Miracle-dz/UniFAR-FTUs huggingface dataset:
List-Mode PET Image Reconstruction Using Deep Image Prior

List-mode positron emission tomography (PET) image reconstruction is an important tool for PET scanners with many lines-of-response and additional information such as time-of-flight and depth-of-interaction. Deep learning is one possible solution to enhance the quality of PET image reconstruction. However, the application of deep learning techniques to list-mode PET image reconstruction has not been progressed because list data is a sequence of bit codes and unsuitable for processing by convolutional neural networks (CNN). In this study, we propose a novel list-mode PET image reconstruction method using an unsupervised CNN called deep image prior (DIP) which is the first trial to integrate list-mode PET image reconstruction and CNN. The proposed list-mode DIP reconstruction (LM-DIPRecon) method alternatively iterates the regularized list-mode dynamic row action maximum likelihood algorithm (LM-DRAMA) and magnetic resonance imaging conditioned DIP (MR-DIP) using an alternating direction method of multipliers. We evaluated LM-DIPRecon using both simulation and clinical data, and it achieved sharper images and better tradeoff curves between contrast and noise than the LM-DRAMA, MR-DIP and sinogram-based DIPRecon methods. These results indicated that the LM-DIPRecon is useful for quantitative PET imaging with limited events while keeping accurate raw data information. In addition, as list data has finer temporal information than dynamic sinograms, list-mode deep image prior reconstruction is expected to be useful for 4D PET imaging and motion correction.
Improving Multimodal Speech Recognition by Data Augmentation and Speech Representations

Multimodal speech recognition aims to improve the performance of automatic speech recognition (ASR) systems by leveraging additional visual information that is usually associated to the audio input. While previous approaches make crucial use of strong visual representations, e.g. by finetuning pretrained image recognition networks, significantly less attention has been paid to its counterpart: the speech component. In this work, we investigate ways of improving the base speech recognition system by following similar techniques to the ones used for the visual encoder, namely, transferring representations and data augmentation. First, we show that starting from a pretrained ASR significantly improves the state-of-the-art performance; remarkably, even when building upon a strong unimodal system, we still find gains by including the visual modality. Second, we employ speech data augmentation techniques to encourage the multimodal system to attend to the visual stimuli. This technique replaces previously used word masking and comes with the benefits of being conceptually simpler and yielding consistent improvements in the multimodal setting. We provide empirical results on three multimodal datasets, including the newly introduced Localized Narratives.
Robust stabilization of polytopic systems via fast and reliable neural network-based approximations

We consider the design of fast and reliable neural network (NN)-based approximations of traditional stabilizing controllers for linear systems with polytopic uncertainty, including control laws with variable structure and those based on a (minimal) selection policy. Building upon recent approaches for the design of reliable control surrogates with guaranteed structural properties, we develop a systematic procedure to certify the closed-loop stability and performance of a linear uncertain system when a trained rectified linear unit (ReLU)-based approximation replaces such traditional controllers. First, we provide a sufficient condition, which involves the worst-case approximation error between ReLU-based and traditional controller-based state-to-input mappings, ensuring that the system is ultimately bounded within a set with adjustable size and convergence rate. Then, we develop an offline, mixed-integer optimization-based method that allows us to compute that quantity exactly.
Signal Recovery with Non-Expansive Generative Network Priors

We study compressive sensing with a deep generative network prior. Initial theoretical guarantees for efficient recovery from compressed linear measurements have been developed for signals in the range of a ReLU network with Gaussian weights and logarithmic expansivity: that is when each layer is larger than the previous one by a logarithmic factor. It was later shown that constant expansivity is sufficient for recovery. It has remained open whether the expansivity can be relaxed, allowing for networks with contractive layers (as often the case of real generators). In this work we answer this question, proving that a signal in the range of a Gaussian generative network can be recovered from few linear measurements provided that the width of the layers is proportional to the input layer size (up to log factors). This condition allows the generative network to have contractive layers. Our result is based on showing that Gaussian matrices satisfy a matrix concentration inequality which we term Range Restricted Weight Distribution Condition (R2WDC) and that weakens the Weight Distribution Condition (WDC) upon which previous theoretical guarantees were based. The WDC has also been used to analyze other signal recovery problems with generative network priors. By replacing the WDC with the R2WDC, we are able to extend previous results for signal recovery with expansive generative network priors to non-expansive ones. We discuss these extensions for phase retrieval, denoising, and spiked matrix recovery.
Local Dynamical Effects of Scale Invariance: the Lunar Recession

