An inclusive approach to art appreciation through visual-taste cross-sensory design by exploration with Kandinsky’s Grey Circle

Cross-sensory translation enables people with diverse abilities to perceive and experience art through sensory modalities other than vision alone. While it is not difficult to find museums or art galleries that are beginning to incorporate auditory, tactile, olfactory, or even gustatory senses into their curatorial practice, findings from recent work on cross-modal correspondences have not been applied to the challenge of “translating” a specific visual artwork to taste sensations. This project seeks to explore a more inclusive approach to artwork appreciation by transforming visual experiences into tasting experiences, thus expanding the perceptual dimensions of artworks for a broader audience. First, this study synthesizes prior work on plastic semiotics with empirical findings from research on cross-modal correspondences to produce a conceptual model that suggests how a sensorial reading of a painting can inform a chef’s “translation” from visual cues of an artwork into a culinary experience. Then, this study practically examined the validity of the conceptual model through interviews and a co-design session with culinary professionals, seeking to understand how they recommend translating Wassily Kandinsky’s painting Grey Circle into a culinary experience. The research findings show how the interpretations of the specific painting through taste modalities echoed the cross-modal mappings from visual to gustatory experiences suggested by the conceptual model. Finally, the study tests the extent to which people can perceive the properties of the given painting through a gustatory experience by hosting a tasting session. The result reveals that participants can receive the intended interpretations mediated by the curated foods’ properties, which are designed to reflect and afford the sensorial expressions in the painting. Furthermore, the gustatory experience also helps visitors dive deeper into the artwork to a certain extent

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

Raptor Encoding for Low-Latency Concurrent Multi-PDU Session Transmission with Security Consideration in B5G Edge Network

In B5G edge networks, end-to-end low-latency and high-reliability transmissions between edge computing nodes and terminal devices are essential. This paper investigates the queue-aware coding scheduling transmission of randomly arriving data packets, taking into account potential eavesdroppers in edge networks. To address these concerns, we introduce SCLER, a Protocol Data Units (PDU) Raptor-encoded multi-path transmission method that overcomes the challenges of a larger attack surface in Concurrent Multipath Transfer (CMT), excessive delay due to asymmetric delay\&bandwidth, and lack of interaction among PDU session bearers. We propose a secure and reliable transmission scheme based on Raptor encoding and distribution that incorporates a queue length-aware encoding strategy. This strategy is modeled using Constrained Markov Decision Process (CMDP), and we solve the constraint optimization problem of optimal decision-making based on a threshold strategy. Numerical results indicate that SCLER effectively reduces data leakage risks while achieving the optimal balance between delay and reliability, thereby ensuring data security. Importantly, the proposed system is compatible with current mobile networks and demonstrates practical applicability

The efficacy and safety of quantitative flow ratio-guided complete revascularization in patients with ST-segment elevation myocardial infarction and multivessel disease: A pilot randomized controlled trial

Background: In patients with ST-segment elevation myocardial infarction (STEMI) and multivessel disease (MVD), the treatment strategy for non-infarct-related artery (non-IRA) remains controversial. Quantitative flow ratio (QFR) is a new angiography-based physiological assessment index. However, there is little evidence on the practical clinical application of QFR. Methods: Two hundred and twenty-nine patients with STEMI and MVD were recruited for this study. Patients were randomly assigned to either receive QFR-guided complete revascularization (QFR-G-CR) of non-IRA or receive no further invasive treatment. The primary (1º) endpoint analyzed included death due to all causes, non-fatal myocardial infarction (MI), and ischemia-induced revascularization at 12 months post-surgery. Secondary (2º) endpoints included cardiovascular death, unstable angina, stent thrombosis, New York Heart Association (NYHA) class IV heart failure (HF), and stroke at 1 year post surgery. Massive bleeding and contrast-associated acute kidney injury (CAKI) were used as safety endpoints. Results: Around the 12 month follow up, the 1º outcome was recorded in 11/115 patients (9.6%) in the QFR-G-CR population, relative to 23/114 patients (20.1%) in the IRA-only PCI population (hazard ratio [HR]: 0.45; 95% confidence interval [CI]: 0.22–0.92; p = 0.025). Unstable angina in 6 (5.2%) and 16 (14.0%) patients (HR: 0.36; 95% CI: 0.14–0.92; p = 0.026), respectively. No marked alterations were found in the massive bleeding and CAKI categories. Conclusions: In conclusion, STEMI and MVD patients can benefit from QFR-G-CR of non-IRA lesions in the initial stages of acute MI. This can help reduce incidences of major adverse cardiovascular events and unstable angina, relative to IRA treatment only. Chinese Clinical Trial Registration number: ChiCTR2100044120

Pathogenic characteristics and drug resistance spectrum of Enteroaggregative Escherichia coli in infant food in Liaoning Province from 2018 to 2020

