Research on Mechanical and Control System Design Based on 3D Printing Equipment

Based on the development requirements of printing equipment, the system design, system processing, installation and debugging, performance testing experiments, system trial operation and system improvement of 3D printing equipment are studied in several stages, and the technical standards for the relevant machinery and control systems of the equipment are studied. Design and optimization were carried out, and the development of the equipment was completed

Enabling Large Language Models to Generate Text with Citations

Large language models (LLMs) have emerged as a widely-used tool for information seeking, but their generated outputs are prone to hallucination. In this work, our aim is to allow LLMs to generate text with citations, improving their factual correctness and verifiability. Existing work mainly relies on commercial search engines and human evaluation, making it challenging to reproduce and compare different modeling approaches. We propose ALCE, the first benchmark for Automatic LLMs' Citation Evaluation. ALCE collects a diverse set of questions and retrieval corpora and requires building end-to-end systems to retrieve supporting evidence and generate answers with citations. We develop automatic metrics along three dimensions -- fluency, correctness, and citation quality -- and demonstrate their strong correlation with human judgements. Our experiments with state-of-the-art LLMs and novel prompting strategies show that current systems have considerable room for improvement -- For example, on the ELI5 dataset, even the best models lack complete citation support 50% of the time. Our analyses further highlight promising future directions, including developing better retrievers, advancing long-context LLMs, and improving the ability to synthesize information from multiple sources.Comment: Accepted by EMNLP 2023. Code and data are available at https://github.com/princeton-nlp/ALC

Exemplar-based Video Colorization with Long-term Spatiotemporal Dependency

Exemplar-based video colorization is an essential technique for applications like old movie restoration. Although recent methods perform well in still scenes or scenes with regular movement, they always lack robustness in moving scenes due to their weak ability in modeling long-term dependency both spatially and temporally, leading to color fading, color discontinuity or other artifacts. To solve this problem, we propose an exemplar-based video colorization framework with long-term spatiotemporal dependency. To enhance the long-term spatial dependency, a parallelized CNN-Transformer block and a double head non-local operation are designed. The proposed CNN-Transformer block can better incorporate long-term spatial dependency with local texture and structural features, and the double head non-local operation further leverages the performance of augmented feature. While for long-term temporal dependency enhancement, we further introduce the novel linkage subnet. The linkage subnet propagate motion information across adjacent frame blocks and help to maintain temporal continuity. Experiments demonstrate that our model outperforms recent state-of-the-art methods both quantitatively and qualitatively. Also, our model can generate more colorful, realistic and stabilized results, especially for scenes where objects change greatly and irregularly

Erosion-reducing potential of Salix psammophila roots in the water–wind crisscrossed erosion region of the Chinese Loess Plateau: A simulated investigation

Laboratory-simulated experiments under a one-way wind erosion–rain erosion sequence were conducted to investigate the effect of S. psammophila roots on wind and water erosion processes and quantify its erosion-reducing potential. With the collected soil of sandy loam and planted shrub of S. psammophila, 16 soil boxes including bare and root-permeated soils were arranged in March 2017 and conducted in August 2017. With the wind speeds of 11 and 14 m s−1 and rainfall intensities of 60 and 100 mm h−1, two levels of interaction (11 m s−1 × 60 mm h−1 and 14 m s−1 × 100 mm h−1) were designed. The particle-size composition and sediment transport flux were examined in the former wind tunnel experiments, and the runoff hydrodynamic parameters and runoff and water erosion rates were determined in the following rainfall tests. The sediment reduction effect by roots (%) was used to quantify the erosion-reducing potential of roots. The results demonstrated that in the former wind tunnel experiments, compared with the bare soils, the root-permeated soils showed a slight coarsening of surface soil and had 18.03% and 35.71% less sediment transport flux at wind speeds of 11 and 14 m s−1, respectively. In the following rainfall tests, S. psammophila roots weakened the hydrodynamic intensity of overland flow and decreased runoff and water erosion rates by 13.34%, 30.70% and 4.44%, 43.72% at rainfall intensities of 60 and 100 mm h−1, respectively. Different from the water erosion process of bare soils, which showed an increased fluctuated trend, the root-permeated soils presented a steady increase in the early stage of rainfall and then a decrease-stable trend at the mid and end of rainfall. In the wind tunnel–rainfall experiments, the sediment reduction effect by Salix psammophila roots showed 24.37% and 39.72% at levels of 11 m s−1 × 60 mm h−1 and 14 m s−1 × 100 mm h−1, respectively. This kind of study may provide more insights into understanding ecological impacts of sandy vegetation construction on the water–wind crisscrossed erosion region of the Chinese Loess Plateau and also sandy land

Co-exposure to multiple vitamins and the risk of all-cause mortality in patients with diabetes

