centered on parametrically driven, Josephson-junction based three-wave mixing, that realizes all-to-all couplings among four detachable quantum modules. We demonstrate coherent exchange among all four communication modes, with an average full-iSWAP time of 764ns and average inferred inter-module exchange fidelity of 0.969, limited by mode coherence. We also demonstrate photon transfer and pairwise entanglement between module qubits, and parallel operation of simultaneous iSWAP exchange across the router. Our router-module architecture serves as a prototype of modular quantum computer that has great potential for enabling flexible, demountable, large-scale quantum networks of superconducting qubits and cavities.

quant-ph↗

arXiv2023

A new Mertens decomposition of $\mathscr{Y}^{g,ξ}$-submartingale systems. Application to BSDEs with weak constraints at stopping times

We first introduce the concept of $\mathscr{Y}^{g,ξ}$-submartingale systems, where the nonlinear operator $\mathscr{Y}^{g,ξ}$ corresponds to the first component of the solution of a reflected BSDE with generator $g$ and lower obstacle $ξ$. We first show that, in the case of a left-limited right-continuous obstacle, any $\mathscr{Y}^{g,ξ}$-submartingale system can be aggregated by a process which is right-lower semicontinuous. We then prove a \textit{Mertens decomposition}, by using an original approach which does not make use of the standard penalization technique. These results are in particular useful for the treatment of control/stopping game problems and, to the best of our knowledge, they are completely new in the literature. As an application, we introduce a new class of \textit{Backward Stochastic Differential Equations (in short BSDEs) with weak constraints at stopping times}, which are related to the partial hedging of American options. We study the wellposedness of such equations and, using the $\mathscr{Y}^{g,ξ}$-Mertens decomposition, we show that the family of minimal time-$t$-values $Y_t$, with $(Y,Z)$ a supersolution of the BSDE with weak constraints, admits a representation in terms of a reflected backward stochastic differential equation.

math.OC↗

arXiv2023

Robust Equilibrium Strategy for Mean-Variance Portfolio Selection

The classical mean-variance portfolio selection problem induces time-inconsistent (precommited) strategies (see Zhou and Li (2000)). To overcome this time-inconsistency, Basak and Chabakauri (2010) introduce the game theoretical approach and look for (sub-game perfect Nash) equilibrium strategies, which is solved from the corresponding partial differential equations (PDE) system. In their model, the investor perfectly knows the drift and volatility of the assets. However, in reality investors only have an estimate on them, e.g, a 95% confidence interval. In this case, some literature (e.g., Pham, Wei and Zhou (2022)) derives the optimal precommited strategy under the worst parameters, which is the robust control. The relation between the equilibrium strategy and the PDE system has not been justified when incorporating robust control. In this paper, we consider a general dynamic mean-variance framework and propose a novel definition of the robust equilibrium strategy. Under our definition, a classical solution to the corresponding PDE system implies a robust equilibrium strategy. We then explicitly solve for some special examples.

q-fin.MF↗

arXiv2023

Transmit Power Minimization for STAR-RIS Empowered Symbiotic Radio Communications

In this paper, we propose a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) empowered transmission scheme for symbiotic radio (SR) systems to make more flexibility for network deployment and enhance system performance. The STAR-RIS is utilized to not only beam the primary signals from the base station (BS) towards multiple primary users on the same side of the STAR-RIS, but also achieve the secondary transmission to the secondary users on another side. We consider both the broadcasting signal model and unicasting signal model at the BS. For each model, we aim for minimizing the transmit power of the BS by designing the active beamforming and simultaneous reflection and transmission coefficients under the practical phase correlation constraint. To address the challenge of solving the formulated problem, we propose a block coordinate descent based algorithm with the semidefinite relaxation, penalty dual decomposition and successive convex approximation methods, which decomposes the original problem into one sub-problem about active beamforming and the other sub-problem about simultaneous reflection and transmission coefficients, and iteratively solve them until the convergence is achieved. Numerical results indicate that the proposed scheme can reduce up to 150.6% transmit power compared to the backscattering device enabled scheme.

cs.IT↗

arXiv2023

Robust Secure Transmission for Active RIS Enabled Symbiotic Radio Multicast Communications

