Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID-view: Diagnosis of COVID-19 using Chest CT

Significant work has been done towards deep learning (DL) models for automatic lung and lesion segmentation and classification of COVID-19 on chest CT data. However, comprehensive visualization systems focused on supporting the dual visual+DL diagnosis of COVID-19 are non-existent. We present COVID-view, a visualization application specially tailored for radiologists to diagnose COVID-19 from chest CT data. The system incorporates a complete pipeline of automatic lungs segmentation, localization/isolation of lung abnormalities, followed by visualization, visual and DL analysis, and measurement/quantification tools. Our system combines the traditional 2D workflow of radiologists with newer 2D and 3D visualization techniques with DL support for a more comprehensive diagnosis. COVID-view incorporates a novel DL model for classifying the patients into positive/negative COVID-19 cases, which acts as a reading aid for the radiologist using COVID-view and provides the attention heatmap as an explainable DL for the model output. We designed and evaluated COVID-view through suggestions, close feedback and conducting case studies of real-world patient data by expert radiologists who have substantial experience diagnosing chest CT scans for COVID-19, pulmonary embolism, and other forms of lung infections. We present requirements and task analysis for the diagnosis of COVID-19 that motivate our design choices and results in a practical system which is capable of handling real-world patient cases.
Adversarial Motorial Prototype Framework for Open Set Recognition

Open set recognition is designed to identify known classes and to reject unknown classes simultaneously. Specifically, identifying known classes and rejecting unknown classes correspond to reducing the empirical risk and the open space risk, respectively. First, the motorial prototype framework (MPF) is proposed, which classifies known classes according to the prototype classification idea. Moreover, a motorial margin constraint term is added into the loss function of the MPF, which can further improve the clustering compactness of known classes in the feature space to reduce both risks. Second, this paper proposes the adversarial motorial prototype framework (AMPF) based on the MPF. On the one hand, this model can generate adversarial samples and add these samples into the training phase; on the other hand, it can further improve the differential mapping ability of the model to known and unknown classes with the adversarial motion of the margin constraint radius. Finally, this paper proposes an upgraded version of the AMPF, AMPF++, which adds much more generated unknown samples into the training phase. In this paper, a large number of experiments prove that the performance of the proposed models is superior to that of other current works.
Vacuum Semiclassical Gravity Does Not Leave Space for Safe Singularities

General relativity predicts its own demise at singularities but also appears to conveniently shield itself from the catastrophic consequences of such singularities, making them safe. For instance, if strong cosmic censorship were ultimately satisfied, spacetime singularities, although present, would not pose any practical problems to predictability. Here, we argue that under semiclassical effects, the situation should be rather different: the potential singularities which could appear in the theory will generically affect predictability, and so one will be forced to analyse whether there is a way to regularise them. For these possible regularisations, the presence and behaviour of matter during gravitational collapse and stabilisation into new structures will play a key role. First, we show that the static semiclassical counterparts to the Schwarzschild and Reissner–Nordström geometries have singularities which are no longer hidden behind horizons. Then, we argue that in dynamical scenarios of formation and evaporation of black holes, we are left with only three possible outcomes which could avoid singularities and eventual predictability issues. We briefly analyse the viability of each one of them within semiclassical gravity and discuss the expected characteristic timescales of their evolution.
Global Hölder continuity of solutions to quasilinear equations with Morrey data

We deal with general quasilinear divergence-form coercive operators whose prototype is the [Formula: see text]-Laplacean operator. The nonlinear terms are given by Carathéodory functions and satisfy controlled growth structure conditions with data belonging to suitable Morrey spaces. The fairly non-regular boundary of the underlying domain is supposed to satisfy a capacity density condition which allows domains with exterior corkscrew property. We prove global boundedness and Hölder continuity up to the boundary for the weak solutions of such equations, generalizing this way the classical [Formula: see text]-result of Ladyzhenskaya and Ural’tseva to the settings of the Morrey spaces.
High Order Strong Stability Preserving MultiDerivative Implicit and IMEX Runge-Kutta Methods with Asymptotic Preserving Properties

