Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoupling of a giant planet from its disk in an inclined binary system

We explore the dynamical evolution of a planet embedded in a disk surrounding a star part of a binary system where the orbital plane of the binary is significantly tilted respect to the initial disk plane. Our aim is to test whether the planet remains within the disk and continues to migrate towards the star in a Type I/II mode in spite of the secular perturbations of the companion star. This would explain observed exoplanets with significant inclination respect to the equatorial plane of their host star. We have used two different SPH codes, vine and phantom, to model the evolution of a system star+disk+planet and companion star with time. After an initial coupled evolution, the inclination of the disk and that of the planet begin to differ significantly. The period of oscillation of the disk inclination, respect to the initial plane, is shorter than that of the planet which evolves independently after about 10^4 yr following a perturbed N-body behavior. However, the planet keeps migrating towards the star because during its orbital motion it crosses the disk plane and the friction with the gas causes angular momentum loss. Disk and planet in a significantly inclined binary system are not dynamically coupled for small binary separations but evolve almost independently. The planet abandons the disk and, due to the onset of a significant mutual inclination, it interacts with the gas only when its orbit intersects the disk plane. The drift of the planet towards the star is not due to type I/II with the planet embedded in the disk but to the friction with the gas during the disk crossing.
Elastic waves generated by impact and vibration in confined granular media

SUMMARY Observational data of asteroids can be explained by considering them as an agglomerate of granular material. Understanding the mechanical properties of these objects is relevant for many scientiﬁc reasons: space missions design, evaluation of impact threats to our planet, and understanding the nature of asteroids and their implication in the origin of the solar system. In-situ measurements of mechanical properties require complex and costly space missions. Here a laboratory-scale characterization of wave propagation in granular media is presented using a novel experimental setup as well as numerical simulations. The pressure inside an asteroid is still a matter of debate, but it deﬁnitely presents a pressure gradient towards the interior. This is why impact characterization needs to be performed as a function of the conﬁning pressure. Our experimental setup allows for the simultaneous measurement of the external conﬁning pressure, internal pressure, total strain, and acceleration in a 50 cm side squared box ﬁlled up with a billion grains. We study the propagation of impact-generated and shaker-born seismic body waves in the 500 Hz range. Through subsequent compression-relaxation cycles, it was observed that the granular media behaves on average like a solid with a constant elastic modulus during each compression. Effective medium theory (EMT) for granular media explains the data at low pressure. After each compression-relaxation cycle, the elastic modulus increases, and a high hysteresis is observed: relaxation shows a more complex behavior than compression. We show that seismic waves generated by both impact and vibration travels at the pressure wave speed. Thanks to a numerical model, we measure a strong wave attenuation α ∼ 3 . 4 Np/m. We found that the wave speed increases with the conﬁning pressure with a p 1 / 2 dependency, in disagreement with theoretical models that predicts a shallower dependency. The dependency of the elasticity with the conﬁning pressure can be explained by a modiﬁed EMT model with a coordination number proportional to the pressure, or equivalently by a mesoscopic nonlinear model based on third-order nonlinear elastic energy. The interpretation of these models is a deep reorganization in the particle contact network.
No Pairs Left Behind: Improving Metric Learning with Regularized Triplet Objective

We propose a novel formulation of the triplet objective function that improves metric learning without additional sample mining or overhead costs. Our approach aims to explicitly regularize the distance between the positive and negative samples in a triplet with respect to the anchor-negative distance. As an initial validation, we show that our method (called No Pairs Left Behind [NPLB]) improves upon the traditional and current state-of-the-art triplet objective formulations on standard benchmark datasets. To show the effectiveness and potentials of NPLB on real-world complex data, we evaluate our approach on a large-scale healthcare dataset (UK Biobank), demonstrating that the embeddings learned by our model significantly outperform all other current representations on tested downstream tasks. Additionally, we provide a new model-agnostic single-time health risk definition that, when used in tandem with the learned representations, achieves the most accurate prediction of subjects' future health complications. Our results indicate that NPLB is a simple, yet effective framework for improving existing deep metric learning models, showcasing the potential implications of metric learning in more complex applications, especially in the biological and healthcare domains.
Tidal Stripping of a White Dwarf by an Intermediate-mass Black Hole

