Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of combined heaving and pitching on propulsion of single and tandem flapping foils

In this study, we present a two and three-dimensional numerical investigation to understand the combined eﬀects of the non-dimensional heave amplitude varying from 0 to 1 and the pitch amplitude ranging from 0 ◦ to 30 ◦ on the propulsive performance for a single and tandem foil system. Flow dynamics across single and tandem ﬂapping foils has been considered at Reynolds number of Re = 1100 and reduced frequency of f ∗ = 0 . 2. The numerical framework consists of arbitrary Lagrangian-Eulerian moving mesh based algorithm coupled with the variational modeling of the incompressible ﬂow equations. We initially present a systematic analysis on the thrust generation due to the kinematic parameters for a single foil with the aid of eﬀective angle of attack, projected area of the foil to the ﬂow direction, time-averaged pressure and streamwise velocity in the wake & wake signature. The signiﬁcance of eﬀective angle of attack and the projected area of the foil has been emphasized in comprehending the dynamics of lift and drag forces and their relationship with the propulsion. We next investigate the relation between the streamwise gap and kinematic parameters on propulsion for the tandem foil system. We show that the propulsive performance strongly depends on the upstream wake interacting with the downstream foil, and the timing of the interaction due to the gap and phase between the kinematic motion of the foils. Through a control volume analysis for both single and tandem foils, the time-averaged pressure and streamwise velocity have been investigated to explain the eﬀect of kinematic parameters on the hydrodynamic forces. Typically in the literature, the formation of jet in the wake has been attributed to thrust generation. However, in this study, we emphasize and show the signiﬁcance of the time-averaged pressure in the wake apart from the streamwise velocity (jet) for predicting the thrust forces. The study is concluded with a three-dimensional demonstration of the tandem foils to understand the possible three-dimensional eﬀects due to the large amplitude ﬂapping and wake-foil interaction.
Unsupervised Learning From Incomplete Measurements for Inverse Problems

In many real-world inverse problems, only incomplete measurement data are available for training which can pose a problem for learning a reconstruction function. Indeed, unsupervised learning using a fixed incomplete measurement process is impossible in general, as there is no information in the nullspace of the measurement operator. This limitation can be overcome by using measurements from multiple operators. While this idea has been successfully applied in various applications, a precise characterization of the conditions for learning is still lacking. In this paper, we fill this gap by presenting necessary and sufficient conditions for learning the underlying signal model needed for reconstruction which indicate the interplay between the number of distinct measurement operators, the number of measurements per operator, the dimension of the model and the dimension of the signals. Furthermore, we propose a novel and conceptually simple unsupervised learning loss which only requires access to incomplete measurement data and achieves a performance on par with supervised learning when the sufficient condition is verified. We validate our theoretical bounds and demonstrate the advantages of the proposed unsupervised loss compared to previous methods via a series of experiments on various imaging inverse problems, such as accelerated magnetic resonance imaging, compressed sensing and image inpainting.
Objects Matter: Learning Object Relation Graph for Robust Camera Relocalization

—Visual relocalization aims to estimate the pose of a camera from one or more images. In recent years deep learning based pose regression methods have attracted many attentions. They feature predicting the absolute poses without relying on any prior built maps or stored images, making the relocalization very efﬁcient. However, robust relocalization under environments with complex appearance changes and real dynamics remains very challenging. In this paper, we propose to enhance the distinctiveness of the image features by extracting the deep relationship among objects. In particular, we extract objects in the image and construct a deep object relation graph (ORG) to incorporate the semantic connections and relative spatial clues of the objects. We integrate our ORG module into several popular pose regression models. Extensive experiments on various public indoor and outdoor datasets demonstrate that our method improves the performance signiﬁcantly and outperforms the previous approaches.
Many Sequential Iterative Algorithms Can Be Parallel and (Nearly) Work-efficient

Some recent papers showed that many sequential iterative algorithms can be directly parallelized, by identifying the dependences between the input objects. This approach yields many simple and practical parallel algorithms, but there are still challenges to achieve work-efficiency and high-parallelism. Work-efficiency means that the number of operations is asymptotically the same as the best sequential solution. This can be hard for certain problems where the number of dependences between objects is asymptotically more than optimal sequential work, and we cannot even afford the cost to generate them. To achieve high-parallelism, we always want it to process as many objects as possible in parallel. The goal is to achieve O (D) span for a problem with the deepest dependence length D. We refer to this property as round-efficiency. This paper presents work-efficient and round-efficient algorithms for a variety of classic problems and propose general approaches to do so. To efficiently parallelize many sequential iterative algorithms, we propose the phase-parallel framework. The framework assigns a rank to each object and processes the objects based on the order of their ranks. All objects with the same rank can be processed in parallel. To enable work-efficiency and high parallelism, we use two types of general techniques. Type 1 algorithms aim to use range queries to extract all objects with the same rank to avoid evaluating all the dependences. We discuss activity selection, and Dijkstra's algorithm using Type 1 framework. Type 2 algorithms aim to wake up an object when the last object it depends on is finished. We discuss activity selection, longest increasing subsequence (LIS), greedy maximal independent set (MIS), and many other algorithms using Type 2 framework. All of our algorithms are (nearly) work-efficient and round-efficient, and some of them (e.g., LIS) are the first to achieve the both. Many of them improve the previous best bounds. Moreover, we implement many of them for experimental studies. On inputs with reasonable dependence depth, our algorithms are highly parallelized and significantly outperform their sequential counterparts.
The Physical Properties of Star-forming Galaxies with Strong [O iii] Lines at z = 3.25

