Miracle-dz/UniFAR-FTUs huggingface dataset:
“Ashfall” Induced by Molecular Outflow in Protostar Evolution

Dust growth and its associated dynamics play key roles in the first phase of planet formation in young stellar objects. Observations have detected signs of dust growth in very young protoplanetary disks. Furthermore, signs of planet formation, gaps in the disk at a distance of several tens of au from the central protostar, are also reported. From a theoretical point of view, however, planet formation in the outer regions is difficult due to the rapid inward drift of dust, called the radial drift barrier. Here, on the basis of three-dimensional magnetohydrodynamical simulations of disk evolution with dust growth, we propose a mechanism called the “ashfall” phenomenon, induced by a powerful molecular outflow driven by a magnetic field that may circumvent the radial drift barrier. We find that the large dust that grows to a size of about a centimeter in the inner region of a disk is entrained by an outflow from the disk. Then, large dust decoupled from gas is ejected from the outflow due to centrifugal force, enriching the grown dust in the envelope and eventually falls onto the outer edge of the disk. The overall process is similar to the behavior of ashfall from volcanic eruptions. In the ashfall phenomenon, the Stokes number of dust increases by reaccreting to the less dense disk outer edge. This may allow the dust grains to overcome the radial drift barrier. Consequently, the ashfall phenomenon can provide a crucial assist for making the formation of the planetesimals in outer regions of the disk possible, and hence the formation of wide-orbit planets and gaps.
Joint Progressive and Coarse-to-Fine Registration of Brain MRI via Deformation Field Integration and Non-Rigid Feature Fusion

Registration of brain MRI images requires to solve a deformation field, which is extremely difficult in aligning intricate brain tissues, e.g., subcortical nuclei, etc. Existing efforts resort to decomposing the target deformation field into intermediate sub-fields with either tiny motions, i.e., progressive registration stage by stage, or lower resolutions, i.e., coarse-to-fine estimation of the full-size deformation field. In this paper, we argue that those efforts are not mutually exclusive, and propose a unified framework for robust brain MRI registration in both progressive and coarse-to-fine manners simultaneously. Specifically, building on a dual-encoder U-Net, the fixed-moving MRI pair is encoded and decoded into multi-scale sub-fields from coarse to fine. Each decoding block contains two proposed novel modules: i) in Deformation Field Integration (DFI), a single integrated deformation sub-field is calculated, warping by which is equivalent to warping progressively by sub-fields from all previous decoding blocks, and ii) in Non-rigid Feature Fusion (NFF), features of the fixed-moving pair are aligned by DFI-integrated deformation field, and then fused to predict a finer sub-field. Leveraging both DFI and NFF, the target deformation field is factorized into multi-scale sub-fields, where the coarser fields alleviate the estimate of a finer one and the finer field learns to make up those misalignments insolvable by previous coarser ones. The extensive and comprehensive experimental results on both private and two public datasets demonstrate a superior registration performance of brain MRI images over progressive registration only and coarse-to-fine estimation only, with an increase by at most 8% in the average Dice.
Towards Reinforcement Learning for Pivot-based Neural Machine Translation with Non-autoregressive Transformer

Pivot-based neural machine translation (NMT) is commonly used in low-resource setups, especially for translation between non-English language pairs. It benefits from using high resource source-pivot and pivot-target language pairs and an individual system is trained for both sub-tasks. However, these models have no connection during training, and the source-pivot model is not optimized to produce the best translation for the source-target task. In this work, we propose to train a pivot-based NMT system with the reinforcement learning (RL) approach, which has been investigated for various text generation tasks, including machine translation (MT). We utilize a non-autoregressive transformer and present an end-to-end pivot-based integrated model, enabling training on source-target data.
Multi-way Clustering and Discordance Analysis through Deep Collective Matrix Tri-Factorization

