Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial-Temporal Interactive Dynamic Graph Convolution Network for Traffic Forecasting

Accurate traffic forecasting is essential for smart cities to achieve traffic control, route planning, and flow detection. Although many spatial-temporal methods are currently proposed, these methods are deficient in capturing the spatial-temporal dependence of traffic data synchronously. In addition, most of the methods ignore the dynamically changing correlations between road network nodes that arise as traffic data changes. We propose a neural network-based Spatial-Temporal Interactive Dynamic Graph Convolutional Network (STIDGCN) to address the above challenges for traffic forecasting. Specifically, we propose an interactive dynamic graph convolution structure, which divides the sequences at intervals and synchronously captures the traffic data's spatial-temporal dependence through an interactive learning strategy. The interactive learning strategy makes STIDGCN effective for long-term prediction. We also propose a novel dynamic graph convolution module to capture the dynamically changing correlations in the traffic network, consisting of a graph generator and fusion graph convolution. The dynamic graph convolution module can use the input traffic data and pre-defined graph structure to generate a graph structure. It is then fused with the defined adaptive adjacency matrix to generate a dynamic adjacency matrix, which fills the pre-defined graph structure and simulates the generation of dynamic associations between nodes in the road network. Extensive experiments on four real-world traffic flow datasets demonstrate that STIDGCN outperforms the state-of-the-art baseline.
Ancestor regression in linear structural equation models

We present a new method for causal discovery in linear structural equation models. We propose a simple trick based on statistical testing in linear models that can distinguish between ancestors and non-ancestors of any given variable. Naturally, this can then be extended to estimating the causal order among all variables. Unlike many methods, we provide explicit error control for false causal discovery, at least asymptotically. This holds true even under Gaussianity where various methods fail due to non-identifiable structures. These Type I error guarantees come at the cost of reduced power. Additionally, we provide an asymptotically valid goodness of fit p-value to assess whether multivariate data stems from a linear structural equation model.
Frank Wolfe Meets Metric Entropy

The Frank-Wolfe algorithm has seen a resurgence in popularity due to its ability to efficiently solve constrained optimization problems in machine learning and high-dimensional statistics. As such, there is much interest in establishing when the algorithm may possess a"linear"$O(\log(1/\epsilon))$ dimension-free iteration complexity comparable to projected gradient descent. In this paper, we provide a general technique for establishing domain specific and easy-to-estimate lower bounds for Frank-Wolfe and its variants using the metric entropy of the domain. Most notably, we show that a dimension-free linear upper bound must fail not only in the worst case, but in the \emph{average case}: for a Gaussian or spherical random polytope in $\mathbb{R}^d$ with $\mathrm{poly}(d)$ vertices, Frank-Wolfe requires up to $\tilde\Omega(d)$ iterations to achieve a $O(1/d)$ error bound, with high probability. We also establish this phenomenon for the nuclear norm ball. The link with metric entropy also has interesting positive implications for conditional gradient algorithms in statistics, such as gradient boosting and matching pursuit. In particular, we show that it is possible to extract fast-decaying upper bounds on the excess risk directly from an analysis of the underlying optimization procedure.
Positional Information is All You Need: A Novel Pipeline for Self-Supervised SVDE from Videos

Recently, much attention has been drawn to learning the underlying 3D structures of a scene from monocular videos in a fully self-supervised fashion. One of the most challenging aspects of this task is handling the independently moving objects as they break the rigid-scene assumption. For the first time, we show that pixel positional information can be exploited to learn SVDE (Single View Depth Estimation) from videos. Our proposed moving object (MO) masks, which are induced by shifted positional information (SPI) and referred to as `SPIMO' masks, are very robust and consistently remove the independently moving objects in the scenes, allowing for better learning of SVDE from videos. Additionally, we introduce a new adaptive quantization scheme that assigns the best per-pixel quantization curve for our depth discretization. Finally, we employ existing boosting techniques in a new way to further self-supervise the depth of the moving objects. With these features, our pipeline is robust against moving objects and generalizes well to high-resolution images, even when trained with small patches, yielding state-of-the-art (SOTA) results with almost 8.5x fewer parameters than the previous works that learn from videos. We present extensive experiments on KITTI and CityScapes that show the effectiveness of our method.
No Evidence that the Majority of Black Holes in Binaries Have Zero Spin

