Cosmic constraint on the unified model of dark sectors with or without a cosmic string fluid in the varying gravitational constant theory

Observations indicate that most of the universal matter are invisible and the gravitational constant $G(t)$ maybe depends on the time. A theory of the variational $G$ (VG) is explored in this paper, with naturally producing the useful dark components in universe. We utilize the observational data: lookback time data, model-independent gamma ray bursts, growth function of matter linear perturbations, type Ia supernovae data with systematic errors, CMB and BAO to restrict the unified model (UM) of dark components in VG theory. Using the best-fit values of parameters with the covariance matrix, constraints on the variation of $G$ are $(\frac{G}{G_{0}})_{z=3.5}\simeq 1.0015^{+0.0071}_{-0.0075}$ and $(\frac{\dot{G}}{G})_{today}\simeq -0.7252^{+2.3645}_{-2.3645}\times 10^{-13} yr^{-1}$, the small uncertainties around constants. Limit on the equation of state of dark matter is $w_{0dm}=0.0072^{+0.0170}_{-0.0170}$ with assuming $w_{0de}=-1$ in unified model, and dark energy is $w_{0de}=-0.9986^{+0.0011}_{-0.0011}$ with assuming $w_{0dm}=0$ at prior. Restriction on UM parameters are $B_{s}=0.7442^{+0.0137+0.0262}_{-0.0132-0.0292}$ and $\alpha=0.0002^{+0.0206+0.0441}_{-0.0209-0.0422}$ with $1\sigma$ and $2\sigma$ confidence level. In addition, the effect of a cosmic string fluid on unified model in VG theory are investigated. In this case it is found that the $\Lambda$CDM ($\Omega_{s}=0$, $\beta=0$ and $\alpha=0$) is included in this VG-UM model at $1\sigma$ confidence level, and the larger errors are given: $\Omega_{s}=-0.0106^{+0.0312+0.0582}_{-0.0305-0.0509}$ (dimensionless energy density of cosmic string), $(\frac{G}{G_{0}})_{z=3.5}\simeq 1.0008^{+0.0620}_{-0.0584}$ and $(\frac{\dot{G}}{G})_{today}\simeq -0.3496^{+26.3135}_{-26.3135}\times 10^{-13}yr^{-1}$.Comment: 17 pages,4 figure

Attention, Please! Adversarial Defense via Attention Rectification and Preservation

This study provides a new understanding of the adversarial attack problem by examining the correlation between adversarial attack and visual attention change. In particular, we observed that: (1) images with incomplete attention regions are more vulnerable to adversarial attacks; and (2) successful adversarial attacks lead to deviated and scattered attention map. Accordingly, an attention-based adversarial defense framework is designed to simultaneously rectify the attention map for prediction and preserve the attention area between adversarial and original images. The problem of adding iteratively attacked samples is also discussed in the context of visual attention change. We hope the attention-related data analysis and defense solution in this study will shed some light on the mechanism behind the adversarial attack and also facilitate future adversarial defense/attack model design

MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology

In this paper, we consider enhancing medical visual-language pre-training (VLP) with domain-specific knowledge, by exploiting the paired image-text reports from the radiological daily practice. In particular, we make the following contributions: First, unlike existing works that directly process the raw reports, we adopt a novel triplet extraction module to extract the medical-related information, avoiding unnecessary complexity from language grammar and enhancing the supervision signals; Second, we propose a novel triplet encoding module with entity translation by querying a knowledge base, to exploit the rich domain knowledge in medical field, and implicitly build relationships between medical entities in the language embedding space; Third, we propose to use a Transformer-based fusion model for spatially aligning the entity description with visual signals at the image patch level, enabling the ability for medical diagnosis; Fourth, we conduct thorough experiments to validate the effectiveness of our architecture, and benchmark on numerous public benchmarks, e.g., ChestX-ray14, RSNA Pneumonia, SIIM-ACR Pneumothorax, COVIDx CXR-2, COVID Rural, and EdemaSeverity. In both zero-shot and fine-tuning settings, our model has demonstrated strong performance compared with the former methods on disease classification and grounding

