arXiv subjects

Da Li

Publications and source records attributed to Da Li.

At least 91 records · Page 5Linked to original sources

arXiv2024

Breaking the Degrees-of-Freedom Limit of Holographic MIMO Communications: A 3-D Antenna Array Topology

The performance of holographic multiple-input multiple-output (MIMO) communications, employing two-dimensional (2-D) planar antenna arrays, is typically compromised by finite degrees-of-freedom (DOF) stemming from limited array size. The DOF constraint becomes significant when the element spacing approaches approximately half a wavelength, thereby restricting the overall performance of MIMO systems. To break this inherent limitation, we propose a novel three-dimensional (3-D) antenna array that strategically explores the untapped vertical dimension. We investigate the performance of MIMO systems utilizing 3-D arrays across different multi-path scenarios, encompassing Rayleigh channels with varying angular spreads and the 3rd generation partnership project (3GPP) channels. We subsequently showcase the advantages of these 3-D arrays over their 2-D counterparts with the same aperture sizes. As a proof of concept, a practical dipole-based 3-D array, facilitated by an electromagnetic band-gap (EBG) reflecting surface, is conceived, constructed, and evaluated. The experimental results align closely with full-wave simulations, and channel simulations substantiate that the DOF and capacity constraints of traditional holographic MIMO systems can be surpassed by adopting such a 3-D array configuration.

cs.IT↗

arXiv2024

Experimental and Theoretical Exploration of Terahertz Channel Performance through Glass Doors

In the evolving landscape of terahertz communication, the behavior of channels within indoor environments, particularly through glass doors, has garnered significant attention. This paper comprehensively investigates terahertz channel performance under such conditions, employing a measurement setup operational between 113 and 170 GHz. Analyzing scenarios frequently induced by human activity and environmental factors, like door movements, we established a comprehensive theoretical model. This model seamlessly integrates transmission, reflection, absorption, and diffraction mechanisms, leveraging the Fresnel formula, multi-layer transmission paradigm, and knife-edge diffraction theory. Our experimental results and theoretical predictions harmoniously align, revealing intricate dependencies, such as increased power loss at higher frequencies and larger incident angles. Furthermore, door interactions, whether opening or oscillations, significantly impact the terahertz channel. Notably, door edges lead to a power blockage surpassing the transmission loss of the glass itself but remaining inferior to metallic handle interferences. This paper's insights are pivotal for the design and fabrication of terahertz communication systems within indoor settings, pushing the boundaries of efficient and reliable communication.

physics.optics↗

arXiv2024

Impact of snowfall on terahertz channel performance: measurement and modeling insights

In the evolving domain of wireless communication, the investigation on terahertz (THz) frequency spectrum, spanning 0.1 to 10 THz, has become a critical focus for advancing ultra-high-speed data transmission technologies. The effective deployment of THz wireless communication techniques mandates a complete study of channel performance under various atmospheric conditions, such as rain, fog, cloud, haze, and notably, snow. These environmental elements significantly impact the design of the protocol stack, ranging from physical-layer signal processing to application design and strategic network planning. An in-depth understanding of channel propagation and fading characteristics in real-world environments, especially over ultra-wide bandwidths, is crucial. This work presents a comprehensive measurement-based and theoretical investigation of line-of-sight (LoS) THz channel performance in snowy conditions. It methodically examines both the empirical and predicted aspects of channel power and bit-error-ratio (BER). The effects of snowfall rate, carrier frequency, ambient temperature, and relative humidity on channel performance are analyzed and discussed. Our findings demonstrate that snowy conditions not only amplify power loss but also induce rapid fluctuations in the power levels of the THz channel. Notably, our results reveal an absence of significant multipath effects in these scenarios. This insight highlights the need for further research into the dynamics of snowflake movement and their interaction with THz transmission paths.

physics.app-ph↗

arXiv2024

Feed-Forward Latent Domain Adaptation

We study a new highly-practical problem setting that enables resource-constrained edge devices to adapt a pre-trained model to their local data distributions. Recognizing that device's data are likely to come from multiple latent domains that include a mixture of unlabelled domain-relevant and domain-irrelevant examples, we focus on the comparatively under-studied problem of latent domain adaptation. Considering limitations of edge devices, we aim to only use a pre-trained model and adapt it in a feed-forward way, without using back-propagation and without access to the source data. Modelling these realistic constraints bring us to the novel and practically important problem setting of feed-forward latent domain adaptation. Our solution is to meta-learn a network capable of embedding the mixed-relevance target dataset and dynamically adapting inference for target examples using cross-attention. The resulting framework leads to consistent improvements over strong ERM baselines. We also show that our framework sometimes even improves on the upper bound of domain-supervised adaptation, where only domain-relevant instances are provided for adaptation. This suggests that human annotated domain labels may not always be optimal, and raises the possibility of doing better through automated instance selection.

