Bayesian Robust Tensor Ring Model for Incomplete Multiway Data

Robust tensor completion (RTC) aims to recover a low-rank tensor from its incomplete observation with outlier corruption. The recently proposed tensor ring (TR) model has demonstrated superiority in solving the RTC problem. However, the existing methods either require a pre-assigned TR rank or aggressively pursue the minimum TR rank, thereby often leading to biased solutions in the presence of noise. In this paper, a Bayesian robust tensor ring decomposition (BRTR) method is proposed to give more accurate solutions to the RTC problem, which can avoid exquisite selection of the TR rank and penalty parameters. A variational Bayesian (VB) algorithm is developed to infer the probability distribution of posteriors. During the learning process, BRTR can prune off slices of core tensor with marginal components, resulting in automatic TR rank detection. Extensive experiments show that BRTR can achieve significantly improved performance than other state-of-the-art methods

QARV: Quantization-Aware ResNet VAE for Lossy Image Compression

This paper addresses the problem of lossy image compression, a fundamental problem in image processing and information theory that is involved in many real-world applications. We start by reviewing the framework of variational autoencoders (VAEs), a powerful class of generative probabilistic models that has a deep connection to lossy compression. Based on VAEs, we develop a novel scheme for lossy image compression, which we name quantization-aware ResNet VAE (QARV). Our method incorporates a hierarchical VAE architecture integrated with test-time quantization and quantization-aware training, without which efficient entropy coding would not be possible. In addition, we design the neural network architecture of QARV specifically for fast decoding and propose an adaptive normalization operation for variable-rate compression. Extensive experiments are conducted, and results show that QARV achieves variable-rate compression, high-speed decoding, and a better rate-distortion performance than existing baseline methods. The code of our method is publicly accessible at https://github.com/duanzhiihao/lossy-vaeComment: Technical repor

Multispectral Imaging for Microchip Electrophoresis Enables Point-of-Care Newborn Hemoglobin Variant Screening

Hemoglobin (Hb) disorders affect nearly 7% of the world\u27s population. Globally, around 400,000 babies are born annually with sickle cell disease (SCD), primarily in sub-Saharan Africa where morbidity and mortality rates are high. Screening, early diagnosis, and monitoring are not widely accessible due to technical challenges and cost. We hypothesized that multispectral imaging will allow sensitive hemoglobin variant identification in existing affordable paper-based Hb electrophoresis. To test this hypothesis, we developed the first integrated point-of-care multispectral Hb variant test: Gazelle-Multispectral. Here, we evaluated the accuracy of Gazelle-Multispectral for Hb variant newborn screening in 265 newborns with known hemoglobin variants including hemoglobin A (Hb A), hemoglobin F (Hb F), hemoglobin S (Hb S) and hemoglobin C (Hb C). Gazelle-Multispectral detected levels of Hb A, Hb F, Hb S, and Hb C/E/A2, demonstrated high correlations with the results reported by laboratory gold standard high performance liquid chromatography (HPLC) at Pearson Correlation Coefficient = 0.97, 0.97, 0.93, and 0.95. Gazelle-Multispectral demonstrated accuracy of 96.8% in subjects of 0–3 days, and 96.9% in newborns. The ability to obtain accurate results on newborn samples suggest that Gazelle-Multispectral can be suitable for large-scale newborn screening and for diagnosis of SCD in low resource settings

Genome-wide identification of the TGA genes in common bean (Phaseolus vulgaris) and revealing their functions in response to Fusarium oxysporum f. sp. phaseoli infection

Fusarium wilt, which affects common bean all across the world, is caused by Fusarium oxysporum f. sp. Phaseoli (Fop). It is necessary to have functional genes in response to Fop infection because they might be used to manage disease. As a crucial regulator, TGA-binding transcription factor (TGA) is engaged in the defense mechanism of plants against pathogens. The role of TGA regulators in common bean in response to Fop infection, however, has not been documented. Hence, we performed genome-wide identified and characterized eight TGA genes in common bean. In this study, eight PvTGA genes were distributed on six chromosomes and classified into four subgroups. The PvTGA genes have the same conserved bZIP and DOG1 domains, but there are specific sequence structures in different PvTGAs. Phylogenetic and synteny analysis explained that PvTGA gene has a close genetic relationship with legume TGAs and that PvTGA03 and PvTGA05 may play an important role in evolution. Transcriptome data explained that expression levels of PvTGA genes showed diversity in different tissues. After Fop inoculation, the expression levels of PvTGA03 and PvTGA07 were significantly different between resistant and susceptible genotypes. Under SA treatment, the expression levels of PvTGA03, PvTGA04, PvTGA06, PvTGA07 and PvTGA08 were significantly different. These results imply that PvTGA03 and PvTGA07 play key roles in SA-mediated resistance to Fusarium wilt. Together, these findings advance knowledge of the PvTGA gene family in common bean and will help future studies aimed at reducing Fusarium wilt

RoAST: Robustifying Language Models via Adversarial Perturbation with Selective Training

Fine-tuning pre-trained language models (LMs) has become the de facto standard in many NLP tasks. Nevertheless, fine-tuned LMs are still prone to robustness issues, such as adversarial robustness and model calibration. Several perspectives of robustness for LMs have been studied independently, but lacking a unified consideration in multiple perspectives. In this paper, we propose Robustifying LMs via Adversarial perturbation with Selective Training (RoAST), a simple yet effective fine-tuning technique to enhance the multi-perspective robustness of LMs in a unified way. RoAST effectively incorporates two important sources for the model robustness, robustness on the perturbed inputs and generalizable knowledge in pre-trained LMs. To be specific, RoAST introduces adversarial perturbation during fine-tuning while the model parameters are selectively updated upon their relative importance to minimize unnecessary deviation. Under a unified evaluation of fine-tuned LMs by incorporating four representative perspectives of model robustness, we demonstrate the effectiveness of RoAST compared to state-of-the-art fine-tuning methods on six different types of LMs, which indicates its usefulness in practice.Comment: 33 pages, accepted at EMNLP 2023 Finding

ELUCID. VII. Using constrained hydro simulations to explore the gas component of the cosmic web

Using reconstructed initial conditions in the Sloan Digital Sky Survey (SDSS) survey volume, we carry out constrained hydrodynamic simulations in three regions representing different types of the cosmic web: the Coma cluster of galaxies; the SDSS Great Wall; and a large low-density region at z ∼ 0.05. These simulations, which include star formation and stellar feedback but no active galactic nucleus formation and feedback, are used to investigate the properties and evolution of intergalactic and intracluster media. About half of the warm-hot intergalactic gas is associated with filaments in the local cosmic web. Gas in the outskirts of massive filaments and halos can be heated significantly by accretion shocks generated by mergers of filaments and halos, respectively, and there is a tight correlation between the gas temperature and the strength of the local tidal field. The simulations also predict some discontinuities associated with shock fronts and contact edges, which can be tested using observations of the thermal Sunyaev-Zel’dovich effect and X-rays. A large fraction of the sky is covered by Lyα and O vi absorption systems, and most of the O vi systems and low-column-density H i systems are associated with filaments in the cosmic web. The constrained simulations, which follow the formation and heating history of the observed cosmic web, provide an important avenue to interpret observational data. With full information about the origin and location of the cosmic gas to be observed, such simulations can also be used to develop observational strategie