During the inspiralling of a white dwarf (WD) into an intermediate-mass black hole (∼102−5 M ⊙), both gravitational waves (GWs) and electromagnetic (EM) radiation are emitted. Once the eccentric orbit’s pericenter radius approaches the tidal radius, the WD would be tidally stripped upon each pericenter passage. The accretion of this stripped mass would produce EM radiation. It is suspected that the recently discovered new types of transients, namely the quasiperiodic eruptions and the fast ultraluminous x-ray bursts, might originate from such systems. Modeling these flares requires a prediction of the amount of stripped mass from the WD and the details of the mass supply to the accretion disk. We run hydrodynamical simulations to study the orbital parameter dependence of the stripped mass. We find that our results match the analytical estimate that the stripped mass is proportional to z 5/2, where z is the excess depth by which the WD overfills its instantaneous Roche lobe at the pericenter. The corresponding fallback rate of the stripped mass is calculated, which may be useful in interpreting the individual flaring light curve in candidate EM sources. We further calculate the long-term mass-loss evolution of a WD during its inspiral and the detectability of the GW and EM signals. The EM signal from the mass-loss stage can be easily detected: the limiting distance is ∼320(M h/104 M ⊙) Mpc for the Einstein Probe. The GW signal, for space-borne detectors such as Laser Interferometer Space Antenna or TianQin, can be detected only within the Local Supercluster (∼33 Mpc).
NFGen: Automatic Non-linear Function Evaluation Code Generator for General-purpose MPC Platforms

Due to the absence of a library for non-linear function evaluation, so-called general-purpose secure multi-party computation (MPC) are not as "general'' as MPC programmers expect. Prior arts either naively reuse plaintext methods, resulting in suboptimal performance and even incorrect results, or handcraft ad hoc approximations for specific functions or platforms. We propose a general technique, NFGen1, that utilizes pre-computed discrete piecewise polynomials to accurately approximate generic functions using fixed-point numbers. We implement it using a performance-prediction-based code generator to support different platforms. Conducting extensive evaluations of 23 non-linear functions against six MPC protocols on two platforms, we demonstrate significant performance, accuracy, and generality improvements over existing methods.
A Practical, Progressively-Expressive GNN

Message passing neural networks (MPNNs) have become a dominant flavor of graph neural networks (GNNs) in recent years. Yet, MPNNs come with notable limitations; namely, they are at most as powerful as the 1-dimensional Weisfeiler-Leman (1-WL) test in distinguishing graphs in a graph isomorphism testing frame-work. To this end, researchers have drawn inspiration from the k-WL hierarchy to develop more expressive GNNs. However, current k-WL-equivalent GNNs are not practical for even small values of k, as k-WL becomes combinatorially more complex as k grows. At the same time, several works have found great empirical success in graph learning tasks without highly expressive models, implying that chasing expressiveness with a coarse-grained ruler of expressivity like k-WL is often unneeded in practical tasks. To truly understand the expressiveness-complexity tradeoff, one desires a more fine-grained ruler, which can more gradually increase expressiveness. Our work puts forth such a proposal: Namely, we first propose the (k, c)(<=)-SETWL hierarchy with greatly reduced complexity from k-WL, achieved by moving from k-tuples of nodes to sets with<=k nodes defined over<=c connected components in the induced original graph. We show favorable theoretical results for this model in relation to k-WL, and concretize it via (k, c)(<=)-SETGNN, which is as expressive as (k, c)(<=)-SETWL. Our model is practical and progressively-expressive, increasing in power with k and c. We demonstrate effectiveness on several benchmark datasets, achieving several state-of-the-art results with runtime and memory usage applicable to practical graphs. We open source our implementation at https://github.com/LingxiaoShawn/KCSetGNN.
Less is More: Simplifying Feature Extractors Prevents Overfitting for Neural Discourse Parsing Models

Complex feature extractors are widely employed for text representation building. However, these complex feature extractors can lead to severe overfitting problems especially when the training datasets are small, which is especially the case for several discourse parsing tasks. Thus, we propose to remove additional feature extractors and only utilize self-attention mechanism to exploit pretrained neural language models in order to mitigate the overfitting problem. Experiments on three common discourse parsing tasks (News Discourse Profiling, Rhetorical Structure Theory based Discourse Parsing and Penn Discourse Treebank based Discourse Parsing) show that powered by recent pretrained language models, our simplied feature extractors obtain better generalizabilities and meanwhile achieve comparable or even better system performance. The simplified feature extractors have fewer learnable parameters and less processing time. Codes will be released and this simple yet effective model can serve as a better baseline for future research.
Inception-Based Crowd Counting - Being Fast while Remaining Accurate

