Miracle-dz/UniFAR-FTUs huggingface dataset:
Online Discovery of Evolving Groups over Massive-Scale Trajectory Streams

The increasing pervasiveness of object tracking technologies leads to huge volumes of spatiotemporal data collected in the form of trajectory streams. The discovery of useful group patterns from moving objects' movement behaviours in trajectory streams is critical for real-time applications ranging from transportation management to military surveillance. Motivated by this, we first propose a novel pattern, called evolving group, which models the unusual group events of moving objects that travel together within density connected clusters in evolving streaming trajectories. Our theoretical analysis and empirical study on the Osaka Pedestrian data and Beijing Taxi data demonstrate its effectiveness in capturing the development, evolution, and trend of group events of moving objects in streaming context. Moreover, we propose a discovery method that efficiently supports online detection of evolving groups over massive-scale trajectory streams using a sliding window. It contains three phases along with a set of novel optimization techniques designed to minimize the computation costs. Furthermore, to scale to huge workloads over evolving streams, we extend our discovery method to a parallel framework by using a sector-based partition. Our comprehensive empirical study demonstrates that our online discovery framework is effective and efficient on real-world high-volume trajectory streams.
Automated Market Making and Loss-Versus-Rebalancing

Automated market making (AMM) protocols such as Uniswap have recently emerged as an alternative to the most common market structure for electronic trading, the limit order book. Relative to limit order books, AMMs are both more computationally efficient and do not require the participation of active market making intermediaries. As such, AMMs have emerged as the dominant market mechanism for trust-less decentralized exchanges (DEXs). We develop a model the underlying economics of AMMs from the perspective of their passive liquidity providers (LPs). Our central contribution is a"Black-Scholes formula for AMMs". Like the Black-Scholes formula, we consider the return to LPs once market risk has been hedged. We identify the main adverse selection cost incurred by LPs, which we call"loss-versus-rebalancing"(LVR, pronounced"lever"). LVR captures costs incurred by AMM LPs due to stale prices that are picked off by better informed arbitrageurs. In a continuous time Black-Scholes setting, we are able to derive closed-form expressions for this adverse selection cost, for all automated market makers, including constant function market makers and those featuring concentrated liquidity (e.g., Uniswap v3). Qualitatively, we highlight the main forces that drive AMM LP returns, including asset characteristics (volatility) and AMM characteristics (curvature/marginal liquidity). Quantitatively, we illustrate how our model's expressions for LP returns match actual LP returns for the Uniswap v2 WETH-USDC trading pair. Our model provides tradable insight into both the ex ante and ex post assessment of AMM LP investment decisions. LVR can also inform the design of the next generation of DEX market mechanisms -- in fact, in the short time since our work has been released,"LVR mitigation"has already emerged as the dominant challenge among practitioners in the AMM protocol designer community.
Foliations Formed by Generic Coadjoint Orbits of Lie Groups Corresponding to a Class Seven-Dimensional Solvable Lie Algebras

We consider all connected and simply connected 7-dimensional Lie groups whose Lie algebras have nilradical g 5 , 2 = span { X 1 , X 2 , X 3 , X 4 , X 5 : [ X 1 , X 2 ] = X 4 , [ X 1 , X 3 ] = X 5 } of Dixmier. First, we give a geometric description of the maximal-dimensional orbits in the coadjoint representation of all considered Lie groups. Next, we prove that, for each considered group, the family of the generic coadjoint orbits forms a measurable foliation in the sense of Connes. Finally, the topological classiﬁcation of all these foliations is also provided.
Category-Level Pose Retrieval with Contrastive Features Learnt with Occlusion Augmentation

Pose estimation is usually tackled as either a bin classification or a regression problem. In both cases, the idea is to directly predict the pose of an object. This is a non-trivial task due to appearance variations between similar poses and similarities between dissimilar poses. Instead, we follow the key idea that comparing two poses is easier than directly predicting one. Render-and-compare approaches have been employed to that end, however, they tend to be unstable, computationally expensive, and slow for real-time applications. We propose doing category-level pose estimation by learning an alignment metric in an embedding space using a contrastive loss with a dynamic margin and a continuous pose-label space. For efficient inference, we use a simple real-time image retrieval scheme with a pre-rendered and pre-embedded reference set of renderings. To achieve robustness to real-world conditions, we employ synthetic occlusions, bounding box perturbations, and appearance augmentations. Our approach achieves state-of-the-art performance on PASCAL3D and OccludedPASCAL3D and surpasses the competing methods on KITTI3D in a cross-dataset evaluation setting. The code is currently available at https://github.com/gkouros/contrastive-pose-retrieval.
The Multiple Stellar Populations in ω Centauri

