Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrostatic Solitary Waves in the Earth's Bow Shock: Nature, Properties, Lifetimes, and Origin

We present a statistical analysis of >2,100 bipolar electrostatic solitary waves (ESWs) collected from 10 quasi‐perpendicular Earth's bow shock crossings by Magnetospheric Multiscale spacecraft. We developed and implemented a correction procedure for reconstruction of actual electric fields, velocities, and other properties of ESW, whose spatial scales are typically comparable with or smaller than spatial distance between voltage‐sensitive probes. We found that more than 95% of the ESW are of negative polarity with amplitudes typically below a few Volts and 0.1Te (5–30 V or 0.1–0.3Te for a few percent of ESW), spatial scales of 10–100 m or λD–10λD, and velocities from a few tens to a few hundred km/s that is on the order of local ion‐acoustic speed. The spatial scales of ESW are correlated with local Debye length λD. The ESW have electric fields generally oblique to magnetic field and they propagate highly oblique to shock normal N; more than 80% of ESW propagate within 30° of the shock plane LM. In the shock plane, ESW typically propagates within a few tens of degrees of local magnetic field projection BLM and preferentially opposite to N × BLM. We argue that the ESW of negative polarity are ion holes produced by ion‐ion streaming instabilities. We estimate ion hole lifetimes to be 10–100 ms, or 1–10 km in terms of traveling distance. The revealed statistical properties will be useful for quantitative studies of electron thermalization in the Earth's bow shock.
The Impact of Baryonic Physics on the Abundance, Clustering, and Concentration of Halos

We examine the impact of baryonic physics on the halo distribution in hydrodynamic simulations compared to that in dark matter–only (DMO) simulations. We find that, in general, DMO simulations produce halo mass functions (HMFs) that are shifted to higher halo masses than their hydrodynamic counterparts due to the lack of baryonic physics. However, the exact nature of this mass shift is a complex function of mass, halo definition, redshift, and larger-scale environment, and it depends on the specifics of the baryonic physics implemented in the simulation. We present fitting formulae for the corrections one would need to apply to each DMO halo catalog in order to reproduce the HMF found in its hydrodynamic counterpart. Additionally, we explore the dependence on environment of this HMF discrepancy and find that, in most cases, halos in low-density environments are slightly more impacted by baryonic physics than halos in high-density environments. We thus also provide environment-dependent mass correction formulae that can reproduce the conditional, as well as global, HMF. We show that our mass corrections also repair the large-scale clustering of halos, though the environment-dependent corrections are required to achieve an accuracy better than 2%. Finally, we examine the impact of baryonic physics on the halo mass–concentration relation and find that its slope in hydrodynamic simulations is consistent with that in DMO simulations. Ultimately, we recommend that any future work relying on DMO halo catalogs incorporate our mass corrections to test the robustness of their results to baryonic effects.
Diffusive search and trajectories on spatial networks: a propagator approach

Several organelles in eukaryotic cells, including mitochondria and the endoplasmic reticulum, form interconnected tubule networks extending throughout the cell. These tubular networks host many biochemical pathways that rely on proteins diffusively searching through the network to encounter binding partners or localized target regions. In this work we develop both exact analytical methods to compute mean first passage times and efficient kinetic Monte Carlo algorithms to simulate trajectories of particles diffusing in a tubular network. Our approach leverages exact propagator functions for the distribution of transition times between network nodes and allows large simulation time steps determined by the network structure. The methodology is applied to both synthetic planar networks and organelle network structures, demonstrating key general features such as the heterogeneity of search times in different network regions and the functional advantage of broadly distributing target sites throughout the network. The proposed algorithms pave the way for future exploration of the interrelationship between tubular network structure and biomolecular reaction kinetics.
Red Supergiants in M31 and M33. II. The Mass-loss Rate

