Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative and O2 Enhanced MRI of the Pathologic Lung: Findings in Emphysema, Fibrosis, and Cystic Fibrosis

Purpose: beyond the pure morphological visual representation, MR imaging offers the possibility to quantify parameters in the healthy, as well as, in pathologic lung parenchyma. Gas exchange is the primary function of the lung and the transport of oxygen plays a key role in pulmonary physiology and pathophysiology. The purpose of this review is to present a short overview of the relaxation mechanisms of the lung and the current technical concepts of T1 mapping and methods of oxygen enhanced MR imaging. Material and Methods: molecular oxygen has weak paramagnetic properties so that an increase in oxygen concentration results in shortening of the T1 relaxation time and thus to an increase of the signal intensity in T1 weighted images. A possible way to gain deeper insights into the relaxation mechanisms of the lung is the calculation of parameter Maps. T1 Maps based on a snapshot FLASH sequence obtained during the inhalation of various oxygen concentrations provide data for the creation of the so-called oxygen transfer function (OTF), assigning a measurement for local oxygen transfer. T1 weighted single shot TSE sequences also permit expression of the signal changing effects associated with the inhalation of pure oxygen. Results: the average of the mean T1 values over the entire lung in inspiration amounts to 1199 +/− 117 milliseconds, the average of the mean T1 values in expiration was 1333 +/− 167 milliseconds. T1 Maps of patients with emphysema and lung fibrosis show fundamentally different behavior patterns. Oxygen enhanced MRT is able to demonstrate reduced diffusion capacity and diminished oxygen transport in patients with emphysema and cystic fibrosis. Discussion: results published in literature indicate that T1 mapping and oxygen enhanced MR imaging are promising new methods in functional imaging of the lung and when evaluated in conjunction with the pure morphological images can provide additional valuable information.
Debris disks around main-sequence stars

'Debris disks' are collections of small bodies around stars, such as the Asteroid Belt and Kuiper Belt in our Solar System. These disks are composed of objects smaller than planets, including asteroids, comets, dust, and dwarf planets. We detect debris disks around a significant fraction of stars, and these disks appear to be common components of planetary systems. Extrasolar debris disks have a broad range of locations, shapes and features. This chapter provides an introduction to debris disks around main-sequence stars. It summarises our understanding of the field, and covers a wide range of concepts from observations and theory. It describes how we detect extrasolar debris disks, what we see, and what these observations tell us. It also describes how debris disks evolve, and how they interact with planets. The chapter concludes by discussing several unsolved questions in debris-disk science.
Out-of-Distribution Detection Should Use Conformal Prediction (and Vice-versa?)

Research on Out-Of-Distribution (OOD) detection focuses mainly on building scores that efficiently distinguish OOD data from In Distribution (ID) data. On the other hand, Conformal Prediction (CP) uses non-conformity scores to construct prediction sets with probabilistic coverage guarantees. In this work, we propose to use CP to better assess the efficiency of OOD scores. Specifically, we emphasize that in standard OOD benchmark settings, evaluation metrics can be overly optimistic due to the finite sample size of the test dataset. Based on the work of (Bates et al., 2022), we define new conformal AUROC and conformal FRP@TPR95 metrics, which are corrections that provide probabilistic conservativeness guarantees on the variability of these metrics. We show the effect of these corrections on two reference OOD and anomaly detection benchmarks, OpenOOD (Yang et al., 2022) and ADBench (Han et al., 2022). We also show that the benefits of using OOD together with CP apply the other way around by using OOD scores as non-conformity scores, which results in improving upon current CP methods. One of the key messages of these contributions is that since OOD is concerned with designing scores and CP with interpreting these scores, the two fields may be inherently intertwined.
Analyzing Scalability of Parallel Algorithms and Architectures