Heterogeneous multi-typed, multimodal relational data is increasingly available in many domains and their exploratory analysis poses several challenges. We advance the state-of-the-art in neural unsupervised learning to analyze such data. We design the first neural method for collective matrix tri-factorization of arbitrary collections of matrices to perform spectral clustering of all constituent entities and learn cluster associations. Experiments on benchmark datasets demonstrate its efficacy over previous non-neural approaches. Leveraging signals from multi-way clustering and collective matrix completion we design a unique technique, called Discordance Analysis, to reveal information discrepancies across subsets of matrices in a collection with respect to two entities. We illustrate its utility in quality assessment of knowledge bases and in improving representation learning.
A method to distinguish between micro- and macro-granular surfaces of small Solar System bodies

The surface granularity of small Solar System bodies is diverse through the different types of planetary bodies and even for specific objects it is often not known in detail. One of the physical properties that strongly depends on the surface structure is the surface temperature. In highly porous media with large voids, radiation can efficiently transport heat, whereas more compact, micro-porous structures transport the heat primarily by conduction through the solid material. In this work, we investigate under which conditions a macro-porous surface can be distinguished from a micro-porous one by simply measuring the surface temperature. In our numerical simulations, we included circular and elliptical orbits with and without obliquity and varied the rotation period of the considered objects. We found that daily temperature cycles are rather insensitive to the specific surface granularity. However, the surface temperature at sunrise shows significant dependency on the material structure and this effect becomes even more pronounced when the solar intensity increases. By measuring the sunrise temperature as a function of insolation at noon, a differentiation between micro- and macro-granular surface structures is possible. In this paper, we provide a strategy how remote sensing can be used to derive the surface structure of small Solar System bodies.
Curvature Injected Adaptive Momentum Optimizer for Convolutional Neural Networks

—In this paper, we propose a new approach, hereafter referred as AdaInject, for the gradient descent optimizers by injecting the curvature information with adaptive momentum. Speciﬁcally, the curvature information is used as a weight to inject the second order moment in the update rule. The curvature information is captured through the short-term parameter history. The AdaInject approach boosts the parameter update by exploiting the curvature information. The proposed approach is generic in nature and can be integrated with any existing adaptive momentum stochastic gradient descent optimizers. The effectiveness of the AdaInject optimizer is tested using a theoretical analysis as well as through toy examples. We also show the convergence property of the proposed injection based optimizer. Further, we depict the efﬁcacy of the AdaInject approach through extensive experiments in conjunction with the state-of-the-art optimizers, i.e., AdamInject, diffGradInject, RadamInject, and AdaBeliefInject on four benchmark datasets. Different CNN models are used in the experiments. A highest improvement in the top-1 classiﬁcation error rate of 16 . 54% is observed using diffGradInject optimizer with ResNeXt29 model over the CI-FAR10 dataset. Overall, we observe very promising performance improvement of existing optimizers with the proposed AdaInject approach.
Long-Range Feature Propagating for Natural Image Matting

Natural image matting estimates the alpha values of unknown regions in the trimap. Recently, deep learning based methods propagate the alpha values from the known regions to unknown regions according to the similarity between them. However, we find that more than 50% pixels in the unknown regions cannot be correlated to pixels in known regions due to the limitation of small effective reception fields of common convolutional neural networks, which leads to inaccurate estimation when the pixels in the unknown regions cannot be inferred only with pixels in the reception fields. To solve this problem, we propose Long-Range Feature Propagating Network (LFPNet), which learns the long-range context features outside the reception fields for alpha matte estimation. Specifically, we first design the propagating module which extracts the context features from the downsampled image. Then, we present Center-Surround Pyramid Pooling (CSPP) that explicitly propagates the context features from the surrounding context image patch to the inner center image patch. Finally, we use the matting module which takes the image, trimap and context features to estimate the alpha matte. Experimental results demonstrate that the proposed method performs favorably against the state-of-the-art methods on the AlphaMatting and Adobe Image Matting datasets.
Exactness of Parrilo's Conic Approximations for Copositive Matrices and Associated Low Order Bounds for the Stability Number of a Graph