Mass loss is an important activity for red supergiants (RSGs) which can influence their evolution and final fate. Previous estimations of mass loss rates (MLRs) of RSGs exhibit significant dispersion due to the difference in method and the incompleteness of sample. With the improved quality and depth of the surveys including the UKIRT/WFCAM observation in near infrared, LGGS and PS1 in optical, a rather complete sample of RSGs is identified in M31 and M33 according to their brightness and colors. For about 2000 objects in either galaxy from this ever largest sample, the MLR is derived by fitting the observational optical-to-mid infrared spectral energy distribution (SED) with the DUSTY code of a 1-D dust radiative transfer model. The average MLR of RSGs is found to be around $2.0\times10^{-5}{\text{M}_\odot}/\text{yr}$ with a gas-to-dust ratio of 100, which yields a total contribution to the interstellar dust by RSGs of about $1.1\times10^{-3}{\text{M}_\odot}/\text{yr}$ in M31 and $6.0 \times10^{-4}{\text{M}_\odot}/\text{yr}$ in M33, a non-negligible source in comparison with evolved low-mass stars. The MLRs are divided into three types by the dust properties, i.e. amorphous silicate, amorphous carbon and optically thin, and the relations of MLR with stellar parameters, infrared flux and colors are discussed and compared with previous works for the silicate and carbon dust group respectively.
Using AoI Forecasts in Communicating and Robust Distributed Model-Predictive Control

In order to enhance the performance of cyber-physical systems, this article proposes the integrated design of distributed controllers for distributed plants together with the control of the communication network. Conventional design methods use static interfaces between both entities and, therefore, rely on worst-case estimations of communication delay, often leading to conservative behavior of the overall system. By contrast, the present approach establishes a robust distributed model-predictive control scheme, in which the local subsystem controllers operate under the assumption of a variable communication schedule that is predicted by a network controller. Using appropriate models for the communication network, the network controller applies a predictive network policy for scheduling the communication among the subsystem controllers across the network. Given the resulting time-varying predictions of the age of information, this article shows under which conditions the subsystem controllers can robustly stabilize the distributed system. To illustrate the approach, this article also reports on the application to a vehicle platooning scenario.
Matching Globular Cluster Models to Observations

As ancient, gravitationally bound stellar populations, globular clusters represent abundant, vibrant laboratories, characterized by high frequencies of dynamical interactions, coupled to complex stellar evolution. Using surface brightness and velocity dispersion profiles from the literature, we fit 59 Milky Way globular clusters to dynamical models from the CMC Cluster Catalog. Without performing any interpolation, and without any directed effort to fit any particular cluster, 26 globular clusters are well matched by at least one of our models. We discuss in particular the core-collapsed clusters NGC 6293, NGC 6397, NGC 6681, and NGC 6624, and the non-core-collapsed clusters NGC 288, NGC 4372, and NGC 5897. As NGC 6624 lacks well-fitting snapshots on the main CMC Cluster Catalog, we run six additional models in order to refine the fit. We calculate metrics for mass segregation, explore the production of compact object sources such as millisecond pulsars, cataclysmic variables, low-mass X-ray binaries, and stellar-mass black holes, finding reasonable agreement with observations. In addition, closely mimicking observational cuts, we extract the binary fraction from our models, finding good agreement, except in the dense core regions of core-collapsed clusters. Accompanying this paper are a number of python methods for examining the publicly accessible CMC Cluster Catalog, as well as any other models generated using CMC.
Constrained Learning With Non-Convex Losses

Though learning has become a core component of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced systems. The need to impose requirements on learning is therefore paramount, especially as it reaches critical applications in social, industrial, and medical domains. However, the non-convexity of most modern statistical problems is only exacerbated by the introduction of constraints. Whereas good unconstrained solutions can often be learned using empirical risk minimization, even obtaining a model that satisfies statistical constraints can be challenging. All the more so, a good one. In this paper, we overcome this issue by learning in the empirical dual domain, where constrained statistical learning problems become unconstrained and deterministic. We analyze the generalization properties of this approach by bounding the empirical duality gap—i.e., the difference between our approximate, tractable solution and the solution of the original (non-convex) statistical problem—and provide a practical constrained learning algorithm. These results establish a constrained counterpart to classical learning theory, enabling the explicit use of constraints in learning. We illustrate this theory and algorithm in rate-constrained learning applications arising in fairness and adversarial robustness.
Optimal Pricing with a Single Point