In this contribution I am going to present some preliminary results of a high-resolution spectroscopic campaign focussed on the most metal rich red giant stars in Omega Cen. This study is part of a long term project we started a few years ago, which is aimed at studying the properties of the different stellar populations in Omega Cen. The final goal of the whole project is the global understanding of both the star formation and the chemical evolution history of this complex stellar system.
BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers

Masked image modeling (MIM) has demonstrated impressive results in selfsupervised representation learning by recovering corrupted image patches. However, most existing studies operate on low-level image pixels, which hinders the exploitation of high-level semantics for representation models. In this work, we propose to use a semantic-rich visual tokenizer as the reconstruction target for masked prediction, providing a systematic way to promote MIM from pixel-level to semantic-level. Specifically, we propose vector-quantized knowledge distillation to train the tokenizer, which discretizes a continuous semantic space to compact codes. We then pretrain vision Transformers by predicting the original visual tokens for the masked image patches. Furthermore, we introduce a patch aggregation strategy which associates discrete image patches to enhance global semantic representation. Experiments on image classification and semantic segmentation show that BEIT V2 outperforms all compared MIM methods. On ImageNet-1K (224 size), the base-size BEIT V2 achieves 85.5% top-1 accuracy for fine-tuning and 80.1% top-1 accuracy for linear probing. The large-size BEIT V2 obtains 87.3% top-1 accuracy for ImageNet-1K (224 size) fine-tuning, and 56.7% mIoU on ADE20K for semantic segmentation. The code and pretrained models are available at https://aka.ms/beitv2.
Metric inhomogeneous Diophantine approximation in positive characteristic

We obtain asymptotic formulae for the number of solutions to systems of inhomogeneous linear Diophantine inequalities over the field of formal Laurent series with coefficients from a finite fields, which are valid for almost every such system. Here "almost every" is with respect to Haar measure of the coefficients of the homogeneous part when the number of variables is at least two (singly metric case), and with respect to the Haar measure of all coefficients for any number of variables (doubly metric case). As consequences, we derive zero-one laws in the spirit of the Khintchine-Groshev Theorem and zero-infinity laws for Hausdorff measure in the spirit of Jarnik's Theorem. The latter result depends on extending a recently developed slicing technique of Beresnevich and Velani to the present setup.
Energy-Aware Design of UAV-Mounted RIS Networks for IoT Data Collection

Data collection in massive Internet of Things networks requires novel and flexible methods. Unmanned aerial vehicles (UAVs) are foreseen as a means to collect data rapidly even in remote areas without static telecommunication infrastructure. In this direction, UAV-mounted reconfigurable intelligent surfaces (RISs) aid in reducing the hardware requirements and signal processing complexity at the UAV side, while increasing the network’s energy efficiency and coverage. Hence, in this paper, we propose the utilization of a UAV-mounted RIS for data collection and study the coverage probability in such networks. Additionally, we propose a novel medium access control protocol based on slotted ALOHA and Code Combining to handle the communication of multiple sensors. To account for the crucial energy issue in UAVs, we devise an energy model that considers both the UAV and the RIS weight, as well as the environmental conditions and the UAV’s velocity. Finally, we characterize the performance of the proposed data collection scheme by analyzing the average throughput and the average collected data per flight, while providing useful insights for the design of such networks.
Automatic Sequences in Negative Bases and Proofs of Some Conjectures of Shevelev

We discuss the use of negative bases in automatic sequences. Recently the theorem-prover Walnut has been extended to allow the use of base (—k) to express variables, thus permitting quantification over ℤ instead of ℕ. This enables us to prove results about two-sided (bi-infinite) automatic sequences. We first explain the theory behind negative bases in Walnut. Next, we use this new version of Walnut to give a very simple proof of a strengthened version of a theorem of Shevelev. We use our ideas to resolve two open problems of Shevelev from 2017. We also reprove a 2000 result of Shut involving bi-infinite binary words.
Unifying Gradients to Improve Real-World Robustness for Deep Networks

The wide application of deep neural networks (DNNs) demands an increasing amount of attention to their real-world robustness, i.e., whether a DNN resists black-box adversarial attacks, among which score-based query attacks (SQAs) are the most threatening since they can effectively hurt a victim network with only access to model outputs. Defending against SQAs requires a slight but artful variation of outputs due to the service purpose for users, who share the same output information with SQAs. In this article, we propose a real-world defense by Unifying Gradients (UniG) of different data so that SQAs could only probe a much weaker attack direction that is similar for different samples. Since such universal attack perturbations have been validated as less aggressive than the input-specific perturbations, UniG protects real-world DNNs by indicating to attackers a twisted and less informative attack direction. We implement UniG efficiently by a Hadamard product module, which is plug-and-play. According to extensive experiments on 5 SQAs, 2 adaptive attacks and 7 defense baselines, UniG significantly improves real-world robustness without hurting clean accuracy on CIFAR10 and ImageNet. For instance, UniG maintains a model of 77.80% accuracy under a 2500-query Square attack while the state-of-the-art adversarially trained model only has 67.34% on CIFAR10. Simultaneously, UniG outperforms all compared baselines in terms of clean accuracy and achieves the smallest modification of the model output. The code is released at https://github.com/snowien/UniG-pytorch.
Exoplanet Radio Transits as a Probe for Exoplanetary Magnetic Fields—Time-dependent MHD Simulations

