arXiv subjects

Yang Hao

Publications and source records attributed to Yang Hao.

At least 19 recordsLinked to original sources

arXiv2026

Non-Uniform Antenna Array Design with Large Inter-Element Spacing for Massive MIMO

In massive multiple-input multiple-output (MIMO) systems, uniform arrays are typically configured with inter-element spacing no greater than half a wavelength to avoid grating lobes and spatial aliasing. However, many emerging fifth- and sixth-generation (5G/6G) applications rely on distributed arrays whose inter-element spacing far exceeds half a wavelength. In this paper, we propose an electromagnetic mutual-information-theoretic (EMIT)-guided non-uniform array (NUA) design with large inter-element spacing for massive MIMO systems to address the grating lobes and spatial aliasing artifacts, and in the meantime, to reduce the hardware cost and energy consumption. We start by developing a multipath channel model for non-uniform planar arrays, and analyze the resulting channel characteristics in terms of inter-user interference, aperture efficiency, favorable propagation and channel capacity for the proposed typical NUA patterns. The model is further extended to wideband scenarios, where NUAs demonstrate improved robustness against beam squint due to their more compact element distribution. In addition, we introduce an EMIT approach to NUA design, which links the spatial sampling pattern of an antenna array to the capacity of the resulting MIMO channel. This gives rise to two complementary shaping strategies, amplitude tapering and geometric shaping, and their joint optimization. Numerical results demonstrate that the proposed NUAs significantly outperform conventional uniform arrays in aperture efficiency, channel orthogonality, beam squint mitigation, capacity, and error rate performance.

eess.SP↗

arXiv2026

Enhanced Terahertz Photoresponse via Acoustic Plasmon Cavity Resonances in Scalable Graphene

Precise control and nanoscale confinement of terahertz (THz) fields are essential requirements for emerging applications in photonics, quantum technologies, wireless communications, and sensing. Here, we demonstrate a polaritonic cavity enhanced THz photoresponse in an antenna coupled device based on chemical vapor deposited (CVD) monolayer graphene. The dipole antenna lobes simultaneously serve as two gate electrodes, concentrate the impinging THz field, and efficiently launch acoustic graphene plasmons (AGPs), which drive a strong photo-thermoelectric (PTE) signal. Between 6 and 90 K, the photovoltage exhibits pronounced peaks, modulating the PTE response by up to 40\%, that we attribute to AGPs forming a Fabry P\'erot THz cavity in the full or half graphene channel. Combined full wave and transport thermal simulations accurately reproduce the gate controlled plasmon wavelength, spatial absorption profile, and the resulting nonuniform electron heating responsible for the PTE response. The lateral and vertical maximum confinement factors of the AGP wavelength relative to the incident wavelength are 165 and 4000, respectively, for frequencies from 1.83 to 2.52 THz. These results demonstrate that wafer scalable CVD graphene, without hBN encapsulation, can host coherent AGP resonances and exhibit an efficient polaritonic enhanced photoresponse under appropriate gating, antenna coupling, and AGP cavity design, opening a route to scalable, polarization and frequency selective, liquid nitrogen cooled, and low power consumption THz detection platforms based on plasmon thermoelectric transduction.

cond-mat.mes-hall↗

arXiv2025

TableDreamer: Progressive and Weakness-guided Data Synthesis from Scratch for Table Instruction Tuning

Despite the commendable progress of recent LLM-based data synthesis methods, they face two limitations in generating table instruction tuning data. First, they can not thoroughly explore the vast input space of table understanding tasks, leading to limited data diversity. Second, they ignore the weaknesses in table understanding ability of the target LLM and blindly pursue the increase of data quantity, resulting in suboptimal data efficiency. In this paper, we introduce a progressive and weakness-guided data synthesis framework tailored for table instruction tuning, named TableDreamer, to mitigate the above issues. Specifically, we first synthesize diverse tables and related instructions as seed data, and then perform an iterative exploration of the input space under the guidance of the newly identified weakness data, which eventually serve as the final training data for fine-tuning the target LLM. Extensive experiments on 10 tabular benchmarks demonstrate the effectiveness of the proposed framework, which boosts the average accuracy of Llama3.1-8B-instruct by 11.62% (49.07% to 60.69%) with 27K GPT-4o synthetic data and outperforms state-of-the-art data synthesis baselines which use more training data. The code and data is available at https://github.com/SpursGoZmy/TableDreamer

cs.CL↗

arXiv2025

Super-Resolution Coherent Diffractive Imaging via Titled-Incidence Multi-Rotation-Angle Fusion Ptychography