Towards Generalist Foundation Model for Radiology

In this study, we aim to initiate the development of Radiology Foundation Model, termed as RadFM.We consider the construction of foundational models from the perspectives of data, model design, and evaluation thoroughly. Our contribution can be concluded as follows: (i), we construct a large-scale Medical Multi-modal Dataset, MedMD, consisting of 16M 2D and 3D medical scans. To the best of our knowledge, this is the first multi-modal dataset containing 3D medical scans. (ii), We propose an architecture that enables visually conditioned generative pre-training, allowing for the integration of text input interleaved with 2D or 3D medical scans to generate response for diverse radiologic tasks. The model was initially pre-trained on MedMD and subsequently domain-specific fine-tuned on RadMD, a radiologic cleaned version of MedMD, containing 3M radiologic visual-language pairs. (iii), we propose a new evaluation benchmark that comprises five tasks, aiming to comprehensively assess the capability of foundation models in handling practical clinical problems. Our experimental results confirm that RadFM significantly outperforms existing multi-modal foundation models. The codes, data, and model checkpoint will all be made publicly available to promote further research and development in the field

Loss of STAT1 in Bone Marrow-Derived Cells Accelerates Skeletal Muscle Regeneration

BACKGROUND: Skeletal muscle regeneration is a complex process which is not yet completely understood. Evidence suggested that the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway may have a role in myogenesis. In this study, we aim to explore the possible role of STAT1 in muscle regeneration. METHODS: Wild-type and STAT1 knockout mice were used in this study. Tibialis anterior muscle injury was conducted by cardiotoxin (CTX) injection. Bone marrow transplantation and glucocorticoid treatment were performed to manipulate the immune system of the mice. RESULTS: Muscle regeneration was accelerated in STAT1-/- mice after CTX injury. Bone marrow transplantation experiments showed that the regeneration process relied on the type of donor mice rather than on recipient mice. Levels of pro-inflammatory cytokines, TNFα and IL-1β, were significantly higher in STAT1-/- mice at 1 day and/or 2 days post-injury, while levels of anti-inflammatory cytokine, IL-10, were lower in STAT1-/- mice at 2 days and 3 days post-injury. Levels of IGF-1 were significantly higher in the STAT1-/- mice at 1 day and 2 days post-injury. Furthermore, the muscle regeneration process was inhibited in glucocorticoid-treated mice. CONCLUSIONS: Loss of STAT1 in bone marrow-derived cells accelerates skeletal muscle regeneration

Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images

While multi-modal foundation models pre-trained on large-scale data have been successful in natural language understanding and vision recognition, their use in medical domains is still limited due to the fine-grained nature of medical tasks and the high demand for domain knowledge. To address this challenge, we propose a novel approach called Knowledge-enhanced Auto Diagnosis (KAD) which leverages existing medical domain knowledge to guide vision-language pre-training using paired chest X-rays and radiology reports. We evaluate KAD on {four} external X-ray datasets and demonstrate that its zero-shot performance is not only comparable to that of fully-supervised models, but also superior to the average of three expert radiologists for three (out of five) pathologies with statistical significance. Moreover, when few-shot annotation is available, KAD outperforms all existing approaches in fine-tuning settings, demonstrating its potential for application in different clinical scenarios

Ultrafast Charge Transfer in Atomically Thin MoS2/WS2 Heterostructures

Van der Waals heterostructures have recently emerged as a new class of materials, where quantum coupling between stacked atomically thin two-dimensional (2D) layers, including graphene, hexagonal-boron nitride, and transition metal dichalcogenides (MX2), give rise to fascinating new phenomena. MX2 heterostructures are particularly exciting for novel optoelectronic and photovoltaic applications, because 2D MX2 monolayers can have an optical bandgap in the near-infrared to visible spectral range and exhibit extremely strong light-matter interactions. Theory predicts that many stacked MX2 heterostructures form type-II semiconductor heterojunctions that facilitate efficient electron-hole separation for light detection and harvesting. Here we report the first experimental observation of ultrafast charge transfer in photo-excited MoS2/WS2 heterostructures using both photoluminescence mapping and femtosecond (fs) pump-probe spectroscopy. We show that hole transfer from the MoS2 layer to the WS2 layer takes place within 50 fs after optical excitation, a remarkable rate for van der Waals coupled 2D layers. Such ultrafast charge transfer in van der Waals heterostructures can enable novel 2D devices for optoelectronics and light harvesting