Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards retrospective motion correction and reconstruction for clinical 3D brain MRI protocols with a reference contrast

Motion artifacts often spoil the radiological interpretation of MR images, and in the most severe cases the scan needs be repeated, with additional costs for the provider. We discuss the application of a novel 3D retrospective rigid motion correction and reconstruction scheme for MRI, which leverages multiple scans contained in a MR session. Typically, in a multi-contrast MR session, motion does not equally affect all the scans, and some motion-free scans are generally available, so that we can exploit their anatomic similarity. The uncorrupted scan is used as a reference in a generalized rigid-motion registration problem to remove the motion artifacts affecting the corrupted scans. We discuss the potential of the proposed algorithm with a prospective in-vivo study and clinical 3D brain protocols. This framework can be easily incorporated into the existing clinical practice with no disruption to the conventional workflow.
Pre-acceleration in the Electron Foreshock. II. Oblique Whistler Waves

Thermal electrons have gyroradii many orders of magnitude smaller than the finite width of a shock, thus need to be pre-accelerated before they can cross it and be accelerated by diffusive shock acceleration. One region where pre-acceleration may occur is the inner foreshock, which upstream electrons must pass through before any potential downstream crossing. In this paper, we perform a large-scale particle-in-cell simulation that generates a single shock with parameters motivated from supernova remnants. Within the foreshock, reflected electrons excite the oblique whistler instability and produce electromagnetic whistler waves, which comove with the upstream flow and as nonlinear structures eventually reach radii of up to 5 ion-gyroradii. We show that the inner electromagnetic configuration of the whistlers evolves into complex nonlinear structures bound by a strong magnetic field around four times the upstream value. Although these nonlinear structures do not in general interact with cospatial upstream electrons, they resonate with electrons that have been reflected at the shock. We show that they can scatter, or even trap, reflected electrons, confining around 0.8% of the total upstream electron population to the region close to the shock where they can undergo substantial pre-acceleration. This acceleration process is similar to, yet approximately three times more efficient than, stochastic shock drift acceleration.
ClusTop: An unsupervised and integrated text clustering and topic extraction framework

Text clustering and topic extraction are two important tasks in text mining. Usually, these two tasks are performed separately. For topic extraction to facilitate clustering, we can first project texts into a topic space and then perform a clustering algorithm to obtain clusters. To promote topic extraction by clustering, we can first obtain clusters with a clustering algorithm and then extract cluster-specific topics. However, this naive strategy ignores the fact that text clustering and topic extraction are strongly correlated and follow a chicken-and-egg relationship. Performing them separately fails to make them mutually benefit each other to achieve the best overall performance. In this paper, we propose an unsupervised text clustering and topic extraction framework (ClusTop) which integrates text clustering and topic extraction into a unified framework and can achieve high-quality clustering result and extract topics from each cluster simultaneously. Our framework includes four components: enhanced language model training, dimensionality reduction, clustering and topic extraction, where the enhanced language model can be viewed as a bridge between clustering and topic extraction. On one hand, it provides text embeddings with a strong cluster structure which facilitates effective text clustering; on the other hand, it pays high attention on the topic related words for topic extraction because of its self-attention architecture. Moreover, the training of enhanced language model is unsupervised. Experiments on two datasets demonstrate the effectiveness of our framework and provide benchmarks for different model combinations in this framework.
Dissecting the active galactic nucleus in Circinus IV. MUSE NFM observations unveil a tuning-fork ionised outflow morphology

We present the ionised gas outflow morphology in the Circinus galaxy using the Narrow Field Mode (NFM) of the MUSE instrument on board the Very Large Telescope (VLT). The NFM observations provide a spatial resolution of ∼0.1″, corresponding to a physical scale of ∼2 pc, one of the highest spatial resolution achievable using ground-based AO-assisted observations in the optical wavelengths. The MUSE observations reveal a collimated clumpy outflow profile originating near the AGN location and extending up to 1.5″ (∼30 pc) in the NW direction. The collimated structure then fragments into two filaments, giving the entire outflowing gas a ‘tuning-fork’ morphology. These structures remain undetected in the lower spatial resolution MUSE Wide Field Mode data. We explain the origin of this tuning-fork structure to the interaction of the outflow with a dense clump in the interstellar medium (ISM) as the outflow propagates outward. The origin of the collimated structure itself could be from jet-ISM interactions on small scales. These observations also provide evidence to the origin of the ionised gas filaments previously observed in the Circinus galaxy out to kiloparsec scales. We find instantaneous and time-averaged mass outflow rates of 10−2 M⊙ yr−1 and 10−4 M⊙ yr−1, respectively. Based on the star formation rate in the Circinus galaxy reported in the literature, the observed ionised outflows are not expected to regulate star formation within the ∼100 pc scales probed by the NFM data.
PMT-IQA: Progressive Multi-task Learning for Blind Image Quality Assessment

