Making Linear MDPs Practical via Contrastive Representation Learning

It is common to address the curse of dimensionality in Markov decision processes (MDPs) by exploiting low-rank representations. This motivates much of the recent theoretical study on linear MDPs. However, most approaches require a given representation under unrealistic assumptions about the normalization of the decomposition or introduce unresolved computational challenges in practice. Instead, we consider an alternative definition of linear MDPs that automatically ensures normalization while allowing efficient representation learning via contrastive estimation. The framework also admits confidence-adjusted index algorithms, enabling an efficient and principled approach to incorporating optimism or pessimism in the face of uncertainty. To the best of our knowledge, this provides the first practical representation learning method for linear MDPs that achieves both strong theoretical guarantees and empirical performance. Theoretically, we prove that the proposed algorithm is sample efficient in both the online and offline settings. Empirically, we demonstrate superior performance over existing state-of-the-art model-based and model-free algorithms on several benchmarks.Comment: ICML 2022. The first two authors contribute equall

DeepSeek-VL: Towards Real-World Vision-Language Understanding

We present DeepSeek-VL, an open-source Vision-Language (VL) Model designed for real-world vision and language understanding applications. Our approach is structured around three key dimensions: We strive to ensure our data is diverse, scalable, and extensively covers real-world scenarios including web screenshots, PDFs, OCR, charts, and knowledge-based content, aiming for a comprehensive representation of practical contexts. Further, we create a use case taxonomy from real user scenarios and construct an instruction tuning dataset accordingly. The fine-tuning with this dataset substantially improves the model's user experience in practical applications. Considering efficiency and the demands of most real-world scenarios, DeepSeek-VL incorporates a hybrid vision encoder that efficiently processes high-resolution images (1024 x 1024), while maintaining a relatively low computational overhead. This design choice ensures the model's ability to capture critical semantic and detailed information across various visual tasks. We posit that a proficient Vision-Language Model should, foremost, possess strong language abilities. To ensure the preservation of LLM capabilities during pretraining, we investigate an effective VL pretraining strategy by integrating LLM training from the beginning and carefully managing the competitive dynamics observed between vision and language modalities. The DeepSeek-VL family (both 1.3B and 7B models) showcases superior user experiences as a vision-language chatbot in real-world applications, achieving state-of-the-art or competitive performance across a wide range of visual-language benchmarks at the same model size while maintaining robust performance on language-centric benchmarks. We have made both 1.3B and 7B models publicly accessible to foster innovations based on this foundation model.Comment: https://github.com/deepseek-ai/DeepSeek-V

DeepSeek LLM: Scaling Open-Source Language Models with Longtermism

The rapid development of open-source large language models (LLMs) has been truly remarkable. However, the scaling law described in previous literature presents varying conclusions, which casts a dark cloud over scaling LLMs. We delve into the study of scaling laws and present our distinctive findings that facilitate scaling of large scale models in two commonly used open-source configurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek LLM, a project dedicated to advancing open-source language models with a long-term perspective. To support the pre-training phase, we have developed a dataset that currently consists of 2 trillion tokens and is continuously expanding. We further conduct supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) on DeepSeek LLM Base models, resulting in the creation of DeepSeek Chat models. Our evaluation results demonstrate that DeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in the domains of code, mathematics, and reasoning. Furthermore, open-ended evaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance compared to GPT-3.5

Prevalence and drug resistance of Salmonella in dogs and cats in Xuzhou, China

Salmonellosis is a zoonotic disease, and Salmonella spp. can sometimes be found in dogs and cats, posing a risk to human health. In this study, the prevalence and antimicrobial susceptibility of faecal Salmonella were investigated in pet dogs and cats in Xuzhou, Jiangsu Province, China

Cyclic Response of Additive Manufactured 316L Stainless Steel : The Role of Cell Structures

We report the effect of cell structures on the fatigue behavior of additively manufactured (AM) 316L stainless steel (316LSS). Compared with the cell-free samples, the fatigue process of fully cellular samples only consists of steady and overload stages, without an initial softening stage. Moreover, the fully cellular sample possesses higher strength, lower cyclic softening rate and longer lifetime. Microscopic analyses show no difference in grain orientations, dimensions, and shapes. However, the fully cellular samples show planar dislocation structures, whereas the cell-free samples display wavy dislocation structures. The existence of cell structures promotes the activation of planar slip, delays strain localization, and ultimately enhances the fatigue performance of AM 316LSS.Funding: Swedish Governmental Agency for Innovation Systems (Vinnova)Vinnova [2016-05175]; Science Foundation Ireland (SFI)Science Foundation Ireland [16/RC/3872]; European Regional Development FundEuropean Commission; I-Form industry partners; Ji Hua Laboratroy [X210141TL210]; Center for Additive Manufacturing-metal (CAM2)</p

The Autophagy-Related Protein ATG8 Orchestrates Asexual Development and AFB1 Biosynthesis in <i>Aspergillus flavus</i>

Autophagy, a conserved cellular recycling process, plays a crucial role in maintaining homeostasis under stress conditions. It also regulates the development and virulence of numerous filamentous fungi. In this study, we investigated the specific function of ATG8, a reliable autophagic marker, in the opportunistic pathogen Aspergillus flavus. To investigate the role of atg8 in A. flavus, the deletion and complemented mutants of atg8 were generated according to the homologous recombination principle. Deletion of atg8 showed a significant decrease in conidiation, spore germination, and sclerotia formation compared to the WT and atg8C strains. Additionally, aflatoxin production was found severely impaired in the ∆atg8 mutant. The stress assays demonstrated that ATG8 was important for A. flavus response to oxidative stress. The fluorescence microscopy showed increased levels of reactive oxygen species in the ∆atg8 mutant cells, and the transcriptional result also indicated that genes related to the antioxidant system were significantly reduced in the ∆atg8 mutant. We further found that ATG8 participated in regulating the pathogenicity of A. flavus on crop seeds. These results revealed the biological role of ATG8 in A. flavus, which might provide a potential target for the control of A. flavus and AFB1 biosynthesis