Abstraction-Based Verification of Approximate Pre-Opacity for Control Systems

In this paper, we consider the problem of verifying pre-opacity for discrete-time control systems. Pre-opacity is an important information-flow security property that secures the intention of a system to execute some secret behaviors in the future. Existing works on pre-opacity only consider non-metric discrete systems, where it is assumed that intruders can distinguish different output behaviors precisely. However, for continuous-space control systems whose output sets are equipped with metrics (which is the case for most real-world applications), it is too restrictive to assume precise measurements from outside observers. In this paper, we first introduce a concept of approximate pre-opacity by capturing the security level of control systems with respect to the measurement precision of the intruder. Based on this new notion of pre-opacity, we propose a verification approach for continuous-space control systems by leveraging abstraction-based techniques. In particular, a new concept of approximate pre-opacity preserving simulation relation is introduced to characterize the distance between two systems in terms of preserving pre-opacity. This new system relation allows us to verify pre-opacity of complex continuous-space control systems using their finite abstractions. We also present a method to construct pre-opacity preserving finite abstractions for a class of discrete-time control systems under certain stability assumptions.Comment: Discrete Event Systems, Opacity, Formal Abstraction

Ecology of Yuqing County Carbon Sink Calculation and Ecosystem Protection Measures

Based on the remote sensing statistical data of land use of terrestrial ecosystems in Yuqing County, this paper calculates the amount of carbon sinks in the county according to the existing carbon sink carbon density index, compares the amount of different types of carbon sinks, and analyzes their respective carbon sink potential. The results show that the forest carbon sink is the largest, about 2.2 million tons, accounting for 75% of the total carbon sink in the county, showing the great potential of forest vegetation to absorb CO2 through photosynthesis, followed by the carbon sink produced by dry land (cultivated land), about 400,000 tons, accounting for 13% of the total carbon sink in the county; Although the amount of wetland aquatic carbon sink is small, its carbon density is very large, and it has the advantages of short renewal time and fast carbon sink, so it has great potential and can be artificially regulated to increase carbon sink. Based on the above research and analysis, combined with the spirit of the national carbon peak and carbon neutral policy and the natural law of ecosystem development, three measures to protect and increase carbon sinks in terrestrial ecosystems were put forward: (1) continuing to carry out forestry planting and do a good job in forestry protection; (2) stabilizing the surface water area and developing aquatic carbon sinks; (3) Establish a long-term monitoring system to ensure the contribution of carbon sinks, provide support for the protection of ecosystem and the development of carbon sink potential in Yuqing County from two aspects of science and management, and compare the amount of different types of carbon sinks, and analyze their carbon sink potential. On this basis, combined with the spirit of the national carbon peak and carbon neutral policy and the natural law of ecosystem development, three kinds of terrestrial ecosystem carbon sink protection and increase wording were put forward accordingly, which provided support for ecosystem protection and carbon sink potential development in Yuqing County from two aspects of science and management

Signaling pathways in the development of infantile hemangioma

Infantile hemangioma (IH), which is the most common tumor in infants, is a benign vascular neoplasm resulting from the abnormal proliferation of endothelial cells and pericytes. For nearly a century, researchers have noted that IH exhibits diverse and often dramatic clinical behaviors. On the one hand, most lesions pose no threat or potential for complication and resolve spontaneously without concern in most children with IH. On the other hand, approximately 10% of IHs are destructive, disfiguring and even vision- or life-threatening. Recent studies have provided some insight into the pathogenesis of these vascular tumors, leading to a better understanding of the biological features of IH and, in particular, indicating that during hemangioma neovascularization, two main pathogenic mechanisms prevail, angiogenesis and vasculogenesis. Both mechanisms have been linked to alterations in several important cellular signaling pathways. These pathways are of interest from a therapeutic perspective because targeting them may help to reverse, delay or prevent hemangioma neovascularization. In this review, we explore some of the major pathways implicated in IH, including the VEGF/VEGFR, Notch, β-adrenergic, Tie2/angiopoietins, PI3K/AKT/mTOR, HIF-α-mediated and PDGF/PDGF-R-β pathways. We focus on the role of these pathways in the pathogenesis of IH, how they are altered and the consequences of these abnormalities. In addition, we review the latest preclinical and clinical data on the rationally designed targeted agents that are now being directed against some of these pathways