De Klerk and Pasechnik introduced in 2002 semidefinite bounds [Formula: see text] for the stability number [Formula: see text] of a graph G and conjectured their exactness at order [Formula: see text]. These bounds rely on the conic approximations [Formula: see text] introduced by Parrilo in 2000 for the copositive cone [Formula: see text]. A difficulty in the convergence analysis of the bounds is the bad behavior of Parrilo’s cones under adding a zero row/column: when applied to a matrix not in [Formula: see text] this gives a matrix that does not lie in any of Parrilo’s cones, thereby showing that their union is a strict subset of the copositive cone for any [Formula: see text]. We investigate the graphs for which the bound of order r≤1 is exact: we algorithmically reduce testing exactness of [Formula: see text] to acritical graphs, we characterize the critical graphs with [Formula: see text] exact, and we exhibit graphs for which exactness of [Formula: see text] is not preserved under adding an isolated node. This disproves a conjecture posed by Gvozdenović and Laurent in 2007, which, if true, would have implied the above conjecture by de Klerk and Pasechnik.
Good-case and Bad-case Latency of Unauthenticated Byzantine Broadcast: A Complete Categorization

This paper studies the {\em good-case latency} of {\em unauthenticated} Byzantine fault-tolerant broadcast, which measures the time it takes for all non-faulty parties to commit given a non-faulty broadcaster. For both asynchrony and synchrony, we show that $n\geq 4f$ is the tight resilience threshold that separates good-case 2 rounds and 3 rounds. For asynchronous Byzantine reliable broadcast (BRB), we also investigate the {\em bad-case latency} for all non-faulty parties to commit when the broadcaster is faulty but some non-faulty party commits. We provide matching upper and lower bounds on the resilience threshold of bad-case latency for BRB protocols with optimal good-case latency of 2 rounds. In particular, we show 2 impossibility results and propose 4 asynchronous BRB protocols.
Comparison of Object Detection Algorithms Using Video and Thermal Images Collected from a UAS Platform: An Application of Drones in Traffic Management

There is a rapid growth of applications of Unmanned Aerial Vehicles (UAVs) in traffic management, such as traffic surveillance, monitoring, and incident detection. However, the existing literature lacks solutions to real-time incident detection while addressing privacy issues in practice. This study explored real-time vehicle detection algorithms on both visual and infrared cameras and conducted experiments comparing their performance. Red Green Blue (RGB) videos and thermal images were collected from a UAS platform along highways in the Tampa, Florida, area. Experiments were designed to quantify the performance of a real-time background subtraction-based method in vehicle detection from a stationary camera on hovering UAVs under free-flow conditions. Several parameters were set in the experiments based on the geometry of the drone and sensor relative to the roadway. The results show that a background subtraction-based method can achieve good detection performance on RGB images (F1 scores around 0.9 for most cases), and a more varied performance is seen on thermal images with different azimuth angles. The results of these experiments will help inform the development of protocols, standards, and guidance for the use of drones to detect highway congestion and provide input for the development of incident detection algorithms.
Constraints on the End of Reionization from the Density Fields Surrounding Two Highly Opaque Quasar Sightlines

The observed large-scale scatter in Lyα opacity of the intergalactic medium at z < 6 implies large fluctuations in the neutral hydrogen fraction that are unexpected long after reionization has ended. A number of models have emerged to explain these fluctuations that make testable predictions for the relationship between Lyα opacity and density. We present selections of z = 5.7 Lyα-emitting galaxies (LAEs) in the fields surrounding two highly opaque quasar sightlines with long Lyα troughs. The fields lie toward the z = 6.0 quasar ULAS J0148+0600, for which we reanalyze previously published results using improved photometric selection, and toward the z = 6.15 quasar SDSS J1250+3130, for which results are presented here for the first time. In both fields, we report a deficit of LAEs within 20 h −1 Mpc of the quasar. The association of highly opaque sightlines with galaxy underdensities in these two fields is consistent with models in which the scatter in Lyα opacity is driven by large-scale fluctuations in the ionizing UV background or by an ultra-late reionization that has not yet concluded at z = 5.7.
Robust SLAM Systems: Are We There Yet?