ObjectiveTo give a solid foundation for the epidemiological data of diarrheagenic Escherichia coli （DEC）， and provide a basis for the rational drug use for foodborne diseases caused by DEC， the continuous monitoring of DEC from 2018 to 2020 in Liaoning Province was carried out. The pathogenic characteristics and drug sensitivity characteristics of DEC were investigated.MethodsA total of 208 infant food in Liaoning Province were collected， from which Enterobacteriaceae was isolated， and 16S rRNA gene sequencing analysis and biochemical identification were carried out. The virulence genes of E.coli were detected. The serotype and drug resistance spectrum of EAEC were identified.ResultsIdentified species of Enterobacteriaceae by 16S rRNA gene sequencing method was consistent with the biochemical results. The detection rate of EAEC in infant food was high， and the detection rate of virulence gene pic of EAEC was high. Virulence genotyping was consistent with the serum typing. According to the virulence gene carried by Escherichia coli， 25 strains of EAEC were detected， of which 40% of the strains （serotype O134：H9） carried virulence gene pic， 28% of the strains （serotype O3：H2） carried virulence gene aggR and astA， 16% of the strains （serotype O9：H6） carried virulence gene aggR， and 16% of the strains （serotype O62：H7） carried virulence gene astA. The carrying rate of EAEC virulence gene in 78 strains was 32.1%. The drug resistance of 25 EAEC strains was not optimistic， and there were multiple drug-resistant strains. The resistance was mainly for β lactams， macrolides， quinolones and tetracyclines.ConclusionEAEC was the main contamination of Escherichia coli in infant food in Liaoning Province， and had high drug resistance， which need more attention to be paid to

Efficient photocatalytic degradation of Malachite Green in seawater by the hybrid of Zinc-Oxide Nanorods Grown on Three-Dimensional (3D) reduced graphene oxide(RGO)/Ni foam

A hybrid of ZnO nanorods grown onto three-dimensional (3D) reduced graphene oxide (RGO)@Ni foam (ZnO/RGO@NF) is synthesized by a facile hydrothermal method. The as-prepared hybrid material is physically characterized by SEM, XRD, Raman, and X-ray photoelectron spectroscopy (XPS).When the as-prepared 3D hybrid is investigated as a photocatalyst, it demonstrates significant high photocatalytic activity for the degradation of methylene blue (MB), rhodamine (RhB), and mixed MB/RhB as organic dye pollutants. In addition, the practical application and the durability of the as-prepared catalyst to degradation of malachite green (MG) in seawater are firstly assessed in a continuous flow system. The catalyst shows a high degradation efficiency and stable photocatalytic activity for 5 h continuous operation, which should be a promising catalyst for the degradation of organic dyes in seawater

Incremental value of non-invasive myocardial work for the evaluation and prediction of coronary microvascular dysfunction in angina with no obstructive coronary artery disease

BackgroundEvidence suggests that patients suffering from angina with no obstructive coronary artery disease (ANOCA) experience coronary microvascular dysfunction (CMD). We aimed to understand the diagnosis value of noninvasive myocardial work indices (MWIs) with left ventricular pressure-strain loop (LV PSL) by echocardiography in ANOCA patients with CMD.Methods97 patients with ANOCA were recruited. All subjects underwent standard echocardiography with traditional ultrasound parameters, two-dimensional speckle-tracking echocardiography with global longitudinal strain (GLS), LV PSL with MWIs include global work index (GWI), global constructive work (GCW), global waste work (GWW) and global work efficiency (GWE). In addition, all enrolled cases underwent high-dose adenosine stress echocardiography (SE) with coronary flow velocity reserve (CFVR). CMD was defined as CFVR <2.0.ResultsOf the 97 patients with ANOCA, 52 were placed in the CMD group and 45 in the control group. GWI and GCW were decreased significantly in the CMD group compared with the control group (P < 0.001 for both). GWI and GCW were moderately correlated with CFVR (r = 0.430, P < 0.001 and r = 0.538, P < 0.001, respectively). In the multiple logistic regression analyses, GCW was identified as the only independent echocardiography parameter associated with CMD after adjusting for age and baseline APV [OR (95%CI) 1.009 (1.005–1.013); P < 0.001]. Moreover, the best predictor of CMD in patients with ANOCA using receiver operating characteristic (ROC) curve was GWI and GCW, with an area under the curve (AUC) of 0.800 and 0.832, sensitivity of 67.3% and 78.8%, specificity of 80.0% and 75.6%, respectively.ConclusionMWIs with LV PSL is a new method to detect LV systolic function noninvasively in ANOCA patients with CMD

CancerUniT: Towards a Single Unified Model for Effective Detection, Segmentation, and Diagnosis of Eight Major Cancers Using a Large Collection of CT Scans

Human readers or radiologists routinely perform full-body multi-organ multi-disease detection and diagnosis in clinical practice, while most medical AI systems are built to focus on single organs with a narrow list of a few diseases. This might severely limit AI's clinical adoption. A certain number of AI models need to be assembled non-trivially to match the diagnostic process of a human reading a CT scan. In this paper, we construct a Unified Tumor Transformer (CancerUniT) model to jointly detect tumor existence & location and diagnose tumor characteristics for eight major cancers in CT scans. CancerUniT is a query-based Mask Transformer model with the output of multi-tumor prediction. We decouple the object queries into organ queries, tumor detection queries and tumor diagnosis queries, and further establish hierarchical relationships among the three groups. This clinically-inspired architecture effectively assists inter- and intra-organ representation learning of tumors and facilitates the resolution of these complex, anatomically related multi-organ cancer image reading tasks. CancerUniT is trained end-to-end using a curated large-scale CT images of 10,042 patients including eight major types of cancers and occurring non-cancer tumors (all are pathology-confirmed with 3D tumor masks annotated by radiologists). On the test set of 631 patients, CancerUniT has demonstrated strong performance under a set of clinically relevant evaluation metrics, substantially outperforming both multi-disease methods and an assembly of eight single-organ expert models in tumor detection, segmentation, and diagnosis. This moves one step closer towards a universal high performance cancer screening tool.Comment: ICCV 2023 Camera Ready Versio