Releasing the Unconsciousness | Visualizing the City

This thesis explores how subway stations lost their identity as strategic node of connectivity which constructed the prevailing image of New York City. In Civilization and Its Discontents (1930), Sigmund Freud famously compared the human mind to the city of Rome. He argues that both contain strata of memory and history which have accumulated over the years through a messy and ad-hoc process. Like Rome, New York City also has a layered history, albeit not as deep. This thesis contends that the subway entrance serves as an experiential entre into the unconscious experience of the unknown elements of the past. These subterranean city/mind experiences contribute to one\u27s image of the city. Building on Kevin Lynch\u27s argument that node serve as the strategic foci into which the observer can enter , the subway station is a concentration of doors of decisions )Kevin Lynch, The Image of the City, 1960: 72). However, subway stations are usually detached from the city\u27s above ground structure. Therefore, this thesis challenges the assumed ground/underground plane to integrate subway stations into the past and present urban contexts. This integration reinforces the connections between an already disconnected netherworld and the terrestrial world of Manhattan through the creation of new subway entrances that reveal the unconscious layer of the city. Two of the elements of Lynch\u27s image of the city that are emphasized, in this thesis, are node and district. Focusing on simulating these concepts requires the construction of an occupiable boundary between the node of the neighborhood and the subway stations. Focusing on the nodal point of the subway station reveal the possibility of it becoming the focal point of the district and thereby highlighting its stratified layers of unconscious memory. The dynamic relationship between the subway as a nodal point and the neighborhood as a district foregrounds the occupiable boundaries between the two and the way they create the image of the city

Joint Demosaicing and Denoising with Double Deep Image Priors

Demosaicing and denoising of RAW images are crucial steps in the processing pipeline of modern digital cameras. As only a third of the color information required to produce a digital image is captured by the camera sensor, the process of demosaicing is inherently ill-posed. The presence of noise further exacerbates this problem. Performing these two steps sequentially may distort the content of the captured RAW images and accumulate errors from one step to another. Recent deep neural-network-based approaches have shown the effectiveness of joint demosaicing and denoising to mitigate such challenges. However, these methods typically require a large number of training samples and do not generalize well to different types and intensities of noise. In this paper, we propose a novel joint demosaicing and denoising method, dubbed JDD-DoubleDIP, which operates directly on a single RAW image without requiring any training data. We validate the effectiveness of our method on two popular datasets -- Kodak and McMaster -- with various noises and noise intensities. The experimental results show that our method consistently outperforms other compared methods in terms of PSNR, SSIM, and qualitative visual perception

Predicting the Future of the CMS Detector: Crystal Radiation Damage and Machine Learning at the LHC

The 75,848 lead tungstate crystals in CMS experiment at the CERN Large Hadron Collider are used to measure the energy of electrons and photons produced in the proton-proton collisions. The optical transparency of the crystals degrades slowly with radiation dose due to the beam-beam collisions. The transparency of each crystal is monitored with a laser monitoring system that tracks changes in the optical properties of the crystals due to radiation from the collision products. Predicting the optical transparency of the crystals, both in the short-term and in the long-term, is a critical task for the CMS experiment. We describe here the public data release, following FAIR principles, of the crystal monitoring data collected by the CMS Collaboration between 2016 and 2018. Besides describing the dataset and its access, the problems that can be addressed with it are described, as well as an example solution based on a Long Short-Term Memory neural network developed to predict future behavior of the crystals

Radiomics-Informed Deep Learning for Classification of Atrial Fibrillation Sub-Types from Left-Atrium CT Volumes