Progress in the last decade has brought about significant improvements in the accuracy and speed of SLAM systems, broadening their mapping capabilities. Despite these advancements, long-term operation remains a major challenge, primarily due to the wide spectrum of perturbations robotic systems may encounter.Increasing the robustness of SLAM algorithms is an ongoing effort, however it usually addresses a specific perturbation. Generalisation of robustness across a large variety of challenging scenarios is not well-studied nor understood. This paper presents a systematic evaluation of the robustness of open-source state-of-the-art SLAM algorithms with respect to challenging conditions such as fast motion, non-uniform illumination, and dynamic scenes. The experiments are performed with perturbations present both independently of each other, as well as in combination in long-term deployment settings in unconstrained environments (lifelong operation). The detailed results (approx. 20,000 experiments) along with comprehensive documentation of the benchmarking tool for integrating new datasets and evaluating SLAM algorithms not studied in this work are available at https://robustslam.github. io/evaluation.
Partial to Whole Knowledge Distillation: Progressive Distilling Decomposed Knowledge Boosts Student Better

Knowledge distillation field delicately designs various types of knowledge to shrink the performance gap between compact student and large-scale teacher. These existing distillation approaches simply focus on the improvement of \textit{knowledge quality}, but ignore the significant influence of \textit{knowledge quantity} on the distillation procedure. Opposed to the conventional distillation approaches, which extract knowledge from a fixed teacher computation graph, this paper explores a non-negligible research direction from a novel perspective of \textit{knowledge quantity} to further improve the efficacy of knowledge distillation. We introduce a new concept of knowledge decomposition, and further put forward the \textbf{P}artial to \textbf{W}hole \textbf{K}nowledge \textbf{D}istillation~(\textbf{PWKD}) paradigm. Specifically, we reconstruct teacher into weight-sharing sub-networks with same depth but increasing channel width, and train sub-networks jointly to obtain decomposed knowledge~(sub-networks with more channels represent more knowledge). Then, student extract partial to whole knowledge from the pre-trained teacher within multiple training stages where cyclic learning rate is leveraged to accelerate convergence. Generally, \textbf{PWKD} can be regarded as a plugin to be compatible with existing offline knowledge distillation approaches. To verify the effectiveness of \textbf{PWKD}, we conduct experiments on two benchmark datasets:~CIFAR-100 and ImageNet, and comprehensive evaluation results reveal that \textbf{PWKD} consistently improve existing knowledge distillation approaches without bells and whistles.
MetaDrive: Composing Diverse Driving Scenarios for Generalizable Reinforcement Learning

Driving safely requires multiple capabilities from human and intelligent agents, such as the generalizability to unseen environments, the safety awareness of the surrounding traffic, and the decision-making in complex multi-agent settings. Despite the great success of Reinforcement Learning (RL), most of the RL research works investigate each capability separately due to the lack of integrated environments. In this work, we develop a new driving simulation platform called MetaDrive to support the research of generalizable reinforcement learning algorithms for machine autonomy. MetaDrive is highly compositional, which can generate an infinite number of diverse driving scenarios from both the procedural generation and the real data importing. Based on MetaDrive, we construct a variety of RL tasks and baselines in both single-agent and multi-agent settings, including benchmarking generalizability across unseen scenes, safe exploration, and learning multi-agent traffic. The generalization experiments conducted on both procedurally generated scenarios and real-world scenarios show that increasing the diversity and the size of the training set leads to the improvement of the RL agent's generalizability. We further evaluate various safe reinforcement learning and multi-agent reinforcement learning algorithms in MetaDrive environments and provide the benchmarks. Source code, documentation, and demo video are available at https://metadriverse.github. io/metadrive.
Objective metrics for language lateralization of fMRI examinations: a new model for the classification of hemispheric dominance in healthy subjects and epileptic patients