In this work we present a class of high order unconditionally strong stability preserving (SSP) implicit multi-derivative Runge--Kutta schemes, and SSP implicit-explicit (IMEX) multi-derivative Runge--Kutta schemes where the time-step restriction is independent of the stiff term. The unconditional SSP property for a method of order $p>2$ is unique among SSP methods, and depends on a backward-in-time assumption on the derivative of the operator. We show that this backward derivative condition is satisfied in many relevant cases where SSP IMEX schemes are desired. We devise unconditionally SSP implicit Runge--Kutta schemes of order up to $p=4$, and IMEX Runge--Kutta schemes of order up to $p=3$. For the multi-derivative IMEX schemes, we also derive and present the order conditions, which have not appeared previously. The unconditional SSP condition ensures that these methods are positivity preserving, and we present sufficient conditions under which such methods are also asymptotic preserving when applied to a range of problems, including a hyperbolic relaxation system, the Broadwell model, and the Bhatnagar-Gross-Krook (BGK) kinetic equation. We present numerical results to support the theoretical results, on a variety of problems.
MGIC: Multigrid-in-Channels Neural Network Architectures

We present a multigrid-in-channels (MGIC) approach that tackles the quadratic growth of the number of parameters with respect to the number of channels in standard convolutional neural networks (CNNs). Thereby our approach addresses the redundancy in CNNs that is also exposed by the recent success of lightweight CNNs. Lightweight CNNs can achieve comparable accuracy to standard CNNs with fewer parameters; however, the number of weights still scales quadratically with the CNN's width. Our MGIC architectures replace each CNN block with an MGIC counterpart that utilizes a hierarchy of nested grouped convolutions of small group size to address this. Hence, our proposed architectures scale linearly with respect to the network's width while retaining full coupling of the channels as in standard CNNs. Our extensive experiments on image classification, segmentation, and point cloud classification show that applying this strategy to different architectures like ResNet and MobileNetV3 reduces the number of parameters while obtaining similar or better accuracy.
Suitability of solid-state platforms as sources of N -photon bundles

N-photon bundles constitute a class of highly nonclassical quantum states of light. They are a specific type of photon emission, where light quanta are released from a cavity only in groups of N particles with a characteristic photon number statistics that may be taken as their fingerprint. We consider two solid-state cavity quantum electrodynamics (cQED) systems in terms of their suitability as emitters of N-photon bundles. As one example, we consider a semiconductor quantum-dot– microcavity system coupled to longitudinal acoustic phonons. There, we find the environmental influence to be detrimental to the emission of N-photon bundles. The other example is a superconducting qubit inside a microwave resonator. In these systems, pure dephasing is not important and an experimentally feasible parameter regime is found, where bundle emission can be observed.
Rescalability of integrable mixed twistor $D$-modules.

We study the rescalability of integrable mixed twistor $D$-modules. We prove some basic functoriality of the rescalability and the associated irregular Hodge filtration. We also observe that rescalable integrable mixed twistor $D$-modules are equivalent to exponential Hodge modules.
Merging Tasks for Video Panoptic Segmentation

In this paper, the task of video panoptic segmentation is studied and two different methods to solve the task will be proposed. Video panoptic segmentation (VPS) is a recently introduced computer vision task that requires classifying and tracking every pixel in a given video. The nature of this task makes the cost of annotating datasets for it prohibiting. To understand video panoptic segmentation, first, earlier introduced constituent tasks that focus on semantics and tracking separately will be researched. Thereafter, two data-driven approaches which do not require training on a tailored VPS dataset will be selected to solve it. The first approach will show how a model for video panoptic segmentation can be built by heuristically fusing the outputs of a pre-trained semantic segmentation model and a pre-trained multi-object tracking model. This can be desired if one wants to easily extend the capabilities of either model. The second approach will counter some of the shortcomings of the first approach by building on top of a shared neural network backbone with task-specific heads. This network is designed for panoptic segmentation and will be extended by a mask propagation module to link instance masks across time, yielding the video panoptic segmentation format.
Titan's highly variable plasma environment: A 3D hybrid simulation study

