PaLI-3 Vision Language Models: Smaller, Faster, Stronger

This paper presents PaLI-3, a smaller, faster, and stronger vision language model (VLM) that compares favorably to similar models that are 10x larger. As part of arriving at this strong performance, we compare Vision Transformer (ViT) models pretrained using classification objectives to contrastively (SigLIP) pretrained ones. We find that, while slightly underperforming on standard image classification benchmarks, SigLIP-based PaLI shows superior performance across various multimodal benchmarks, especially on localization and visually-situated text understanding. We scale the SigLIP image encoder up to 2 billion parameters, and achieves a new state-of-the-art on multilingual cross-modal retrieval. We hope that PaLI-3, at only 5B parameters, rekindles research on fundamental pieces of complex VLMs, and could fuel a new generation of scaled-up models

PaLI: A Jointly-Scaled Multilingual Language-Image Model

Effective scaling and a flexible task interface enable large language models to excel at many tasks. PaLI (Pathways Language and Image model) extends this approach to the joint modeling of language and vision. PaLI generates text based on visual and textual inputs, and with this interface performs many vision, language, and multimodal tasks, in many languages. To train PaLI, we make use of large pretrained encoder-decoder language models and Vision Transformers (ViTs). This allows us to capitalize on their existing capabilities and leverage the substantial cost of training them. We find that joint scaling of the vision and language components is important. Since existing Transformers for language are much larger than their vision counterparts, we train the largest ViT to date (ViT-e) to quantify the benefits from even larger-capacity vision models. To train PaLI, we create a large multilingual mix of pretraining tasks, based on a new image-text training set containing 10B images and texts in over 100 languages. PaLI achieves state-of-the-art in multiple vision and language tasks (such as captioning, visual question-answering, scene-text understanding), while retaining a simple, modular, and scalable design

Clinical Study Metformin and Diammonium Glycyrrhizinate Enteric-Coated Capsule versus Metformin Alone versus Diammonium Glycyrrhizinate Enteric-Coated Capsule Alone in Patients with Nonalcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus

Objective. The present study was conducted to compare the efficacy of metformin combined with diammonium glycyrrhizinate enteric-coated capsule (DGEC) versus metformin alone versus DGEC alone for the treatment of nonalcoholic fatty liver disease (NAFLD) in patients with type 2 diabetes mellitus (T2DM). Subjects and Methods. 163 patients with NAFLD and T2DM were enrolled in this 24-week study and were randomized to one of three groups: group 1 was treated with metformin alone; group 2 was treated with DGEC alone; group 3 received metformin plus DGEC combination therapy. Anthropometric parameters, liver function, lipid profile, serum ferritin (SF), metabolic parameters, liver/spleen computed tomography (CT) ratio, and fibroscan value were evaluated at baseline and after 8, 16, and 24 weeks of treatment. Results. After 24 weeks, significant improvements in all measured parameters were observed in three groups ( < 0.05) except for the improvements in low density lipoprotein cholesterol (LDL-C) and metabolic parameters in group 2 which did not reach statistical significance ( > 0.05). Compared with group 1 and group 2, the patients in group 3 had greater reductions in observed parameters apart from CB and TB ( < 0.05). Conclusions. This study showed that metformin plus DGEC was more effective than metformin alone or DGEC alone in reducing liver enzymes, lipid levels, and metabolic parameters and ameliorating the degree of hepatic fibrosis in patients with NAFLD and T2DM

PaLI-X: On Scaling up a Multilingual Vision and Language Model

We present the training recipe and results of scaling up PaLI-X, a multilingual vision and language model, both in terms of size of the components and the breadth of its training task mixture. Our model achieves new levels of performance on a wide-range of varied and complex tasks, including multiple image-based captioning and question-answering tasks, image-based document understanding and few-shot (in-context) learning, as well as object detection, video question answering, and video captioning. PaLI-X advances the state-of-the-art on most vision-and-language benchmarks considered (25+ of them). Finally, we observe emerging capabilities, such as complex counting and multilingual object detection, tasks that are not explicitly in the training mix

System design and optimization of an aerial refueling system for transcontinental flights

