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Realistic image restoration is a crucial task in computer vision, and the use of diffusion-based models for image restoration has garnered significant attention due to their ability to produce realistic results. However, the quality of the generated images is still a significant challenge due to the severity of image degradation and the uncontrollability of the diffusion model. In this work, we delve into the potential of utilizing pre-trained stable diffusion for image restoration and propose MRIR, a diffusion-based restoration method with multimodal insights. Specifically, we explore the problem from two perspectives: textual level and visual level. For the textual level, we harness the power of the pre-trained multimodal large language model to infer meaningful semantic information from low-quality images. Furthermore, we employ the CLIP image encoder with a designed Refine Layer to capture image details as a supplement. For the visual level, we mainly focus on the pixel level control. Thus, we utilize a Pixel-level Processor and ControlNet to control spatial structures. Finally, we integrate the aforementioned control information into the denoising U-Net using multi-level attention mechanisms and realize controllable image restoration with multimodal insights. The qualitative and quantitative results demonstrate our method's superiority over other state-of-the-art methods on both synthetic and real-world datasets.

Image restoration is a classic low-level problem aimed at recovering high-quality images from low-quality images with various degradations such as blur, noise, rain, haze, etc. However, due to the inherent complexity and non-uniqueness of degradation in real-world images, it is challenging for a model trained for single tasks to handle real-world restoration problems effectively. Moreover, existing methods often suffer from over-smoothing and lack of realism in the restored results. To address these issues, we propose Diff-Restorer, a universal image restoration method based on the diffusion model, aiming to leverage the prior knowledge of Stable Diffusion to remove degradation while generating high perceptual quality restoration results. Specifically, we utilize the pre-trained visual language model to extract visual prompts from degraded images, including semantic and degradation embeddings. The semantic embeddings serve as content prompts to guide the diffusion model for generation. In contrast, the degradation embeddings modulate the Image-guided Control Module to generate spatial priors for controlling the spatial structure of the diffusion process, ensuring faithfulness to the original image. Additionally, we design a Degradation-aware Decoder to perform structural correction and convert the latent code to the pixel domain. We conducted comprehensive qualitative and quantitative analysis on restoration tasks with different degradations, demonstrating the effectiveness and superiority of our approach.

Recent advances in non-invasive EEG technology have broadened its application in emotion recognition, yielding a multitude of related datasets. Yet, deep learning models struggle to generalize across these datasets due to variations in acquisition equipment and emotional stimulus materials. To address the pressing need for a universal model that fluidly accommodates diverse EEG dataset formats and bridges the gap between laboratory and real-world data, we introduce a novel deep learning framework: the Contrastive Learning based Diagonal Transformer Autoencoder (CLDTA), tailored for EEG-based emotion recognition. The CLDTA employs a diagonal masking strategy within its encoder to extracts full-channel EEG data's brain network knowledge, facilitating transferability to the datasets with fewer channels. And an information separation mechanism improves model interpretability by enabling straightforward visualization of brain networks. The CLDTA framework employs contrastive learning to distill subject-independent emotional representations and uses a calibration prediction process to enable rapid adaptation of the model to new subjects with minimal samples, achieving accurate emotion recognition. Our analysis across the SEED, SEED-IV, SEED-V, and DEAP datasets highlights CLDTA's consistent performance and proficiency in detecting both task-specific and general features of EEG signals related to emotions, underscoring its potential to revolutionize emotion recognition research.

Colorizing grayscale images offers an engaging visual experience. Existing automatic colorization methods often fail to generate satisfactory results due to incorrect semantic colors and unsaturated colors. In this work, we propose an automatic colorization pipeline to overcome these challenges. We leverage the extraordinary generative ability of the diffusion prior to synthesize color with plausible semantics. To overcome the artifacts introduced by the diffusion prior, we apply the luminance conditional guidance. Moreover, we adopt multimodal high-level semantic priors to help the model understand the image content and deliver saturated colors. Besides, a luminance-aware decoder is designed to restore details and enhance overall visual quality. The proposed pipeline synthesizes saturated colors while maintaining plausible semantics. Experiments indicate that our proposed method considers both diversity and fidelity, surpassing previous methods in terms of perceptual realism and gain most human preference.

The global public health landscape is perpetually challenged by the looming threat of infectious diseases. Central to addressing this concern is the imperative to prevent and manage disease transmission during pandemics, particularly in unique settings. This study addresses the transmission dynamics of infectious diseases within conference venues, presenting a computational model designed to simulate transmission processes within a condensed timeframe (one day), beginning with sporadic cases. Our model intricately captures the activities of individual attendees within the conference venue, encompassing meetings, rest intervals, and meal breaks. While meetings entail proximity seating, rest and lunch periods allow attendees to interact with diverse individuals. Moreover, the restroom environment poses an additional avenue for potential infection transmission. Employing an individual-based model, we meticulously replicated the transmission dynamics of infectious diseases, with a specific emphasis on close-contact interactions between infected and susceptible individuals. Through comprehensive analysis of model simulations, we elucidated the intricacies of disease transmission dynamics within conference settings and assessed the efficacy of control strategies to curb disease dissemination. Ultimately, our study proffers a numerical framework for assessing the risk of infectious disease transmission during short-duration conferences, furnishing conference organizers with valuable insights to inform the implementation of targeted prevention and control measures., Comment: 25 pages; 8 figures

Reading comprehension, a fundamental cognitive ability essential for knowledge acquisition, is a complex skill, with a notable number of learners lacking proficiency in this domain. This study introduces innovative tasks for Brain-Computer Interface (BCI), predicting the relevance of words or tokens read by individuals to the target inference words. We use state-of-the-art Large Language Models (LLMs) to guide a new reading embedding representation in training. This representation, integrating EEG and eye-tracking biomarkers through an attention-based transformer encoder, achieved a mean 5-fold cross-validation accuracy of 68.7% across nine subjects using a balanced sample, with the highest single-subject accuracy reaching 71.2%. This study pioneers the integration of LLMs, EEG, and eye-tracking for predicting human reading comprehension at the word level. We fine-tune the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model for word embedding, devoid of information about the reading tasks. Despite this absence of task-specific details, the model effortlessly attains an accuracy of 92.7%, thereby validating our findings from LLMs. This work represents a preliminary step toward developing tools to assist reading.