Blind image quality assessment (BIQA) remains challenging due to the diversity of distortion and image content variation, which complicate the distortion patterns crossing different scales and aggravate the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and little research has been done on learning strategies to make the regression model produce better performance. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model's performance.
Asymmetric Co-teaching with Multi-view Consensus for Noisy Label Learning

Learning with noisy-labels has become an important research topic in computer vision where state-of-the-art (SOTA) methods explore: 1) prediction disagreement with co-teaching strategy that updates two models when they disagree on the prediction of training samples; and 2) sample selection to divide the training set into clean and noisy sets based on small training loss. However, the quick convergence of co-teaching models to select the same clean subsets combined with relatively fast overfitting of noisy labels may induce the wrong selection of noisy label samples as clean, leading to an inevitable confirmation bias that damages accuracy. In this paper, we introduce our noisy-label learning approach, called Asymmetric Co-teaching (AsyCo), which introduces novel prediction disagreement that produces more consistent divergent results of the co-teaching models, and a new sample selection approach that does not require small-loss assumption to enable a better robustness to confirmation bias than previous methods. More specifically, the new prediction disagreement is achieved with the use of different training strategies, where one model is trained with multi-class learning and the other with multi-label learning. Also, the new sample selection is based on multi-view consensus, which uses the label views from training labels and model predictions to divide the training set into clean and noisy for training the multi-class model and to re-label the training samples with multiple top-ranked labels for training the multi-label model. Extensive experiments on synthetic and real-world noisy-label datasets show that AsyCo improves over current SOTA methods.
DEVELOPMENT OF A TYPING SYSTEM TO DETERMINE MHC HAPLOTYPES IN THE ZEBRA

The outbreak of sarcoid in the Cape mountain zebra herds in South Africa, as the herds were becoming more inbred, raised the possibility of genetic influence. Since susceptibility to sarcoid has been described in different equine breeds all over the world in association with MHC haplotypes these herds were investigated for MHC types. Papillomavirus-induced tumours possessing associations with MHC class II haplotypes have also been encountered in other species. To establish an appropriate typing test, sufficiently simple to test a large amount of zebras, single strand conformational polymorphism (SSCP) of MHC class I and class II genes of zebras was investigated. Primers designed to amplify MHC class I and II genes on DNA samples of horses have been used for the amplification of MHC class I and II genes on DNA samples of Equus zebra hartmannae. The results demonstrated that class I genes were highly complex, class II DRA were not polymorphic among zebras and the primers for DQA failed to amplify products in the zebra. DRB and DQB could however be used to score SSCP gels with and without sarcoid.
TOWARDS TRANSIENT RESTORATION IN SCORE-INFORMED AUDIO DECOMPOSITION

Our goal is to improve the perceptual quality of transient signal components extracted in the context of music source separation. Many state-of-the-art techniques are based on applying a suitable decomposition to the magnitude of the Short-Time Fourier Transform (STFT) of the mixture signal. The phase information required for the reconstruction of individual component signals is usually taken from the mixture, resulting in a complex-valued, modified STFT (MSTFT). There are different methods for reconstructing a time-domain signal whose STFT approximates the target MSTFT. Due to phase inconsistencies, these reconstructed signals are likely to contain artifacts such as pre-echos preceding transient components. In this paper, we propose a simple, yet effective extension of the iterative signal reconstruction procedure by Griffin and Lim to remedy this problem. In a first experiment, under laboratory conditions, we show that our method considerably attenuates pre-echos while still showing similar convergence properties as the original approach. A second, more realistic experiment involving score-informed audio decomposition shows that the proposed method still yields improvements, although to a lesser extent, under non-idealized conditions.
Progressive Text-to-Image Generation

