Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction

Dimensionality reduction is an essential technique for multi-way large-scale data, i.e., tensor. Tensor ring (TR) decomposition has become popular due to its high representation ability and flexibility. However, the traditional TR decomposition algorithms suffer from high computational cost when facing large-scale data. In this paper, taking advantages of the recently proposed tensor random projection method, we propose two TR decomposition algorithms. By employing random projection on every mode of the large-scale tensor, the TR decomposition can be processed at a much smaller scale. The simulation experiment shows that the proposed algorithms are $4-25$ times faster than traditional algorithms without loss of accuracy, and our algorithms show superior performance in deep learning dataset compression and hyperspectral image reconstruction experiments compared to other randomized algorithms.Comment: ICASSP submissio

Tensor Ring Decomposition with Rank Minimization on Latent Space: An Efficient Approach for Tensor Completion

In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity. In particular, for tensor ring (TR) decomposition, the number of model possibilities grows exponentially with the tensor order, which makes it rather challenging to find the optimal TR decomposition. In this paper, by exploiting the low-rank structure of the TR latent space, we propose a novel tensor completion method which is robust to model selection. In contrast to imposing the low-rank constraint on the data space, we introduce nuclear norm regularization on the latent TR factors, resulting in the optimization step using singular value decomposition (SVD) being performed at a much smaller scale. By leveraging the alternating direction method of multipliers (ADMM) scheme, the latent TR factors with optimal rank and the recovered tensor can be obtained simultaneously. Our proposed algorithm is shown to effectively alleviate the burden of TR-rank selection, thereby greatly reducing the computational cost. The extensive experimental results on both synthetic and real-world data demonstrate the superior performance and efficiency of the proposed approach against the state-of-the-art algorithms

ChatAnything: Facetime Chat with LLM-Enhanced Personas

In this technical report, we target generating anthropomorphized personas for LLM-based characters in an online manner, including visual appearance, personality and tones, with only text descriptions. To achieve this, we first leverage the in-context learning capability of LLMs for personality generation by carefully designing a set of system prompts. We then propose two novel concepts: the mixture of voices (MoV) and the mixture of diffusers (MoD) for diverse voice and appearance generation. For MoV, we utilize the text-to-speech (TTS) algorithms with a variety of pre-defined tones and select the most matching one based on the user-provided text description automatically. For MoD, we combine the recent popular text-to-image generation techniques and talking head algorithms to streamline the process of generating talking objects. We termed the whole framework as ChatAnything. With it, users could be able to animate anything with any personas that are anthropomorphic using just a few text inputs. However, we have observed that the anthropomorphic objects produced by current generative models are often undetectable by pre-trained face landmark detectors, leading to failure of the face motion generation, even if these faces possess human-like appearances because those images are nearly seen during the training (e.g., OOD samples). To address this issue, we incorporate pixel-level guidance to infuse human face landmarks during the image generation phase. To benchmark these metrics, we have built an evaluation dataset. Based on it, we verify that the detection rate of the face landmark is significantly increased from 57.0% to 92.5% thus allowing automatic face animation based on generated speech content. The code and more results can be found at https://chatanything.github.io/

Lysosomal Proteases Are a Determinant of Coronavirus Tropism

Cell entry by coronaviruses involves two principal steps, receptor binding and membrane fusion; the latter requires activation by host proteases, particularly lysosomal proteases. Despite the importance of lysosomal proteases in both coronavirus entry and cell metabolism, the correlation between lysosomal proteases and cell tropism of coronaviruses has not been established. Here, we examined the roles of lysosomal proteases in activating coronavirus surface spike proteins for membrane fusion, using the spike proteins from severe acute respiratory syndrome coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) as the model system. To this end, we controlled the contributions from receptor binding and other host proteases, thereby attributing coronavirus entry solely or mainly to the efficiency of lysosomal proteases in activating coronavirus spike-mediated membrane fusion. Our results showed that lysosomal proteases from bat cells support coronavirus spike-mediated pseudovirus entry and cell-cell fusion more effectively than their counterparts from human cells. Moreover, purified lysosomal extracts from bat cells cleave cell surface-expressed coronavirus spikes more efficiently than their counterparts from human cells. Overall, our study suggests that different lysosomal protease activities from different host species and tissue cells are an important determinant of the species and tissue tropism of coronaviruses.IMPORTANCE Coronaviruses are capable of colonizing new species, as evidenced by the recent emergence of SARS and MERS coronaviruses; they can also infect multiple tissues in the same species. Lysosomal proteases play critical roles in coronavirus entry by cleaving coronavirus surface spike proteins and activating the fusion of host and viral membranes; they also play critical roles in cell physiology by processing cellular products. How do different lysosomal protease activities from different cells impact coronavirus entry? Here, we controlled the contributions from known factors that function in coronavirus entry so that lysosomal protease activities became the only or the main determinant of coronavirus entry. Using pseudovirus entry, cell-cell fusion, and biochemical assays, we showed that lysosomal proteases from bat cells activate coronavirus spike-mediated membrane fusion more efficiently than their counterparts from human cells. Our study provides the first direct evidence supporting lysosomal proteases as a determinant of the species and tissue tropisms of coronaviruses