Recent sophisticated CNN-based algorithms have demonstrated their extraordinary ability to automate counting crowds from images, thanks to their structures which are designed to address the issue of various head scales. However, these complicated architectures also increase computational complexity enormously, making real-time estimation implausible. Thus, in this paper, a new method, based on Inception-V3, is proposed to reduce the amount of computation. This proposed approach (ICC), exploits the first five inception blocks and the contextual module designed in CAN to extract features at different receptive fields, thereby being context-aware. The employment of these two different strategies can also increase the model's robustness. Experiments show that ICC can at best reduce 85.3 percent calculations with 24.4 percent performance loss. This high efficiency contributes significantly to the deployment of crowd counting models in surveillance systems to guard the public safety. The code will be available at https://github.com/YIMINGMA/CrowdCounting-ICC,and its pre-trained weights on the Crowd Counting dataset, which comprises a large variety of scenes from surveillance perspectives, will also open-sourced.
DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling

Nowadays, differential privacy (DP) has become a well-accepted standard for privacy protection, and deep neural networks (DNN) have been immensely successful in machine learning. The combination of these two techniques, i.e., deep learning with differential privacy, promises the privacy-preserving release of high-utility models trained with sensitive data such as medical records. A classic mechanism for this purpose is DP-SGD, which is a differentially private version of the stochastic gradient descent (SGD) optimizer commonly used for DNN training. Subsequent approaches have improved various aspects of the model training process, including noise decay schedule, model architecture, feature engineering, and hyperparameter tuning. However, the core mechanism for enforcing DP in the SGD optimizer remains unchanged ever since the original DP-SGD algorithm, which has increasingly become a fundamental barrier limiting the performance of DP-compliant machine learning solutions. Motivated by this, we propose DPIS, a novel mechanism for differentially private SGD training that can be used as a drop-in replacement of the core optimizer of DP-SGD, with consistent and significant accuracy gains over the latter. The main idea is to employ importance sampling (IS) in each SGD iteration for mini-batch selection, which reduces both sampling variance and the amount of random noise injected to the gradients that is required to satisfy DP. Although SGD with IS in the non-private setting has been well-studied in the machine learning literature, integrating IS into the complex mathematical machinery of DP-SGD is highly non-trivial; further, IS involves additional private data release which must be protected under differential privacy, as well as computationally intensive gradient computations. DPIS addresses these challenges through novel mechanism designs, fine-grained privacy analysis, efficiency enhancements, and an adaptive gradient clipping optimization. Extensive experiments on four benchmark datasets, namely MNIST, FMNIST, CIFAR-10 and IMDb, involving both convolutional and recurrent neural networks, demonstrate the superior effectiveness of DPIS over existing solutions for deep learning with differential privacy.
Uncertainty estimation for out-of-distribution detection in computational histopathology

In computational histopathology algorithms now outperform humans on a range of tasks, but to date none are employed for automated diagnoses in the clinic. Before algorithms can be involved in such high-stakes decisions they need to "know when they don’t know", i.e., they need to estimate their predictive uncertainty. This allows them to defer potentially erroneous predictions to a human pathologist, thus increasing their safety. Here, we evaluate the predictive performance and calibration of several uncertainty estimation methods on clinical histopathology data. We show that a distance-aware uncertainty estimation method outperforms commonly used approaches, such as Monte Carlo dropout and deep ensembles. However, we observe a drop in predictive performance and calibration on novel samples across all uncertainty estimation methods tested. We also investigate the use of uncertainty thresholding to reject out-of-distribution samples for selective prediction. We demonstrate the limitations of this approach and suggest areas for future research.
Linear Guardedness and its Implications

Methods for erasing human-interpretable concepts from neural representations that assume linearity have been found to be tractable and useful.However, the impact of this removal on the behavior of downstream classifiers trained on the modified representations is not fully understood.In this work, we formally define the notion of linear guardedness as the inability of an adversary to predict the concept directly from the representation, and study its implications.We show that, in the binary case, under certain assumptions, a downstream log-linear model cannot recover the erased concept.However, we constructively demonstrate that a multiclass log-linear model can be constructed that indirectly recovers the concept in some cases, pointing to the inherent limitations of linear guardedness as a downstream bias mitigation technique.These findings shed light on the theoretical limitations of linear erasure methods and highlight the need for further research on the connections between intrinsic and extrinsic bias in neural models.
AMPNet: Attention as Message Passing for Graph Neural Networks