Recently, Vector Quantized AutoRegressive (VQ-AR) models have shown remarkable results in text-to-image synthesis by equally predicting discrete image tokens from the top left to bottom right in the latent space. Although the simple generative process surprisingly works well, is this the best way to generate the image? For instance, human creation is more inclined to the outline-to-fine of an image, while VQ-AR models themselves do not consider any relative importance of image patches. In this paper, we present a progressive model for high-fidelity text-to-image generation. The proposed method takes effect by creating new image tokens from coarse to fine based on the existing context in a parallel manner, and this procedure is recursively applied with the proposed error revision mechanism until an image sequence is completed. The resulting coarse-to-fine hierarchy makes the image generation process intuitive and interpretable. Extensive experiments in MS COCO benchmark demonstrate that the progressive model produces significantly better results compared with the previous VQ-AR method in FID score across a wide variety of categories and aspects. Moreover, the design of parallel generation in each step allows more than $\times 13$ inference acceleration with slight performance loss.
Fast and Accurate Graph Learning for Huge Data via Minipatch Ensembles

—Gaussian graphical models provide a powerful framework for uncovering conditional dependence relationships between sets of nodes; they have found applications in a wide variety of ﬁelds including sensor and communication networks, image processing and computer vision, physics, ﬁnance, and computational biology. Often, one observes data on the nodes and the task is to learn the graph structure, or perform graphical model selection. While this is a well-studied problem with many popular techniques, there are typically three major practical challenges: i) many existing algorithms become computationally intractable in huge-data settings with tens of thousands of nodes; ii) the need for separate data-driven hyperparameter tuning considerably adds to the computational burden; iii) the statistical accuracy of selected edges often deteriorates as the dimension and/or the complexity of the underlying graph structures increase. We tackle these problems by developing the novel Minipatch Graph (MPGraph) estimator. Our approach breaks up the huge graph learning problem into many smaller problems by creating an ensemble of tiny random subsets of both the observations and the nodes, termed minipatches. We then leverage recent advances that use hard thresholding to solve the latent variable graphical model problem to consistently learn the graph on each minipatch. Our approach is computationally fast, embarrassingly paral- lelizable, memory efﬁcient, and has integrated stability-based hyperparamter tuning. Additionally, we prove that under weaker assumptions than that of the Graphical Lasso, our MPGraph estimator achieves graph selection consistency. We compare our approach to state-of-the-art computational approaches for Gaussian graphical model selection including the BigQUIC algorithm, and empirically demonstrate that our approach is not only more statistically accurate but also extensively faster for huge graph learning problems.
External error modelling with combined model in terrestrial laser scanning

Terrestrial laser scanning (TLS) is a promising tool in geodetic applications. Data quality of outdoor TLS measurements is considerably limited by external errors caused by atmospheric variations and object related factors. In this study, external models related to atmospheric refraction, beam wander and incidence angle were introduced and integrated into a seven-parameter similarity transformation (7PST) model to compose a combined model. The combined model was rigorously adjusted by an iterative Gauss–Helmert (GH) model primarily to estimate external calibration parameters and transformation parameters simultaneously. In the application on a dam surface, a t-test showed that a significant external calibration parameter was the range scale, which mainly corresponded with the incidence angle model. The precision of estimated representative points was improved by approximately 50% with the proposed method. The presented models show a great potential for detecting external errors and registering multiple scans in outdoor applications.
Effects of peristaltic amplitude and frequency on gastric emptying and mixing: a simulation study

The amplitude and frequency of peristaltic contractions are two major parameters for assessing gastric motility. However, it is not fully understood how these parameters affect the important functions of the stomach, such as gastric mixing and emptying. This study aimed to quantify the effects of peristaltic amplitude and frequency on gastric mixing and emptying using computational fluid dynamics simulation of gastric flow with an anatomically realistic model of the stomach. Our results suggest that both the increase and decrease in peristaltic amplitude have a significant impact on mixing strength and emptying rate. For example, when the peristaltic amplitude was 1.2 times higher than normal, the emptying rate was 2.7 times faster, whereas when the amplitude was half, the emptying rate was 4.2 times slower. Moreover, the emptying rate increased more than proportionally with the peristaltic frequency. The nearest contraction wave to the pylorus and the subsequent waves promoted gastric emptying. These results suggest the importance of maintaining parameters within normal ranges to achieve healthy gastric function.
A Closer Look at Indirect Causes of Death After Hurricane Maria Using a Semiparametric Model