Thesis: S.M. in Engineering and Management, Massachusetts Institute of Technology, System Design and Management Program, May, 2020Cataloged from the official version of thesis.Includes bibliographical references (pages 145-149).Currently, intercontinental flights are long-haul flights, and commercial aircraft are not refueled during the flight. As a result, the fuel consumption of intercontinental flights increases exponentially with the distance travelled, because these long-haul flights consume extra fuel due to their weight gain. Intercontinental aviation already accounts for a significant portion of global carbon emissions and this is expected to grow rapidly in the foreseeable future. Therefore, aircraft emissions from transcontinental flights have become a global challenge both socially and technologically. In this study, we propose a floating air refueling system (FARS) to reduce fuel costs on intercontinental flights. In this system, we launch a tanker to refuel incoming intercontinental aircraft. Through the refueling process, intercontinental flights avoid the exponential fuel consumption caused by the additional fuel required, and can potentially reduce aircraft emissions. This thesis presents the design of a floating aerial refueling system, including stakeholder analysis, system architecture design and economic feasibility analysis. In addition, we propose a method for mathematical simulation and optimization of FARS using different techniques. Finally we analyze FARS's feasibility and sensitivity based on case studies. The case study of Singapore Airlines SQ21 shows that our optimized design can save up to 39,415 tons of jet fuel annually over a 25-year life cycle, with a net present value of USD 266 million.by Ir. Keran Rong.S.M. in Engineering and ManagementS.M.inEngineeringandManagement Massachusetts Institute of Technology, System Design and Management Progra

Metformin and Diammonium Glycyrrhizinate Enteric-Coated Capsule versus Metformin Alone versus Diammonium Glycyrrhizinate Enteric-Coated Capsule Alone in Patients with Nonalcoholic Fatty Liver Disease and Type 2 Diabetes Mellitus

Objective. The present study was conducted to compare the efficacy of metformin combined with diammonium glycyrrhizinate enteric-coated capsule (DGEC) versus metformin alone versus DGEC alone for the treatment of nonalcoholic fatty liver disease (NAFLD) in patients with type 2 diabetes mellitus (T2DM). Subjects and Methods. 163 patients with NAFLD and T2DM were enrolled in this 24-week study and were randomized to one of three groups: group 1 was treated with metformin alone; group 2 was treated with DGEC alone; group 3 received metformin plus DGEC combination therapy. Anthropometric parameters, liver function, lipid profile, serum ferritin (SF), metabolic parameters, liver/spleen computed tomography (CT) ratio, and fibroscan value were evaluated at baseline and after 8, 16, and 24 weeks of treatment. Results. After 24 weeks, significant improvements in all measured parameters were observed in three groups (P<0.05) except for the improvements in low density lipoprotein cholesterol (LDL-C) and metabolic parameters in group 2 which did not reach statistical significance (P>0.05). Compared with group 1 and group 2, the patients in group 3 had greater reductions in observed parameters apart from CB and TB (P<0.05). Conclusions. This study showed that metformin plus DGEC was more effective than metformin alone or DGEC alone in reducing liver enzymes, lipid levels, and metabolic parameters and ameliorating the degree of hepatic fibrosis in patients with NAFLD and T2DM

Shikonin Attenuates Concanavalin A-Induced Acute Liver Injury in Mice via Inhibition of the JNK Pathway

Objective. Shikonin possesses anti-inflammatory effects. However, its function in concanavalin A-induced acute liver injury remains uncertain. The aim of the present study was to investigate the functions of shikonin and its mechanism of protection on ConA-induced acute liver injury. Materials and Methods. Balb/C mice were exposed to ConA (20 mg/kg) via tail vein injection to establish acute liver injury; shikonin (7.5 mg/kg and 12.5 mg/kg) was intraperitoneally administered 2 h before the ConA injection. The serum liver enzyme levels and the inflammatory cytokine levels were determined at 3, 6, and 24 h after ConA injection. Results. After the injection of ConA, inflammatory cytokines IL-1β, TNF-α, and IFN-γ were significantly increased. Shikonin significantly ameliorated liver injury and histopathological changes and suppressed the release of inflammatory cytokines. The expressions of Bcl-2 and Bax were markedly affected by shikonin pretreatment. LC3, Beclin-1, and p-JNK expression levels were decreased in the shikonin-pretreated groups compared with the ConA-treated groups. Shikonin attenuated ConA-induced liver injury by reducing apoptosis and autophagy through the inhibition of the JNK pathway. Conclusion. Our results indicated that shikonin pretreatment attenuates ConA-induced acute liver injury by inhibiting apoptosis and autophagy through the suppression of the JNK pathway