We study the following fundamental data-driven pricing problem. How can/should a decision-maker price its product based on observations at a single historical price? The decision-maker optimizes over (potentially randomized) pricing policies to maximize the worst-case ratio of the revenue it can garner compared to an oracle with full knowledge of the distribution of values, when the latter is only assumed to belong to broad non-parametric set. In particular, our framework applies to the widely used regular and monotone non-decreasing hazard rate (mhr) classes of distributions. For settings where the seller knows the exact probability of sale associated with one historical price or only a confidence interval for it, we fully characterize optimal performance and near-optimal pricing algorithms that adjust to the information at hand. As examples, against mhr distributions, we show that it is possible to guarantee $85%$ of oracle performance if one knows that half of the customers have bought at the historical price, and if only $1%$ of the customers bought, it still possible to guarantee $51%$ of oracle performance. The framework we develop leads to new insights on the value of information for pricing, as well as the value of randomization. In addition, it is general and allows to characterize optimal deterministic mechanisms and incorporate uncertainty in the probability of sale.
LCDNet: Deep Loop Closure Detection for LiDAR SLAM based on Unbalanced Optimal Transport

Loop closure detection is an essential component of Simultaneous Localization and Mapping (SLAM) systems, which reduces the drift accumulated over time. Over the years, several deep learning approaches have been proposed to address this task, however their performance has been subpar compared to handcrafted techniques, especially while dealing with reverse loops. In this paper, we introduce the novel LCDNet that effectively detects loop closures in LiDAR point clouds by simultaneously identifying previously visited places and estimating the 6-DoF relative transformation between the current scan and the map. LCDNet is composed of a shared encoder, a place recognition head that extracts global descriptors, and a relative pose head that estimates the transformation between two point clouds. We introduce a novel relative pose head based on the unbalanced optimal transport theory that we implement in a differentiable manner to allow for end-to-end training. Extensive evaluations of LCDNet on multiple real-world autonomous driving datasets show that our approach outperforms state-of-the-art techniques by a large margin even while dealing with reverse loops. Moreover, we integrate our proposed loop closure detection approach into a LiDAR SLAM library to provide a complete mapping system and demonstrate the generalization ability using different sensor setup in an unseen city.
Sustainable improvement of seeds vigor using dry atmospheric plasma priming: Evidence through coating wettability, water uptake, and plasma reactive chemistry

Lentil seeds have been packed in a dielectric barrier device and exposed for several minutes to a cold atmospheric plasma generated in helium with/without a reactive gas (nitrogen or oxygen). While no impact is evidenced on germination rates (caping nearly at 100% with/without plasma exposure), seeds vigor is clearly improved with a median germination time decreasing from 1850 min (31h) to 1500 min (26 h), hence representing a time saving of at least 5 hours. We show that the admixture of nitrogen to helium can further increase this time saving up to 8 hours. Contrarily, we demonstrate that the addition of molecular oxygen to the helium discharge does not promote seeds vigor. Whatever the plasma chemistry utilized, these biological effects are accompanied with strong hydrophilization of the seed coating (with a decrease in contact angles from 118° to 25°) as well as increased water absorption (water uptakes measured 8 hours after imbibition are close to 50% for plasma-treated seeds instead of 37% for seeds from the control group). A follow-up of the seeds over a 45-days ageing period shows the sustainability of the plasma-triggered biological effects: whatever the plasma treatment, seeds vigor remains stable and much higher than for seeds unexposed to plasma). For these reasons, the seed-packed dielectric barrier device (SP-DBD) supplied with a He-N2 gas mixture can be considered as a relevant dry atmospheric priming plasma (DAPP) in the same way as those used in routine by seed companies.
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph Analysis

