18 accepted papers
Modern foundation models such as large language models (LLMs) require a massive amount of computational and memory resources. We propose a new framework to convert such LLMs into a reduced-dimension latent structure. Our method extends a local activation-aware tensor decomposition to a global attent
This paper addresses the integration of millimeter-wave (mmWave) Wi-Fi communication and sensing during data transmission intervals (DTIs). We leverage prior knowledge from codebook beam training conducted during preceding beacon transmission intervals (BTIs) and association beamforming training (A-…
Fine-tuning large language models on private data for downstream applications poses significant privacy risks in potentially exposing sensitive information. Several popular community platforms now offer convenient distribution of a large variety of pre-trained models, allowing anyone to publish with…
This paper introduces Quantum-PEFT that leverages quantum computations for parameter-efficient fine-tuning (PEFT). Unlike other additive PEFT methods, such as low-rank adaptation (LoRA), Quantum-PEFT exploits an underlying full-rank yet surprisingly parameter efficient _quantum unitary parameterizat…
Point cloud delivery over wireless and mobile channels will be a key technology for untethered users to realize extended reality via wireless and mobile terminals. A key challenge of point cloud delivery is efficiently delivering the point cloud over unstable and band-limited channels. Graph Fourier…
In contrast to existing multi-band Wi-Fi fusion in a frame-to-frame basis for simple classification, this paper considers asynchronous sequence-to-sequence fusion between sub-7GHz channel state information (CSI) and 60GHz beam SNR for more challenging downstream tasks such as continuous regression.…
Text-conditioned image-to-video generation (TI2V) aims to synthesize a realistic video starting from a given image (e.g. a woman's photo) and a text description (e.g. "a woman is drinking water."). Existing TI2V frameworks often require costly training on video-text datasets and specific model desig…
In frequency-modulated continuous-wave (FMCW) lidar, the distance to an illuminated target is proportional to the beat frequency of the interference signal. Laser phase noise often limits the range accuracy of FMCW lidar, and existing frequency estimation methods make overly simplistic assumptions a…
Holographic-type communication, i.e., three-dimensional (3D) content delivery, will be a crucial application for modern wireless and mobile networks. In this paper, we propose a novel soft delivery scheme to realize efficient 3D content delivery. Specifically, the proposed scheme sends a single 2D i…
Conditional generative models typically demand large annotated training sets to achieve high-quality synthesis. As a result, there has been significant interest in designing models that perform plug-and-play generation, i.e., to use a predefined or pretrained model, which is not explicitly trained o…
We leverage standards-compliant beam training measurements from commercial-of-the-shelf (COTS) 802.11ad/ay devices for localization of a moving object. Two technical challenges need to be addressed: (1) the beam training measurements are intermittent due to beam scanning overhead control and content…
Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi) localization, it is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a nov…
This paper considers indoor localization using multi-modal wireless signals including Wi-Fi, inertial measurement unit (IMU), and ultra-wideband (UWB). By formulating the localization as a multi-modal sequence regression problem, a multi-stream recurrent fusion method is proposed to combine the curr…
Previous studies have verified the usefulness of visual haptics for achieving the appropriate grasping force and task success rate to operate remote machines. However, its capabilities have not been evaluated objectively and quantitatively. We comprehensively compare three feedback modalities (i.e.,
Motivated by real-world automotive radar measurements that are distributed around object (e.g., vehicles) edges with a certain volume, a novel hierarchical truncated Gaussian measurement model is proposed to resemble the underlying spatial distribution of radar measurements. With the proposed measur…
Modern face alignment methods have become quite accurate at predicting the locations of facial landmarks, but they do not typically estimate the uncertainty of their predicted locations nor predict whether landmarks are visible. In this paper, we present a novel framework for jointly predicting land…
This paper studies parameter estimation of a coupled mixture of polynomial phase signal (PPS) and sinusoidal frequency modulated (FM) signal, a newly introduced model motivated by industrial applications. Particularly, we analytically evaluate the estimation performance (or performance loss) via the…
In this paper, we propose a Bayesian inference approach to extract the binary reflectance pattern of samples from compressed measurements in the terahertz (THz) frequency band. Compared with existing compressed THz imaging methods relying on the sparsity of the reflectance pattern, the proposed Baye…