ObjectiveAlthough the effect of vitamins on the risk of mortality in diabetic patients has been reported, most studies focus on individual vitamins. However, humans are often exposed to multiple vitamins simultaneously in daily life. Therefore, it is worth exploring the effects of co-exposure to multiple vitamins on the risk of mortality in diabetic patients.MethodsThis study included diabetic patients aged ≥20WD years who participated in NHANES from 2003 to 2006. An unsupervised K-means clustering method was used to cluster eight vitamins in serum into several patterns of co-exposure to multiple vitamins, and the Cox proportional hazards model was used to evaluate the impact of different patterns of co-exposure to multiple vitamins on the risk of all-cause mortality in diabetic patients.ResultsThree patterns of co-exposure to multiple vitamins were generated based on K-means clustering, namely, low-level, moderate-level, and high-level. Among the 484 diabetic patients, with a median follow-up of 13.7 years, a total of 211 deaths occurred. After adjusting for covariates, the individual vitamins had varying effects on the risk of all-cause mortality in diabetic patients. Compared to the low-level group of co-exposure to multiple vitamins, the high-level group significantly reduced the risk of all-cause mortality in diabetic patients, with a HR of 0.42 (95% CI: 0.20, 0.87). Subgroup analysis demonstrated that high levels of co-exposure to multiple vitamins significantly reduced the risk of all-cause mortality in males, individuals aged ≥ 60 years, and non-Hispanic White people with diabetes compared to the low-level group, with HR of 0.42 (95% CI: 0.18, 0.98), 0.53 (95% CI: 0.26, 0.98), and 0.26 (95% CI: 0.12, 0.58) respectively.ConclusionWhile individual vitamins had different effects on the risk of all-cause mortality in patients with diabetes, high-level co-exposure to multiple vitamins significantly reduced the risk of all-cause mortality in patients with diabetes, with differences observed among genders, ages, and race. This suggests that when developing vitamin intervention strategies for patients with diabetes, consideration should be given not only to the dosage of individual vitamins but also to the variations between different population groups

Bulked segregant RNA-seq reveals complex resistance expression profile to powdery mildew in wild emmer wheat W762

Powdery mildew, caused by Blumeria graminis f. sp. tritici (Bgt), is one of the most destructive fungal diseases threatening global wheat production. Exploring powdery mildew resistance (Pm) gene(s) and dissecting the molecular mechanism of the host resistance are critical to effectively and reasonably control this disease. Durum wheat (Triticum turgidum L. var. durumDesf.) is an important gene donor for wheat improvement against powdery mildew. In this study, a resistant durum wheat accession W762 was used to investigate its potential resistance component(s) and profile its expression pattern in responding to Bgt invasion using bulked segregant RNA-Seq (BSR-Seq) and further qRT-PCR verification. Genetic analysis showed that the powdery mildew resistance in W762 did not meet monogenic inheritance and complex genetic model might exist within the population of W762 × Langdon (susceptible durum wheat). After BSR-Seq, 6,196 consistently different single nucleotide polymorphisms (SNPs) were called between resistant and susceptible parents and bulks, and among them, 763 SNPs were assigned to the chromosome arm 7B. Subsequently, 3,653 differentially expressed genes (DEGs) between resistant and susceptible parents and bulks were annotated and analyzed by Gene Ontology (GO), Cluster of Orthologous Groups (COG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. The potential regulated genes were selected and analyzed their temporal expression patterns following Bgt inoculation. As a result, nine disease-related genes showed distinctive expression profile after Bgt invasion and might serve as potential targets to regulate the resistance against powdery mildew in W762. Our study could lay a foundation for analysis of the molecular mechanism and also provide potential targets for the improvement of durable resistance against powdery mildew

How to train your instruction-following text encoder without labeling

Text embedding models encode the semantic content of natural language inputs into fixed-length vectors. Contrastive learning has been the go-to training strategy to ensure semantically similar vectors are closely positioned in the embedding space. While successful, this training recipe requires large amounts of labeled training data in order to cover diverse domains. In addition, current text embedding models cannot adhere to user instructions when encoding inputs. In this thesis, we provide the first attempt to build text embedding models that can (1) adhere to user instructions, and (2) generalize without domain-specific annotated data. We leverage the recent success of generative large language models (LLMs), which exhibit strong domain generalization and rich latent knowledge. Transferring these properties to text encoders can enrich their contextualized representations and allow for instruction-controlled representations. In this work, we rely on the state-space model (SSM) parametrization to achieve such goals. SSMs are defined through ordinary differential equations with respect to the state vector, capturing the dynamics of an information system over time. State vectors are a fixed-length compression of the system’s past trajectory. We show that, empirically, state vectors in learned, discretized SSMs still preserve this information-rich property in language modeling tasks, and can be applied off-the-shelf to perform text embedding tasks. The instruction-following ability of pretrained generative LMs allows the state vectors to be sensitive to user intentions and can successfully compress different information, given different prompts

Impact of Digital Strategic Orientation on Organizational Performance through Digital Competence

In the era of the digital economy, enterprises need a comprehensive digital transformation of strategy, business, organization, competence, and operation. However, being limited themselves to the development of digital technology, previous studies mainly focused on the development and application of digital technology, single case studies, and multi-case studies of digital transformation. Few researchers systematically studied the digital transformation mechanism at the organizational level. Therefore, this study explored the relationship between a strategic orientation and organizational performance though digital competence at the organizational level. To accomplish the task, this study basically constructed the dimensions of digital competence according to core competence theory. Digital competence contains three hub-factors: digital infrastructure, digital integration, and digital management. This study collected 160 questionnaires from Chinese enterprises and analyzed the data using SmartPLS 3. This study analyzed the positive relationship between digital strategic orientation, digital competence, and organization performance. This study identified the importance of digital competence through the empirical analysis of enterprises that are undergoing digital transformation or had completed a digital transformation. Therefore, enterprises need to pay attention to the impact of digital competence on organizational performance. Digital competence is a reshaping of corporate resources when facing a turbulent digital environment. Moreover, digital competence can ultimately achieve value delivery through the improvement of enterprise organizational performance