Scale invariance is expected in empty Universe models, while the presence of matter tends to suppress it. As shown recently, scale invariance is certainly absent in cosmological models with densities equal to or above the critical value $\varrho_{\mathrm{c}} =3H^2_0/(8 \pi G)$. For models with densities below $\varrho_{\mathrm{c}}$, the possibility of limited effects remains open. If present, scale invariance would be a global cosmological property. Some traces could be observable locally. For the Earth-Moon two-body system, the predicted additional lunar recession would be increased by 0.92 cm/yr, while the tidal interaction would also be slightly increased. The Earth-Moon distance is the most systematically measured distance in the Solar System, thanks to the Lunar Laser Ranging (LLR) experiment active since 1970. The observed lunar recession from LLR amounts to 3.83 ($\pm 0.009$) cm/yr; implying a tidal change of the length-of-the-day (LOD) by 2.395 ms/cy. However, the observed change of the LOD since the Babylonian Antiquity is only 1.78 ms/cy, a result supported by paleontological data, and implying a lunar recession of 2.85 cm/yr. The significant difference of (3.83-2.85) cm/yr = 0.98 cm/yr, already pointed out by several authors over the last two decades, corresponds well to the predictions of the scale-invariant theory, which is also supported by several other astrophysical tests.
Stability of Cyber-Physical Systems of Numerical Methods for Stochastic Differential Equations: Integrating the Cyber and the Physical of Stochastic Systems

This paper presents the cyber-physical system (CPS) of a numerical method (the widely-used Euler-Maruyama method) and establishes a foundational theory of the CPSs of numerical methods for stochastic differential equations (SDEs), which transforms the way we understand the relationship between the numerical method and the underlying SDE. The CPS is a seamless integration of the SDE and the numerical method, unlike in the literature where they are treated as separate systems linked by inequalities. We formulate a new and general class of stochastic impulsive differential equations (SiDEs) that can serve as a canonic form of the CPSs and establish a Lyapunov stability theory as a theoretic foundation for our class of SiDEs. By the CPS approach, we show the equivalence and intrinsic relationship between the stability of the SDE and the stability of the numerical method. As application of our proposed results, we develop the CPS theory for linear systems and present the CPS Lyapunov inequality that is the necessary and sufficient condition for mean-square stability of the CPS of the Euler-Maruyama method for linear SDEs. Our proposed CPS theory initiates the study of systems numerics and provokes many open and interesting problems for future work.
Zero-Error Feedback Capacity for Bounded Stabilization and Finite-State Additive Noise Channels

This article studies the zero-error feedback capacity of <italic>causal</italic> discrete channels with memory. First, by extending the classical zero-error feedback capacity concept, a new notion of <italic>uniform zero-error feedback capacity</italic> <inline-formula> <tex-math notation="LaTeX">$C_{0f} $ </tex-math></inline-formula> for such channels is introduced. Using this notion a tight condition for bounded stabilization of unstable noisy linear systems via causal channels is obtained, assuming no channel state information at either end of the channel. Furthermore, the zero-error feedback capacity of a class of additive noise channels is investigated. It is known that for a discrete channel with correlated additive noise, the ordinary capacity with or without feedback is equal <inline-formula> <tex-math notation="LaTeX">$\log q-\mathcal {H}_{ch} $ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$\mathcal {H}_{ch} $ </tex-math></inline-formula> is the entropy rate of the noise process and <inline-formula> <tex-math notation="LaTeX">$q $ </tex-math></inline-formula> is the input alphabet size. In this paper, for a class of finite-state additive noise channels (FSANCs), it is shown that the zero-error feedback capacity is either zero or <inline-formula> <tex-math notation="LaTeX">$C_{0f} =\log q -h_{ch} $ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$h_{ch} $ </tex-math></inline-formula> is the <italic>topological entropy</italic> of the noise process. A condition is given to determine when the zero-error capacity with or without feedback is zero. This, in conjunction with the stabilization result, leads to a “Small-Entropy Theorem”, stating that stabilization over FSANCs can be achieved if the sum of the topological entropies of the linear system and the channel is smaller than <inline-formula> <tex-math notation="LaTeX">$\log q$ </tex-math></inline-formula>.
A Machine Learning Approach for Automated Filling of Categorical Fields in Data Entry Forms