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for interdisciplinary research that bridges cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals' neural states during a semantic relation reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use a feature engineering approach to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60\% across 12 subjects. Words of high relevance to the inference keyword had significantly more eye fixations per word: 1.0584 compared to 0.6576 when excluding no-fixation words, and 1.5126 compared to 1.4026 when including them. This study represents the first attempt to classify brain states at a word level using LLM knowledge. It provides valuable insights into human cognitive abilities and the realm of Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies.

In the field of intelligent education, knowledge tracing (KT) has attracted increasing attention, which estimates and traces students' mastery of knowledge concepts to provide high-quality education. In KT, there are natural graph structures among questions and knowledge concepts so some studies explored the application of graph neural networks (GNNs) to improve the performance of the KT models which have not used graph structure. However, most of them ignored both the questions' difficulties and students' attempts at questions. Actually, questions with the same knowledge concepts have different difficulties, and students' different attempts also represent different knowledge mastery. In this paper, we propose a difficulty and attempts boosted graph-based KT (DAGKT), using rich information from students' records. Moreover, a novel method is designed to establish the question similarity relationship inspired by the F1 score. Extensive experiments on three real-world datasets demonstrate the effectiveness of the proposed DAGKT., Comment: 12 pages, 3figures, conference:ICONIP

Knowledge tracing (KT) is a fundamental task in educational data mining that mainly focuses on students' dynamic cognitive states of skills. The question-answering process of students can be regarded as a thinking process that considers the following two problems. One problem is which skills are needed to answer the question, and the other is how to use these skills in order. If a student wants to answer a question correctly, the student should not only master the set of skills involved in the question but also think and obtain the associative path on the skills graph. The nodes in the associative path refer to the skills needed and the path shows the order of using them. The associative path is referred to as the skill mode. Thus, obtaining the skill modes is the key to answering questions successfully. However, most existing KT models only focus on a set of skills, without considering the skill modes. We propose a KT model, called APGKT, that exploits skill modes. Specifically, we extract the subgraph topology of the skills involved in the question and combine the difficulty level of the skills to obtain the skill modes via encoding; then, through multi-layer recurrent neural networks, we obtain a student's higher-order cognitive states of skills, which is used to predict the student's future answering performance. Experiments on five benchmark datasets validate the effectiveness of the proposed model.

Recent years have witnessed increasing interest in few-shot knowledge graph completion (FKGC), which aims to infer unseen query triples for a few-shot relation using a few reference triples about the relation. The primary focus of existing FKGC methods lies in learning relation representations that can reflect the common information shared by the query and reference triples. To this end, these methods learn entity-pair representations from the direct neighbors of head and tail entities, and then aggregate the representations of reference entity pairs. However, the entity-pair representations learned only from direct neighbors may have low expressiveness when the involved entities have sparse direct neighbors or share a common local neighborhood with other entities. Moreover, merely modeling the semantic information of head and tail entities is insufficient to accurately infer their relational information especially when they have multiple relations. To address these issues, we propose a Relation-Specific Context Learning (RSCL) framework, which exploits graph contexts of triples to learn global and local relation-specific representations for few-shot relations. Specifically, we first extract graph contexts for each triple, which can provide long-term entity-relation dependencies. To encode the extracted graph contexts, we then present a hierarchical attention network to capture contextualized information of triples and highlight valuable local neighborhood information of entities. Finally, we design a hybrid attention aggregator to evaluate the likelihood of the query triples at the global and local levels. Experimental results on two public datasets demonstrate that RSCL outperforms state-of-the-art FKGC methods., Comment: 12 pages, 6 figures

SPPC (Super Proton-Proton Collider) is a discovery machine that is designed for energy frontier research in two decades from now, as the second stage of the CEPC-SPPC project. The main objective is to carry out experiments at 125 TeV in center-of-mass energy in a two-ring collider of 100 km in circumference and 20 T in dipole field. This white paper about SPPC describes the machine related issues, including performance, design overview, design challenges, key technologies and their maturity and required R&D, staged options and upgrades, synergies with other facilities, and environmental impacts., Comment: contribution to Snowmass 2021

Completion through the embedding representation of the knowledge graph (KGE) has been a research hotspot in recent years. Realistic knowledge graphs are mostly related to time, while most of the existing KGE algorithms ignore the time information. A few existing methods directly or indirectly encode the time information, ignoring the balance of timestamp distribution, which greatly limits the performance of temporal knowledge graph completion (KGC). In this paper, a temporal KGC method is proposed based on the direct encoding time information framework, and a given time slice is treated as the finest granularity for balanced timestamp distribution. A large number of experiments on temporal knowledge graph datasets extracted from the real world demonstrate the effectiveness of our method., Comment: 14 pages, 1 figures