Titan, der groste Mond des Planeten Saturn, besitzt eine ausgedehnte Atmosphare, die hauptsachlich aus molekularem Stickstoff besteht. Im Gegensatz zu Erde und Saturn besitzt Titan kein signifikantes intrinsisches Magnetfeld. Der Orbit von Titan befindet sich in den auseren Regionen von Saturns Magnetosphare. Da Titans Umlaufdauer um Saturn erheblich groser als die Rotationsperiode des Gasriesen ist, wird Titan standig vom korotierenden magnetospharischen Plasma angestromt. Im Rahmen dieser Arbeit wurde die Wechselwirkung zwischen Titans Ionosphare und dem anstromenden Plasma mittels dreidimensionaler numerischer Simulationen analysiert. Da die Gyrationsradien der beteiligten Ionensorten mit dem Durchmesser von Titan vergleichbar sind, konnen magnetohydrodynamische Modelle nur eine grobe Beschreibung von Titans Plasmaumgebung liefern. Daher wurde Titans Plasmawechselwirkung mittels eines dreidimensionalen Hybrid-Simulationscodes untersucht. Der Hybrid-Ansatz beschreibt die Elektronen des Plasmas als masselose Flussigkeit; die Ionen werden dagegen durch Makropartikel reprasentiert. Bis zu funf verschiedene Ionenspezies wurden in die Berechnungen einbezogen. Im ersten Teil der Arbeit werden die Strukturen in Titans Plasmaumgebung in Abhangigkeit von der Position des Mondes in der Magnetosphare diskutiert. Die asymmetrische Struktur von Titans Plasmaschweif wird detailliert beschrieben. Der zweite Teil der Arbeit stellt den Simulationsergebnissen Magnetfeld- und Plasmadaten gegenuber, die wahrend der Cassini-Mission gesammelt wurden.
Tate resolutions on toric varieties

We develop an analogue of Eisenbud-Floystad-Schreyer's Tate resolutions for toric varieties. Our construction, which is given by a noncommutative analogue of a Fourier- Mukai transform, works quite generally and provides a new perspective on the relationship between Tate resolutions and Beilinson's resolution of the diagonal. We also develop a Beilinson-type resolution of the diagonal for toric varieties.
Towards to Robust and Generalized Medical Image Segmentation Framework

Deep learning-based computer-aided diagnosis is gradually deployed to review and analyze medical images. However, this paradigm is restricted in real-world clinical applications due to the poor robustness and generalization. The issue is more sinister with a lack of training data. In this paper, we address the challenge from the transfer learning point of view. Different from the common setting that transferring knowledge from the natural image domain to the medical image domain, we find the knowledge from the same domain further boosts the model robustness and generalization. Therefore, we propose a novel two-stage framework for robust generalized medical image segmentation. Firstly, an unsupervised tile-wise autoencoder pretraining architecture is proposed to learn local and global knowledge. Secondly, the downstream segmentation model coupled with an auxiliary reconstruction network is designed. The reconstruction branch encourages the model to capture more general semantic features. Experiments of lung segmentation on multi chest X-ray datasets are conducted. Comprehensive results demonstrate the superior robustness of the proposed framework to corruption and high generalization performance on unseen datasets, especially under the scenario of the limited training data.
DRINet: A Dual-Representation Iterative Learning Network for Point Cloud Segmentation

We present a novel and flexible architecture for point cloud segmentation with dual-representation iterative learning. In point cloud processing, different representations have their own pros and cons. Thus, finding suitable ways to represent point cloud data structure while keeping its own internal physical property such as permutation and scale-invariant is a fundamental problem. Therefore, we propose our work, DRINet, which serves as the basic network structure for dual-representation learning with great flexibility at feature transferring and less computation cost, especially for large-scale point clouds. DRINet mainly consists of two modules called Sparse Point-Voxel Feature Extraction and Sparse Voxel-Point Feature Extraction. By utilizing these two modules iteratively, features can be propagated between two different representations. We further propose a novel multi-scale pooling layer for pointwise locality learning to improve context information propagation. Our network achieves state-of-the-art results for point cloud classification and segmentation tasks on several datasets while maintaining high runtime efficiency. For large-scale outdoor scenarios, our method outperforms state-of-the-art methods with a real-time inference time of 62ms per frame.
Universal thermodynamics of an 
SU(N)
 Fermi-Hubbard model

