DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement

Existing unsupervised low-light image enhancement methods lack enough effectiveness and generalization in practical applications. We suppose this is because of the absence of explicit supervision and the inherent gap between real-world scenarios and the training data domain. In this paper, we develop Diffusion-based domain calibration to realize more robust and effective unsupervised Low-Light Enhancement, called DiffLLE. Since the diffusion model performs impressive denoising capability and has been trained on massive clean images, we adopt it to bridge the gap between the real low-light domain and training degradation domain, while providing efficient priors of real-world content for unsupervised models. Specifically, we adopt a naive unsupervised enhancement algorithm to realize preliminary restoration and design two zero-shot plug-and-play modules based on diffusion model to improve generalization and effectiveness. The Diffusion-guided Degradation Calibration (DDC) module narrows the gap between real-world and training low-light degradation through diffusion-based domain calibration and a lightness enhancement curve, which makes the enhancement model perform robustly even in sophisticated wild degradation. Due to the limited enhancement effect of the unsupervised model, we further develop the Fine-grained Target domain Distillation (FTD) module to find a more visual-friendly solution space. It exploits the priors of the pre-trained diffusion model to generate pseudo-references, which shrinks the preliminary restored results from a coarse normal-light domain to a finer high-quality clean field, addressing the lack of strong explicit supervision for unsupervised methods. Benefiting from these, our approach even outperforms some supervised methods by using only a simple unsupervised baseline. Extensive experiments demonstrate the superior effectiveness of the proposed DiffLLE

A SIMPLE Approach to Provably Reconstruct Ising Model with Global Optimality

Reconstruction of interaction network between random events is a critical problem arising from statistical physics and politics to sociology, biology, and psychology, and beyond. The Ising model lays the foundation for this reconstruction process, but finding the underlying Ising model from the least amount of observed samples in a computationally efficient manner has been historically challenging for half a century. By using the idea of sparsity learning, we present a approach named SIMPLE that has a dominant sample complexity from theoretical limit. Furthermore, a tuning-free algorithm is developed to give a statistically consistent solution of SIMPLE in polynomial time with high probability. On extensive benchmarked cases, the SIMPLE approach provably reconstructs underlying Ising models with global optimality. The application on the U.S. senators voting in the last six congresses reveals that both the Republicans and Democrats noticeably assemble in each congresses; interestingly, the assembling of Democrats is particularly pronounced in the latest congress

Reliability evaluation of reservoir bank slopes with weak interlayers considering spatial variability

Reservoir bank slopes with weak interlayers are common in the Three Gorges Reservoir area. Their stabilities are affected by multi-coupled factors (e.g., reservoir water fluctuations, rainfall, and earthquakes in the reservoir area). Meanwhile, the differences in mechanical parameters of reservoir banks make it more difficult to determine the dynamic stability of bank slopes under complex mechanical environments. In this paper, the multiple disaster-causing factors and spatial variability of the landslide were comprehensively considered to study the long-term evolution trend of the bank slopes with weak interlayers. Specifically, the limit equilibrium method combined with the random field was performed to calculate the reliability. Furthermore, the long-term effects of dry-wet cycles on reservoir bank landslides and the sensitivity analysis of the statistical parameters of the random field were discussed. The results show that the earthquake action had the most significant impact on the failure probability of the landslide. The failure probability was more significantly affected by the vertical fluctuation range of the parameters and the coefficient of variation of the internal friction angle. The increase in failure probability under the action of dry-wet cycles was mainly caused by the reduction of the parameters of the weak interlayer. The reliability evaluation method of reservoir bank slopes can be applied to predict the long-term stability of the coastal banks

CritiqueLLM: Scaling LLM-as-Critic for Effective and Explainable Evaluation of Large Language Model Generation

Since the natural language processing (NLP) community started to make large language models (LLMs), such as GPT-4, act as a critic to evaluate the quality of generated texts, most of them only train a critique generation model of a specific scale on specific datasets. We argue that a comprehensive investigation on the key factor of LLM-based evaluation models, such as scaling properties, is lacking, so that it is still inconclusive whether these models have potential to replace GPT-4's evaluation in practical scenarios. In this paper, we propose a new critique generation model called CritiqueLLM, which includes a dialogue-based prompting method for high-quality referenced / reference-free evaluation data. Experimental results show that our model can achieve comparable evaluation performance to GPT-4 especially in system-level correlations, and even outperform GPT-4 in 3 out of 8 tasks in a challenging reference-free setting. We conduct detailed analysis to show promising scaling properties of our model in the quality of generated critiques. We also demonstrate that our generated critiques can act as scalable feedback to directly improve the generation quality of LLMs.Comment: 18 pages, 5 figure

Graphene-Based Biosensors for Detection of Composite Vibrational Fingerprints in the Mid-Infrared Region.

