Massively Parallel Ray Tracing Algorithm Using GPU

Ray tracing is a technique for generating an image by tracing the path of light through pixels in an image plane and simulating the effects of high-quality global illumination at a heavy computational cost. Because of the high computation complexity, it can't reach the requirement of real-time rendering. The emergence of many-core architectures, makes it possible to reduce significantly the running time of ray tracing algorithm by employing the powerful ability of floating point computation. In this paper, a new GPU implementation and optimization of the ray tracing to accelerate the rendering process is presented

Social Preferences in Behavioral Economics: The Study of Reciprocal Altruism under Different Conditions

Different external interventions prompt people to perceive different motivation which in turncauses different reactions. In our study, we propose that under different circumstances, the degree of the“reciprocal altruism heuristic” varies. This paper is aiming at carrying out an ultimatum game under twoscenarios and compares the results to demonstrate the effect of different external interventions on thetendency of reciprocal altruism. All 10 participants in the experiment, as a result, have shown differentinclination under the implementation of various external interventions, which strongly suggests the existenceof determinants that control the inclination of mutual cooperation and the provide insights for futurepsychological and educational related research to develop a more advanced system of human cognitivemodels under external interferences

Optimization of probabilistic quantum search algorithm with a priori information

A quantum computer encodes information in quantum states and runs quantum algorithms to surpass the classical counterparts by exploiting quantum superposition and quantum correlation. Grover's quantum search algorithm is a typical quantum algorithm that proves the superiority of quantum computing over classical computing. It has a quadratic reduction in the query complexity of database search, and is known to be optimal when no a priori information about the elements of the database is provided. In this work, we consider a probabilistic Grover search algorithm allowing nonzero probability of failure for a database with a general a priori probability distribution of the elements, and minimize the number of oracle calls by optimizing the initial state of the quantum system and the reflection axis of the diffusion operator. The initial state and the reflection axis are allowed to not coincide, and thus the quantum search algorithm rotates the quantum system in a three-dimensional subspace spanned by the initial state, the reflection axis and the search target state in general. The number of oracle calls is minimized by a variational method, and formal results are obtained with the assumption of low failure probability. The results show that for a nonuniform a priori distribution of the database elements, the number of oracle calls can be significantly reduced given a small decrease in the success probability of the quantum search algorithm, leading to a lower average query complexity to find the solution of the search problem. The results are applied to a simple but nontrivial database model with two-value a priori probabilities to show the power of the optimized quantum search algorithm. The paper concludes with a discussion about the generalization to higher-order results that allows for a larger failure probability for the quantum search algorithm.Comment: v2: Main text expanded to include analysis of the first-order optimization result. Close to the published versio

Human Papillomavirus Infection in Relation to Vaginal Microflora and Immune Factors

Objective: Clarify the vaginal microflora and immune factors in women with human papilloma virus (HPV) infection, and explore its association with HPV infection. Methods: This study collected vaginal secretions and blood from 160 women initially diagnosed as HPV positive in our hospital from June 2020 to December 2020 and 80 healthy women with HPV negative physical examination in the same period. The vaginal microflora of the patients were detected by 16S rDNA sequencing and the expression of immune factors was measured by a high-performance liquid phase chip. Results: The different types of HPV were HPV mix (64,40%), HPV52 (39,24.375%), HPV16 (30,18.750%), HPV58 (18,11.250%), HPV18 (6,3.750%), HPV53 (1,0.625%), HPV55 (1,0.625%), and HPV68 (1,0.625%).α diversity analysis showed that there was no significant difference in vaginal microflora between different HPV types (P=0.733). The genus level abundance of vaginal microflora in each group was mainly Lactobacillus, followed by Gardnerella and Prevotella. LEfSe Analysis showed that the mix group was Gardnerella and the type HPV16 group was Streptococcus. The immune comparison showed that MIP-1β was significantly upregulated in the HPV-positive group, but EGF in the HPV-negative group. Conclusion: This study revealed that HPV infection can change the proportion of vaginal microbial bacteria and the expression of immune factors, which provides a basis for local vaginal treatment and prevention of HPV infection after HPV infection

Troika: Multi-Path Cross-Modal Traction for Compositional Zero-Shot Learning

Recent compositional zero-shot learning (CZSL) methods adapt pre-trained vision-language models (VLMs) by constructing trainable prompts only for composed state-object pairs. Relying on learning the joint representation of seen compositions, these methods ignore the explicit modeling of the state and object, thus limiting the exploitation of pre-trained knowledge and generalization to unseen compositions. With a particular focus on the universality of the solution, in this work, we propose a novel paradigm for CZSL models that establishes three identification branches (i.e., Multi-Path) to jointly model the state, object, and composition. The presented Troika is our implementation that aligns the branch-specific prompt representations with decomposed visual features. To calibrate the bias between semantically similar multi-modal representations, we further devise a Cross-Modal Traction module into Troika that shifts the prompt representation towards the current visual content. We conduct extensive experiments on three popular benchmarks, where our method significantly outperforms existing methods in both closed-world and open-world settings.Comment: 14 page

LayoutLMv3: Pre-training for Document AI with Unified Text and Image Masking

Self-supervised pre-training techniques have achieved remarkable progress in Document AI. Most multimodal pre-trained models use a masked language modeling objective to learn bidirectional representations on the text modality, but they differ in pre-training objectives for the image modality. This discrepancy adds difficulty to multimodal representation learning. In this paper, we propose LayoutLMv3 to pre-train multimodal Transformers for Document AI with unified text and image masking. Additionally, LayoutLMv3 is pre-trained with a word-patch alignment objective to learn cross-modal alignment by predicting whether the corresponding image patch of a text word is masked. The simple unified architecture and training objectives make LayoutLMv3 a general-purpose pre-trained model for both text-centric and image-centric Document AI tasks. Experimental results show that LayoutLMv3 achieves state-of-the-art performance not only in text-centric tasks, including form understanding, receipt understanding, and document visual question answering, but also in image-centric tasks such as document image classification and document layout analysis. The code and models are publicly available at https://aka.ms/layoutlmv3.Comment: Work in Progres