The spin properties of merging black holes observed with gravitational waves can offer novel information about the origin of these systems. The magnitudes and orientations of black hole spins offer a record of binaries’ evolutionary history, encoding information about massive stellar evolution and the astrophysical environments in which binary black holes are assembled. Recent analyses of the binary black hole population have yielded conflicting portraits of the black hole spin distribution. Some works suggest that black hole spins are small but nonzero and exhibit a wide range of misalignment angles relative to binaries’ orbital angular momenta. Other works conclude that the majority of black holes are nonspinning while the remainder are rapidly rotating and primarily aligned with their orbits. We revisit these conflicting conclusions, employing a variety of complementary methods to measure the distribution of spin magnitudes and orientations among binary black hole mergers. We find that the existence of a subpopulation of black holes with vanishing spins is not required by current data. Should such a subpopulation exist, we conclude that it must contain ≲60% of binaries. Additionally, we find evidence for significant spin–orbit misalignment among the binary black hole population, with some systems exhibiting misalignment angles greater than 90°, and see no evidence for an approximately spin-aligned subpopulation.
Team formation and team performance: The balance between team freshness and repeat collaboration

Incorporating fresh members in teams is considered a pathway to team creativity. However, whether freshness improves team performance or not remains unclear, as well as the optimal involvement of fresh members for team performance. This study uses a group of authors on the byline of a publication as a proxy for a scientific team. We extend an indicator, i.e., team freshness, to measure the extent to which a scientific team incorporates new members, by calculating the fraction of new collaboration relations established within the team. Based on more than 43 million scientific publications covering more than a half-century of research from Microsoft Academic Graph, this study provides a holistic picture of the current development of team freshness by outlining the temporal evolution of freshness, and its disciplinary distribution. Subsequently, using a multivariable regression approach, we examine the association between team freshness and papers’ short-term and long-term citations. The major findings are as follows: (1) team freshness in scientific teams has been increasing in the past half-century; (2) there exists an inverted-U-shaped association between team freshness and papers’ citations in all the disciplines and in different periods; (3) the inverted-U-shaped relationship between team freshness and papers’ citations is only found in small teams, while, in large teams, team freshness is significantly positively related to papers’ citations. How team
Dielectric constant of gray Tin: A first-principles study

α -Sn (gray tin) is a group-IV, zero-gap semiconductor with potential use in infrared detectors, necessitating a clear understanding of its dielectric properties. We report the ﬁrst-principles calculations of the band structure and dielectric function of α -Sn using density functional theory, emphasizing the eﬀects of strain, spin-orbit interaction, and pseudo-potentials on the electronic and optical properties of α -Sn in the infrared region (photon energy < 1eV). In α -Sn, spin-orbit coupling greatly inﬂuences the electronic band structure that leads to unusual optical behavior. We explain an apparently anomalous absorption at ∼ 0.41 eV caused by interbank transitions within the valence band. Infrared spectroscopic ellipsometry on several α -Sn ﬁlms grown by molecular beam epitaxy validate our band-structure calculations. Our computational methods and results are discussed in detail.
Rank-based Heuristics for Optimizing the Execution of Product Data Models

The Product Data Model (PDM) is an example of a data-centric approach to modelling information-intensive business processes, which offers exibility and facilitates process optimization. Because the approach is declarative in nature, there may be multiple, alternative execution plans that can produce the desired end product. To generate such plans, several heuristics have been proposed in the literature. The contributions of this work are twofold: (i) we propose new heuristics that capitalize on established techniques for optimizing data-intensive work ows in terms of execution time and cost and transfer them to business processes; and (ii) we extensively evaluate the existing solutions. Our results shed light on the merits of each heuristic and show that our new heuristics can yield significant benefits.
Hyperparameter Optimization with Neural Network Pruning

Since the deep learning model is highly dependent on hyperparameters, hyperparameter optimization is essential in developing deep learning model-based applications, even if it takes a long time. As service development using deep learning models has gradually become competi-tive, many developers highly demand rapid hyperparameter optimization algorithms. In order to keep pace with the needs of faster hyperparameter optimization algorithms, researchers are focusing on improving the speed of hyperparameter optimization algorithm. However, the huge time consumption of hyperparameter optimization due to the high computational cost of the deep learning model itself has not been dealt with in-depth. Like using surrogate model in Bayesian optimization, to solve this problem, it is necessary to consider proxy model for a neural network ( N B ) to be used for hyperparameter optimization. Inspired by the main goal of neural network pruning, i.e., high computational cost reduction and performance preservation, we presumed that the neural network ( N P ) obtained through neural network pruning would be a good proxy model of N B . In order to verify our idea, we performed extensive experiments by using CIFAR10, CFIAR100, and TinyImageNet datasets and three generally-used neural networks and three representative hyperparameter optmization methods. Through these experiments, we verified that N P can be a good proxy model of N B for rapid hyperparameter optimization. The proposed hyperparameter optimization framework can reduce the amount of time up to 37%.
Kink correlations, domain-size distribution, and emptiness formation probability after a Kibble-Zurek quench in the quantum Ising chain

