Opekline izazvane curenjem monokloroctene kiseline u kemijskom pogonu – prikaz slučaja

The patient, a 45-year-old male chemical factory worker, was burned by monochloroacetic acid discharged from a ruptured pipe. The patient was merely flushed with water and did not leave the workplace immediately. As a result, he suffered local burn symptoms, which gradually worsened. Two and a half hours after the accident, he developed symptoms of systemic poisoning, such as lethargy and dyspnoea. After a thorough debridement of the wound surface and subsequent skin grafting combined with early glucocorticoid therapy and haemofiltration, a satisfactory result was achieved, and the patient eventually recovered. With the widespread use of monochloroacetic acid in China, incidents of poisoning with this chemical are becoming increasingly common, with more than 100 cases reported in the past ten years in China alone.Radnik u kemijskoj tvornici u dobi od 45 godina zadobio je opekline izazvane monokloroctenom kiselinom koja je iscurila iz napukle cijevi. Ranu je samo isprao vodom i nije odmah napustio radno mjesto. Zbog toga je imao lokalizirane simptome opekline koji su se s vremenom pogoršavali. Dva i pol sata nakon nesreće pojavili su se simptomi sistemskog otrovanja poput letargije i zaduhe (dispneje). Cjelovito uklanjanje oštećenoga tkiva s površine i presađivanje kože u kombinaciji s ranim liječenjem glukokortikosteroidom i hemofiltracijom bilo je uspješno i bolesnik se naposljetku oporavio. S raširenom primjenom monokloroctene kiseline povećan je i broj otrovanja, koji je u posljednjih deset godina samo u Kini dosegnuo broj veći od 100 slučajeva

AE-GPT: Using Large Language Models to Extract Adverse Events from Surveillance Reports-A Use Case with Influenza Vaccine Adverse Events

Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cataloging AEs within clinical reports. Utilizing data from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study particularly focuses on AEs to evaluate LLMs' capability for AE extraction. A variety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama 2, were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5 model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match and 0.816 for relaxed match. The encouraging performance of the AE-GPT underscores LLMs' potential in processing medical data, indicating a significant stride towards advanced AE detection, thus presumably generalizable to other AE extraction tasks

Black-box Dataset Ownership Verification via Backdoor Watermarking

Deep learning, especially deep neural networks (DNNs), has been widely and successfully adopted in many critical applications for its high effectiveness and efficiency. The rapid development of DNNs has benefited from the existence of some high-quality datasets ($e.g.$, ImageNet), which allow researchers and developers to easily verify the performance of their methods. Currently, almost all existing released datasets require that they can only be adopted for academic or educational purposes rather than commercial purposes without permission. However, there is still no good way to ensure that. In this paper, we formulate the protection of released datasets as verifying whether they are adopted for training a (suspicious) third-party model, where defenders can only query the model while having no information about its parameters and training details. Based on this formulation, we propose to embed external patterns via backdoor watermarking for the ownership verification to protect them. Our method contains two main parts, including dataset watermarking and dataset verification. Specifically, we exploit poison-only backdoor attacks ($e.g.$, BadNets) for dataset watermarking and design a hypothesis-test-guided method for dataset verification. We also provide some theoretical analyses of our methods. Experiments on multiple benchmark datasets of different tasks are conducted, which verify the effectiveness of our method. The code for reproducing main experiments is available at \url{https://github.com/THUYimingLi/DVBW}.Comment: This paper is accepted by IEEE TIFS. 15 pages. The preliminary short version of this paper was posted on arXiv (arXiv:2010.05821) and presented in a non-archival NeurIPS Workshop (2020

Towards Robust Model Watermark via Reducing Parametric Vulnerability

Deep neural networks are valuable assets considering their commercial benefits and huge demands for costly annotation and computation resources. To protect the copyright of DNNs, backdoor-based ownership verification becomes popular recently, in which the model owner can watermark the model by embedding a specific backdoor behavior before releasing it. The defenders (usually the model owners) can identify whether a suspicious third-party model is ``stolen'' from them based on the presence of the behavior. Unfortunately, these watermarks are proven to be vulnerable to removal attacks even like fine-tuning. To further explore this vulnerability, we investigate the parameter space and find there exist many watermark-removed models in the vicinity of the watermarked one, which may be easily used by removal attacks. Inspired by this finding, we propose a mini-max formulation to find these watermark-removed models and recover their watermark behavior. Extensive experiments demonstrate that our method improves the robustness of the model watermarking against parametric changes and numerous watermark-removal attacks. The codes for reproducing our main experiments are available at \url{https://github.com/GuanhaoGan/robust-model-watermarking}.Comment: This paper is accepted by ICCV 202