We present an analysis of physical properties of 34 [O iii] emission-line galaxies (ELGs) at z = 3.254 ± 0.029 in the Extended Chandra Deep Field South (ECDFS). These ELGs are selected from deep narrow H2S(1) and broad K s imaging of 383 arcmin2 obtained with CFHT/WIRCam. We construct spectral energy distributions (SEDs) from U to K s to derive the physical properties of ELGs. These [O iii] ELGs are identified as starburst galaxies with strong [O iii] lines of L OIII ∼ 1042.6–1044.2 erg s−1 and have stellar masses of M * ∼ 109.0–1010.6 M ⊙ and star formation rates of ∼10–210 M ⊙ yr−1. Our results show that 24% of our sample galaxies are dusty with A V > 1 mag and EW([O iii])rest ∼ 70–500 Å, which are often missed in optically selected [O iii] ELG samples. Their rest-frame UV and optical morphologies from HST/ACS and HST/WFC3 deep imaging reveal that these [O iii] ELGs are mostly multiple-component systems (likely mergers) or compact. And 20% of them are nearly invisible in the rest-frame UV owing to heavy dust attenuation. Interestingly, we find that our sample ELGs reside in an overdensity consisting of two components: one southeast (SE) with an overdensity factor of δ gal ∼ 41 over a volume of 133 cMpc3, and the other northwest (NW) with δ gal ∼ 38 over a volume of 103 cMpc3. The two overdense substructures are expected to be virialized at z = 0 with a total mass of ∼ 1.1 × 1015 M ⊙ and ∼ 4.8 × 1014 M ⊙ and probably merge into a Coma-like galaxy cluster.
Exploring Map-based Features for Efficient Attention-based Vehicle Motion Prediction

Motion prediction (MP) of multiple agents is a crucial task in arbitrarily complex environments, from social robots to self-driving cars. Current approaches tackle this problem using end-to-end networks, where the input data is usually a rendered top-view of the scene and the past trajectories of all the agents; leveraging this information is a must to obtain optimal performance. In that sense, a reliable Autonomous Driving (AD) system must produce reasonable predictions on time, however, despite many of these approaches use simple ConvNets and LSTMs, models might not be efficient enough for real-time applications when using both sources of information (map and trajectory history). Moreover, the performance of these models highly depends on the amount of training data, which can be expensive (particularly the annotated HD maps). In this work, we explore how to achieve competitive performance on the Argoverse 1.0 Benchmark using efficient attention-based models, which take as input the past trajectories and map-based features from minimal map information to ensure efficient and reliable MP. These features represent interpretable information as the driveable area and plausible goal points, in opposition to black-box CNN-based methods for map processing.
Ergodic Capacity Maximization of RIS-assisted Millimeter-Wave MIMO-OFDM Communication Systems

Reconﬁgurable intelligent surface (RIS) has attracted extensive attentions in recent years. However, most research focuses on the scenario of the narrowband and/or instantaneous channel state information (CSI), while wide bandwidth with the use of millimeter-wave (mmWave) (including sub-THz) spectrum is a major trend in next-generation wireless communications, and statistical CSI is more practical to obtain in realistic systems. Thus, we ﬁll the blank by looking at the ergodic capacity of RIS-assisted mmWave multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) communication systems. The widely used Saleh-Valenzuela channel model is adopted to characterize the mmWave channels and only the statistical CSI is available. We ﬁrst derive the approximations of the ergodic capacity by means of the majorization theory and Jensen’s inequality. Then, an alternating optimization based algorithm is proposed to maximize the ergodic capacity by jointly designing the transmit covariance matrix at the base station and the reﬂection coefﬁcients at the RIS. Speciﬁcally, the design of the transmit covariance matrix is transformed into a power allocation problem and solved by spatial-frequency water-ﬁlling. The reﬂection coefﬁcients are optimized by the Riemannian conjugate gradient algorithm. Simulation results corroborate the closeness of the derived ergodic capacity approximations and the effectiveness of the proposed algorithms.
Benchmarking of Deep Learning models on 2D Laminar Flow behind Cylinder