Abstract The scalability of a parallel algorithm on a parallel architecture is a measure of its capacity to effectively utilize an increasing number of processors. Scalability analysis may be used to select the best algorithm-architecture combination for a problem under different constraints on the growth of the problem size and the number of processors. It may be used to predict the performance of a parallel algorithm and a parallel architecture for a large number of processors from the known performance on fewer processors. For a fixed problem size, it may be used to determine the optimal number of processors to be used and the maximum possible speedup that can be obtained. The objectives of this paper are to critically assess the state of the art in the theory of scalability analysis, and to motivate further research on the development of new and more comprehensive analytical tools to study the scalability of parallel algorithms and architectures. We survey a number of techniques and formalisms that have been developed for studying scalability issues, and discuss their interrelationships. For example, we derive an important relationship between time-constrained scaling and the isoefficiency function. We point out some of the weaknesses of the existing schemes for measuring scalability, and discuss possible ways of extending them.
Hierarchical Spatial Proximity Reasoning for Vision-and-Language Navigation

Most Vision-and-Language Navigation (VLN) algorithms tend to make decision errors, primarily due to a lack of visual common sense and insufficient reasoning capabilities. To address this issue, this paper proposes a Hierarchical Spatial Proximity Reasoning (HSPR) model. Firstly, we design a Scene Understanding Auxiliary Task (SUAT) to assist the agent in constructing a knowledge base of hierarchical spatial proximity for reasoning navigation. Specifically, this task utilizes panoramic views and object features to identify regions in the navigation environment and uncover the adjacency relationships between regions, objects, and region-object pairs. Secondly, we dynamically construct a semantic topological map through agent-environment interactions and propose a Multi-step Reasoning Navigation Algorithm (MRNA) based on the map. This algorithm continuously plans various feasible paths from one region to another, utilizing the constructed proximity knowledge base, enabling more efficient exploration. Additionally, we introduce a Proximity Adaptive Attention Module (PAAM) and Residual Fusion Method (RFM) to enable the model to obtain more accurate navigation decision confidence. Finally, we conduct experiments on publicly available datasets including REVERIE, SOON, R2R, and R4R to validate the effectiveness of the proposed approach.
Towards Understanding the Relationship between In-context Learning and Compositional Generalization

According to the principle of compositional generalization, the meaning of a complex expression can be understood as a function of the meaning of its parts and of how they are combined. This principle is crucial for human language processing and also, arguably, for NLP models in the face of out-of-distribution data. However, many neural network models, including Transformers, have been shown to struggle with compositional generalization. In this paper, we hypothesize that forcing models to in-context learn can provide an inductive bias to promote compositional generalization. To test this hypothesis, we train a causal Transformer in a setting that renders ordinary learning very difficult: we present it with different orderings of the training instance and shuffle instance labels. This corresponds to training the model on all possible few-shot learning problems attainable from the dataset. The model can solve the task, however, by utilizing earlier examples to generalize to later ones (i.e. in-context learning). In evaluations on the datasets, SCAN, COGS, and GeoQuery, models trained in this manner indeed show improved compositional generalization. This indicates the usefulness of in-context learning problems as an inductive bias for generalization.
Identification and Investigation of Drosophila Postsynaptic Density Homologs

AMPA receptors are responsible for fast excitatory transmission in the CNS and the trafficking of these receptors has been implicated in LTP and learning and memory. These receptors reside in the postsynaptic density, a network of proteins that links the receptors to downstream signaling components and to the neuronal cytoskeleton. To determine whether the fruit fly, Drosophila melanogaster, possesses a similar array of proteins as are found at the mammalian PSD, we identified Drosophila homologs of 95.8% of mammalian PSD proteins. We investigated, for the first time, the role of one of these PSD proteins, Pod1 in GluR cluster formation at the Drosophila neuromuscular junction and found that mutations in pod1 resulted in a specific loss of A-type receptors at the synapse.
Prohormone Convertase 1/3 Is Essential for Processing of the Glucose-dependent Insulinotropic Polypeptide Precursor*