cs.LG↗

arXiv2023

High-speed surface-property recognition by 140-GHz frequency

In the field of integrated sensing and communication, there's a growing need for advanced environmental perception. The terahertz (THz) frequency band, significant for ultra-high-speed data connections, shows promise in environmental sensing, particularly in detecting surface textures crucial for autonomous system's decision-making. However, traditional numerical methods for parameter estimation in these environments struggle with accuracy, speed, and stability, especially in high-speed scenarios like vehicle-to-everything communications. This study introduces a deep learning approach for identifying surface roughness using a 140-GHz setup tailored for high-speed conditions. A high-speed data acquisition system was developed to mimic real-world scenarios, and a diverse set of rough surface samples was collected for realistic high-speed datasets to train the models. The model was trained and validated in three challenging scenarios: random occlusions, sparse data, and narrow-angle observations. The results demonstrate the method's effectiveness in high-speed conditions, suggesting terahertz frequencies' potential in future sensing and communication applications.

physics.app-ph↗

arXiv2023

Mössbauer spectroscopy study of the magnetostructural and spin-state transitions in the breathing pyrochlore LiFeCr$_{4}$O$_{8}$

We report on investigations of the complex magnetostructural and spin-state transitions in the breathing pyrochlore LiFeCr$_{4}$O$_{8}$ by means of magnetization, Mössbauer spectroscopy, and density functional theory (DFT) calculations. Three transitions corresponding to the ferrimagnetic transition at $T_N\sim94$ K, the spin-gap transition at $T_{SG}\sim50$ K, and the magnetostructural transition at $T_{MS}\sim19$ K were observed from the $χ$(T) curve, whereas only $T_N$ and $T_{MS}$ were evidenced for the Fe site from our Mössbauer measurements, suggesting that the spin-gap transition is absent at the Fe site. This indicates that the spin-gap transition is an effect of the breathing Cr$_4$ lattice, in agreement with our DFT calculations from which we see nearly decoupled electronic states for the FeO$_4$ and CrO$_6$ units. From the temperature dependence of the hyperfine magnetic field we also observed a spin-state transition for the Fe spins at $T_{MS}$ consistent with earlier neutron diffraction measurements. These local characteristics are believed to be important for a complete understanding of the complex magnetostructural coupling effects observed in similar systems.

cond-mat.str-el↗

arXiv2023

Extraction of n = 0 pick-up by locked mode detectors based on neural networks in J-TEXT

Measurement of locked mode (LM) is important for the physical research of Magnetohydrodynamic (MHD) instabilities and plasma disruption. The n = 0 pick-up need to be extracted and subtracted to calculate the amplitude and phase of the LM. A new method to extract this pick-up has been developed by predicting the n = 0 pick-up brn=0 by the LM detectors based on Neural Networks (NNs) in J-TEXT. An approach called Power Multiple Time Scale (PMTS) has been developed with outstanding regressing effect in multiple frequency ranges. Three models have been progressed based on PMTS NNs. PMTS could fit the brn=0 on the LM detectors with little errors both in time domain and frequency domain. The n>0 pick-up brn>0 generated by resonant magnetic perturbations (RMPs) can be obtained after subtracting the extracted brn=0. This new method uses only one LM instead of 4 LM detectors to extract brn=0. Therefore, the distribution of the LM detectors can also be optimized based on this new method.

physics.plasm-ph↗

arXiv2023

Sketch-based Video Object Segmentation: Benchmark and Analysis

Reference-based video object segmentation is an emerging topic which aims to segment the corresponding target object in each video frame referred by a given reference, such as a language expression or a photo mask. However, language expressions can sometimes be vague in conveying an intended concept and ambiguous when similar objects in one frame are hard to distinguish by language. Meanwhile, photo masks are costly to annotate and less practical to provide in a real application. This paper introduces a new task of sketch-based video object segmentation, an associated benchmark, and a strong baseline. Our benchmark includes three datasets, Sketch-DAVIS16, Sketch-DAVIS17 and Sketch-YouTube-VOS, which exploit human-drawn sketches as an informative yet low-cost reference for video object segmentation. We take advantage of STCN, a popular baseline of semi-supervised VOS task, and evaluate what the most effective design for incorporating a sketch reference is. Experimental results show sketch is more effective yet annotation-efficient than other references, such as photo masks, language and scribble.