Linear quench of the transverse ﬁeld drives the quantum Ising chain across a quantum critical point from the paramagnetic to the ferromagnetic phase. We focus on normal and anomalous quadratic correlators between fermionic kink creation and annihilation operators. They depend not only on the Kibble-Zurek (KZ) correlation length but also on a dephasing length scale, which differs from the KZ length by a logarithmic correction. Additional slowing down of the ramp in the ferromagnetic phase further increases the dephasing length and suppresses the anomalous correlator. The quadratic correlators enter Pfafﬁans that yield experimentally relevant kink correlation functions, the probability distribution of ferromagnetic domain sizes, and, closely related, emptiness formation probability. The latter takes the form of a Pfafﬁan of a block Toeplitz matrix that allows for some analytic asymptotes. Finally, we obtain further insight into the structure of the state at the end of the ramp by interpreting it as a paired state of fermionic kinks characterized by its pair wave function. All those quantities are sensitive to quantum coherence between eigenstates with different numbers of kinks, thus making them a convenient probe of the quantumness of a quantum simulator platform.
Programming of channels in generalized probabilistic theories

For a given target system and apparatus described by quantum theory, the so-called quantum no-programming theorem indicates that a family of states called programs in the apparatus with a fixed unitary operation on total system programs distinct unitary dynamics to the target system only if the initial programs are orthogonal to each other. The current study aims at revealing whether a similar behavior can be observed in generalized probabilistic theories (GPTs). Generalizing the programming scheme to GPTs, we derive a similar theorem to the quantum no-programming theorem. We, furthermore, demonstrate that programming of reversible dynamics is closely related to a curious structure named a quasi-classical structure on the state space. Programming of irreversible dynamics, i.e., channels, in GPTs is also investigated.
Finding Qs: Profiling QAnon Supporters on Parler

The social media platform "Parler'' has emerged into a prominent fringe community where a significant part of the user base are self-reported supporters of QAnon, a far-right conspiracy theory alleging that a cabal of elites controls global politics. QAnon is considered to have had an influential role in the public discourse during the 2020 U.S. presidential election. However, little is known about QAnon supporters on Parler and what sets them aside from other users. Building up on social identity theory, we aim to profile the characteristics of QAnon supporters on Parler. We analyze a large-scale dataset with more than 600,000 profiles of English-speaking users on Parler. Based on users' profiles, posts, and comments, we then extract a comprehensive set of user features, linguistic features, network features, and content features. This allows us to perform user profiling and understand to what extent these features discriminate between QAnon and non-QAnon supporters on Parler. Our analysis is three-fold: (1) We quantify the number of QAnon supporters on Parler, finding that 34,913 users (5.5% of all users) openly report supporting the conspiracy. (2) We examine differences between QAnon vs. non-QAnon supporters. We find that QAnon supporters differ statistically significantly from non-QAnon supporters across multiple dimensions. For example, they have, on average, a larger number of followers, followees, and posts, and thus have a large impact on the Parler network. (3) We use machine learning to identify which user characteristics discriminate QAnon from non-QAnon supporters. We find that user features, linguistic features, network features, and content features, can - to a large extent - discriminate QAnon vs. non-QAnon supporters on Parler. In particular, we find that user features are highly discriminatory, followed by content features and linguistic features.
Text Detection & Recognition in the Wild for Robot Localization

Signage is everywhere and a robot should be able to take advantage of signs to help it localize (including Visual Place Recognition (VPR)) and map. Robust text detection&recognition in the wild is challenging due to such factors as pose, irregular text, illumination, and occlusion. We propose an end-to-end scene text spotting model that simultaneously outputs the text string and bounding boxes. This model is more suitable for VPR. Our central contribution is introducing utilizing an end-to-end scene text spotting framework to adequately capture the irregular and occluded text regions in different challenging places. To evaluate our proposed architecture's performance for VPR, we conducted several experiments on the challenging Self-Collected Text Place (SCTP) benchmark dataset. The initial experimental results show that the proposed method outperforms the SOTA methods in terms of precision and recall when tested on this benchmark.
VRAG: Region Attention Graphs for Content-Based Video Retrieval

Content-based Video Retrieval (CBVR) is used on media- sharing platforms for applications such as video recommendation and ﬁltering. To manage databases that scale to billions of videos, video-level approaches that use ﬁxed-size embeddings are preferred due to their efﬁciency. In this paper, we introduce Video Region Attention Graph Networks (VRAG) that improves the state-of-the-art of video-level methods. We represent videos at a ﬁner granularity via region-level features and encode video spatio-temporal dynamics through region- level relations. Our VRAG captures the relationships between regions based on their semantic content via self-attention and the permutation invariant aggregation of Graph Convolution. In addition, we show that the performance gap be- tween video-level and frame-level methods can be reduced by segmenting videos into shots and using shot embeddings for video retrieval. We evaluate our VRAG over several video retrieval tasks and achieve a new state-of-the-art for video-level retrieval. Furthermore, our shot-level VRAG shows higher retrieval precision than other existing video-level methods, and closer performance to frame-level methods at faster evaluation speeds. Finally, our code will be made publicly available.
Positivity of minuscule quantum K-theory