Coherent diffractive imaging (CDI) enables lensless imaging with experimental simplicity and a flexible field of view, yet its resolution is fundamentally constrained by the Abbe diffraction limit. To overcome this limitation, we introduce a novel Tilted-Incidence Multi-Rotation-Angle Fusion Ptychography technique. This approach leverages a tilted-incidence geometry to extend the collection angle beyond the Abbe limit, achieving up to a -fold resolution enhancement. By acquiring diffraction patterns at multiple sample rotation angles, we capture complementary spatial frequency information. A tilted-incidence multi-rotation-angle fusion ptychographic iterative engine (tmf-PIE) algorithm is then employed to integrate these datasets, enabling super-resolution image reconstruction. Additionally, this method mitigates the anisotropic resolution artifacts inherent to tilted CDI geometries. Our technique represents a novel advancement in super-resolution imaging, providing a novel alternative alongside established methods such as STED, SIM, and SMLM.

physics.optics↗

arXiv2022

Pervasive wireless channel modeling theory and applications to 6G GBSMs for all frequency bands and all scenarios

In this paper, a pervasive wireless channel modeling theory is first proposed, which uses a unified channel modeling method and a unified equation of channel impulse response (CIR), and can integrate important channel characteristics at different frequency bands and scenarios. Then, we apply the proposed theory to a three dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) for the sixth generation (6G) wireless communication systems. The proposed 6G pervasive channel model (6GPCM) can characterize statistical properties of channels at all frequency bands from sub-6 GHz to visible light communication (VLC) bands and all scenarios such as unmanned aerial vehicle (UAV), maritime, (ultra-)massive multiple-input multiple-output (MIMO), reconfigurable intelligent surface (RIS), and industry Internet of things (IIoT) scenarios. By adjusting channel model parameters, the 6GPCM can be reduced to various simplified channel models for specific frequency bands and scenarios. Also, it includes standard fifth generation (5G) channel models as special cases. In addition, key statistical properties of the proposed 6GPCM are derived, simulated, and verified by various channel measurement results, which clearly demonstrates its accuracy, pervasiveness, and applicability.

eess.SP↗

arXiv2022

Self-Supervised Audio-and-Text Pre-training with Extremely Low-Resource Parallel Data

Multimodal pre-training for audio-and-text has recently been proved to be effective and has significantly improved the performance of many downstream speech understanding tasks. However, these state-of-the-art pre-training audio-text models work well only when provided with large amount of parallel audio-and-text data, which brings challenges on many languages that are rich in unimodal corpora but scarce of parallel cross-modal corpus. In this paper, we investigate whether it is possible to pre-train an audio-text multimodal model with extremely low-resource parallel data and extra non-parallel unimodal data. Our pre-training framework consists of the following components: (1) Intra-modal Denoising Auto-Encoding (IDAE), which is able to reconstruct input text (audio) representations from a noisy version of itself. (2) Cross-modal Denoising Auto-Encoding (CDAE), which is pre-trained to reconstruct the input text (audio), given both a noisy version of the input text (audio) and the corresponding translated noisy audio features (text embeddings). (3) Iterative Denoising Process (IDP), which iteratively translates raw audio (text) and the corresponding text embeddings (audio features) translated from previous iteration into the new less-noisy text embeddings (audio features). We adapt a dual cross-modal Transformer as our backbone model which consists of two unimodal encoders for IDAE and two cross-modal encoders for CDAE and IDP. Our method achieves comparable performance on multiple downstream speech understanding tasks compared with the model pre-trained on fully parallel data, demonstrating the great potential of the proposed method. Our code is available at: \url{https://github.com/KarlYuKang/Low-Resource-Multimodal-Pre-training}.