cs.CV↗

arXiv2023

FedL2P: Federated Learning to Personalize

Federated learning (FL) research has made progress in developing algorithms for distributed learning of global models, as well as algorithms for local personalization of those common models to the specifics of each client's local data distribution. However, different FL problems may require different personalization strategies, and it may not even be possible to define an effective one-size-fits-all personalization strategy for all clients: depending on how similar each client's optimal predictor is to that of the global model, different personalization strategies may be preferred. In this paper, we consider the federated meta-learning problem of learning personalization strategies. Specifically, we consider meta-nets that induce the batch-norm and learning rate parameters for each client given local data statistics. By learning these meta-nets through FL, we allow the whole FL network to collaborate in learning a customized personalization strategy for each client. Empirical results show that this framework improves on a range of standard hand-crafted personalization baselines in both label and feature shift situations.

cs.LG↗

arXiv2023

Better Practices for Domain Adaptation

Distribution shifts are all too common in real-world applications of machine learning. Domain adaptation (DA) aims to address this by providing various frameworks for adapting models to the deployment data without using labels. However, the domain shift scenario raises a second more subtle challenge: the difficulty of performing hyperparameter optimisation (HPO) for these adaptation algorithms without access to a labelled validation set. The unclear validation protocol for DA has led to bad practices in the literature, such as performing HPO using the target test labels when, in real-world scenarios, they are not available. This has resulted in over-optimism about DA research progress compared to reality. In this paper, we analyse the state of DA when using good evaluation practice, by benchmarking a suite of candidate validation criteria and using them to assess popular adaptation algorithms. We show that there are challenges across all three branches of domain adaptation methodology including Unsupervised Domain Adaptation (UDA), Source-Free Domain Adaptation (SFDA), and Test Time Adaptation (TTA). While the results show that realistically achievable performance is often worse than expected, they also show that using proper validation splits is beneficial, as well as showing that some previously unexplored validation metrics provide the best options to date. Altogether, our improved practices covering data, training, validation and hyperparameter optimisation form a new rigorous pipeline to improve benchmarking, and hence research progress, within this important field going forward.

cs.LG↗

arXiv2023

Stochastic Gradient Descent in the Viewpoint of Graduated Optimization

Stochastic gradient descent (SGD) method is popular for solving non-convex optimization problems in machine learning. This work investigates SGD from a viewpoint of graduated optimization, which is a widely applied approach for non-convex optimization problems. Instead of the actual optimization problem, a series of smoothed optimization problems that can be achieved in various ways are solved in the graduated optimization approach. In this work, a formal formulation of the graduated optimization is provided based on the nonnegative approximate identity, which generalizes the idea of Gaussian smoothing. Also, an asymptotic convergence result is achieved with the techniques in variational analysis. Then, we show that the traditional SGD method can be applied to solve the smoothed optimization problem. The Monte Carlo integration is used to achieve the gradient in the smoothed problem, which may be consistent with distributed computing schemes in real-life applications. From the assumptions on the actual optimization problem, the convergence results of SGD for the smoothed problem can be derived straightforwardly. Numerical examples show evidence that the graduated optimization approach may provide more accurate training results in certain cases.

math.OC↗

arXiv2023

Feed-Forward Source-Free Domain Adaptation via Class Prototypes

Source-free domain adaptation has become popular because of its practical usefulness and no need to access source data. However, the adaptation process still takes a considerable amount of time and is predominantly based on optimization that relies on back-propagation. In this work we present a simple feed-forward approach that challenges the need for back-propagation based adaptation. Our approach is based on computing prototypes of classes under the domain shift using a pre-trained model. It achieves strong improvements in accuracy compared to the pre-trained model and requires only a small fraction of time of existing domain adaptation methods.

cs.CV↗

arXiv2023

Label Calibration for Semantic Segmentation Under Domain Shift

Performance of a pre-trained semantic segmentation model is likely to substantially decrease on data from a new domain. We show a pre-trained model can be adapted to unlabelled target domain data by calculating soft-label prototypes under the domain shift and making predictions according to the prototype closest to the vector with predicted class probabilities. The proposed adaptation procedure is fast, comes almost for free in terms of computational resources and leads to considerable performance improvements. We demonstrate the benefits of such label calibration on the highly-practical synthetic-to-real semantic segmentation problem.

