Foreign body responses in central nervous system mimic natural wound responses and alter biomaterial functions

Biomaterials hold promise for diverse therapeutic applications in the central nervous system (CNS). Little is known about molecular factors that determine CNS foreign body responses (FBRs) in vivo , or about how such responses influence biomaterial function. Here, we probed these factors using a platform of injectable hydrogels readily modified to present interfaces with different representative physiochemical properties to host cells. We show that biomaterial FBRs mimic specialized multicellular CNS wound responses not present in peripheral tissues, which serve to isolate damaged neural tissue and restore barrier functions. Moreover, we found that the nature and intensity of CNS FBRs are determined by definable properties. For example, cationic, anionic or nonionic interfaces with CNS cells elicit quantifiably different levels of stromal cell infiltration, inflammation, neural damage and amyloid production. The nature and intensity of FBRs significantly influenced hydrogel resorption and molecular delivery functions. These results characterize specific molecular mechanisms that drive FBRs in the CNS and have important implications for developing effective biomaterials for CNS applications

Analysis the Role Conflict of Trade Union Chairman and Its Types

With the development of the market economy, the trade union work has become increasingly complex. As a special group, trade union chairman plays multiple roles. On the one hand, the trade union chairman should safeguard the rights and interests of enterprise employees, on the other hand, as the workers of the enterprise, they have to create benefits for the enterprise, this kind of dual role trigger a trade union chairman role conflict. In this paper, through reviewing the relevant literature, from the personal angle of the trade union chairman, trade union chairman of role conflict from three dimensions divided responsibility, time, interests. Through the division of the three dimension classifying trade union chairman of role conflict, and explain the causes of different types of role conflict and the corresponding countermeasures and suggestions, so as to eliminate the role of the trade union chairman conflict, give full play to the role of the trade union chairman

Better Explain Transformers by Illuminating Important Information

Transformer-based models excel in various natural language processing (NLP) tasks, attracting countless efforts to explain their inner workings. Prior methods explain Transformers by focusing on the raw gradient and attention as token attribution scores, where non-relevant information is often considered during explanation computation, resulting in confusing results. In this work, we propose highlighting the important information and eliminating irrelevant information by a refined information flow on top of the layer-wise relevance propagation (LRP) method. Specifically, we consider identifying syntactic and positional heads as important attention heads and focus on the relevance obtained from these important heads. Experimental results demonstrate that irrelevant information does distort output attribution scores and then should be masked during explanation computation. Compared to eight baselines on both classification and question-answering datasets, our method consistently outperforms with over 3\% to 33\% improvement on explanation metrics, providing superior explanation performance. Our anonymous code repository is available at: https://github.com/LinxinS97/Mask-LR

Fraction Constraint in Partial Wave Analysis

To resolve the non-convex optimization problem in partial wave analysis, this paper introduces a novel approach that incorporates fraction constraints into the likelihood function. This method offers significant improvements in both the efficiency of pole searching and the reliability of resonance selection within partial wave analysis

PWACG: Partial Wave Analysis Code Generator supporting Newton-conjugate gradient method

This paper introduces a novel Partial Wave Analysis Code Generator (PWACG) that automatically generates high-performance partial wave analysis codes. This is achieved by leveraging the JAX automatic differentiation library and the jinja2 template engine. The resulting code is constructed using the high-performance API of JAX, and includes support for the Newton's Conjugate Gradient optimization method, as well as the full utilization of parallel computing capabilities offered by GPUs. By harnessing these advanced computing techniques, PWACG demonstrates a significant advantage in efficiently identifying global optimal points compared to conventional partial wave analysis software packages

Event Generation and Consistence Test for Physics with Sliced Wasserstein Distance

In the field of modern high-energy physics research, there is a growing emphasis on utilizing deep learning techniques to optimize event simulation, thereby expanding the statistical sample size for more accurate physical analysis. Traditional simulation methods often encounter challenges when dealing with complex physical processes and high-dimensional data distributions, resulting in slow performance. To overcome these limitations, we propose a solution based on deep learning with the sliced Wasserstein distance as the loss function. Our method shows its ability on high precision and large-scale simulations, and demonstrates its effectiveness in handling complex physical processes. By employing an advanced transformer learning architecture, we initiate the learning process from a Monte Carlo sample, and generate high-dimensional data while preserving all original distribution features. The generated data samples have passed the consistence test, that is developed to calculate the confidence of the high-dimentional distributions of the generated data samples through permutation tests. This fast simulation strategy, enabled by deep learning, holds significant potential not only for increasing sample sizes and reducing statistical uncertainties but also for applications in numerical integration, which is crucial in partial wave analysis, high-precision sample checks, and other related fields. It opens up new possibilities for improving event simulation in high-energy physics research

CustomListener: Text-guided Responsive Interaction for User-friendly Listening Head Generation

Listening head generation aims to synthesize a non-verbal responsive listener head by modeling the correlation between the speaker and the listener in dynamic conversion.The applications of listener agent generation in virtual interaction have promoted many works achieving the diverse and fine-grained motion generation. However, they can only manipulate motions through simple emotional labels, but cannot freely control the listener's motions. Since listener agents should have human-like attributes (e.g. identity, personality) which can be freely customized by users, this limits their realism. In this paper, we propose a user-friendly framework called CustomListener to realize the free-form text prior guided listener generation. To achieve speaker-listener coordination, we design a Static to Dynamic Portrait module (SDP), which interacts with speaker information to transform static text into dynamic portrait token with completion rhythm and amplitude information. To achieve coherence between segments, we design a Past Guided Generation Module (PGG) to maintain the consistency of customized listener attributes through the motion prior, and utilize a diffusion-based structure conditioned on the portrait token and the motion prior to realize the controllable generation. To train and evaluate our model, we have constructed two text-annotated listening head datasets based on ViCo and RealTalk, which provide text-video paired labels. Extensive experiments have verified the effectiveness of our model.Comment: Accepted by CVPR 202

tau-FPL: Tolerance-Constrained Learning in Linear Time

Learning a classifier with control on the false-positive rate plays a critical role in many machine learning applications. Existing approaches either introduce prior knowledge dependent label cost or tune parameters based on traditional classifiers, which lack consistency in methodology because they do not strictly adhere to the false-positive rate constraint. In this paper, we propose a novel scoring-thresholding approach, tau-False Positive Learning (tau-FPL) to address this problem. We show the scoring problem which takes the false-positive rate tolerance into accounts can be efficiently solved in linear time, also an out-of-bootstrap thresholding method can transform the learned ranking function into a low false-positive classifier. Both theoretical analysis and experimental results show superior performance of the proposed tau-FPL over existing approaches.Comment: 32 pages, 3 figures. This is an extended version of our paper published in AAAI-1