Users frequently interact with software systems through data entry forms. However, form filling is time-consuming and error-prone. Although several techniques have been proposed to auto-complete or pre-fill fields in the forms, they provide limited support to help users fill categorical fields, i.e., fields that require users to choose the right value among a large set of options. In this article, we propose LAFF, a learning-based automated approach for filling categorical fields in data entry forms. LAFF first builds Bayesian Network models by learning field dependencies from a set of historical input instances, representing the values of the fields that have been filled in the past. To improve its learning ability, LAFF uses local modeling to effectively mine the local dependencies of fields in a cluster of input instances. During the form filling phase, LAFF uses such models to predict possible values of a target field, based on the values in the already-filled fields of the form and their dependencies; the predicted values (endorsed based on field dependencies and prediction confidence) are then provided to the end-user as a list of suggestions. We evaluated LAFF by assessing its effectiveness and efficiency in form filling on two datasets, one of them proprietary from the banking domain. Experimental results show that LAFF is able to provide accurate suggestions with a Mean Reciprocal Rank value above 0.73. Furthermore, LAFF is efficient, requiring at most 317 ms per suggestion.
Forecasting brain activity based on models of spatiotemporal brain dynamics: A comparison of graph neural network architectures

Abstract Comprehending the interplay between spatial and temporal characteristics of neural dynamics can contribute to our understanding of information processing in the human brain. Graph neural networks (GNNs) provide a new possibility to interpret graph-structured signals like those observed in complex brain networks. In our study we compare different spatiotemporal GNN architectures and study their ability to model neural activity distributions obtained in functional MRI (fMRI) studies. We evaluate the performance of the GNN models on a variety of scenarios in MRI studies and also compare it to a VAR model, which is currently often used for directed functional connectivity analysis. We show that by learning localized functional interactions on the anatomical substrate, GNN-based approaches are able to robustly scale to large network studies, even when available data are scarce. By including anatomical connectivity as the physical substrate for information propagation, such GNNs also provide a multimodal perspective on directed connectivity analysis, offering a novel possibility to investigate the spatiotemporal dynamics in brain networks.
Mechanistic insight into spontaneous transition from cellular alternans to arrhythmia-A simulation study. Computational