The SU(2) symmetric Fermi-Hubbard model (FHM) plays an essential role in strongly correlated fermionic many-body systems. In the one particle per site and strongly interacting limit ${U/t \gg 1}$, it is effectively described by the Heisenberg Hamiltonian. In this limit, enlarging the spin and extending the typical SU(2) symmetry to SU($N$) has been predicted to give exotic phases of matter in the ground state, with a complicated dependence on $N$. This raises the question of what --- if any --- are the finite-temperature signatures of these phases, especially in the currently experimentally relevant regime near or above the superexchange energy. We explore this question for thermodynamic observables by numerically calculating the thermodynamics of the SU($N$) FHM in the two-dimensional square lattice near densities of one particle per site, using determinant Quantum Monte Carlo and Numerical Linked Cluster Expansion. Interestingly, we find that for temperatures above the superexchange energy, where the correlation length is short, the energy, number of on-site pairs, and kinetic energy are universal functions of $N$. Although the physics in the regime studied is well beyond what can be captured by low-order high-temperature series, we show that an analytic description of the scaling is possible in terms of only one- and two-site calculations.
Unique Reconstruction of the Heat-Reflection Indices at Solid Interfaces

We show the unique reconstruction of the heat-reflection coefficients in a phonon transport equation. This is a mathematical model used to characterize the dynamics of heat-conducting phonons in multiple layers of media, commonly composed of metals and silicon. In experiments, the heat-reflection indices are inferred by measuring the temperature at the surface of the exterior metal after applying heat sources. In this article, we rigorously justify the unique reconstruction of these indices by using such procedures.
Edge-augmented Graph Transformers: Global Self-attention is Enough for Graphs

Transformer neural networks have achieved state-of-the-art results for unstructured data such as text and images but their adoption for graph-structured data has been limited. This is partly due to the difﬁculty in incorporating complex structural information in the basic transformer framework. We propose a simple yet powerful extension to the transformer – residual edge channels. The resultant framework, which we call Edge-augmented Graph Transformer (EGT), can directly accept, process and output structural information as well as node information. This simple addition allows us to use global self-attention, the key element of transformers, directly for graphs and comes with the beneﬁt of long-range interaction among nodes. Moreover, the edge channels allow the structural information to evolve from layer to layer, and prediction tasks on edges can be derived directly from these channels. In addition to that, we introduce positional encodings based on Singular Value Decomposition which can improve the performance of EGT. Our framework, which relies on global node feature aggregation, achieves better performance compared to Graph Convolutional Networks (GCN), which rely on local feature aggregation within a neighborhood. We verify the performance of EGT in a supervised learning setting on a wide range of experiments on benchmark datasets. Our ﬁndings indicate that convolutional aggregation is not an essential inductive bias for graphs and global self-attention can serve as a ﬂexible and adaptive alternative to graph convolution.
Detection of Flare Multiperiodic Pulsations in Mid-ultraviolet Balmer Continuum, Lyα, Hard X-Ray, and Radio Emissions Simultaneously

Quasi-periodic pulsations (QPPs), which usually appear as temporal pulsations of the total flux, are frequently detected in the light curves of solar/stellar flares. In this study, we present the investigation of nonstationary QPPs with multiple periods during the impulsive phase of a powerful flare on 2017 September 6, which were simultaneously measured by the Hard X-ray Modulation Telescope (Insight-HXMT), as well as the ground-based BLENSW. The multiple periods, detected by applying a wavelet transform and Lomb–Scargle periodogram to the detrended light curves, are found to be ∼20–55 s in the Lyα and mid-ultraviolet Balmer continuum emissions during the flare impulsive phase. Similar QPPs with multiple periods are also found in the hard X-ray emission and low-frequency radio emission. Our observations suggest that the flare QPPs could be related to nonthermal electrons accelerated by the repeated energy release process, i.e., triggering of repetitive magnetic reconnection, while the multiple periods might be modulated by the sausage oscillation of hot plasma loops. For the multiperiodic pulsations, other generation mechanisms could not be completely ruled out.
iGibson 2.0: Object-Centric Simulation for Robot Learning of Everyday Household Tasks