The rapidly advancing ﬁeld of Fluid Mechanics has recently employed Deep Learning to solve various problems within that ﬁeld. In that same spirit we try to perform Direct Numerical Simulation(DNS) which is one of the tasks in Computational Fluid Dynamics, using three fundamental architectures in the ﬁeld of Deep Learning that were each used to solve various high dimensional problems. We train these three models in an autoencoder manner, for this the dataset is treated like sequential frames given to the model as input. We observe that recently introduced architecture called Transformer signiﬁcantly outperforms its counterparts on the selected dataset. Furthermore, we conclude that using Transformers for doing DNS in the ﬁeld of CFD is an interesting research area worth exploring.
Selective Classification Via Neural Network Training Dynamics

Selective classification is the task of rejecting inputs a model would predict incorrectly on through a trade-off between input space coverage and model accuracy. Current methods for selective classification impose constraints on either the model architecture or the loss function; this inhibits their usage in practice. In contrast to prior work, we show that state-of-the-art selective classification performance can be attained solely from studying the (discretized) training dynamics of a model. We propose a general framework that, for a given test input, monitors metrics capturing the disagreement with the final predicted label over intermediate models obtained during training; we then reject data points exhibiting too much disagreement at late stages in training. In particular, we instantiate a method that tracks when the label predicted during training stops disagreeing with the final predicted label. Our experimental evaluation shows that our method achieves state-of-the-art accuracy/coverage trade-offs on typical selective classification benchmarks.
Deep Active Learning with Noise Stability

Uncertainty estimation for unlabeled data is crucial to active learning. With a deep neural network employed as the backbone model, the data selection process is highly challenging due to the potential over-confidence of the model inference. Existing methods resort to special learning fashions (e.g. adversarial) or auxiliary models to address this challenge. This tends to result in complex and inefficient pipelines, which would render the methods impractical. In this work, we propose a novel algorithm that leverages noise stability to estimate data uncertainty. The key idea is to measure the output derivation from the original observation when the model parameters are randomly perturbed by noise. We provide theoretical analyses by leveraging the small Gaussian noise theory and demonstrate that our method favors a subset with large and diverse gradients. Our method is generally applicable in various tasks, including computer vision, natural language processing, and structural data analysis. It achieves competitive performance compared against state-of-the-art active learning baselines.
Penalizing Proposals using Classifiers for Semi-Supervised Object Detection

Obtaining gold standard annotated data for object detection is often costly, involving human-level effort. Semi-supervised object detection algorithms solve the problem with a small amount of gold-standard labels and a large unlabelled dataset used to generate silver-standard labels. But training on the silver standard labels does not produce good results, because they are machine-generated annotations. In this work, we design a modified loss function to train on large silver standard annotated sets generated by a weak annotator. We include a confidence metric associated with the annotation as an additional term in the loss function, signifying the quality of the annotation. We test the effectiveness of our approach on various test sets and use numerous variations to compare the results with some of the current approaches to object detection. In comparison with the baseline where no confidence metric is used, we achieved a 4% gain in mAP with 25% labeled data and 10% gain in mAP with 50% labeled data by using the proposed confidence metric.
Homoclinic orbits, multiplier spectrum and rigidity theorems in complex dynamics

Abstract The aims of this paper are to answer several conjectures and questions about the multiplier spectrum of rational maps and giving new proofs of several rigidity theorems in complex dynamics by combining tools from complex and non-Archimedean dynamics. A remarkable theorem due to McMullen asserts that, aside from the flexible Lattès family, the multiplier spectrum of periodic points determines the conjugacy class of rational maps up to finitely many choices. The proof relies on Thurston’s rigidity theorem for post-critically finite maps, in which Teichmüller theory is an essential tool. We will give a new proof of McMullen’s theorem (and therefore a new proof of Thurston’s theorem) without using quasiconformal maps or Teichmüller theory. We show that, aside from the flexible Lattès family, the length spectrum of periodic points determines the conjugacy class of rational maps up to finitely many choices. This generalizes the aforementioned McMullen’s theorem. We will also prove a rigidity theorem for marked length spectrum. Similar ideas also yield a simple proof of a rigidity theorem due to Zdunik. We show that a rational map is exceptional if and only if one of the following holds: (i) the multipliers of periodic points are contained in the integer ring of an imaginary quadratic field, and (ii) all but finitely many periodic points have the same Lyapunov exponent. This solves two conjectures of Milnor.
An experimental modal testing method for subcritical flow around a cylinder

