Miracle-dz/UniFAR-FTUs huggingface dataset:
Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric

Heterogeneous graphs offer powerful data representations for traffic, given their ability to model the complex interaction effects among a varying number of traffic participants and the underlying road infrastructure. With the recent advent of graph neural networks (GNNs) as the accompanying deep learning framework, the graph structure can be efficiently leveraged for various machine learning applications such as trajectory prediction. As a first of its kind, our proposed Python framework offers an easy-to-use and fully customizable data processing pipeline to extract standardized graph datasets from traffic scenarios. Providing a platform for GNN-based autonomous driving research, it improves comparability between approaches and allows researchers to focus on model implementation instead of dataset curation.
Phase transition of parallelizability in assembly systems

We propose a phase transition on the feasibility of efficient parallel assembly. By introducing the parallel efficiency that measures how efficiently the parallel assembly works, the parallelizable phase is defined by its positive value. The parallelizable/unparallelizable transition is then identified by the non-analytic change in the parallel efficiency from a positive value to zero. We present two analyzable models to demonstrate this phase transition in the limit of infinite system size.
SSO-Monitor: Fully-Automatic Large-Scale Landscape, Security, and Privacy Analyses of Single Sign-On in the Wild

Single Sign-On (SSO) shifts the crucial authentication process on a website to to the underlying SSO protocols and their correct implementation. To strengthen SSO security, organizations, such as IETF and W3C, maintain advisories to address known threats. One could assume that these security best practices are widely deployed on websites. We show that this assumption is a fallacy. We present SSO-MONITOR, an open-source fully-automatic large-scale SSO landscape, security, and privacy analysis tool. In contrast to all previous work, SSO-MONITOR uses a highly extensible, fully automated workflow with novel visual-based SSO detection techniques, enhanced security and privacy analyses, and continuously updated monitoring results. It receives a list of domains as input to discover the login pages, recognize the supported Identity Providers (IdPs), and execute the SSO. It further reveals the current security level of SSO in the wild compared to the security best practices on paper. With SSO-MONITOR, we automatically identified 1,632 websites with 3,020 Apple, Facebook, or Google logins within the Tranco 10k. Our continuous monitoring also revealed how quickly these numbers change over time. SSO-MONITOR can automatically login to each SSO website. It records the logins by tracing HTTP and in-browser communication to detect widespread security and privacy issues automatically. We introduce a novel deep-level inspection of HTTP parameters that we call SMARTPARMS. Using SMARTPARMS for security analyses, we uncovered URL parameters in 5 Client Application (Client) secret leakages and 337 cases with weak CSRF protection. We additionally identified 447 cases with no CSRF protection, 342 insecure SSO flows and 9 cases with nested URL parameters, leading to an open redirect in one case. SSO-MONITOR reveals privacy leakages that deanonymize users in 200 cases.
A Universal Technique for Machine-Certified Proofs of Linearizable Algorithms

Linearizability has been the long standing gold standard for consistency in concurrent data structures. However, proofs of linearizability can be long and intricate, hard to produce, and extremely time consuming even to verify. In this work, we address this issue by introducing simple $universal$, $sound$, and $complete$ proof methods for producing machine-verifiable proofs of linearizability and its close cousin, strong linearizability. Universality means that our method works for any object type; soundness means that an algorithm can be proved correct by our method only if it is linearizable (resp. strong linearizable); and completeness means that any linearizable (resp. strong linearizable) implementation can be proved so using our method. We demonstrate the simplicity and power of our method by producing proofs of linearizability for the Herlihy-Wing queue and Jayanti's single-scanner snapshot, as well as a proof of strong linearizability of the Jayanti-Tarjan union-find object. All three of these proofs are machine-verified by TLAPS (the Temporal Logic of Actions Proof System).
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics

Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$). MD is often used to compute equilibrium properties, which requires sampling from an equilibrium distribution such as the Boltzmann distribution. However, many important processes, such as binding and folding, occur over timescales of milliseconds or beyond, and cannot be efficiently sampled with conventional MD. Furthermore, new MD simulations need to be performed for each molecular system studied. We present Timewarp, an enhanced sampling method which uses a normalising flow as a proposal distribution in a Markov chain Monte Carlo method targeting the Boltzmann distribution. The flow is trained offline on MD trajectories and learns to make large steps in time, simulating the molecular dynamics of $10^{5} - 10^{6}\:\textrm{fs}$. Crucially, Timewarp is transferable between molecular systems: once trained, we show that it generalises to unseen small peptides (2-4 amino acids) at all-atom resolution, exploring their metastable states and providing wall-clock acceleration of sampling compared to standard MD. Our method constitutes an important step towards general, transferable algorithms for accelerating MD.
Learning in Multi-Memory Games Triggers Complex Dynamics Diverging from Nash Equilibrium

Repeated games consider a situation where multiple agents are motivated by their independent rewards throughout learning. In general, the dynamics of their learning become complex. Especially when their rewards compete with each other like zero-sum games, the dynamics often do not converge to their optimum, i.e., the Nash equilibrium. To tackle such complexity, many studies have understood various learning algorithms as dynamical systems and discovered qualitative insights among the algorithms. However, such studies have yet to handle multi-memory games (where agents can memorize actions they played in the past and choose their actions based on their memories), even though memorization plays a pivotal role in artificial intelligence and interpersonal relationship. This study extends two major learning algorithms in games, i.e., replicator dynamics and gradient ascent, into multi-memory games. Then, we prove their dynamics are identical. Furthermore, theoretically and experimentally, we clarify that the learning dynamics diverge from the Nash equilibrium in multi-memory zero-sum games and reach heteroclinic cycles (sojourn longer around the boundary of the strategy space), providing a fundamental advance in learning in games.
De Novo Molecular Generation via Connection-aware Motif Mining

De novo molecular generation is an essential task for science discovery. Recently, fragment-based deep generative models have attracted much research attention due to their flexibility in generating novel molecules based on existing molecule fragments. However, the motif vocabulary, i.e., the collection of frequent fragments, is usually built upon heuristic rules, which brings difficulties to capturing common substructures from large amounts of molecules. In this work, we propose a new method, MiCaM, to generate molecules based on mined connection-aware motifs. Specifically, it leverages a data-driven algorithm to automatically discover motifs from a molecule library by iteratively merging subgraphs based on their frequency. The obtained motif vocabulary consists of not only molecular motifs (i.e., the frequent fragments), but also their connection information, indicating how the motifs are connected with each other. Based on the mined connection-aware motifs, MiCaM builds a connection-aware generator, which simultaneously picks up motifs and determines how they are connected. We test our method on distribution-learning benchmarks (i.e., generating novel molecules to resemble the distribution of a given training set) and goal-directed benchmarks (i.e., generating molecules with target properties), and achieve significant improvements over previous fragment-based baselines. Furthermore, we demonstrate that our method can effectively mine domain-specific motifs for different tasks.
Faster Convergence with Lexicase Selection in Tree-based Automated Machine Learning

In many evolutionary computation systems, parent selection methods can affect, among other things, convergence to a solution. In this paper, we present a study comparing the role of two commonly used parent selection methods in evolving machine learning pipelines in an automated machine learning system called Tree-based Pipeline Optimization Tool (TPOT). Specifically, we demonstrate, using experiments on multiple datasets, that lexicase selection leads to significantly faster convergence as compared to NSGA-II in TPOT. We also compare the exploration of parts of the search space by these selection methods using a trie data structure that contains information about the pipelines explored in a particular run.
Rainbow triangles sharing one common vertex or edge