cs.SD↗

arXiv2022

Formula graph self-attention network for representation-domain independent materials discovery

The success of machine learning (ML) in materials property prediction depends heavily on how the materials are represented for learning. Two dominant families of material descriptors exist, one that encodes crystal structure in the representation and the other that only uses stoichiometric information with the hope of discovering new materials. Graph neural networks (GNNs) in particular have excelled in predicting material properties within chemical accuracy. However, current GNNs are limited to only one of the above two avenues owing to the little overlap between respective material representations. Here, we introduce a new concept of formula graph which unifies stoichiometry-only and structure-based material descriptors. We further develop a self-attention integrated GNN that assimilates a formula graph and show that the proposed architecture produces material embeddings transferable between the two domains. Our model can outperform some previously proposed structure-agnostic models and their structure-based counterparts while exhibiting better sample efficiency and faster convergence. Finally, the model is applied in a challenging exemplar to predict the complex dielectric function of materials and nominate new substances that potentially exhibit epsilon-near-zero phenomena.

cs.LG↗

arXiv2021

NeuCrowd: Neural Sampling Network for Representation Learning with Crowdsourced Labels

Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart city, education, etc. In practice, people refer to crowdsourcing to get annotated labels. However, due to issues like data privacy, budget limitation, shortage of domain-specific annotators, the number of crowdsourced labels is still very limited. Moreover, because of annotators' diverse expertise, crowdsourced labels are often inconsistent. Thus, directly applying existing supervised representation learning (SRL) algorithms may easily get the overfitting problem and yield suboptimal solutions. In this paper, we propose \emph{NeuCrowd}, a unified framework for SRL from crowdsourced labels. The proposed framework (1) creates a sufficient number of high-quality \emph{n}-tuplet training samples by utilizing safety-aware sampling and robust anchor generation; and (2) automatically learns a neural sampling network that adaptively learns to select effective samples for SRL networks. The proposed framework is evaluated on both one synthetic and three real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC. To encourage reproducible results, we make our code publicly available at \url{https://github.com/tal-ai/NeuCrowd_KAIS2021}.

cs.LG↗

arXiv2021

A Novel 3D Non-Stationary GBSM for 6G THz Ultra-Massive MIMO Wireless Systems

Terahertz (THz) communication is now being considered as one of possible technologies for the sixth generation (6G) wireless communication systems. In this paper, a novel three-dimensional (3D) space-time-frequency non-stationary theoretical channel model is first proposed for 6G THz wireless communication systems employing ultra-massive multiple-input multiple-output (MIMO) technologies with long traveling paths. Considering frequency-dependent diffuse scattering, which is a special property of THz channels different from millimeter wave (mmWave) channels, the relative angles and delays of rays within one cluster will evolve in the frequency domain. Then, a corresponding simulation model is proposed with discrete angles calculated using the method of equal area (MEA). The statistical properties of the proposed theoretical and simulation models are derived and compared, showing good agreements. The accuracy and flexibility of the proposed simulation model are demonstrated by comparing the simulation results of the relative angle spread and root mean square (RMS) delay spread with corresponding measurements.

eess.SP↗

arXiv2021

Multi-Task Learning based Online Dialogic Instruction Detection with Pre-trained Language Models

In this work, we study computational approaches to detect online dialogic instructions, which are widely used to help students understand learning materials, and build effective study habits. This task is rather challenging due to the widely-varying quality and pedagogical styles of dialogic instructions. To address these challenges, we utilize pre-trained language models, and propose a multi-task paradigm which enhances the ability to distinguish instances of different classes by enlarging the margin between categories via contrastive loss. Furthermore, we design a strategy to fully exploit the misclassified examples during the training stage. Extensive experiments on a real-world online educational data set demonstrate that our approach achieves superior performance compared to representative baselines. To encourage reproducible results, we make our implementation online available at \url{https://github.com/AIED2021/multitask-dialogic-instruction}.