The physiology of the incretin hormones, glucagon-like peptide 1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), and their role in type 2 diabetes currently attract great interest. Recently we reported an essential role for prohormone convertase (PC) 1/3 in the cleavage of intestinal proglucagon, resulting in formation of GLP-1, as demonstrated in PC1/3-deficient mice. However, little is known about the endoproteolytic processing of the GIP precursor. This study investigates the processing of proGIP in PC1/3 and PC2 null mice and in cell lines using adenovirus-mediated overexpression. Supporting a role for PC1/3 in proGIP processing, we found co-localization of GIP and PC1/3 but not PC2 in intestinal sections by immunohistochemistry, and analysis of intestinal extracts from PC1/3-deficient animals demonstrated severely impaired processing to GIP, whereas processing to GIP was unaltered in PC2-deficient mice. Accordingly, overexpression of preproGIP in the neuroendocrine AtT-20 cell line that expresses high levels of endogenous PC1/3 and negligible levels of PC2 resulted in production of GIP. Similar results were obtained after co-expression of preproGIP and PC1/3 in GH4 cells that express no PC2 and only low levels of PC1/3. In addition, studies in GH4 cells and the α-TC1.9 cell line, expressing PC2 but not PC1/3, indicate that PC2 can mediate processing to GIP but also to other fragments not found in intestinal extracts. Taken together, our data indicate that PC1/3 is essential and sufficient for the production of the intestinal incretin hormone GIP, whereas PC2, although capable of cleaving proGIP, does not participate in intestinal proGIP processing and is not found in intestinal GIP-expressing cells.
Treatment Evolution in High-Risk Congenital Diaphragmatic Hernia: Ten Years' Experience With Diaphragmatic Agenesis

Objective:The objective of this study was to evaluate the impact of newer therapies on the highest risk patients with congenital diaphragmatic hernia (CDH), those with agenesis of the diaphragm. Summary Background Data:CDH remains a significant cause of neonatal mortality. Many novel therapeutic interventions have been used in these infants. Those children with large defects or agenesis of the diaphragm have the highest mortality and morbidity. Methods:Twenty centers from 5 countries collected data prospectively on all liveborn infants with CDH over a 10-year period. The treatment and outcomes in these patients were examined. Patients were followed until death or hospital discharge. Results:A total of 1569 patients with CDH were seen between January 1995 and December 2004 in 20 centers. A total of 218 patients (14%) had diaphragmatic agenesis and underwent repair. The overall survival for all patients was 68%, while survival was 54% in patients with agenesis. When patients with diaphragmatic agenesis from the first 2 years were compared with similar patients from the last 2 years, there was significantly less use of ECMO (75% vs. 52%) and an increased use of inhaled nitric oxide (iNO) (30% vs. 80%). There was a trend toward improved survival in patients with agenesis from 47% in the first 2 years to 59% in the last 2 years. The survivors with diaphragmatic agenesis had prolonged hospital stays compared with patients without agenesis (median, 68 vs. 30 days). For the last 2 years of the study, 36% of the patients with agenesis were discharged on tube feedings and 22% on oxygen therapy. Conclusions:There has been a change in the management of infants with CDH with less frequent use of ECMO and a greater use of iNO in high-risk patients with a potential improvement in survival. However, the mortality, hospital length of stay, and morbidity in agenesis patients remain significant.
A Maximum Entropy Principle in Deep Thermalization and in Hilbert-Space Ergodicity