In this paper, we propose a robust secure transmission scheme for an active reconfigurable intelligent surface (RIS) enabled symbiotic radio (SR) system in the presence of multiple eavesdroppers (Eves). In the considered system, the active RIS is adopted to enable the secure transmission of primary signals from the primary transmitter to multiple primary users in a multicasting manner, and simultaneously achieve its own information delivery to the secondary user by riding over the primary signals. Taking into account the imperfect channel state information (CSI) related with Eves, we formulate the system power consumption minimization problem by optimizing the transmit beamforming and reflection beamforming for the bounded and statistical CSI error models, taking the worst-case SNR constraints and the SNR outage probability constraints at the Eves into considerations, respectively. Specifically, the S-Procedure and the Bernstein-Type Inequality are implemented to approximately transform the worst-case SNR and the SNR outage probability constraints into tractable forms, respectively. After that, the formulated problems can be solved by the proposed alternating optimization (AO) algorithm with the semi-definite relaxation and sequential rank-one constraint relaxation techniques. Numerical results show that the proposed active RIS scheme can reduce up to 27.0% system power consumption compared to the passive RIS.

cs.IT↗

arXiv2023

OcTr: Octree-based Transformer for 3D Object Detection

A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects. Albeit recent efforts made by Transformers with the long sequence modeling capability, they fail to properly balance the accuracy and efficiency, suffering from inadequate receptive fields or coarse-grained holistic correlations. In this paper, we propose an Octree-based Transformer, named OcTr, to address this issue. It first constructs a dynamic octree on the hierarchical feature pyramid through conducting self-attention on the top level and then recursively propagates to the level below restricted by the octants, which captures rich global context in a coarse-to-fine manner while maintaining the computational complexity under control. Furthermore, for enhanced foreground perception, we propose a hybrid positional embedding, composed of the semantic-aware positional embedding and attention mask, to fully exploit semantic and geometry clues. Extensive experiments are conducted on the Waymo Open Dataset and KITTI Dataset, and OcTr reaches newly state-of-the-art results.

cs.CV↗

arXiv2023

Parallel Driving for Fast Quantum Computing Under Speed Limits

Increasing quantum circuit fidelity requires an efficient instruction set to avoid errors from decoherence. The choice of a two-qubit (2Q) hardware basis gate depends on a quantum modulator's native Hamiltonian interactions and applied control drives. In this paper, we propose a collaborative design approach to select the best ratio of drive parameters that determine the best basis gate for a particular modulator. This requires considering the theoretical computing power of the gate along with the practical speed limit of that gate, given the modulator drive parameters. The practical speed limit arises from the couplers' tolerance for strong driving when one or more pumps is applied, for which some combinations can result in higher overall speed limits than others. Moreover, as this 2Q basis gate is typically applied multiple times in succession, interleaved by 1Q gates applied directly to the qubits, the speed of the 1Q gates can become a limiting factor for the quantum circuit. We propose a parallel-drive approach that drives the modulator and qubits simultaneously, allowing a richer capability of the 2Q basis gate and in some cases for this 1Q drive time to be absorbed entirely into the 2Q operation. This allows increasingly short duration 2Q gates while mitigating a significant source of overhead in some quantum systems. On average, this approach can decrease circuit duration by 17.84% and decrease infidelity for random 2Q gates by 10.5% compared to the best basic 2Q gate, $\sqrt{\texttt{iSWAP}}$.

quant-ph↗

arXiv2023

High-Rate Point-to-Multipoint Quantum Key Distribution using Coherent States

Quantum key distribution (QKD) which enables information-theoretically security is now heading towards quantum secure networks. It requires high-performance and cost-effective protocols while increasing the number of users. Unfortunately, qubit-implemented protocols only allow one receiver to respond to the prepared signal at a time, thus cannot support multiple users natively and well satisfy the network demands. Here, we show a 'protocol solution' using continuous-variable quantum information. A coherent-state point-to-multipoint protocol is proposed to simultaneously support multiple independent QKD links between a single transmitter and massive receivers. Every prepared coherent state is measured by all receivers to generate raw keys, then processed with a secure and high-efficient key distillation method to remove the correlations between different QKD links. It can achieve remarkably high key rates even with a hundred of access points and shows the potential improvement of two orders of magnitude. This scheme is a promising step towards a high-rate multi-user solution in a scalable quantum secure network.