Let G be an edge-colored graph on n vertices. For a vertex v , the color degree of v in G , denoted by d c ( v ), is the number of colors appearing on the edges incident with v . Denote by δ c ( G ) = min { d c ( v ) : v ∈ V ( G ) } . By a theorem of H. Li, an n -vertex edge-colored graph G contains a rainbow triangle if δ c ( G ) ≥ n +12 . Inspired by this result, we consider two related questions concerning edge-colored books and friendship subgraphs of edge-colored graphs. Let k ≥ 2 be a positive integer. We prove that if δ c ( G ) ≥ n + k − 1 2 where n ≥ 3 k − 2, then G contains k rainbow triangles sharing one common edge; and if δ c ( G ) ≥ n +2 k − 3 2 where n ≥ 2 k + 9, then G contains k rainbow triangles sharing one common vertex. The special case k = 2 of both results improves H. Li’s theorem. The main novelty of our proof of the ﬁrst result is a combination of the recent new technique for ﬁnding rainbow cycles due to Czygrinow, Molla, Nagle, and Oursler and some recent counting technique from [26]. The proof of the second result is with the aid of the machine implicitly in the work of Tur´an numbers for matching numbers due to Erd˝os and Gallai.
Can shallow quantum circuits scramble local noise into global white noise?

Shallow quantum circuits are believed to be the most promising candidates for achieving early practical quantum advantage—this has motivated the development of a broad range of error mitigation techniques whose performance generally improves when the quantum state is well approximated by a global depolarising (white) noise model. While it has been crucial for demonstrating quantum supremacy that random circuits scramble local noise into global white noise—a property that has been proved rigorously—we investigate to what degree practical shallow quantum circuits scramble local noise into global white noise. We define two key metrics as (a) density matrix eigenvalue uniformity and (b) commutator norm that quantifies stability of the dominant eigenvector. While the former determines the distance from white noise, the latter determines the performance of purification based error mitigation. We derive analytical approximate bounds on their scaling and find in most cases they nicely match numerical results. On the other hand, we simulate a broad class of practical quantum circuits and find that white noise is in certain cases a bad approximation posing significant limitations on the performance of some of the simpler error mitigation schemes. On a positive note, we find in all cases that the commutator norm is sufficiently small guaranteeing a very good performance of purification-based error mitigation. Lastly, we identify techniques that may decrease both metrics, such as increasing the dimensionality of the dynamical Lie algebra by gate insertions or randomised compiling.
Bibliometric and social network analysis on the use of satellite imagery in agriculture: an entropy-based approach

Satellite imagery is gaining popularity as a valuable tool to lower the impact on natural resources and increase profits for farmers. The purpose of this study is twofold: to mine the scientific literature to reveal the structure of this research domain, and to investigate to what extent scientific results can reach a wider public audience. To meet these two objectives, a Web of Science and a Twitter dataset were retrieved and analysed, respectively. For the academic literature, different performances of various countries were observed: the USA and China resulted as the leading actors, both in terms of published papers and employed researchers. Among the categorised keywords, “resolution”, “Landsat”, “yield”, “wheat” and “multispectral” are the most used. Then, analysing the semantic network of the words used in the various abstracts, the different facets of the research in satellite remote sensing were detected. The importance of retrieving meteorological parameters through remote sensing and the broad use of vegetation indexes emerged from these analyses. As emerging topics, classification tasks for land use assessment and crop recognition stand out, alongside the use of hyperspectral sensors. Regarding the interaction of academia with the public, the analysis showed that it is practically absent on Twitter: most of the activity therein stems from private companies advertising their business. This shows that there is still a communication gap between academia and actors from other societal sectors.
La conjecture du facteur direct (d'apr\`es Y.Andr\'e et B.Bhatt)