Feature-level interactions between nodes can carry crucial information for understanding complex interactions in graph-structured data. Current interpretability techniques, however, are limited in their ability to capture feature-level interactions between different nodes. In this work, we propose AMPNet, a general Graph Neural Network (GNN) architecture for uncovering feature-level interactions be-tween different nodes in a graph. Our framework applies a multiheaded attention operation during message-passing to contextualize messages based on the feature interactions between different nodes. We utilize subgraph sampling and node feature downsampling in our experiments to improve the scalability of our architecture to large networks. We evaluate AMPNet on several benchmark and real-world datasets, and develop a synthetic benchmark based on cyclic cellular automata to test the ability of our framework to recover the underlying generation rules of the cellular automata based on feature-interactions.
Decentralized optimization over time-varying graphs

Decentralized optimization over time-varying networks has a wide range of applications in distributed learning, signal processing and various distributed control problems. The agents of the distributed system locally hold optimization objectives and can communicate to their immediate neighbors over a network that changes from time to time. In this paper, we survey state-of-the-art results and describe the techniques for optimization over time-varying graphs. We also give an overview of open questions in the ﬁeld and formulate hypotheses and directions for future work.
AlignOT: An Optimal Transport Based Algorithm for Fast 3D Alignment With Applications to Cryogenic Electron Microscopy Density Maps

Aligning electron density maps from Cryogenic electron microscopy (cryo-EM) is a first key step for studying multiple conformations of a biomolecule. As this step remains costly and challenging, with standard alignment tools being potentially stuck in local minima, we propose here a new procedure, called AlignOT, which relies on the use of computational optimal transport (OT) to align EM maps in 3D space. By embedding a fast estimation of OT maps within a stochastic gradient descent algorithm, our method searches for a rotation that minimizes the Wasserstein distance between two maps, represented as point clouds. We quantify the impact of various parameters on the precision and accuracy of the alignment, and show that AlignOT can outperform the standard local alignment methods, with an increased range of rotation angles leading to proper alignment. We further benchmark AlignOT on various pairs of experimental maps, which account for different types of conformational heterogeneities and geometric properties. As our experiments show good performance, we anticipate that our method can be broadly applied to align 3D EM maps.
What You See is What You Get: Principled Deep Learning via Distributional Generalization

Having similar behavior at training time and test time $-$ what we call a"What You See Is What You Get"(WYSIWYG) property $-$ is desirable in machine learning. Models trained with standard stochastic gradient descent (SGD), however, do not necessarily have this property, as their complex behaviors such as robustness or subgroup performance can differ drastically between training and test time. In contrast, we show that Differentially-Private (DP) training provably ensures the high-level WYSIWYG property, which we quantify using a notion of distributional generalization. Applying this connection, we introduce new conceptual tools for designing deep-learning methods by reducing generalization concerns to optimization ones: to mitigate unwanted behavior at test time, it is provably sufficient to mitigate this behavior on the training data. By applying this novel design principle, which bypasses"pathologies"of SGD, we construct simple algorithms that are competitive with SOTA in several distributional-robustness applications, significantly improve the privacy vs. disparate impact trade-off of DP-SGD, and mitigate robust overfitting in adversarial training. Finally, we also improve on theoretical bounds relating DP, stability, and distributional generalization.
First Detection of Silicon-bearing Molecules in η Car

We present ALMA band 6 observations of the luminous blue variable η Car obtained within the ALMAGAL program. We report SiO J = 5 → 4, SiS J = 12 → 11, and SiN N = 5 → 4 emission in the equatorial region of the Homunculus nebula, constituting the first detection of silicon- and sulfur-bearing molecules in the outskirts of a highly evolved, early-type massive star. The SiO, SiS, and SiN trace a clumpy equatorial ring that surrounds the central binary at a projected distance of ∼2″, delineating the inner rims of the butterfly-shaped dusty region. The formation of silicon-bearing compounds is presumably related to the continuous recycling of dust due to the variable wind regime of η Car, which destroys grains and releases silicon back to the gas phase. We discuss possible formation routes for the observed species, contextualizing them within the current molecular inventory of η Car. We find that the SiO and SiS fractional abundances in localized clumps of the ring, 6.7 × 10−9 and 1.2 × 10−8, respectively, are exceptionally lower than those measured in C- and O-rich AGB stars and cool supergiants, while the higher SiN abundance, 3.6 × 10−8, evidences the nitrogen-rich chemistry of the ejecta. These abundances must be regarded as strict upper limits, since the distribution of H2 in the Homunculus is unknown. In any case, these findings shed new light on the peculiar molecular ecosystem of η Car and establish its surroundings as a new laboratory to investigate the life cycle of silicate dust in extreme astrophysical conditions.