Modal analysis of fluid flows is essential to understanding flow physics and fluid-solid interaction mechanisms, and to implementing flow control. Unlike unstable flows, the intrinsic stability of subcritical flows has led to failures in experimentally extracting the clear structure of the subcritical flow modes. To this end, this paper proposes an experimental modal testing method for subcritical flows. Using Dynamic Mode Decomposition (DMD), dominant modes of flow around a cylinder at subcritical Reynolds numbers are extracted experimentally for the first time. The extracted structure and parameters of the modes are in agreement with the numerical results in the literature. It is found that the first-order mode is the stable von Kármán mode and can be observed at a Reynolds number as low as 19.3, which is nearly identical to the lower boundary of subcritical vortex-induced vibration (VIV). This finding provides the first experimental evidence of the correlation between the von Kármán mode and fluid-solid interaction instability in subcritical flows.
GraphPMU: Event Clustering via Graph Representation Learning Using Locationally-Scarce Distribution-Level Fundamental and Harmonic PMU Measurements

This paper is concerned with the complex task of identifying the <italic>type</italic> and <italic>cause</italic> of the events that are captured by distribution-level phasor measurement units (D-PMUs) in order to enhance situational awareness in power distribution systems. Our goal is to address two fundamental challenges in this field: a) <italic>scarcity in measurement locations</italic> due to the high cost of purchasing, installing, and streaming data from D-PMUs; b) <italic>limited prior knowledge about the event signatures</italic> due to the fact that the events are diverse and infrequent, and have unknown characteristics. To tackle these challenges, we propose an <italic>unsupervised graph-representation learning</italic> method, called GraphPMU, to significantly improve the performance in event clustering under <italic>locationally-scarce data availability</italic> by proposing the following two new directions: 1) using the <italic>topological information</italic> about the <italic>relative location</italic> of the few available phasor measurement units on the graph of the power distribution network; 2) utilizing not only the commonly used <italic>fundamental</italic> phasor measurements, bus also the less explored <italic>harmonic</italic> phasor measurements in the process of analyzing the signatures of various events. Through a detailed analysis of several case studies, we show that GraphPMU can highly outperform the prevalent methods in the literature.
Denoising gravitational-wave signals from binary black holes with a dilated convolutional autoencoder

The broadband frequency output of gravitational-wave (GW) detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the GW signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expose the astrophysical signals by employing a convolutional neural network in the encoder-decoder configuration, i.e. apply the denoising procedure of coalescing binary black hole signals to the publicly available LIGO O1 time series strain data. The denoising convolutional autoencoder neural network is trained on a dataset of simulated astrophysical signals injected into the real detector’s noise and a dataset of detector noise artifacts (‘glitches’), and its fidelity is tested on real GW events from O1 and O2 LIGO-Virgo observing runs.
Embedding Principle in Depth for the Loss Landscape Analysis of Deep Neural Networks

Understanding the relation between deep and shallow neural networks is extremely important for the theoretical study of deep learning. In this work, we discover an embedding principle in depth that loss landscape of an NN"contains"all critical points of the loss landscapes for shallower NNs. The key tool for our discovery is the critical lifting operator proposed in this work that maps any critical point of a network to critical manifolds of any deeper network while preserving the outputs. This principle provides new insights to many widely observed behaviors of DNNs. Regarding the easy training of deep networks, we show that local minimum of an NN can be lifted to strict saddle points of a deeper NN. Regarding the acceleration effect of batch normalization, we demonstrate that batch normalization helps avoid the critical manifolds lifted from shallower NNs by suppressing layer linearization. We also prove that increasing training data shrinks the lifted critical manifolds, which can result in acceleration of training as demonstrated in experiments. Overall, our discovery of the embedding principle in depth uncovers the depth-wise hierarchical structure of deep learning loss landscape, which serves as a solid foundation for the further study about the role of depth for DNNs.
Wasserstein Distributionally Robust Gaussian Process Regression and Linear Inverse Problems

We study a distributionally robust optimization formulation (i.e., a min-max game) for problems of nonparametric estimation: Gaussian process regression and, more gen-erally, linear inverse problems. We choose the best mean-squared error predictor on an inﬁnite-dimensional space against an adversary who chooses the worst-case model in a Wasserstein ball around an inﬁnite-dimensional Gaussian model. The Wasserstein cost function is chosen to control features such as the degree of roughness of the sample paths that the adversary is allowed to inject. We show that the game has a well-deﬁned value (i.e., strong duality holds in the sense that max-min equals min-max) and show existence of a unique Nash equilibrium that can be computed by a sequence of ﬁnite-dimensional approximations. Crucially, the worst-case distribution is itself Gaussian. We explore properties of the Nash equilibrium and the eﬀects of hy-perparameters through a set of numerical experiments, demonstrating the versatility of our modeling framework.