Abstract Objective: The coronavirus disease 2019 (COVID-19) pandemic as well as other recent natural emergencies have put the spotlight on emergency planning. One important aspect is that natural disasters or emergencies often lead to indirect deaths, and studying the behavior of indirect deaths during emergencies can guide emergency planning. While many studies have suggested many indirect deaths in Puerto Rico due to Hurricane Maria; the specific causes of these deaths have not been carefully studied. Methods: In this study, we use a semiparametric model and mortality data to evaluate cause of death trends. Our model adjusts for cause of death effect potentially varying over time while also inferring on how long excess deaths occurred. Results: From September 2017 to March 2018, after adjusting for intra-annual variability and population displacement, we find evidence of significant excess deaths due to Alzheimer’s/Parkinson, heart disease, sepsis, diabetes, renal failure, and pneumonia and influenza. Conclusions: In contrast, for the same time period we find no evidence of significant excess deaths due to cancer, hypertension, respiratory diseases, cerebrovascular disease, suicide, homicide, falling accidents, and traffic accidents.
A Comparative Investigation of Anaphoric Reference Devices in Anglo-Norman and Middle English Personal Letters

Abstract This paper compares the use of anaphoric reference terms, such as le dit (and its English equivalent the said), a characteristic feature of ‘curial style’, in Anglo-Norman (hence AN) and Middle English (hence ME) personal letters. Whilst we know that this style was prevalent in the official AN letters that were used to conduct English parliamentary business until the end of the 1300s, we do not yet have a clear understanding of the extent to which it was prevalent in both AN and ME personal letters, defined here as being written to one addressee who was known to the writer. The results show that there more anaphoric reference terms in the AN epistolary material than in the ME, and that the difference is statistically significant. However, these anaphoric reference devices are very much in evidence in the ME material as well, albeit in smaller numbers, suggesting a degree of influence, or emulation, or both. It is furthermore suggested that the use of anaphoric reference devices in both the AN and ME personal letters is more similar to their use in the literary texts discussed by Burnley (1986) than to their use in their more official, administrative epistolary forebears, i.e., they are often used in a looser, relaxed way, as a kind of ‘connective convenience’ (Burnley 1986: 610). Results relating to diachronic variation demonstrate that the reference terms are most common in the latest (1380s) AN letters and earliest (pre-1431) ME letters, perhaps suggesting a period of overlap. In relation to geographic distribution, ME anaphoric reference terms appear to be used more in letters written in London and Oxfordshire than in the East Anglian or Northern letters. Finally, in the AN corpora, the anaphoric reference devices are most frequently used by writers from the gentry and professions, a finding mirrored in the ME material. Overall, the paper highlights the importance of taking different discoursal contexts, and the deliberate emulation of styles within those contexts, into account when investigating the interaction between Anglo Norman and Middle English during the medieval period.
Impact of Privacy Settings on Intention of Mobile Shoppers to Disclose Information from the Perspective of Technical Features: Trust as Mediator Variable

Widespread use of big data technology in mobile shopping provides users with personalized services but can also lead to infringements of their privacy, which would not help users to maintain their trust in mobile shopping platforms and be willing to disclose personal information. Therefore, privacy protection technologies are necessary. Privacy settings allow users to choose which information to disclose, enhance user trust, and ultimately, affect their intention to disclose personal information. Based on innovation diffusion theory and technology acceptance model, from the perspective of technical features, this study used structural equation model to examine the impact of privacy settings on the intention of mobile shoppers to disclose information with trust for mediation. The findings show that trust has a significant positive effect on their intention. Among the technical features of privacy settings, applicability, ease of use, and effectiveness enhance trust. The first two features enhance the comparative advantages (effectiveness and interactivity) of privacy settings while applicability also enhances ease of use. This paper provides recommendations to mobile service providers for improving their privacy settings and creating more secure mobile shopping environments.
HessianAuth: An ECC-based Distributed and Efficient Authentication Mechanism for 6LoWPAN Networked IoT Devices

Internet of Things (IoT) constitutes an ecosystem of networked resource-constrained devices that mandates light-weighted security solutions. In most IoT applications, co-located and proximal devices are connected in 6LoWPAN networks and use the IEEE 802.15 standard for local interactions via device-to-device (D2D) communication. The resource-limited 6LoWPAN networked devices need an efficient authentication mechanism to validate requesters’ identities such that less resources and time are required. In this paper, we propose a novel Elliptic Curve Cryptography (ECC)-based distributed authentication scheme called HessianAuth to achieve efficiencies in resource usage and latency as well as security. We simulated a 6LoWPAN network to support a D2D communication scenario and implemented the proposed authentication mechanism using the Cooja network simulator. We have considered AES-CBC and standard Weierstrass curve-based ECC as the benchmarks and carried out performance analysis with respect to CPU cycles and delay. Further, we carry out a security analysis of HessianAuth using a state-of-the-art tool called AVISPA and demonstrate that the former is secure against man-in-the-middle and replay attacks.