La conjecture du facteur direct (théorème 1.1 ci-dessous) est un énoncé d’algèbre commutative presque aussi élémentaire que le Théorème de Fermat et qui est resté ouvert pendant près de 50 ans : énoncée en 1969 (cf. Hochster (1973) pour la version publiée), elle a été démontrée par André en 2016 (cf. André (2018b) pour la version publiée). Cet énoncé fait partie d’un faisceau de conjectures, les « conjectures homologiques » dont la liste et les relations donnent un peu le tournis . En particulier, Hochster (1975, 1983) avait prouvé que l’existence de A-algèbres (ou même seulement de A-modules) de Cohen–Macaulay (voir § 1.5), pour tout anneau local noethérien A, impliquait la plupart de ces conjectures, par exemple celle du facteur direct. Hochster et Huneke (1992) avaient aussi montré (2) l’existence de telles A-algèbres dans le cas d’égale caractéristique, i.e. quand A contient un corps.
Unsupervised Entity Alignment for Temporal Knowledge Graphs

Entity alignment (EA) is a fundamental data integration task that identifies equivalent entities between different knowledge graphs (KGs). Temporal Knowledge graphs (TKGs) extend traditional knowledge graphs by introducing timestamps, which have received increasing attention. State-of-the-art time-aware EA studies have suggested that the temporal information of TKGs facilitates the performance of EA. However, existing studies have not thoroughly exploited the advantages of temporal information in TKGs. Also, they perform EA by pre-aligning entity pairs, which can be labor-intensive and thus inefficient. In this paper, we present DualMatch that effectively fuses the relational and temporal information for EA. DualMatch transfers EA on TKGs into a weighted graph matching problem. More specifically, DualMatch is equipped with an unsupervised method, which achieves EA without necessitating the seed alignment. DualMatch has two steps: (i) encoding temporal and relational information into embeddings separately using a novel label-free encoder, Dual-Encoder; and (ii) fusing both information and transforming it into alignment using a novel graph-matching-based decoder, GM-Decoder. DualMatch is able to perform EA on TKGs with or without supervision, due to its capability of effectively capturing temporal information. Extensive experiments on three real-world TKG datasets offer the insight that DualMatch significantly outperforms the state-of-the-art methods.
Classifying topology of consistent thermodynamics of four-dimensional Lorentzian NUT-charged spacetimes

A BSTRACT : In this paper, employing the uniform modiﬁed form of the generalized off-shell Helmholtz free energy, we investigate the topological numbers for the whole class of four-dimensional NUT-charged spacetimes. We ﬁnd that, while the topological numbers of almost all four-dimensional NUT-charged spacetimes (except the Taub-NUT spacetime and Kerr-NUT spacetime) vary depending on the values of the solution parameters, the entire class of four-dimensional NUT-charged spacetime solutions can still be classiﬁed as one of the three types of well-known black hole solutions. Thus, the whole class of four-dimensional NUT-charged spacetimes should be viewed as generic black holes from the viewpoint of the thermodynamic topological approach. Furthermore, the four-dimensional Reissner-Nordstr ¨ om-Taub-NUT (RN-NUT) spacetime, Kerr-Newman-NUT (KN-NUT) spacetime, and RN-NUT-AdS spacetime provide three counterexamples that are in sharp contrast to the conclusions reached in all previous works, which conclude that a black hole solution only has one certain topological number, independent of the values of the solution parameters. What is more, these topological numbers could help us unravel the mysteries of NUT-charged spacetimes with their so many peculiar properties and shed new light on the research of black hole thermodynamics.
Plasmonics: Future Outlook

Plasma resonance in metals exhibits some unique optical phenomena that occur on the surface of metal with nanostructures. The use of surface plasmons has been proposed in various fields, such as nanometer-resolution near-field optical microscopy, nanoscale optical circuits, single-molecule detection, molecular sensors, cancer treatment, solar cells, lasers, and holography. The study of plasma resonance is called “plasmonics” and is expected as a new field of nanophotonics. In this report, I review the principles and limits of plasmonics and give a future outlook.
Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils

Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving the way for significant changes in computational engineering applications. Here, we investigate the use of DeepONets to infer flow fields around unseen airfoils with the aim of shape optimization, an important design problem in aerodynamics that typically taxes computational resources heavily. We present results which display little to no degradation in prediction accuracy, while reducing the online optimization cost by orders of magnitude. We consider NACA airfoils as a test case for our proposed approach, as their shape can be easily defined by the four-digit parametrization. We successfully optimize the constrained NACA four-digit problem with respect to maximizing the lift-to-drag ratio and validate all results by comparing them to a high-order CFD solver. We find that DeepONets have low generalization error, making them ideal for generating solutions of unseen shapes. Specifically, pressure, density, and velocity fields are accurately inferred at a fraction of a second, hence enabling the use of general objective functions beyond the maximization of the lift-to-drag ratio considered in the current work.
Variational Autoencoder Learns Better Feature Representations for EEG-based Obesity Classification

Obesity is a common issue in modern societies today that can lead to various diseases and significantly reduced quality of life. Currently, research has been conducted to investigate resting state EEG (electroencephalogram) signals with an aim to identify possible neurological characteristics associated with obesity. In this study, we propose a deep learning-based framework to extract the resting state EEG features for obese and lean subject classification. Specifically, a novel variational autoencoder framework is employed to extract subject-invariant features from the raw EEG signals, which are then classified by a 1-D convolutional neural network. Comparing with conventional machine learning and deep learning methods, we demonstrate the superiority of using VAE for feature extraction, as reflected by the significantly improved classification accuracies, better visualizations and reduced impurity measures in the feature representations. Future work can be directed to gaining an in-depth understanding regarding the spatial patterns that have been learned by the proposed model from a neurological view, as well as improving the interpretability of the proposed model by allowing it to uncover any temporal-related information.
Hydrogen-like Ion Emission in the Spectra of Low-Mass X-Ray Binaries

We study the emission from H-like ions, particularly oxygen and neon, observed in ASCA spectra of low-mass X-ray binaries (LMXBs). The observed strengths of Lyα emission lines relative to the recombination continua differ from the classical recombination model. This suggests a different excitation mechanism for the lines. Various mechanisms are considered, which include three-body recombination, collisional excitation, recombination satellite line emission, and resonant fluorescent excitation. We find that the detection of Lyman recombination continuum implies that the plasma is photoionized, in agreement with previous studies by Hatchett, Buff, & McCray and Liedahl & Paerels. Under collisional ionization conditions, on the other hand, the Lyman continuum should be negligible unless the temperature were greater than 3 × 106 K. Otherwise, the enhancement of Lyα emission with respect to the continuum may be due to (1) three-body recombination at electron densities greater than 1017 cm-3 and temperatures below 2 × 105 K, (2) contributions to the recombination spectrum from collisional excitation of the H-like ionization stage, (3) contributions to recombination Lyα emission from satellite lines due to recombination onto the He-like ionization stage, and (4) contributions to the recombination spectrum from resonant fluorescence excitation of the H-like ionization stage. Further observations of higher order Lyman series lines and lines from other ionization stages are suggested to discriminate between the various excitation mechanisms. In addition, the implications of the different spectral formation mechanisms to the hydrodynamic accretion model of LMXBs are discussed.
New equidistributions on plane trees and decompositions of $132$-avoiding permutations

Our main results in this paper are new equidistributions on plane trees and $132$-avoiding permutations, two closely related objects. As for the former, we discover a characteristic for vertices of plane trees that is equally distributed as the height for vertices. The latter is concerned with four distinct ways of decomposing a $132$-avoiding permutation into subsequences. We show combinatorially that the subsequence length distributions of the four decompositions are mutually equivalent, and there is a way to group the four into two groups such that each group is symmetric and the joint length distribution of one group is the same as that of the other. Some consequences are discussed. For instance, we provide a new refinement of the equidistribution of internal vertices and leaves, and present new sets of $132$-avoiding permutations that are counted by the Motzkin numbers and their refinements.