ChatDoctor: A Medical Chat Model Fine-tuned on LLaMA Model using Medical Domain Knowledge

Recent large language models (LLMs) in the general domain, such as ChatGPT, have shown remarkable success in following instructions and producing human-like responses. However, such language models have yet to be adapted for the medical domain, resulting in poor accuracy of responses and an inability to provide sound advice on medical diagnoses, medications, etc. To address this problem, we fine-tuned our ChatDoctor model based on 100k real-world patient-physician conversations from an online medical consultation site. Besides, we add autonomous knowledge retrieval capabilities to our ChatDoctor, for example, Wikipedia or a disease database as a knowledge brain. By fine-tuning the LLMs using these 100k patient-physician conversations, our model showed significant improvements in understanding patients' needs and providing informed advice. The autonomous ChatDoctor model based on Wikipedia and Database Brain can access real-time and authoritative information and answer patient questions based on this information, significantly improving the accuracy of the model's responses, which shows extraordinary potential for the medical field with a low tolerance for error. To facilitate the further development of dialogue models in the medical field, we make available all source code, datasets, and model weights available at: https://github.com/Kent0n-Li/ChatDoctor

NerfAcc: Efficient Sampling Accelerates NeRFs

Optimizing and rendering Neural Radiance Fields is computationally expensive due to the vast number of samples required by volume rendering. Recent works have included alternative sampling approaches to help accelerate their methods, however, they are often not the focus of the work. In this paper, we investigate and compare multiple sampling approaches and demonstrate that improved sampling is generally applicable across NeRF variants under an unified concept of transmittance estimator. To facilitate future experiments, we develop NerfAcc, a Python toolbox that provides flexible APIs for incorporating advanced sampling methods into NeRF related methods. We demonstrate its flexibility by showing that it can reduce the training time of several recent NeRF methods by 1.5x to 20x with minimal modifications to the existing codebase. Additionally, highly customized NeRFs, such as Instant-NGP, can be implemented in native PyTorch using NerfAcc.Comment: Website: https://www.nerfacc.co

PortraitNet:Real-time portrait segmentation network for mobile device

Real-time portrait segmentation plays a significant role in many applications on mobile device, such as background replacement in video chat or teleconference. In this paper, we propose a real-time portrait segmentation model, called PortraitNet, that can run effectively and efficiently on mobile device. PortraitNet is based on a lightweight U-shape architecture with two auxiliary losses at the training stage, while no additional cost is required at the testing stage for portrait inference. The two auxiliary losses are boundary loss and consistency constraint loss. The former improves the accuracy of boundary pixels, and the latter enhances the robustness in complex lighting environment. We evaluate PortraitNet on portrait segmentation dataset EG1800 and Supervise-Portrait. Compared with the state-of-the-art methods, our approach achieves remarkable performance in terms of both accuracy and efficiency, especially for generating results with sharper boundaries and under severe illumination conditions. Meanwhile, PortraitNet is capable of processing 224 × 224 RGB images at 30 FPS on iPhone 7

Scheduling with a Limited Testing Budget

Scheduling with testing falls under the umbrella of the research on optimization with explorable uncertainty. In this model, each job has an upper limit on its processing time that can be decreased to a lower limit (possibly unknown) by some preliminary action (testing). Recently, D{\"{u}}rr et al. \cite{DBLP:journals/algorithmica/DurrEMM20} has studied a setting where testing a job takes a unit time, and the goal is to minimize total completion time or makespan on a single machine. In this paper, we extend their problem to the budget setting in which each test consumes a job-specific cost, and we require that the total testing cost cannot exceed a given budget. We consider the offline variant (the lower processing time is known) and the oblivious variant (the lower processing time is unknown) and aim to minimize the total completion time or makespan on a single machine. For the total completion time objective, we show NP-hardness and derive a PTAS for the offline variant based on a novel LP rounding scheme. We give a $(4+\epsilon)$-competitive algorithm for the oblivious variant based on a framework inspired by the worst-case lower-bound instance. For the makespan objective, we give an FPTAS for the offline variant and a $(2+\epsilon)$-competitive algorithm for the oblivious variant. Our algorithms for the oblivious variants under both objectives run in time $O(poly(n/\epsilon))$. Lastly, we show that our results are essentially optimal by providing matching lower bounds.Comment: To appear in ESA 202