1 Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy; 2 Department of Neuroradiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy; 3 Claudio Munari Epilepsy Surgery Center, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy; 4 Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133 Milan, Italy;
Speeding-up One-vs-All Training for Extreme Classification via Smart Initialization

In this paper we show that a simple, data dependent way of setting the initial vector can be used to substantially speed up the training of linear one-versus-all (OVA) classifiers in extreme multi-label classification (XMC). We discuss the problem of choosing the initial weights from the perspective of three goals. We want to start in a region of weight space a) with low loss value, b) that is favourable for second-order optimization, and c) where the conjugate-gradient (CG) calculations can be performed quickly. For margin losses, such an initialization is achieved by selecting the initial vector such that it separates the mean of all positive (relevant for a label) instances from the mean of all negatives -- two quantities that can be calculated quickly for the highly imbalanced binary problems occurring in XMC. We demonstrate a speedup of $\approx 3\times$ for training with squared hinge loss on a variety of XMC datasets. This comes in part from the reduced number of iterations that need to be performed due to starting closer to the solution, and in part from an implicit negative mining effect that allows to ignore easy negatives in the CG step. Because of the convex nature of the optimization problem, the speedup is achieved without any degradation in classification accuracy.
Obstructions for Matroids of Path-Width at most k and Graphs of Linear Rank-Width at most k

Every minor-closed class of matroids of bounded branch-width can be characterized by a list of excluded minors, but unlike graphs, this list may need to be infinite in general. However, for each fixed finite field $\mathbb F$, the list needs to contain only finitely many $\mathbb F$-representable matroids, due to the well-quasi-ordering of $\mathbb F$-representable matroids of bounded branch-width under taking matroid minors [J. F. Geelen, A. M. H. Gerards, and G. Whittle (2002)]. But this proof is non-constructive and does not provide any algorithm for computing these $\mathbb F$-representable excluded minors in general. We consider the class of matroids of path-width at most $k$ for fixed $k$. We prove that for a finite field $\mathbb F$, every $\mathbb F$-representable excluded minor for the class of matroids of path-width at most $k$ has at most $2^{|\mathbb{F}|^{O(k^2)}}$ elements. We can therefore compute, for any integer $k$ and a fixed finite field $\mathbb F$, the set of $\mathbb F$-representable excluded minors for the class of matroids of path-width $k$, and this gives as a corollary a polynomial-time algorithm for checking whether the path-width of an $\mathbb F$-represented matroid is at most $k$. We also prove that every excluded pivot-minor for the class of graphs having linear rank-width at most $k$ has at most $2^{2^{O(k^2)}}$ vertices, which also results in a similar algorithmic consequence for linear rank-width of graphs.
Finding Taint-Style Vulnerabilities in Linux-based Embedded Firmware with SSE-based Alias Analysis

Although the importance of using static analysis to detect taint-style vulnerabilities in Linux-based embedded firmware is widely recognized, existing approaches are plagued by three major limitations. (a) Approaches based on symbolic execution may miss alias information and therefore suffer from a high false-negative rate. (b) Approaches based on VSA (value set analysis) often provide an over-approximate pointer range. As a result, many false positives could be produced. (c) Existing work for detecting taint-style vulnerability does not consider indirect call resolution, whereas indirect calls are frequently used in Internet-facing embedded devices. As a result, many false negatives could be produced. In this work, we propose a precise demand-driven flow-, context- and field-sensitive alias analysis approach. Based on this new approach, we also design a novel indirect call resolution scheme. Combined with sanitization rule checking, our solution discovers taint-style vulnerabilities by static taint analysis. We implemented our idea with a prototype called EmTaint and evaluated it against 35 real-world embedded firmware samples from six popular vendors. EmTaint discovered at least 192 bugs, including 41 n-day bugs and 151 0-day bugs. At least 115 CVE/PSV numbers have been allocated from a subset of the reported vulnerabilities at the time of writing. Compared to state-of-the-art tools such as KARONTE and SaTC, EmTaint found significantly more bugs on the same dataset in less time.