Cardiac electrical alternans (CEA), manifested as T-wave alternans in ECG, is a clinical biomarker for predicting cardiac arrhythmias and sudden death. However, the mechanism underlying the spontaneous transition from CEA to arrhythmias remains incompletely eluci-dated. In this study, multiscale rabbit ventricular models were used to study the transition and a potential role of I Na in perpetuating such a transition. It was shown CEA evolved into either concordant or discordant action potential (AP) conduction alternans in a homogeneous one-dimensional tissue model, depending on tissue AP duration and conduction velocity (CV) restitution properties. Discordant alternans was able to cause conduction failure in the model, which was promoted by impaired sodium channel with either a reduced or increased channel current. In a two-dimensional homogeneous tissue model, a combined effect of rate- and curvature-dependent CV broke-up alternating wavefronts at localised points, facilitating a spontaneous transition from CEA to re-entry. Tissue inhomogeneity or anisotropy observations that are closely associated with cardiac arrhythmias and sudden death. TWA is believed to be underlined by cardiac alternans at the cellular level, but the extract mechanism for the transition from cellular alternans to that at the tissue level, and how this further spontaneously evolves into cardiac arrhythmias remains incompletely elucidated. In this study, multiscale rabbit ventricular computational models were used to address this issue by investigating the underlying mechanism(s) for the arrhythmogenesis of cardiac alternans, as well as a possible role of sodium channel on perpetuating cardiac arrhythmias. Our results demonstrated a spontaneous development of re-entry from cellular alternans, arising from a combined action of APD and CV restitution properties with the curvature-dependence of CV. Tissue inhomogeneity and anisotropy further promoted break-up of excitation waves, leading to multiple re-entrant excitation waves. It was also found impaired sodium channel with either increased or decreased channel current facili-tated the arrhythmogenesis. This study provides new insights into underlying the mechanism, by which cellular cardiac alternans spontaneously evolves into cardiac arrhythmias. Similar results were observed in human atrial tissue models, suggesting our major findings are model-independent and of potential clinical relevance. correlated with the one generating AP alternans, demonstrating that large and small APs were associated with fast and slow wave propagation in tissue respectively. It was also shown that a
ScholarWorks at WMU ScholarWorks at WMU Characterization and Expression of Characterization and Expression of Pseudomonas Fluorescens L-L-Arabinose Dehydrogenase: Genetic Elements in Arabinose Dehydrogenase: Genetic Elements in Pseudomonas Putida

An assay based on the spectrophotometeric properties of NAD/NADH was developed to detect the degradation of L-arabinose to L-arabinolactone. This assay was used to determine the expression of the L-arabinose utilization genes contained in Pseudomonas fluorescens and in the plasmid, designated pPZ221, contained in Pseudomonas putida. The results from this assay, in conjunction with various growth kinetics, indicate that pPZ221 is inducible and repressible for L-arabinose utilization. Thus, the P. fluorescens chromosomal insert in pPZ221 for L-arabinose utilization does appear to contain the regulatory genes necessary for controlling the expression of an L-ar<:1,binose operon.
Protection against Omicron re-infection conferred by prior heterologous SARS-CoV-2 infection, with and without mRNA vaccination

Importance. Omicron is phylogenetically- and antigenically-distinct from earlier SARS-CoV-2 variants and the original vaccine strain. Protection conferred by prior SARS-CoV-2 infection against Omicron re-infection, and the added value of vaccination, require quantification. Objective. To estimate protection against Omicron re-infection and hospitalization conferred by prior heterologous SARS-CoV-2 (non-Omicron) infection and/or up to three doses of (ancestral, Wuhan-like) mRNA vaccine. Design. Test-negative study between December 26 (epi-week 52), 2021 and March 12 (epi-week 10), 2022. Setting. Population-based, province of Quebec, Canada Participants. Community-dwelling [≥]12-year-olds tested for SARS-CoV-2. Exposures. Prior laboratory-confirmed infection with/without mRNA vaccination. Outcomes. Laboratory-confirmed SARS-CoV-2 re-infection and hospitalization, presumed Omicron by genomic surveillance. The odds of prior non-Omicron infection with/without vaccination were compared among Omicron cases/hospitalizations versus test-negative controls (single randomly-selected per individual). Adjusted odds ratios controlled for age, sex, testing-indication and epi-week. Analyses were stratified by severity and time since last non-Omicron infection or vaccine dose. Results. Without vaccination, prior non-Omicron infection reduced the Omicron re-infection risk by 44% (95%CI:38-48), decreasing from 66% (95%CI:57-73) at 3-5 months to 35% (95%CI:21-47) at 9-11 months post-infection and <30% thereafter. The more severe the prior infection, the greater the risk reduction: 8% (95%CI:17-28), 43% (95%CI:37-49) and 68% (95%CI:51-80) for prior asymptomatic, symptomatic ambulatory or hospitalized infections. mRNA vaccine effectiveness against Omicron infection was consistently significantly higher among previously-infected vs. non-infected individuals at 65% (95%CI:63-67) vs. 20% (95%CI:16-24) for one-dose; 68% (95%CI:67-70) vs. 42% (95%CI:41-44) for two doses; and 83% (95%CI:81-84) vs. 73% (95%CI:72-73) for three doses. Infection-induced protection against Omicron hospitalization was 81% (95%CI: 66-89) increasing to 86% (95%CI:77-99) with one, 94% (95%CI:91-96) with two and 97%(95%CI:94-99) with three mRNA vaccine doses. Two-dose effectiveness against hospitalization among previously-infected individuals did not wane across 11 months and did not significantly differ from three-dose effectiveness despite longer follow-up (median 158 and 27 days, respectively). Conclusions and relevance. Prior heterologous SARS-CoV-2 infection provided substantial and sustained protection against Omicron hospitalization, greatest among those also vaccinated. In the context of program goals to prevent severe outcomes and preserve healthcare system capacity, >2 doses of ancestral Wuhan-like vaccine may be of marginal incremental value to previously-infected individuals.
Effects of gender-affirming hormone therapy on gray matter density, microstructure and monoamine oxidase A levels in transgender subjects

