Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Complex Systems Analysis to Identify Organizational Interventions

Using Complex Systems Analysis to Identify Organizational Interventions Sebastian Benthall Information Law Institute NYU School of Law The IETF has historically emphasized the individuality of its participants. Consider this summary statement from “The Tao of IETF”: “Participation in the IETF or of its WGs is not fee-based or organizationally defined, but is based upon self-identification and active participation by individuals. ” 1 Nevertheless, it is well known that many participants in IETF are operating on behalf of their employer. This potentially dual nature of IETF participation raises interesting normative questions. Are IETF participants, as individuals, expected to make arguments and deploy reasons in a kind of Habermasian discourse for the public’s interest? (Froomkin, 2002) Or is the IETF rather the site of contesting, powerful commercial interests? (Kuerbis & Mueller, 2011) Does the latter tendency erode the IETF’s legitimacy as a standards organization? This proposal does not aim to answer these questions. Rather, it addresses a descriptive question of whether it is possible to detect, empirically, when an organization is acting within the IETF through individuals that it employs, as opposed to letting those employees act in a more personal capacity. I aim to address this question using BigBang (Benthall, 2015), an open source toolkit for studying the dynamics of collaboration that has been used to instrument and analyze Internet standards setting organizations. (ten Oever, N., Milan, S., & Beraldo, D., 2020) To explore this hypothesis, I will draw on structural aspects of public IETF data. In particular, the IETF is organized into a number of different working groups, each of which has a set of (often overlapping) individual participants. These participants are identified individually, but may also have an affiliation that is either explicit (in a RFC authorship listing) or implicit (in an email address domain). Hence, IETF data can be viewed at multiple different scales of analysis: individual, email domain, and working group. I draw on the general complex systems theory result of Siegenfeld and Bar-Yam (2020) that systems can vary by their complexity profile , the plot of their complexity at different scales, depending on whether they are “random”, “coordinated”, or “coherent” systems. The actions of individuals that are acting on behalf of an organization will be more coherent -appearing with complexity that is similar across different scales of analysis -than the actions of individuals who are interacting more randomly. In other words, those acting as individuals should be identifiable by virtue of how they do not contribute to complexity at higher scales of analysis. Preliminary results have been promising. An analysis of email domains used in IETF working group mailing lists has shown that representativeemail domains that are typed as either generic hosts (gmail.com, gmx.de), companies (apple.com, cisco.com), or personal (mnot.net,
A Study on the Contribution of Medial and Lateral Longitudinal Foot Arch to Human Gait

This study aimed to investigate the contribution of medial longitudinal arch and lateral longitudinal arch in human gait and to study the correlation between foot features and gait characteristics. The foot arch plays a significant role in human movements, and understanding its contribution to spatiotemporal gait parameters is vital in predicting and rectifying gait patterns. To serve the objectives, the study developed a new foot feature measurement system and measured the foot features and spatiotemporal gait parameters of 17 young healthy subjects without any foot structure abnormality. The foot-feature parameters were measured under three movement conditions which were sitting, standing, and one-leg standing conditions. The spatiotemporal gait parameters were measured at three speeds which were fast, preferred, and slow speeds. The correlation study showed that medial longitudinal arch characteristics were found to be associated with temporal gait parameters while lateral longitudinal arch characteristics were found to be associated with spatial gait parameters. The developed system not only eases the burden of manual measuring but also secures accuracy of the collected data. Inviting variety of subjects including athletes and people with abnormal foot structures would extend the scope of this study in the future. The findings of this study break new ground in the field of the foot- and gait-related research work.Clinical Relevance—This study demonstrated that the medial longitudinal arch and lateral longitudinal arch characteristics were related to the temporal and spatial gait parameters, respectively. These underlying findings can be applied to investigate relationships between foot abnormality and gait characteristics.
Predictors of Adverse Human Responses to Traffic Noise

A field study was carried out measuring the responses to traffic noise of 1150 subjects at 46 different sites. The merits of a number of statistical noise measures were compared in terms of the accuracy with which they predicted responses. Measures most representative of only local traffic noise were most successful and included L 1 0 and L e q values. The inclusion of measures of noise level fluctuations was found to be of minimal importance, and suggested that increased levels of fluctuations decreased adverse responses. The difference between day and night time noise levels also had only a small influence on responses and was largely due to the more disturbing nature of night time noise. Vehicle flow measures, particularly the logarithm of the total vehicle flow rate, were found to quite accurately predict responses. Results supported the concept of disturbance relating to noise measures representative of the energy equivalent level of only the particular noise in question (here traffic noise) as a potentially unifying basis for a wide range of statistical noise measures.
Sequence analysis of chloroplast psbA-trnH region in Citrus L. (Rutaceae) species from the Aegean region of Turkey