cs.CL↗

arXiv2021

A Multimodal Machine Learning Framework for Teacher Vocal Delivery Evaluation

The quality of vocal delivery is one of the key indicators for evaluating teacher enthusiasm, which has been widely accepted to be connected to the overall course qualities. However, existing evaluation for vocal delivery is mainly conducted with manual ratings, which faces two core challenges: subjectivity and time-consuming. In this paper, we present a novel machine learning approach that utilizes pairwise comparisons and a multimodal orthogonal fusing algorithm to generate large-scale objective evaluation results of the teacher vocal delivery in terms of fluency and passion. We collect two datasets from real-world education scenarios and the experiment results demonstrate the effectiveness of our algorithm. To encourage reproducible results, we make our code public available at \url{https://github.com/tal-ai/ML4VocalDelivery.git}.

cs.SD↗

arXiv2021

Temporal-aware Language Representation Learning From Crowdsourced Labels

Learning effective language representations from crowdsourced labels is crucial for many real-world machine learning tasks. A challenging aspect of this problem is that the quality of crowdsourced labels suffer high intra- and inter-observer variability. Since the high-capacity deep neural networks can easily memorize all disagreements among crowdsourced labels, directly applying existing supervised language representation learning algorithms may yield suboptimal solutions. In this paper, we propose \emph{TACMA}, a \underline{t}emporal-\underline{a}ware language representation learning heuristic for \underline{c}rowdsourced labels with \underline{m}ultiple \underline{a}nnotators. The proposed approach (1) explicitly models the intra-observer variability with attention mechanism; (2) computes and aggregates per-sample confidence scores from multiple workers to address the inter-observer disagreements. The proposed heuristic is extremely easy to implement in around 5 lines of code. The proposed heuristic is evaluated on four synthetic and four real-world data sets. The results show that our approach outperforms a wide range of state-of-the-art baselines in terms of prediction accuracy and AUC. To encourage the reproducible results, we make our code publicly available at \url{https://github.com/CrowdsourcingMining/TACMA}.

cs.CL↗

arXiv2021

Analogical discovery of disordered perovskite oxides by crystal structure information hidden in unsupervised material fingerprints

Compositional disorder induces myriad captivating phenomena in perovskites. Target-driven discovery of perovskite solid solutions has been a great challenge due to the analytical complexity introduced by disorder. Here, we demonstrate that an unsupervised deep learning strategy can find fingerprints of disordered materials that embed perovskite formability and underlying crystal structure information by learning only from the chemical composition, manifested in (A1-xA'x)BO3 and A(B1-xB'x)O3 formulae. This phenomenon can be capitalized to predict the crystal symmetry of experimental compositions, outperforming several supervised machine learning (ML) algorithms. The educated nature of material fingerprints has led to the conception of analogical materials discovery that facilitates inverse exploration of promising perovskites based on similarity investigation with known materials. The search space of unstudied perovskites is screened from ~600,000 feasible compounds using experimental data powered ML models and automated web mining tools at a 94% success rate. This concept further provides insights on possible phase transitions and computational modelling of complex compositions. The proposed quantitative analysis of materials analogies is expected to bridge the gap between the existing materials literature and the undiscovered terrain.

cond-mat.mtrl-sci↗

arXiv2021

Reconfigurable Intelligent Surfaces for 6G Systems: Principles, Applications, and Research Directions

Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), are regarded as one of the most promising and revolutionizing techniques for enhancing the spectrum and/or energy efficiency of wireless systems. These devices are capable of reconfiguring the wireless propagation environment by carefully tuning the phase shifts of a large number of low-cost passive reflecting elements. In this article, we aim for answering four fundmental questions: 1) Why do we need RISs? 2) What is an RIS? 3) What are RIS's applications? 4) What are the relevant challenges and future research directions? In response, eight promising research directions are pointed out.

eess.SP↗

arXiv2021

STAR: Simultaneous Transmission And Reflection for 360° Coverage by Intelligent Surfaces