cs.CV↗

arXiv2023

Neural Fine-Tuning Search for Few-Shot Learning

In few-shot recognition, a classifier that has been trained on one set of classes is required to rapidly adapt and generalize to a disjoint, novel set of classes. To that end, recent studies have shown the efficacy of fine-tuning with carefully crafted adaptation architectures. However this raises the question of: How can one design the optimal adaptation strategy? In this paper, we study this question through the lens of neural architecture search (NAS). Given a pre-trained neural network, our algorithm discovers the optimal arrangement of adapters, which layers to keep frozen and which to fine-tune. We demonstrate the generality of our NAS method by applying it to both residual networks and vision transformers and report state-of-the-art performance on Meta-Dataset and Meta-Album.

cs.CV↗

arXiv2023

Co-Design for Spectral Coexistence between RIS-aided MIMO Radar and MIMO Communication Systems

Reconfigurable intelligent surface (RIS) refers to a signal reflection surface containing a large number of low-cost passive reflecting elements. RIS can improve the performance of radar and communication systems by dynamically modulating the wireless channels. In this paper, we consider the co-design for improving the co-existence between multiple-input-multiple-output (MIMO) radar and MIMO communication system with the aid of RIS.The design purpose is to improve the radar detection performance and guarantee the communication capability. Due to the unimodular constraint on the RIS coefficients and the constant-envelope constraint on the radar transmit waveforms, the associated optimization problem is non-convex.To tackle this problem, we develop a cyclic method based on minorization-maximization, semi-definite programming, and alternating direction method of multipliers. Numerical examples verify the effectiveness of the proposed algorithm.

eess.SP↗

arXiv2023

Meta Omnium: A Benchmark for General-Purpose Learning-to-Learn

Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction. This naturally raises the question of whether there is any few-shot meta-learning algorithm capable of generalizing across these diverse task types? To support the community in answering this question, we introduce Meta Omnium, a dataset-of-datasets spanning multiple vision tasks including recognition, keypoint localization, semantic segmentation and regression. We experiment with popular few-shot meta-learning baselines and analyze their ability to generalize across tasks and to transfer knowledge between them. Meta Omnium enables meta-learning researchers to evaluate model generalization to a much wider array of tasks than previously possible, and provides a single framework for evaluating meta-learners across a wide suite of vision applications in a consistent manner.

cs.CV↗

arXiv2023

Modulated non-collinear magnetic structure of (Co$_{0.97}$$^{57}$Fe$_{0.03}$)$_{4}$Nb$_{2}$O$_{9}$ as revealed by Mössbauer spectroscopy

In this work, we present detailed $^{57}$Fe Mössbauer spectroscopy investigations of (Co$_{0.97}$$^{57}$Fe$_{0.03}$)$_{4}$Nb$_{2}$O$_{9}$ compound to study its possible magnetic structures. We have shown that the previously reported magnetic structures can not satisfactorily describe our low temperature Mössbauer spectra. Therefore, in combination with theoretical calculations, we have proposed a modulated helicoidal magnetic structure that can be used to simulate the whole series of our low temperature Mössbauer spectra. Our results suggest that the combination of previously reported different magnetic structures are only approximations of the average magnetic structure from our modulated helicoidal model. We anticipate that the proposed modulated non-collinear magnetic structure might shed light on the understanding of the complex magnetoelectric effects observed in this system.

cond-mat.str-el↗

arXiv2023

Relative Entropy-Based Waveform Optimization for Rician Target Detection with Dual-Function Radar Communication Systems

In this paper, we consider waveform design for dualfunction radar-communication systems based on multiple-inputmultiple-out arrays. To achieve better Rician target detection performance, we use the relative entropy associated with the formulated detection problem as the design metric. We also impose a multiuser interference energy constraint on the waveforms to ensure the achievable sum-rate of the communications. Two algorithms are presented to tackle the nonlinear non-convex waveform design problem. In the first algorithm, we derive a quadratic function to minorize the objective function. To tackle the quadratically constrained quadratic programming problem at each iteration, a semidefinite relaxation approach followed by a rank-one decomposition procedure and an efficient alternating direction method of multipliers (ADMM) are proposed, respectively. In the second algorithm, we present a novel ADMM algorithm to tackle the optimization problem and employ an efficient minorization-maximization approach in the inner loop of the ADMM algorithm. Numerical results demonstrate the superiority of both algorithms. Moreover, the presented algorithms can be extended to synthesize peak-to-average-power ratio constrained waveforms, which allows the radio frequency amplifier to operate at an increased efficiency.

cs.IT↗