The aim of the study was to analyze the sequence of some citrus species found in the Aegean Sea region of Turkey based on the psbA-trnH cpDNA region. Genomic DNA was isolated from healthy and green leaves. Total genomic DNA was extracted using the GeneMark DNA isolation Plant Kit. The psbA-trnH region of chloroplast DNA was amplified using primers psbA and trnH. DNA sequences were edited using the Sequencher 5.4. 6 and sequencing data were analyzed using the MEGA 6.0 software. The maximum likelihood (ML) tree was created to determine the relationships between Citrus taxa. Amplified cpDNA psbA-trnH sequences ranged between 426 and 470 nucleotides. The maximum likelihood phylogenetic tree was composed of two clades. The divergence values differed between 0.000 and 0.012. In addition, the sequences of some species belonging to the Rutaceae family were obtained from NCBI and a maximum likelihood tree was constructed. The phylogenetic relationship between the sequence data of some species belonging to the Rutaceae family taken from NCBI and Citrus species was revealed.
Over 1100 nm wavelength tuning in superstructure grating (SSG) DBR lasers

A broad range tuning of over 100 nm in tunable DBR lasers with superstructure grating (SSG) is reported. The SSG reflectors for 100 nm were designed and patterned by electron beam lithography. 1.55 μm DBR lasers with SSG reflector operate in a single mode with a tuning range of 83 nm under CW conditions. With inclusion of the multimode operating region, the tuning range becomes as wide as 103 nm.
A Transdiagnostic Biotype Detection Method for Schizophrenia and Autism Spectrum Disorder Based on Graph Kernel

Psychiatric diagnoses based on clinical manifestations are prone to be inaccurate. Schizophrenia (SZ) and autism spectrum disorder (ASD) were historically considered as the same disorder, and they still have many overlaps of clinical symptoms in the current standard. Therefore, there is an urgent need to explore the potential biotypes for them using neuroimaging measures such as brain functional connectivity (FC). However, previous studies have not effectively leveraged FC in detecting biotypes. Considering that graph theory helps reveal the topological information in FC, in this paper, we propose a graph kernel-based clustering method to explore transdiagnostic biotypes using FC estimated from functional magnetic resonance imaging (fMRI) data. In our method, frequent subnetworks are identified from the whole-brain FCs of all subjects, and then the graph kernel similarity is computed to measure the relationship between subjects for clustering. Based on fMRI data of 137 SZ and 150 ASD subjects, we obtained meaningful biotypes using our method, which shows significant differences between the identified biotypes in FC. In brief, our graph kernel-based clustering method is promising for transdiagnostic biotype detection.
CPUE standardization for the Pacific saury (Cololabis saira) fishery in the Northwest Pacific

In stock assessment and fisheries management, the standardization of catch per unit effort (CPUE) is critical to derive an effective index of the abundance of exploited fish stocks. In addition to developing various statistical models, a method including the relative fishing power (RFP) of individual vessels has been developed from an intuition concept and applied to bottom trawl fisheries using logbookbased data. This is a convenient and straightforward approach for standardizing the catch-effort data of a fishery. The Pacific saury is a pelagic fish species, and its stock in the Northwest Pacific has been targeted by international fleets since the early 1980s; annual production ranged from 328,000 to 630,000 tons during 2001-2016. The Pacific saury stock has been one of the resources managed by the North Pacific Fisheries Commission (NPFC) since 2015. In this study, the RFP method in CPUE standardization was applied to the Taiwanese Pacific saury fishery in the Northwest Pacific, and the effects of varying the parameters of the model were analyzed. The results indicate that more than 98.8% of the fishing effort can be standardized according to the estimated RFP, with 20 being the minimum number of required comparisons in each of the 4-year periods during 2001-2016. The RFP values of the model generally correlate with the length and gross tonnage of fishing vessels in the saury fishery. The result of the model, applied to the Pacific saury fishery, is robust when the model considers the effects of varying the model parameters (the minimum number of comparisons and standard vessel). This study suggests that first, the RFP method is appropriate for the CPUE standardization of the Pacific saury fishery, and that second, the resulting CPUE can be an effective index of abundance that reflects the annual variability of the Pacific saury stock in the Northwest Pacific.
Unsupervised Generative Adversarial Network for Plantar Pressure Image-to-Image Translation