We prove that the Schubert structure constants of the quantum $K$-theory ring of any minuscule flag variety or quadric hypersurface have signs that alternate with codimension. We also prove that the powers of the deformation parameter $q$ that occur in the product of two Schubert classes in the quantum cohomology or quantum $K$-theory ring of a cominuscule flag variety form an integer interval. Our proofs are based on several new results, including an explicit description of the most general non-empty intersection of two Schubert varieties in an arbitrary flag manifold, and a computation of the cohomology groups of any negative line bundle restricted to a Richardson variety in a cominuscule flag variety. We also give a type-uniform proof of the quantum-to-classical theorem, which asserts that the (3-point, genus 0) Gromov-Witten invariants of any cominuscule flag variety are classical triple-intersection numbers on an associated flag variety. Finally, we prove several new results about the geometry and combinatorics related to this theorem.
Benefits and costs of matching prior to a Difference in Differences analysis when parallel trends does not hold

The Difference in Difference (DiD) estimator is a popular estimator built on the"parallel trends"assumption, which is an assertion that the treatment group, absent treatment, would change"similarly"to the control group over time. To bolster such a claim, one might generate a comparison group, via matching, that is similar to the treated group with respect to pre-treatment outcomes and/or pre-treatment covariates. Unfortunately, as has been previously pointed out, this intuitively appealing approach also has a cost in terms of bias. To assess the trade-offs of matching in our application, we first characterize the bias of matching prior to a DiD analysis under a linear structural model that allows for time-invariant observed and unobserved confounders with time-varying effects on the outcome. Given our framework, we verify that matching on baseline covariates generally reduces bias. We further show how additionally matching on pre-treatment outcomes has both cost and benefit. First, matching on pre-treatment outcomes partially balances unobserved confounders, which mitigates some bias. This reduction is proportional to the outcome's reliability, a measure of how coupled the outcomes are with the latent covariates. Offsetting these gains, matching also injects bias into the final estimate by undermining the second difference in the DiD via a regression-to-the-mean effect. Consequently, we provide heuristic guidelines for determining to what degree the bias reduction of matching is likely to outweigh the bias cost. We illustrate our guidelines by reanalyzing a principal turnover study that used matching prior to a DiD analysis and find that matching on both the pre-treatment outcomes and observed covariates makes the estimated treatment effect more credible.
Coherence Properties of Electron-Beam-Activated Emitters in Hexagonal Boron Nitride Under Resonant Excitation

Two dimensional materials are becoming increasingly popular as a platform for studies of quantum phenomena and for the production of prototype quantum technologies. Quantum emitters in 2D materials can host two level systems that can act as qubits for quantum information processing. Here, we characterize the behavior of position-controlled quantum emitters in hexagonal boron nitride at cryogenic temperatures. Over two dozen sites, we observe an ultra-narrow distribution of the zero phonon line at ~436 nm, together with strong linearly polarized emission. We employ resonant excitation to characterize the emission lineshape and find spectral diffusion and phonon broadening contribute to linewidths in the range 1-2 GHz. Rabi oscillations are observed at a range of resonant excitation powers, and under 1 μW excitation a coherent superposition is maintained up to 0.90 ns. Our results are promising for future employment of quantum emitters in hBN for scalable quantum technologies.
The overfull conjecture on graphs of odd order and large minimum degree

Let be a simple graph with maximum degree . A subgraph of is overfull if . Chetwynd and Hilton in 1986 conjectured that a graph with has chromatic index if and only if contains no overfull subgraph. Let , be sufficiently large, and be graph on vertices with minimum degree at least . It was shown that the conjecture holds for if is even. In this paper, the same result is proved if is odd. As far as we know, this is the first result on the Overfull Conjecture for graphs of odd order and with a minimum degree constraint.
Anomaly detection using prediction error with Spatio-Temporal Convolutional LSTM

In this paper, we propose a novel method for video anomaly detection motivated by an existing architecture for sequence-to-sequence prediction and reconstruction using a spatio-temporal convolutional Long Short-Term Memory (convLSTM). As in previous work on anomaly detection, anomalies arise as spatially localised failures in reconstruction or prediction. In experiments with five benchmark datasets, we show that using prediction gives superior performance to using reconstruction. We also compare performance with different length input/output sequences. Overall, our results using prediction are comparable with the state of the art on the benchmark datasets.