In this study, a label-free multi-resonant graphene-based biosensor with periodic graphene nanoribbons is proposed for detection of composite vibrational fingerprints in the mid-infrared range. The multiple vibrational signals of biomolecules are simultaneously enhanced and detected by different resonances in the transmission spectrum. Each of the transmission dips can be independently tuned by altering the gating voltage applied on the corresponding graphene nanoribbon. Geometric parameters are investigated and optimized to obtain excellent sensing performance. Limit of detection is also evaluated in an approximation way. Besides, the biosensor can operate in a wide range of incident angles. Electric field intensity distributions are depicted to reveal the physical insight. Moreover, another biosensor based on periodic graphene nanodisks is further proposed, whose performance is insensitive to the polarization of incidence. Our research may have a potential for designing graphene-based biosensor used in many promising bioanalytical and pharmaceutical applications

Stabilization of Bio-Oss® particulates using photocurable hydrogel to enhance bone regeneration by regulating macrophage polarization

Bone substitutes are widely used in maxillofacial and oral surgeries. However, in clinical practice, bone substitutes with various forms, including separated particulates, powders, and blocks, have exhibited poor handling properties and space maintenance characteristics, resulting in long surgery procedures and unstable volume of the newly formed bone. Movable separated particulates with high stiffness have induced local inflammatory responses that hinder bone regeneration. The present study aimed to develop a new method to enhance the stability and operability of bone substitutes commonly used in dentistry by premixing with photocurable hydrogel GelMA. The GelMA-encapsulated particulate had a strong capacity to aggregate separated particulates and firmly attach to the host bone defect after photocuring compared to particulates alone. Additionally, macrophages at the surface of the GelMA-stabilized particulates tended to present a more M2-like phenotype than those at the surface of Bio-Oss®, leading to more MMR+ multinucleated giant cell formation and the induction of blood vessel invasion and new bone formation. In conclusion, this hydrogel-coated bone substitute strategy facilitates bone regeneration with increased operability, a stable volume of osteogenic space, and a favorable osteogenic microenvironment, indicating its potential value in the field of maxillofacial and oral surgeries when bone substitutes are needed

Elucidating shared biomarkers in gastroesophageal reflux disease and idiopathic pulmonary fibrosis: insights into novel therapeutic targets and the role of angelicae sinensis radix

Background: The etiological underpinnings of gastroesophageal reflux disease (GERD) and idiopathic pulmonary fibrosis (IPF) remain elusive, coupled with a scarcity of effective therapeutic interventions for IPF. Angelicae sinensis radix (ASR, also named Danggui) is a Chinese herb with potential anti-fibrotic properties, that holds promise as a therapeutic agent for IPF.Objective: This study seeks to elucidate the causal interplay and potential mechanisms underlying the coexistence of GERD and IPF. Furthermore, it aims to investigate the regulatory effect of ASR on this complex relationship.Methods: A two-sample Mendelian randomization (TSMR) approach was employed to delineate the causal connection between gastroesophageal reflux disease and IPF, with Phennoscanner V2 employed to mitigate confounding factors. Utilizing single nucleotide polymorphism (SNPs) and publicly available microarray data, we analyzed potential targets and mechanisms related to IPF in GERD. Network pharmacology and molecular docking were employed to explore the targets and efficacy of ASR in treating GERD-related IPF. External datasets were subsequently utilized to identify potential diagnostic biomarkers for GERD-related IPF.Results: The IVW analysis demonstrated a positive causal relationship between GERD and IPF (IVW: OR = 1.002, 95%CI: 1.001, 1.003; p < 0.001). Twenty-five shared differentially expressed genes (DEGs) were identified. GO functional analysis revealed enrichment in neural, cellular, and brain development processes, concentrated in chromosomes and plasma membranes, with protein binding and activation involvement. KEGG analysis unveiled enrichment in proteoglycan, ERBB, and neuroactive ligand-receptor interaction pathways in cancer. Protein-protein interaction (PPI) analysis identified seven hub genes. Network pharmacology analysis demonstrated that 104 components of ASR targeted five hub genes (PDE4B, DRD2, ERBB4, ESR1, GRM8), with molecular docking confirming their excellent binding efficiency. GRM8 and ESR1 emerged as potential diagnostic biomarkers for GERD-related IPF (ESR1: AUCGERD = 0.762, AUCIPF = 0.725; GRM8: AUCGERD = 0.717, AUCIPF = 0.908). GRM8 and ESR1 emerged as potential diagnostic biomarkers for GERD-related IPF, validated in external datasets.Conclusion: This study establishes a causal link between GERD and IPF, identifying five key targets and two potential diagnostic biomarkers for GERD-related IPF. ASR exhibits intervention efficacy and favorable binding characteristics, positioning it as a promising candidate for treating GERD-related IPF. The potential regulatory mechanisms may involve cell responses to fibroblast growth factor stimulation and steroidal hormone-mediated signaling pathways