Recent research in embodied AI has been boosted by the use of simulation environments to develop and train robot learning approaches. However, the use of simulation has skewed the attention to tasks that only require what robotics simulators can simulate: motion and physical contact. We present iGibson 2.0, an open-source simulation environment that supports the simulation of a more diverse set of household tasks through three key innovations. First, iGibson 2.0 supports object states, including temperature, wetness level, cleanliness level, and toggled and sliced states, necessary to cover a wider range of tasks. Second, iGibson 2.0 implements a set of predicate logic functions that map the simulator states to logic states like Cooked or Soaked. Additionally, given a logic state, iGibson 2.0 can sample valid physical states that satisfy it. This functionality can generate potentially infinite instances of tasks with minimal effort from the users. The sampling mechanism allows our scenes to be more densely populated with small objects in semantically meaningful locations. Third, iGibson 2.0 includes a virtual reality (VR) interface to immerse humans in its scenes to collect demonstrations. As a result, we can collect demonstrations from humans on these new types of tasks, and use them for imitation learning. We evaluate the new capabilities of iGibson 2.0 to enable robot learning of novel tasks, in the hope of demonstrating the potential of this new simulator to support new research in embodied AI. iGibson 2.0 and its new dataset are publicly available at http://svl.stanford. edu/igibson/.
Impact of Aliasing on Generalization in Deep Convolutional Networks

We investigate the impact of aliasing on generalization in Deep Convolutional Networks and show that data augmentation schemes alone are unable to prevent it due to structural limitations in widely used architectures. Drawing insights from frequency analysis theory, we take a closer look at ResNet and EfficientNet architectures and review the trade-off between aliasing and information loss in each of their major components. We show how to mitigate aliasing by inserting non-trainable low-pass filters at key locations, particularly where networks lack the capacity to learn them. These simple architectural changes lead to substantial improvements in generalization on i.i. d. and even more on out-of-distribution conditions, such as image classification under natural corruptions on ImageNet-C [11] and few-shot learning on Meta-Dataset [26]. State-of-the art results are achieved on both datasets without introducing additional trainable parameters and using the default hyper-parameters of open source codebases.
A Self-Configurable Grouping Method for Integrated Wi-SUN FAN and TSCH-based Networks

Recent applications in large-scale wireless mesh networks (WSN), e.g., Advanced Metering Infrastructure (AMI) scenarios, expect to support an extended number of nodes with higher throughput, which cannot be sufficiently supported by the current WSN protocols. Two prior protocols, Wi-SUN Field Area Network (Wi-SUN FAN) and IETF 6TiSCH standards, are popularly used that are respectively based on asynchronous Carrier Sense Multiple Access / Collision Avoidance (CSMA/CA) and IEEE 802.15. 4 Time Scheduled Channel Hopping (TSCH). However, the former one with CSMA/CA can be prone to the Hidden Node Problem (HNP) that leads to a degradation of reliability, while the latter one with TSCH avoids HNP by using synchronously scheduling but requires massive control signalling that degrades the upper-bound of throughput. Accordingly, this paper tackles the challenge of how to improve the upper-bound of throughput without loss of reliability. To do so, we first present an in-depth evaluation of reliability and throughput between the two existing standards via system-level simulation. We then propose a self-configurable grouping (SCG) method to cluster nodes without using location information of each node. This SCG method eliminates around 99% HNP in CSMA/CA, thus greatly improves its reliability with maintaining the relatively high upper-bound of throughput. Our results show that the SCG method almost doubles the network throughput compared with both 6TiSCH and Wi-SUN FAN in heavy traffic scenarios, while providing extremely high reliability of more than 99.999%.