Application Effect of Limited Fluid Resuscitation in Emergency Patients with Multiple Trauma Complicated with Shock

This article explores the methods and effects of limited fluid resuscitation in the treatment of hemorrhagic shock caused by multiple trauma, which is common in clinic. 80 patients with multiple trauma complicated with shock were randomly selected from the emergency department of our hospital and divided into the observation group and the control group, with 40 members in each group. Patients in the observation group were treated with limited fluid resuscitation, while those in the control group were treated with aggressive fluid resuscitation. By comparing the therapeutic effects of the two groups, it is concluded that the therapeutic effect of the observation group is significantly better than that of the control group. Therefore, adopting limited fluid resuscitation in the clinical treatment of patients with multiple trauma complicated with shock can realize faster recovery, as well as protect patients’ coagulation function, effectively reducing complications and mortality. Moreover, it can also reduce the injury of trauma perfusion to the body, ensuring the recovery of patients

Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning

As a pivotal component to attaining generalizable solutions in human intelligence, reasoning provides great potential for reinforcement learning (RL) agents' generalization towards varied goals by summarizing part-to-whole arguments and discovering cause-and-effect relations. However, how to discover and represent causalities remains a huge gap that hinders the development of causal RL. In this paper, we augment Goal-Conditioned RL (GCRL) with Causal Graph (CG), a structure built upon the relation between objects and events. We novelly formulate the GCRL problem into variational likelihood maximization with CG as latent variables. To optimize the derived objective, we propose a framework with theoretical performance guarantees that alternates between two steps: using interventional data to estimate the posterior of CG; using CG to learn generalizable models and interpretable policies. Due to the lack of public benchmarks that verify generalization capability under reasoning, we design nine tasks and then empirically show the effectiveness of the proposed method against five baselines on these tasks. Further theoretical analysis shows that our performance improvement is attributed to the virtuous cycle of causal discovery, transition modeling, and policy training, which aligns with the experimental evidence in extensive ablation studies.Comment: 28 pages, 5 figures, under revie

Score-based Likelihood Ratios for Camera Device Identification

Many areas of forensics are moving away from the notion of classifying evidence simply as a match or non-match. Instead, some use score-based likelihood ratios (SLR) to quantify the similarity between two pieces of evidence, such as a fingerprint obtained from a crime scene and a fingerprint obtained from a suspect. We apply trace-anchored score-based likelihood ratios to the camera device identification problem. We use photo-response non-uniformity (PRNU) as a camera fingerprint and one minus the normalized correlation as a similarity score. We calculate trace-anchored SLRs for 10,000 images from seven camera devices from the BOSSbase image dataset. We include a comparison between our results the universal detector method

A Wild Manhunt for Stego Images Created by Modbile Apps.

Background: As mobile Internet and telecommunication technology develops at high speed, the digital image forensics academic community is facing a growing challenge. • Mobile applications (Apps) allow a user to easily edit/process an image for a variety of purposes. • Thanks to the improved cameras and editing apps on smartphones, the volume of images presented to digital image forensic practitioners increases every day. • Unfortunately, terrorists, spies and child pornography predators are also taking the advantage of the mobile app ecosystem to exchange illegal files and photos