We report universal statistical properties displayed by ensembles of pure states that naturally emerge in quantum many-body systems. Specifically, two classes of state ensembles are considered: those formed by i) the temporal trajectory of a quantum state under unitary evolution or ii) the quantum states of small subsystems obtained by partial, local projective measurements performed on their complements. These cases respectively exemplify the phenomena of"Hilbert-space ergodicity"and"deep thermalization. "In both cases, the resultant ensembles are defined by a simple principle: the distributions of pure states have maximum entropy, subject to constraints such as energy conservation, and effective constraints imposed by thermalization. We present and numerically verify quantifiable signatures of this principle by deriving explicit formulae for all statistical moments of the ensembles; proving the necessary and sufficient conditions for such universality under widely-accepted assumptions; and describing their measurable consequences in experiments. We further discuss information-theoretic implications of the universality: our ensembles have maximal information content while being maximally difficult to interrogate, establishing that generic quantum state ensembles that occur in nature hide (scramble) information as strongly as possible. Our results generalize the notions of Hilbert-space ergodicity to time-independent Hamiltonian dynamics and deep thermalization from infinite to finite effective temperature. Our work presents new perspectives to characterize and understand universal behaviors of quantum dynamics using statistical and information theoretic tools.
Inductive Intrusion Detection in Flow-Based Network Data Using One-Class Support Vector Machines

Despite extensive research effort, ordinary anomaly detection systems still suffer from serious drawbacks such as high false alarm rates due to the enormous variety of network traffic. Also, increasingly fast network speeds pose performance problems to systems which base upon deep packet inspection. In this paper, we address these problems by proposing a novel inductive network intrusion detection system. The system operates on lightweight network flows and uses One-Class Support Vector Machines for analysis. In contrast to traditional anomaly detection systems, the system is trained with malicious rather than with benign network data. The system is suited for the load of large-scale networks and is less affected by typical problems of ordinary anomaly detection systems. Evaluations brought satisfying results which indicate that the proposed approach is interesting for further research and perfectly complements traditional signature-based intrusion detection systems.
Kinetic-type Mean Field Games with Non-separable Local Hamiltonians

We prove well-posedness of a class of kinetic-type Mean Field Games, which typically arise when agents control their acceleration. Such systems include independent variables representing the spatial position as well as velocity. We consider non-separable Hamiltonians without any structural conditions, which depend locally on the density variable. Our analysis is based on two main ingredients: an energy method for the forward-backward system in Sobolev spaces, on the one hand and on a suitable vector field method to control derivatives with respect to the velocity variable, on the other hand. The careful combination of these two techniques reveals interesting phenomena applicable for Mean Field Games involving general classes of drift-diffusion operators and nonlinearities. While many prior existence theories for general Mean Field Games systems take the final datum function to be smoothing, we can allow this function to be non-smoothing, i.e. also depending locally on the final measure. Our well-posedness results hold under an appropriate smallness condition, assumed jointly on the data.
Adding Sensitivity to the MLBF Doppler Centroid Estimator

The multilook beat frequency (MLBF) algorithm is the Doppler centroid estimator most commonly used in practice to solve the Doppler ambiguity. However, it still makes errors, notably in medium- or low-contrast scenes. In this paper, we present two ways in which the estimation sensitivity of the MLBF algorithm can be improved. First, we give a more thorough frequency-domain explanation of how the MLBF algorithm works and explain how cross beating and range migration cause estimation difficulties. The first improvement to the algorithm replaces the fast Fourier transform (FFT)-based beat frequency estimator with a more accurate one that uses phase increments. It avoids the FFT limitations of resolution and quantization, especially when the signal is discontinuous in one range cell due to range cell migration or burst mode operation (ScanSAR). A second improvement uses range cell migration correction to straighten the target trajectories before the beat frequency estimator is applied. This has the effect of narrowing the bandwidth of the beat signal and reducing the effect of cross beating. Finally, experiments with RADARSAT-1 data are used to illustrate the improved estimation accuracy of the modified algorithm
Q-MKL: Matrix-induced Regularization in Multi-Kernel Learning with Applications to Neuroimaging