Scene understanding is a critical problem in computer vision. In this paper, we propose a 3D point-based scene graph generation (SGGpoint) framework to effectively bridge perception and reasoning to achieve scene under-standing via three sequential stages, namely scene graph construction, reasoning, and inference. Within the reasoning stage, an EDGE-oriented Graph Convolutional Network (EdgeGCN) is created to exploit multi-dimensional edge features for explicit relationship modeling, together with the exploration of two associated twinning interaction mechanisms between nodes and edges for the independent evolution of scene graph representations. Overall, our integrated SGGpoint framework is established to seek and infer scene structures of interest from both real-world and synthetic 3D point-based scenes. Our experimental results show promising edge-oriented reasoning effects on scene graph generation studies. We also demonstrate our method advantage on several traditional graph representation learning benchmark datasets, including the node-wise classification on citation networks and whole-graph recognition problems for molecular analysis.
A Flow-Injection Potentiometric System for Selective and Sensitive Determination of Serum Lithium Level

A potentiometric flow injection system was adopted by using a Li+-selective electrode with improved selectivity for sensitive and selective determination of Li+ in human serum spiked samples. For the optimization of the offered flow injection system, multivariate method was utilized. Potentiometric characteristics of the system were investigated (linear range: 5.0×10−5−1.0×10−1 mol L−1, slope: 56.93 mV per decade, detection limit: 3.4×10−5 mol L−1, sampling-rate: 40 injections per hour). The detection of Li+ in real serum samples was achieved. The obtained results for the spiked serum samples were in good agreement with IC results at 95 % confidence level.
Probing of the Ligand Anatomy: Effects of the Chelating Alkoxy Ligand Modifications on the Structure and Catalytic Activity of Ruthenium Carbene Complexes

Structural modifications of the Hoveyda-Grubbs ruthenium metathesis complex via electronic and structural withdrawing of the chelating alkoxy ligand were investigated. By decreasing the donor properties of the oxygen atom, an acceleration in catalytic activity was achieved based on facilitation of the initiation step. Conformational constraints of the chelating ether linkage led to the unexpected disturbance of the complex geometry and a vast improvement of the activity.
DISC: A Dynamic Shape Compiler for Machine Learning Workloads

Many recent machine learning models show dynamic shape characteristics. However, existing AI compiler optimization systems suffer a lot from problems brought by dynamic shape models, including compilation overhead, memory usage, optimization pipeline and deployment complexity. This paper provides a compiler system to natively support optimization for dynamic shape workloads, named DISC. DISC enriches a set of IR to form a fully dynamic shape representation. It generates the runtime flow at compile time to support processing dynamic shape based logic, which avoids the interpretation overhead at runtime and enlarges the opportunity of host-device co-optimization. It addresses the kernel fusion problem of dynamic shapes with shape propagation and constraints collecting methods. This is the first work to demonstrate how to build an end-to-end dynamic shape compiler based on MLIR infrastructure. Experiments show that DISC achieves up to 3.3× speedup than TensorFlow/PyTorch, and 1.8× than Nimble.
Attention-driven read-aloud technology increases reading comprehension in children with reading disabilities

The paper presents the design of an assistive reading tool that integrates read-aloud technology with eye-tracking to regulate the speed of reading and support struggling readers in following the text while listening to it. The paper describes the design rationale of this approach, following the theory of auditory-visual integration, in terms of an automatic self-adaptable technique based on the reader's gaze that provides an individualized interaction experience. This tool has been assessed in a controlled experiment with 20 children (aged 8-10 years) with a diagnosis of dyslexia and a control group of 20 children with typical reading abilities. The results show that children with reading difficulties improved their comprehension scores by 24% measured on a standardized instrument for the assessment of reading comprehension, and that children with more inaccurate reading (N=9) tended to benefit more. The findings are discussed in terms of a better integration between audio and visual text information, paving the way to improve standard read-aloud technology with gaze-contingency and self-adaptable techniques to personalize the reading experience.
MWQ: Multiscale Wavelet Quantized Neural Networks