Analyzing human gait from plantar pressure is critical for human health. The majority of works focus on classifying the healthy plantar pattern from unhealthy ones. Different from previous works, we adopt a generative adversarial network to produce healthy plantar pressure image for individual patients. In this work, we do not have pairs of images for training thus we cast the problem as an unsupervised generative adversarial learning task. Our network benefits from multiple components: an encoder-decoder generator, a convolution-based discriminator, a convolution-based evaluation network, and a new term in the loss function to preserve the person’s gait style. Our method achieves high performance (99.8%) on the CAD WALK databases which have patients with hallux valgus disease.
3D Dense Volumetric Network for Accurate Automated Pancreas Segmentation

Pancreatic cancer poses a great threat to our health with an overall five-year survival rate of 8%. Automatic and accurate segmentation of pancreas plays an important and prerequisite role in computer-assisted diagnosis and treatment. Due to the ambiguous pancreas borders and intertwined surrounding tissues, it is a challenging task. In this paper, we propose a novel 3D Dense Volumetric Network (3D2VNet) to improve the segmentation accuracy of pancreas organ. Firstly, 3D fully convolutional architecture is applied to effectively incorporate the 3D pancreas and geometric cues for volume-to-volume segmentation. Then, dense connectivity is introduced to preserve the maximum information flow between layers and reduce the overfitting on limited training data. In addition, a auxiliary side path is constructed to help the gradient propagation to stabilize the training process. Adequate experiments are conducted on a challenging pancreas dataset in Medical Segmentation Decathlon challenge. The results demonstrate our method can outperform other comparison methods on the task of automated pancreas segmentation using limited data.Clinical relevance—This paper proposes an accurate automated pancreas segmentation method, which can provide assistance to clinicians in the diagnosis and treatment of pancreatic cancer.
Altered Connection and Diagnosis Utility of White Matter in Alzheimer’s Disease: A Multi-site Automated Fiber Quantification Study

Alzheimer's disease (AD) is a typical neurodegenerative disease that is associated with cognitive decline, memory loss, and functional disconnection. Diffusion tensor imaging (DTI) has been widely used to investigate the integrity and degeneration of white matter in AD. In this study, with one of the world’s largest DTI biobanks (865 individuals), we aim to explore the diagnosis utility and stability of tractbased features (extracted by automated fiber quantification (AFQ) pipeline) in AD. First, we studied the clinical association of tract-based features by detecting AD-associated alterations of diffusion properties along fiber bundles. Then, a binary classification experiment between AD and normal controls was performed using tract-based diffusion properties as features and support vector machine (SVM) as a classifier with an independent site cross-validation strategy. The average accuracy of 77.90% (the highest was 88.89%) showed that white matter properties as biomarkers had a relatively stable role in the clinical diagnosis of AD.Clinical Relevance— White matter characteristics are valid and robust biomarkers of AD, which have high accuracy and generalizability in the AD diagnosis in a large multi-site dataset.
Recent Advances in the Endogenous Brain Renin-Angiotensin System and Drugs Acting on It

The RAS (renin-angiotensin system) is the part of the endocrine system that plays a prime role in the control of essential hypertension. Since the discovery of brain RAS in the seventies, continuous efforts have been put by the scientific committee to explore it more. The brain has shown the presence of various components of brain RAS such as angiotensinogen (AGT), converting enzymes, angiotensin (Ang), and specific receptors (ATR). AGT acts as the precursor molecule for Ang peptides—I, II, III, and IV—while the enzymes such as prorenin, ACE, and aminopeptidases A and N synthesize it. AT1, AT2, AT4, and mitochondrial assembly receptor (MasR) are found to be plentiful in the brain. The brain RAS system exhibits pleiotropic properties such as neuroprotection and cognition along with regulation of blood pressure, CVS homeostasis, thirst and salt appetite, stress, depression, alcohol addiction, and pain modulation. The molecules acting through RAS predominantly ARBs and ACEI are found to be effective in various ongoing and completed clinical trials related to cognition, memory, Alzheimer's disease (AD), and pain. The review summarizes the recent advances in the brain RAS system highlighting its significance in pathophysiology and treatment of the central nervous system-related disorders.
High-Resolution Magnetic Resonance Spectroscopic Imaging using a Multi-Encoder Attention U-Net with Structural and Adversarial Loss