We perform a series of time-dependent magnetohydrodynamic simulations of the HD 189733 star–planet system in order to predict radio transit modulations due to the interaction between the stellar wind and planetary magnetic field. The simulation combines a model for the stellar corona and wind with an exoplanet that is orbiting the star in a fully dynamic, time-dependent manner. Our simulations generate synthetic radio images that enable us to obtain synthetic radio light curves in different frequencies. We find a clear evidence for the planetary motion in the radio light curves. Moreover, we find specific repeated features in the light curves that are attributed to the passage of the planetary magnetosphere in front of the star during transit. More importantly, we find a clear dependence in magnitude and phase of these light-curve features on the strength of the planetary magnetic field. Our work demonstrates that if radio transits could be observed, they could indeed provide information about the magnetic field strength of the transiting exoplanet. Future work to parameterize these light-curve features and their dependence on the planetary field strength would provide tools to search for these features in radio observation data sets. As we only consider the thermal radio emission from the host star for our study, very sensitive radio interferometers are necessary to detect these kinds of planetary transits in radio.
Gaussian process surrogate models for neural networks

Not being able to understand and predict the behavior of deep learning systems makes it hard to decide what architecture and algorithm to use for a given problem. In science and engineering, modeling is a methodology used to understand complex systems whose internal processes are opaque. Modeling replaces a complex system with a simpler, more interpretable surrogate. Drawing inspiration from this, we construct a class of surrogate models for neural networks using Gaussian processes. Rather than deriving kernels for infinite neural networks, we learn kernels empirically from the naturalistic behavior of finite neural networks. We demonstrate our approach captures existing phenomena related to the spectral bias of neural networks, and then show that our surrogate models can be used to solve practical problems such as identifying which points most influence the behavior of specific neural networks and predicting which architectures and algorithms will generalize well for specific datasets.
Measurements of the five-dimensional phase space distribution of an intense ion beam

No simulation of intense beam transport has accurately reproduced measurements at the level of beam halo. One potential explanation of this discrepancy is a lack of knowl-edge of the initial distribution of particles in six-dimensional (6D) phase space. A direct 6D measurement of an ion beam was recently performed at the Spallation Neutron Source (SNS) Beam Test Facility (BTF), revealing nonlinear transverse-longitudinal correlations in the beam core that affect downstream evolution. Unfortunately, direct 6D measurements are limited in resolution and dynamic range; here, we discuss the use of three slits and one screen to measure a 5D projection of the 6D phase space distribution, overcoming these limitations at the cost of one dimension. We examine the measured 5D distribution before and after transport through the BTF and compare to particle-in-cell simulations. We also discuss the possibility of reconstructing the 6D distribution from 5D and 4D projections.
Algebraic Reduction of Hidden Markov Models

The problem of reducing a hidden Markov model (HMM) to one of smaller dimension that exactly reproduces the same marginals is tackled by using a system-theoretic approach. Realization theory tools are extended to HMMs by leveraging suitable algebraic representations of probability spaces. We propose two algorithms that return coarse-grained equivalent HMMs obtained by stochastic projection operators: the first returns models that exactly reproduce the single-time distribution of a given output process, while in the second, the full (multitime) distribution is preserved. The reduction method exploits not only the structure of the observed output but also its initial condition, whenever the latter is known or belongs to a given subclass. Optimal algorithms are derived for a class of HMMs, namely observable ones.
Dropout is NOT All You Need to Prevent Gradient Leakage

Gradient inversion attacks on federated learning systems reconstruct client training data from exchanged gradient information. To defend against such attacks, a variety of defense mechanisms were proposed. However, they usually lead to an unacceptable trade-off between privacy and model utility. Recent observations suggest that dropout could mitigate gradient leakage and improve model utility if added to neural networks. Unfortunately, this phenomenon has not been systematically researched yet. In this work, we thoroughly analyze the effect of dropout on iterative gradient inversion attacks. We find that state of the art attacks are not able to reconstruct the client data due to the stochasticity induced by dropout during model training. Nonetheless, we argue that dropout does not offer reliable protection if the dropout induced stochasticity is adequately modeled during attack optimization. Consequently, we propose a novel Dropout Inversion Attack (DIA) that jointly optimizes for client data and dropout masks to approximate the stochastic client model. We conduct an extensive systematic evaluation of our attack on four seminal model architectures and three image classification datasets of increasing complexity. We find that our proposed attack bypasses the protection seemingly induced by dropout and reconstructs client data with high fidelity. Our work demonstrates that privacy inducing changes to model architectures alone cannot be assumed to reliably protect from gradient leakage and therefore should be combined with complementary defense mechanisms.
Gas Bubble Photonics: Manipulating Sonoluminescence Light with Fluorescent and Plasmonic Nanoparticles