Model quantization can reduce the model size and computational latency, it has become an essential technique for the deployment of deep neural networks on resourceconstrained hardware (e.g., mobile phones and embedded devices). The existing quantization methods mainly consider the numerical elements of the weights and activation values, ignoring the relationship between elements. The decline of representation ability and information loss usually lead to the performance degradation. Inspired by the characteristics of images in the frequency domain, we propose a novel multiscale wavelet quantization (MWQ) method. This method decomposes original data into multiscale frequency components by wavelet transform, and then quantizes the components of different scales, respectively. It exploits the multiscale frequency and spatial information to alleviate the information loss caused by quantization in the spatial domain. Because of the flexibility of MWQ, we demonstrate three applications (e.g., model compression, quantized network optimization, and information enhancement) on the ImageNet and COCO datasets. Experimental results show that our method has stronger representation ability and can play an effective role in quantized neural networks.
Flow time scheduling with uncertain processing time

We consider the problem of online scheduling on a single machine in order to minimize weighted flow time. The existing algorithms for this problem (STOC ’01, SODA ’03, FOCS ’18) all require exact knowledge of the processing time of each job. This assumption is crucial, as even a slight perturbation of the processing time would lead to polynomial competitive ratio. However, this assumption very rarely holds in real-life scenarios. In this paper, we present the first algorithm for weighted flow time which do not require exact knowledge of the processing times of jobs. Specifically, we introduce the Scheduling with Predicted Processing Time (SPPT) problem, where the algorithm is given a prediction for the processing time of each job, instead of its real processing time. For the case of a constant factor distortion between the predictions and the real processing time, our algorithms match all the best known competitiveness bounds for weighted flow time – namely O(logP), O(logD) and O(logW), where P,D,W are the maximum ratios of processing times, densities, and weights, respectively. For larger errors, the competitiveness of our algorithms degrades gracefully.
PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency

Removing outlier correspondences is one of the critical steps for successful feature-based point cloud registration. Despite the increasing popularity of introducing deep learning techniques in this field, spatial consistency, which is essentially established by a Euclidean transformation between point clouds, has received almost no individual attention in existing learning frameworks. In this paper, we present PointDSC, a novel deep neural network that explicitly incorporates spatial consistency for pruning outlier correspondences. First, we propose a nonlocal feature aggregation module, weighted by both feature and spatial coherence, for feature embedding of the input correspondences. Second, we formulate a differentiable spectral matching module, supervised by pairwise spatial compatibility, to estimate the inlier confidence of each correspondence from the embedded features. With modest computation cost, our method outperforms the state-of-the-art hand- crafted and learning-based outlier rejection approaches on several real-world datasets by a significant margin. We also show its wide applicability by combining PointDSC with different 3D local descriptors. [code release]
On the Oracle Complexity of Higher-Order Smooth Non-Convex Finite-Sum Optimization

We prove lower bounds for higher-order methods in smooth non-convex finite-sum optimization. Our contribution is threefold: We first show that a deterministic algorithm cannot profit from the finite-sum structure of the objective, and that simulating a pth-order regularized method on the whole function by constructing exact gradient information is optimal up to constant factors. We further show lower bounds for randomized algorithms and compare them with the best known upper bounds. To address some gaps between the bounds, we propose a new second-order smoothness assumption that can be seen as an analogue of the first-order mean-squared smoothness assumption. We prove that it is sufficient to ensure state-of-the-art convergence guarantees, while allowing for a sharper lower bound.