Common to most medical imaging techniques, the spatial resolution of Magnetic Resonance Spectroscopic Imaging (MRSI) is ultimately limited by the achievable SNR. This work presents a deep learning method for 1H-MRSI spatial resolution enhancement, based on the observation that multi-parametric MRI images provide relevant spatial priors for MRSI enhancement. A Multi-encoder Attention U-Net (MAU-Net) architecture was constructed to process a MRSI metabolic map and three different MRI modalities through separate encoding paths. Spatial attention modules were incorporated to automatically learn spatial weights that highlight salient features for each MRI modality. MAU-Net was trained based on in vivo brain imaging data from patients with high-grade gliomas, using a combined loss function consisting of pixel, structural and adversarial loss. Experimental results showed that the proposed method is able to reconstruct high-quality metabolic maps with a high-resolution of 64×64 from a low-resolution of 16 × 16, with better performance compared to several baseline methods.
Functional Performance Evaluation of Hospital Radiology Facilities and Equipment Using FA and AHP: A Case Study

Result of evaluates the usage function at radiology facilities and equipment based on the professional opinions of radiology department relevant medical personnel in Taiwan, using of factor analysis (FA), analytic hierarchy process (AHP), and simple weight method is : The among the four evaluation dimensions for the usage function evaluation model of radiology facilities and equipment, the safety equipment dimension is far more important than the other so that needs to be improved (The total number of institutions, whose evaluation score falling below the dimension average value, are 9, 4, 7 and 5, respectively). Of the 17 medical institutions, the overall performance of the best (90.95) and the worst (61.47) the gap between is up to 47%.
A proof-of-concept study for the prediction of the de-novo atherosclerotic plaque development using finite elements*

The type of the atherosclerotic plaque has significant clinical meaning since plaque vulnerability depends on its type. In this work, we present a computational approach which predicts the development of new plaques in coronary arteries. More specifically, we employ a multi-level model which simulates the blood fluid dynamics, the lipoprotein transport and their accumulation in the arterial wall and the triggering of inflammation using convection-diffusion-reaction equations and in the final level, we estimate the plaque volume which causes the arterial wall thickening. The novelty of this work relies on the conceptual approach that using the information from 94 patients with computed tomography coronary angiography (CTCA) imaging at two time points we identify the correlation of the computational results with the real plaque components detected in CTCA. In the next step, we use these correlations to generate two types of de-novo plaques: calcified and non-calcified. Evaluation of the model’s performance is achieved using eleven patients, who present de-novo plaques at the follow-up imaging. The results demonstrate that the computationally generated plaques are associated significantly with the real plaques indicating that the proposed approach could be used for the prediction of specific plaque type formation.
Research on DC-DC power health management technology based on degradation test and deep learning

The health state of DC-DC power supply is the key factor to determine whether the electronic equipment can operate normally. The failure and deterioration of the power supply system will lead to the collapse of the entire electronic system. The research in this paper is based on the long-term high temperature degradation test data of a certain type of DC-DC power supply. The degradation law of power supply is studied by data preprocessing and noise reduction of sensitive parameters such as input current, output current, input voltage and output voltage. On this basis, we use the method of deep learning to model the efficiency of power supply in the process of degradation test. The experimental results show that the efficiency time series modeling of power supply degradation using LSTM method can effectively reflect the law of power supply efficiency degradation. Based on the DC-DC power health management technology combining degradation test and deep learning, the advanced fault prediction model is used to reflect the change law of power supply in the real degradation process. This method has certain theoretical and engineering value for power PHM modeling and application.
PREDICTING THE FUTURE OF STEALTH ATTACKS

This paper takes an in-depth look into the attack strategies of recent rootkits and analyses what has worked for them. In doing so it highlights some of the profi table attack methodologies from the perspective of kernel rootkits. The discussion in this paper about prediction of the future of stealth attacks is derived from our analysis of multiple rootkits over many years and also based on current trends and some specifi c techniques. The main aim of this discussion is to help reanalyse rootkit defences and decide what technological improvements (if any) are needed in current and future products to better combat the ever changing stealth threat landscape.
Fault diagnosis of rolling bearing based on multi-scale one-dimensional convolutional neural network

Aiming at the typical non-stationary and nonlinear characteristics of rolling bearing vibration signals, a multi-scale convolutional neural network method for bearing fault diagnosis based on wavelet transform and one-dimensional convolutional neural network is proposed. First, the signal is decomposed into multi scale components with wavelet transform, and then each scale component is reconstructed. The reconstructed signal is subjected to the Fourier transform to obtain the frequency spectrum representation, which is used as the input of the one-dimensional convolutional neural network. Finally, one-dimensional convolution neural network is used to learn the features of the input data and recognize the bearing fault. The performance of the model is verified by using data sets of rolling bearing. The results show that this method can intelligent feature extraction and obtain 99.94% diagnostic accuracy.