Protective effect of recombinant staphylococcal enterotoxin A entrapped in polylactic-co-glycolic acid microspheres against Staphylococcus aureus infection

Staphylococcus aureus is an important cause of nosocomial and community-acquired infections in humans and animals, as well as the cause of mastitis in dairy cattle. Vaccines aimed at preventing S. aureus infection in bovine mastitis have been studied for many years, but have so far been unsuccessful due to the complexity of the bacteria, and the lack of suitable vaccine delivery vehicles. The current study developed an Escherichia coli protein expression system that produced a recombinant staphylococcal enterotoxin A (rSEA) encapsulated into biodegradable microparticles generated by polylactic-co-glycolic acid (PLGA) dissolved in methylene chloride and stabilized with polyvinyl acetate. Antigen loading and surface properties of the microparticles were investigated to optimize particle preparation protocols. The prepared PLGA-rSEA microspheres had a diameter of approximately 5 μm with a smooth and regular surface. The immunogenicity of the PLGA-rSEA vaccine was assessed using mice as an animal model and showed that the vaccine induced a strong humoral immune response and increased the percent survival of challenged mice and bacterial clearance. Histological analysis showed moderate impairment caused by the pathogen upon challenge afforded by immunization with PLGA-rSEA microspheres. Antibody titer in the sera of mice immunized with PLGA-rSEA microparticles was higher than in vaccinated mice with rSEA. In conclusion, the PLGA-rSEA microparticle vaccine developed here could potentially be used as a vaccine against enterotoxigenic S. aureus

3DMIT: 3D Multi-modal Instruction Tuning for Scene Understanding

The remarkable potential of multi-modal large language models (MLLMs) in comprehending both vision and language information has been widely acknowledged. However, the scarcity of 3D scenes-language pairs in comparison to their 2D counterparts, coupled with the inadequacy of existing approaches in understanding of 3D scenes by LLMs, poses a significant challenge. In response, we collect and construct an extensive dataset comprising 75K instruction-response pairs tailored for 3D scenes. This dataset addresses tasks related to 3D VQA, 3D grounding, and 3D conversation. To further enhance the integration of 3D spatial information into LLMs, we introduce a novel and efficient prompt tuning paradigm, 3DMIT. This paradigm eliminates the alignment stage between 3D scenes and language and extends the instruction prompt with the 3D modality information including the entire scene and segmented objects. We evaluate the effectiveness of our method across diverse tasks in the 3D scene domain and find that our approach serves as a strategic means to enrich LLMs' comprehension of the 3D world. Our code is available at https://github.com/staymylove/3DMIT.Comment: 9 pages, 5 figure

A fast responsive chromogenic and near-infrared fluorescence lighting-up probe for visual detection of toxic thiophenol in environmental water and living cells

Thiophenols as high toxic environmental pollutants are poisonous for animals and aquatic organisms. Therefore, it is indispensable to monitor thiophenols in the environment. Herein, a novel near-infrared fluorescent probe was developed for the detection of thiophenols, which was easily prepared by one-step coupling of 2,4-dini trobenzenesulfonyl chloride with Nile blue. The probe showed a significant near infrared (∼675 nm) fluores cence “turn-on” response to thiophenols with some good features including chromogenic reaction, high sensi tivity and selectivity, fast response, near-infrared emission along with low detection limit (1.8 nM). The probe was employed to rapidly and visually determine thiophenols in several industrial wastewaters with good re coveries (90–110%). Moreover, this probe has been demonstrated good capability for imaging thiophenol in HeLa cellsinfo:eu-repo/semantics/publishedVersio