Atrial Fibrillation (AF) is characterized by rapid, irregular heartbeats, and can lead to fatal complications such as heart failure. The disease is divided into two sub-types based on severity, which can be automatically classified through CT volumes for disease screening of severe cases. However, existing classification approaches rely on generic radiomic features that may not be optimal for the task, whilst deep learning methods tend to over-fit to the high-dimensional volume inputs. In this work, we propose a novel radiomics-informed deep-learning method, RIDL, that combines the advantages of deep learning and radiomic approaches to improve AF sub-type classification. Unlike existing hybrid techniques that mostly rely on na\"ive feature concatenation, we observe that radiomic feature selection methods can serve as an information prior, and propose supplementing low-level deep neural network (DNN) features with locally computed radiomic features. This reduces DNN over-fitting and allows local variations between radiomic features to be better captured. Furthermore, we ensure complementary information is learned by deep and radiomic features by designing a novel feature de-correlation loss. Combined, our method addresses the limitations of deep learning and radiomic approaches and outperforms state-of-the-art radiomic, deep learning, and hybrid approaches, achieving 86.9% AUC for the AF sub-type classification task. Code is available at https://github.com/xmed-lab/RIDL.Comment: Accepted by MICCAI2

Genetic Diversity Analysis of Hypsizygus marmoreus

Hypsizygus marmoreus is an industrialized edible mushroom. In the present paper, the genetic diversity among 20 strains collected from different places of China was evaluated by target region amplification polymorphism (TRAP) analysis; the common fragment of TRAPs was sequenced and analyzed. Six fixed primers were designed based on the analysis of H. marmoreus sequences from GenBank database. The genomic DNA extracted from H. marmoreus was amplified with 28 TRAP primer combinations, which generated 287 bands. The average of amplified bands per primer was 10.27 (mean polymorphism is 69.73%). The polymorphism information content (PIC) value for TRAPs ranged from 0.32 to 0.50 (mean PIC value per TRAP primer combination is 0.48), which indicated a medium level of polymorphism among the strains. A total of 36 sequences were obtained from TRAP amplification. Half of these sequences could encode the known or unknown proteins. According to the phylogenetic analysis based on TRAP result, the 20 strains of H. marmoreus were classified into two main groups

Acute combined effects of concurrent physical activities on autonomic nervous activation during cognitive tasks

Backgrounds: The validity of heart rate variability (HRV) has been substantiated in mental workload assessments. However, cognitive tasks often coincide with physical exertion in practical mental work, but their synergic effects on HRV remains insufficiently established. The study aims were to investigate the combined effects of cognitive and physical load on autonomic nerve functions.Methods: Thirty-five healthy male subjects (aged 23.5 ± 3.3 years) were eligible and enrolled in the study. The subjects engaged in n-back cognitive tasks (1-back, 2-back, and 3-back) under three distinct physical conditions, involving isotonic contraction of the left upper limb with loads of 0 kg, 3 kg, and 5 kg. Electrocardiogram signals and cognitive task performance were recorded throughout the tasks, and post-task assessment of subjective experiences were conducted using the NASA-TLX scale.Results: The execution of n-back tasks resulted in enhanced perceptions of task-load feelings and increased reaction times among subjects, accompanied by a decline in the accuracy rate (p < 0.05). These effects were synchronously intensified by the imposition of physical load. Comparative analysis with a no-physical-load scenario revealed significant alterations in the HRV of the subjects during the cognitive task under moderate and high physical conditions. The main features were a decreased power of the high frequency component (p < 0.05) and an increased low frequency component (p < 0.05), signifying an elevation in sympathetic activity. This physiological response manifested similarly at both moderate and high physical levels. In addition, a discernible linear correlation was observed between HRV and task-load feelings, as well as task performance under the influence of physical load (p < 0.05).Conclusion: HRV can serve as a viable indicator for assessing mental workload in the context of physical activities, making it suitable for real-world mental work scenarios

Rethink Transfer Learning in Medical Image Classification

Transfer learning (TL) with deep convolutional neural networks (DCNNs) is crucial for modern medical image classification (MIC). However, the current practice of finetuning the entire pretrained model is puzzling, as most MIC tasks rely only on low- to mid-level features that are learned by up to mid layers of DCNNs. To resolve the puzzle, we perform careful empirical comparisons of several existing deep and shallow models, and propose a novel truncated TL method that consistently leads to comparable or superior performance and compact models on two MIC tasks. Our results highlight the importance of transferring the right level of pretrained visual features commensurate with the intrinsic complexity of the task