Alterations in gray matter (GM) and monoamine oxidase A (MAO-A) distribution across the brain have been found in various neuropsychiatric conditions. MAO-A catalyzes the oxidative degradation of various monoamines and is thus implicated in neuroplastic processes that influence GM density (GMD) and microstructure (GMM) of the brain. Sex-specific differences in these patterns are well documented, however studying the long-term effects of certain sex steroids on the brain are limited due to hormonal fluctuations under naturalistic conditions. Due to the exact monitoring of plasma hormone levels and sex steroid intake, transgender individuals undergoing gender-affirming hormone therapy represent a valuable cohort to investigate such changes of GM and concomitant MAO-A density. Here, we investigated the effects of long-term gender-affirming hormone therapy over a median time period of 4.5 months on GMD and GMM as well as MAO-A distribution volume. To this end, 20 cisgender women, 11 cisgender men, 20 transgender women and 10 transgender men were recruited. All participants underwent two MRI scans in a longitudinal design. PET scans using [11C]harmine were performed before each MRI session in a subset of 35 individuals. Between baseline and follow-up imaging, transgender subjects underwent gender-affirming hormone therapy. GM changes determined by diffusion weighted imaging (DWI) metrics for GMM and voxel based morphometry (VBM) for GMD were estimated using repeated measures ANOVA. Regions showing significant changes of both GMM and GMD were used for the subsequent analysis of MAO-A density. These involved the fusiform gyrus, rolandic operculum, inferior occipital cortex, middle and anterior cingulum, bilateral insula, cerebellum and the lingual gyrus (post-hoc tests: pFWE+Bonferroni < 0.025). In terms of MAO-A distribution volume, no significant effects were found. The present results are indicative of a reliable influence of gender-affirming hormone therapy on GMD and GMM following an interregional pattern. Nevertheless, future studies with larger sample sizes are needed to further investigate the relationship between sex steroids, gray matter alterations and MAO-A density.
Heterogeneity and Intrinsic Variation in Spatial Genome Heterogeneity and Intrinsic Variation in Spatial Genome Organization [preprint] Organization [preprint] us know how access to this document benefits you.

: The genome is hierarchically organized in 3D space and its architecture is altered in differentiation, development and disease. Some of the general principles that determine global 3D genome organization have been established. However, the extent and nature of cell-to-cell and cell-intrinsic variability in genome architecture are poorly characterized. Here, we systematically probe the heterogeneity in genome organization in human fibroblasts by combining high-resolution Hi-C datasets and high-throughput genome imaging. Optical mapping of several hundred genome interaction pairs at the single cell level demonstrates low steady-state frequencies of colocalization in the population and independent behavior of individual alleles in single nuclei. Association frequencies are determined by genomic distance, higher-order chromatin architecture and chromatin environment. These observations reveal extensive variability and heterogeneity in genome organization at the level of single cells and alleles and they demonstrate the coexistence of a broad spectrum of chromatin and genome conformations in a cell population.