In Search of the Long-Tail: Systematic Generation of Long-Tail Knowledge via Logical Rule Guided Search

Since large language models have approached human-level performance on many tasks, it has become increasingly harder for researchers to find tasks that are still challenging to the models. Failure cases usually come from the long-tail distribution - data that an oracle language model could assign a probability on the lower end of its distribution. Current methodology such as prompt engineering or crowdsourcing are insufficient for creating long-tail examples because humans are constrained by cognitive bias. We propose a Logic-Induced-Knowledge-Search (LINK) framework for systematically generating long-tail knowledge statements. Grounded by a symbolic rule, we search for long-tail values for each variable of the rule by first prompting a LLM, then verifying the correctness of the values with a critic, and lastly pushing for the long-tail distribution with a reranker. With this framework we construct a dataset, Logic-Induced-Long-Tail (LINT), consisting of 200 symbolic rules and 50K knowledge statements spanning across four domains. Human annotations find that 84% of the statements in LINT are factually correct. In contrast, ChatGPT and GPT4 struggle with directly generating long-tail statements under the guidance of logic rules, each only getting 56% and 78% of their statements correct. Moreover, their "long-tail" generations in fact fall into the higher likelihood range, and thus are not really long-tail. Our findings suggest that LINK is effective for generating data in the long-tail distribution while enforcing quality. LINT can be useful for systematically evaluating LLMs' capabilities in the long-tail distribution. We challenge the models with a simple entailment classification task using samples from LINT. We find that ChatGPT and GPT4's capability in identifying incorrect knowledge drop by ~3% in the long-tail distribution compared to head distribution

Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain. Prior works have achieved this goal by jointly training one model on multi-site datasets, which achieve competitive performance on average but such methods rely on the assumption about the availability of all training data, thus limiting its effectiveness in practical deployment. In this paper, we propose a novel multi-site segmentation framework called incremental-transfer learning (ITL), which learns a model from multi-site datasets in an end-to-end sequential fashion. Specifically, "incremental" refers to training sequentially constructed datasets, and "transfer" is achieved by leveraging useful information from the linear combination of embedding features on each dataset. In addition, we introduce our ITL framework, where we train the network including a site-agnostic encoder with pre-trained weights and at most two segmentation decoder heads. We also design a novel site-level incremental loss in order to generalize well on the target domain. Second, we show for the first time that leveraging our ITL training scheme is able to alleviate challenging catastrophic forgetting problems in incremental learning. We conduct experiments using five challenging benchmark datasets to validate the effectiveness of our incremental-transfer learning approach. Our approach makes minimal assumptions on computation resources and domain-specific expertise, and hence constitutes a strong starting point in multi-site medical image segmentation

Ferroptosis in inflammatory arthritis: A promising future

Ferroptosis is a kind of regulatory cell death (RCD) caused by iron accumulation and lipid peroxidation, which is characterized by mitochondrial morphological changes and has a complex regulatory network. Ferroptosis has been gradually emphasized in the pathogenesis of inflammatory arthritis. In this review, we summarized the relevant research on ferroptosis in various inflammatory arthritis including rheumatoid arthritis (RA), osteoarthritis, gout arthritis, and ankylosing spondylitis, and focused on the relationship between RA and ferroptosis. In patients with RA and animal models of RA, there was evidence of iron overload and lipid peroxidation, as well as mitochondrial dysfunction that may be associated with ferroptosis. Ferroptosis inducers have shown good application prospects in tumor therapy, and some anti-rheumatic drugs such as methotrexate and sulfasalazine have been shown to have ferroptosis modulating effects. These phenomena suggest that the role of ferroptosis in the pathogenesis of inflammatory arthritis will be worth further study. The development of therapeutic strategies targeting ferroptosis for patients with inflammatory arthritis may be a promising future