Oscillations of gas bubbles in liquids irradiated with acoustic pressure waves may result in an intriguing physical phenomenon called sonoluminescence, where a collapsing bubble emits the in a broad optical spectral range. However, the intensity of the so-generated light is typically weak for practical purposes. Recently, it has been demonstrated that nanoparticles can be used to increase the efficiency of sonoluminescence, thereby enabling one to generate light that is intense enough for a number of applications in photonics, biomedicine, and materials science. In this article, we review the latest achievements in the field of nanoparticle-enhanced sonoluminescence and showcase the perspectives of their practical applications.
Observations of Thomson Scattering from a Loop-prominence System

We describe observations of the white-light structures in the low corona following the X8.2 flare SOL 2017-09-10, as observed in full Stokes parameters by the Helioseismic and Magnetic Imager (HMI) of the Solar Dynamics Observatory. These data show both bright loops and a diffuse emission region above them. We interpret the loops as the white-light counterpart of a classical loop-prominence system, intermediate between the hot X-ray loops and coronal rain. The diffuse emission external to the loops is linearly polarized and has a natural interpretation in terms of Thomson scattering from the hot plasma seen prior to its cooling and recombination. The polarimetric data from HMI enable us to distinguish this contribution of scattering from the HMI pseudocontinuum measurement, and to make a direct estimation of the coronal mass in the polarized source. For a snapshot at 16:19 UT, we estimate a mass 8 × 1014 g. We further conclude that the volumetric filling factor of this source is near unity.
Vortex structure deformation of rotating Lifshitz holographic superconductors

We construct a type-II holographic superconductor from an anisotropic rotating background metric. We study the eﬀects of the magnetic ﬁeld on the vortex structure, including continuous deformations both from triangular to square lattices or vice versa. Our holographic model reproduces known experimental vortex lattice deformations and the increment of the vortex population by increasing the external magnetic ﬁeld.
Changes in the Na D1 Absorption Components of η Carinae Provide Clues on the Location of the Dissipating Central Occulter

The Na D absorption doublet in the spectrum of η Carinae is complex, with multiple absorption features associated with the Great Eruption (1840s), the Lesser Eruption (1890s), and the interstellar clouds. The velocity profile is further complicated by the P Cygni profile originating in the system’s stellar winds and blending with the He i λ5876 profile. The Na D profile contains a multitude of absorption components, including those at velocities of −145 km s−1, −168 km s−1, and +87 km s−1, which we concentrate on in this analysis. Ground-based spectra recorded from 2008 to 2021 show significant variability of the −145 km s−1 absorption throughout long-term observations. In the high-ionization phases of η Carinae prior to the 2020 periastron passage, this feature disappeared completely but briefly reappeared across the 2020 periastron, along with a second absorption at −168 km s−1. Over the past few decades, η Carinae has been gradually brightening, which is shown to be caused by a dissipating occulter. The decreasing absorption of the −145 km s−1 component, coupled with similar trends seen in absorptions of ultraviolet resonant lines, indicate that this central occulter was possibly a large clump associated with the Little Homunculus or another clump between the Little Homunculus and the star. We also report on a foreground absorption component at +87 km s−1. Comparison of Na D absorption in the spectra of nearby systems demonstrates that this redshifted component likely originates in an extended foreground structure consistent with a previous ultraviolet spectral survey in the Carina Nebula.
Modular interface for managing cognitive bias in experts

Expert knowledge is required to interpret data across a range of fields. Experts bridge gaps that often exists in our knowledge about relationships between data and the parameters of interest. This is especially true in geoscientific applications, where knowledge of the Earth is derived from interpretations of observable features and relies on predominantly unproven but widely accepted theories. Thus, experts facilitate solutions to otherwise unsolvable problems. However, experts are inherently subjective, and susceptible to cognitive biases and adverse external effects. This work examines this problem within geoscience. Three compelling examples are provided of the prevalence of cognitive biases from previous work. The problem is then formally defined, and a set of design principles which ensure that any solution is sufficiently flexible to be readily applied to the range of geoscientific problems. No solutions exist that reliably capture and reduce cognitive bias in experts. However, formal expert elicitation methods can be used to assess expert variation, and a variety of approaches exist that may help to illuminate uncertainties, avoid misunderstandings, and reduce herding behaviours or single-expert over-dominance. This work combines existing and future