Multiple Kernel Learning (MKL) generalizes SVMs to the setting where one simultaneously trains a linear classifier and chooses an optimal combination of given base kernels. Model complexity is typically controlled using various norm regularizations on the base kernel mixing coefficients. Existing methods neither regularize nor exploit potentially useful information pertaining to how kernels in the input set 'interact'; that is, higher order kernel-pair relationships that can be easily obtained via unsupervised (similarity, geodesics), supervised (correlation in errors), or domain knowledge driven mechanisms (which features were used to construct the kernel? ). We show that by substituting the norm penalty with an arbitrary quadratic function Q 0, one can impose a desired covariance structure on mixing weights, and use this as an inductive bias when learning the concept. This formulation significantly generalizes the widely used 1- and 2-norm MKL objectives. We explore the model's utility via experiments on a challenging Neuroimaging problem, where the goal is to predict a subject's conversion to Alzheimer's Disease (AD) by exploiting aggregate information from many distinct imaging modalities. Here, our new model outperforms the state of the art (p-values ⪡ 10-3). We briefly discuss ramifications in terms of learning bounds (Rademacher complexity).
Improving LoRA in Privacy-preserving Federated Learning

Low-rank adaptation (LoRA) is one of the most popular task-specific parameter-efficient fine-tuning (PEFT) methods on pre-trained language models for its good performance and computational efficiency. LoRA injects a product of two trainable rank decomposition matrices over the top of each frozen pre-trained model module. However, when applied in the setting of privacy-preserving federated learning (FL), LoRA may become unstable due to the following facts: 1) the effects of data heterogeneity and multi-step local updates are non-negligible, 2) additive noise enforced on updating gradients to guarantee differential privacy (DP) can be amplified and 3) the final performance is susceptible to hyper-parameters. A key factor leading to these phenomena is the discordance between jointly optimizing the two low-rank matrices by local clients and separately aggregating them by the central server. Thus, this paper proposes an efficient and effective version of LoRA, Federated Freeze A LoRA (FFA-LoRA), to alleviate these challenges and further halve the communication cost of federated fine-tuning LLMs. The core idea of FFA-LoRA is to fix the randomly initialized non-zero matrices and only fine-tune the zero-initialized matrices. Compared to LoRA, FFA-LoRA is motivated by practical and theoretical benefits in privacy-preserved FL. Our experiments demonstrate that FFA-LoRA provides more consistent performance with better computational efficiency over vanilla LoRA in various FL tasks.
Weakly nonlinear analysis of Turing pattern dynamics on curved surfaces

Pattern dynamics on curved surfaces are ubiquitous. Although the effect of surface topography on pattern dynamics has gained much interest, there is a limited understanding of the roles of surface geometry and topology in pattern dynamics. Recently, we reported that a static pattern on a flat plane can become a propagating pattern on a curved surface [Nishide and Ishihara, Phys. Rev. Lett. 2022]. By examining reaction-diffusion equations on axisymmetric surfaces, certain conditions for the onset of pattern propagation were determined. However, this analysis was limited by the assumption that the pattern propagates at a constant speed. Here, we investigate the pattern propagation driven by surface curvature using weakly nonlinear analysis, which enables a more comprehensive approach to the aforementioned problem. The analysis reveals consistent conditions of the pattern propagation similar to our previous results, and further predicts that rich dynamics other than pattern propagation, such as periodic and chaotic behaviors, can arise depending on the surface geometry. This study provides a new perspective on the relationship between surfaces and pattern dynamics and a basis for controlling pattern dynamics on surfaces.
The SIS process on Erd\"os-R\'enyi graphs: determining the infected fraction

The SIS process on a graph poses many challenges. An important problem is to identify characteristics of the metastable behaviour. Existing mean-field methods (such as Heterogeneous Mean Field and the N-intertwined Mean Field Approximation) overestimate the metastable infected fraction, because they ignore correlations. Especially in sparse graphs, this leads to serious inaccuracies. We propose quenched and annealed methods incorporating correlations and giving significantly more accurate approximations. We use the Erd\"os-R\'enyi graph as a test case, but the methods can be generalized easily. Our methods are computationally very friendly and can be applied to fairly large graphs, in contrast with some other second-order mean field approximations.