quant-ph↗

arXiv2022

Co-Designed Architectures for Modular Superconducting Quantum Computers

Noisy, Intermediate Scale Quantum (NISQ) computers have reached the point where they can show the potential for quantum advantage over classical computing. Unfortunately, NISQ machines introduce sufficient noise that even for moderate size quantum circuits the results can be unreliable. We propose a co-designed superconducting quantum computer using a Superconducting Nonlinear Asymmetric Inductive eLement (SNAIL) modulator. The SNAIL modulator is designed by considering both the ideal fundamental qubit gate operation while maximizing the qubit coupling capabilities. First, the SNAIL natively implements $\sqrt[n]{\texttt{iSWAP}}$ gates realized through proportionally scaled pulse lengths. This naturally includes $\sqrt{\texttt{iSWAP}}$, which provides an advantage over $\texttt{CNOT}$ as a basis gate. Second, the SNAIL enables high-degree couplings that allow rich and highly parallel qubit connection topologies without suffering from frequency crowding. Building on our previously demonstrated SNAIL-based quantum state router we propose a quantum 4-ary tree and a hypercube inspired corral built from interconnected quantum modules. We compare their advantage in data movement based on necessary \texttt{SWAP} gates to the traditional lattice and heavy-hex lattice used in latest commercial quantum computers. We demonstrate the co-design advantage of our SNAIL-based machine with $\sqrt{\texttt{iSWAP}}$ basis gates and rich topologies against $\texttt{CNOT}$/heavy-hex and $\texttt{FSIM}$/lattice for 16-20 qubit and extrapolated designs circa 80 qubit architectures. We compare total circuit time and total gate count to understand fidelity for systems dominated by decoherence and control imperfections, respectively. Finally, we provide a gate duration sensitivity study on further decreasing the SNAIL pulse length to realize $\sqrt[n]{\texttt{iSWAP}}$ qubit systems to reduce decoherence times.

quant-ph↗

arXiv2022

Object-Attentional Untargeted Adversarial Attack

Deep neural networks are facing severe threats from adversarial attacks. Most existing black-box attacks fool target model by generating either global perturbations or local patches. However, both global perturbations and local patches easily cause annoying visual artifacts in adversarial example. Compared with some smooth regions of an image, the object region generally has more edges and a more complex texture. Thus small perturbations on it will be more imperceptible. On the other hand, the object region is undoubtfully the decisive part of an image to classification tasks. Motivated by these two facts, we propose an object-attentional adversarial attack method for untargeted attack. Specifically, we first generate an object region by intersecting the object detection region from YOLOv4 with the salient object detection (SOD) region from HVPNet. Furthermore, we design an activation strategy to avoid the reaction caused by the incomplete SOD. Then, we perform an adversarial attack only on the detected object region by leveraging Simple Black-box Adversarial Attack (SimBA). To verify the proposed method, we create a unique dataset by extracting all the images containing the object defined by COCO from ImageNet-1K, named COCO-Reduced-ImageNet in this paper. Experimental results on ImageNet-1K and COCO-Reduced-ImageNet show that under various system settings, our method yields the adversarial example with better perceptual quality meanwhile saving the query budget up to 24.16\% compared to the state-of-the-art approaches including SimBA.

cs.CV↗

arXiv2022

Optimal Controls for Forward-Backward Stochastic Differential Equations: Time-Inconsistency and Time-Consistent Solutions

This paper is concerned with an optimal control problem for a forward-backward stochastic differential equation (FBSDE, for short) with a recursive cost functional determined by a backward stochastic Volterra integral equation (BSVIE, for short). It is found that such an optimal control problem is time-inconsistent in general, even if the cost functional is reduced to a classical Bolza type one as in Peng [50], Lim-Zhou [41], and Yong [74]. Therefore, instead of finding a global optimal control (which is time-inconsistent), we will look for a time-consistent and locally optimal equilibrium strategy, which can be constructed via the solution of an associated equilibrium Hamilton-Jacobi-Bellman (HJB, for short) equation. A verification theorem for the local optimality of the equilibrium strategy is proved by means of the generalized Feynman-Kac formula for BSVIEs and some stability estimates of the representation for parabolic partial differential equations (PDEs, for short). Under certain conditions, it is proved that the equilibrium HJB equation, which is a nonlocal PDE, admits a unique classical solution. As special cases and applications, the linear-quadratic problems, a mean-variance model, a social planner problem with heterogeneous Epstein-Zin utilities, and a Stackelberg game are briefly investigated. It turns out that our framework can cover not only the optimal control problems for FBSDEs studied in [50,41,74], and so on, but also the problems of the general discounting and some nonlinear appearance of conditional expectations for the terminal state, studied in Yong [75,77] and Björk-Khapko-Murgoci [7].

math.OC↗