Genome-Wide Analysis of the Zn(II)₂Cys₆ Zinc Cluster-Encoding Gene Family in <i>Tolypocladium</i> <i>guangdongense</i> and Its Light-Induced Expression

The Zn(II)2Cys6 zinc cluster gene family is a subclass of zinc-finger proteins, which are transcriptional regulators involved in a wide variety of biological processes in fungi. We performed genome-wide identification and characterization of Zn(II)2Cys6 zinc-cluster gene (C6 zinc gene) family in Tolypocladium guangdongense, Cordyceps militaris and Ophiocordyceps sinensis. Based on the structures of the C6 zinc domains, these proteins were observed to be evolutionarily conserved in ascomycete fungi. We focused on T. guangdongense, a medicinal fungus, and identified 139 C6 zinc genes which could be divided into three groups. Among them, 49.6% belonged to the fungal specific transcriptional factors, and 16% had a DUF3468 domain. Homologous and phylogenetic analysis indicated that 29 C6 zinc genes were possibly involved in the metabolic process, while five C6 zinc genes were supposed to be involved in asexual or sexual development. Gene expression analysis revealed that 54 C6 zinc genes were differentially expressed under light, including two genes that possibly influenced the development, and seven genes that possibly influenced the metabolic processes. This indicated that light may affect the development and metabolic processes, at least partially, through the regulation of C6 zinc genes in T. guangdongense. Our results provide comprehensive data for further analyzing the functions of the C6 zinc genes

Whole Genome Sequence of an Edible and Potential Medicinal Fungus, Cordyceps guangdongensis

Cordyceps guangdongensis is an edible fungus which was approved as a novel food by the Chinese Ministry of Public Health in 2013. It also has a broad prospect of application in pharmaceutical industries, with many medicinal activities. In this study, the whole genome of C. guangdongensis GD15, a single spore isolate from a wild strain, was sequenced and assembled with Illumina and PacBio sequencing technology. The generated genome is 29.05 Mb in size, comprising nine scaffolds with an average GC content of 57.01%. It is predicted to contain a total of 9150 protein-coding genes. Sequence identification and comparative analysis indicated that the assembled scaffolds contained two complete chromosomes and four single-end chromosomes, showing a high level assembly. Gene annotation revealed a diversity of transposons that could contribute to the genome size and evolution. Besides, approximately 15.57% and 12.01% genes involved in metabolic processes were annotated by KEGG and COG respectively. Genes belonging to CAZymes accounted for 3.15% of the total genes. In addition, 435 transcription factors, involved in various biological processes, were identified. Among the identified transcription factors, the fungal transcription regulatory proteins (18.39%) and fungal-specific transcription factors (19.77%) represented the two largest classes of transcription factors. This genomic resource provided a new insight into better understanding the relevance of phenotypic characters and genetic mechanisms in C. guangdongensis

False positive elimination in intrusion detection based on clustering

In order to solve the problem of high false positive in network intrusion detection systems, we adopted clustering algorithms, the K-means algorithm and the Fuzzy C Mean (FCM) algorithm, to identify false alerts, to reduce invalid alerts and to purify alerts for a better analysis. In this paper, we first introduced typical clustering algorithms, including the partition clustering, the hierarchical clustering, the density and grid clustering, and the fuzzy clustering, and then analyzed their feasibilities in security data processing. Furthermore, we introduced an intrusion detection framework, and tested the validity and feasibility of false positive elimination in intrusion detection. The process steps of false positive elimination were clearly described, and additionally, two typical clustering algorithms, the K-means algorithm and the FCM algorithm, were implemented for false alerts identification and filtration. Also, we defined three evaluation indexes: the elimination rate, the false elimination rate and the miss elimination rate. Accordingly, we used DARPA 2000 LLDOS1.0 dataset for our experiments, and adopted Snort as our intrusion detection system. Eventually, the results showed that the method proposed by us has a satisfactory validity and feasibility in false positive elimination, and the clustering algorithms we adopted can achieve a high elimination rate