A novel simultaneously transmitting and reflecting (STAR) system design relying on reconfigurable intelligent surfaces (RISs) is conceived. First, an existing prototype is reviewed and the potential benefits of STAR-RISs are discussed. Then, the key differences between conventional reflecting-only RISs and STAR-RISs are identified from the perspectives of hardware design, physics principles, and communication system design. Furthermore, the basic signal model of STAR-RISs is introduced, and three practical protocols are proposed for their operation, namely energy splitting, mode switching, and time switching. Based on the proposed protocols, a range of promising application scenarios are put forward for integrating STAR-RISs into next-generation wireless networks. By considering the downlink of a typical RIS-aided multiple-input single-output (MISO) system, numerical case studies are provided for revealing the superiority of STAR-RISs over other baselines, when employing the proposed protocols. Finally, several open research problems are discussed.

cs.IT↗

arXiv2020

6G Oriented Wireless Communication Channel Characteristics Analysis and Modeling

Based on the vision on the 6G wireless communication network, i.e., global coverage, all spectrums and all applications, we comprehensively survey 6G related wireless channel measurements, channel characteristics, and channel models for all frequency bands and all scenarios. Millimeter wave (mmWave), terahertz (THz), optical band, satellite, unmanned aerial vehicle (UAV), maritime, underwater acoustic, high-speed train (HST), vehicle-to-vehicle (V2V), massive/ ultra-massive multiple-input multiple-output (MIMO), orbital angular momentum (OAM), and industry Internet of things (IoT) communication channels were particularly investigated. The related 6G channel measurement and modeling results were also given. Finally, future research challenges on 6G channel measurements and modeling were pointed out.

eess.SP↗

arXiv2020

Deep learning framework for subject-independent emotion detection using wireless signals

Emotion states recognition using wireless signals is an emerging area of research that has an impact on neuroscientific studies of human behaviour and well-being monitoring. Currently, standoff emotion detection is mostly reliant on the analysis of facial expressions and/or eye movements acquired from optical or video cameras. Meanwhile, although they have been widely accepted for recognizing human emotions from the multimodal data, machine learning approaches have been mostly restricted to subject dependent analyses which lack of generality. In this paper, we report an experimental study which collects heartbeat and breathing signals of 15 participants from radio frequency (RF) reflections off the body followed by novel noise filtering techniques. We propose a novel deep neural network (DNN) architecture based on the fusion of raw RF data and the processed RF signal for classifying and visualising various emotion states. The proposed model achieves high classification accuracy of 71.67 % for independent subjects with 0.71, 0.72 and 0.71 precision, recall and F1-score values respectively. We have compared our results with those obtained from five different classical ML algorithms and it is established that deep learning offers a superior performance even with limited amount of raw RF and post processed time-sequence data. The deep learning model has also been validated by comparing our results with those from ECG signals. Our results indicate that using wireless signals for stand-by emotion state detection is a better alternative to other technologies with high accuracy and have much wider applications in future studies of behavioural sciences.

eess.SP↗

arXiv2020

Polar nano-clusters in nominally paraelectric ceramics demonstrating high microwave tunability for wireless communication

Dielectric materials, with high tunability at microwave frequencies, are key components in the design of microwave communication systems. Dense Ba0.6Sr0.4TiO3 (BST) ceramics, with different grain sizes, were prepared in order to optimise the dielectric tunability via polar nano cluster effects. Dielectric permittivity and loss measurements were carried at both high and low frequencies and were supported by results from X-ray powder diffraction, scanning and transmission electron microscopies, Raman spectroscopy and piezoresponse force microscopy. The concentration of polar nano clusters, whose sizes are found to be in the range 20 to 50 nm, and the dielectric tunability increase with increasing grain size. A novel method for measurement of the microwave tunability in bulk dielectrics is presented. The highest tunability of 32% is achieved in ceramics with an average grain size of 10 um. The tunability of BST ceramics with applied DC field is demonstrated in a prototype small resonant antenna.

physics.app-ph↗