Miracle-dz/UniFAR-FTUs huggingface dataset:
So2Sat LCZ42: A Benchmark Dataset for Global Local Climate Zones Classification

Access to labeled reference data is one of the grand challenges in supervised machine learning endeavors. This is especially true for an automated analysis of remote sensing images on a global scale, which enables us to address global challenges such as urbanization and climate change using state-of-the-art machine learning techniques. To meet these pressing needs, especially in urban research, we provide open access to a valuable benchmark dataset named "So2Sat LCZ42," which consists of local climate zone (LCZ) labels of about half a million Sentinel-1 and Sentinel-2 image patches in 42 urban agglomerations (plus 10 additional smaller areas) across the globe. This dataset was labeled by 15 domain experts following a carefully designed labeling work flow and evaluation process over a period of six months. As rarely done in other labeled remote sensing dataset, we conducted rigorous quality assessment by domain experts. The dataset achieved an overall confidence of 85%. We believe this LCZ dataset is a first step towards an unbiased globallydistributed dataset for urban growth monitoring using machine learning methods, because LCZ provide a rather objective measure other than many other semantic land use and land cover classifications. It provides measures of the morphology, compactness, and height of urban areas, which are less dependent on human and culture. This dataset can be accessed from this http URL.
Unsupervised Deep Feature Transfer for Low Resolution Image Classification /Author=Zhang, Ziming; Wu, Yuanwei; Wang, Guanghui /CreationDate=October 31, 2019 /Subject=Artificial Intelligence, Computer Vision, Machine Learning

In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is required in our method. We use pre-trained convenet to extract features for both high and low resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer. A SVM classifier is learned directly using these transferred low resolution features. Our network can be embedded into the state-of-the-art deep neural networks as a plug-in feature enhancement module. It preserves data structures in feature space for high resolution images, and transfers the distinguishing features from a wellstructured source domain (high resolution features space) to a not well-organized target domain (low resolution features space). Extensive experiments on VOC2007 test set show that the proposed method achieves significant improvements over the baseline of using feature extraction. IEEE International Conference on Computer Vision Workshops (ICCV) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c © Mitsubishi Electric Research Laboratories, Inc., 2019 201 Broadway, Cambridge, Massachusetts 02139 Unsupervised Deep Feature Transfer for Low Resolution Image Classification Yuanwei Wu∗, Guanghui Wang EECS, The University of Kansas 1450 Jayhawk Blvd., Lawrence, KS 66045 {y262w558, ghwang}@ku.edu Ziming Zhang† Mitsubishi Electric Research Laboratories 201 Broadway, Cambridge, MA 02139-1955
How Implicit Regularization of ReLU Neural Networks Characterizes the Learned Function -- Part I: the 1-D Case of Two Layers with Random First Layer

In this paper, we consider one dimensional (shallow) ReLU neural networks in which weights are chosen randomly and only the terminal layer is trained. First, we mathematically show that for such networks L2-regularized regression corresponds in function space to regularizing the estimate's second derivative for fairly general loss functionals. For least squares regression, we show that the trained network converges to the smooth spline interpolation of the training data as the number of hidden nodes tends to infinity. Moreover, we derive a novel correspondence between the early stopped gradient descent (without any explicit regularization of the weights) and the smoothing spline regression.
MIMO Precoding and Relay Selection for the Decode-and-Forward Relay Networks

We consider the joint relay and pre-coder selection in the decode-and-forward (DaF) relay network, where multiple relays are employed with multiple antennas. Depending on the channel information, the destination informs the relay and pre-coder selection information to the source and relays with finite bits of information. The source and the selected relay use their own pre-coders to transmit the signal using orthogonal channels. We present a selection criterion based on an upper bound of the pair-wise symbol error probability and provide the conditions for the code-books to achieve the full diversity. For a single antenna destination, we show that the proposed criterion guarantees the diversity order of M_s + KM_r, where M_s,M_r are the numbers of antennas at the source and the relay respectively and K is the number of relays in the network.
The holographic glass bead game : from superconductivity to time machines

The thesis is devoted to applications of the anti de Sitter/Conformal field theory correspondence (AdS/CFT, also called holography) to various problems in different areas of theoretical physics. We make use of AdS/CFT to get insights into the physics of superconductors, quark-gluon plasma, and to study properties of quantum field theories on non-causal backgrounds. In the first part of the thesis we construct a holographic model of a Bardeen-Cooper-Schrieffer-like superconductor and demonstrate that the Cooper instability of Fermi-liquids can be naturally described in holographic terms. We show that both the superconducting phase and the BEC/BCS crossover appear naturally. In the second part we address the problem of quark-gluon plasma formation in heavy ion collisions. We study the effect of finite chemical potential on this process and calculate physical properties of the resulting deconfined state. The third part is dedicated to study of evolution of a quantum system with violated causality. Using AdS/CFT we can map the complicated and poorly defined non-causal quantum dynamics onto a classical spacetime with closed timelike curves. Doing that we have derived the Green's function of the QFT and demonstrated that evolution of a quantum system can be consistent and non-pathological even at broken causality.
3D-FSE Inner Volume Imaging using 3D selective excitation

Citing this paper Please note that where the full-text provided on King's Research Portal is the Author Accepted Manuscript or Post-Print version this may differ from the final Published version. If citing, it is advised that you check and use the publisher's definitive version for pagination, volume/issue, and date of publication details. And where the final published version is provided on the Research Portal, if citing you are again advised to check the publisher's website for any subsequent corrections.
Spin and Geometric Phase Control Four‐Wave Mixing from Metasurfaces

The spin angular momentum of light plays an important role in nonlinear interactions in optical systems with rotational symmetries. Here, the existence of the nonlinear geometric Berry phase is demonstrated in the four‐wave mixing process and applied to spin‐controlled nonlinear light generation from plasmonic metasurfaces. The polarization state of four‐wave mixing from the ultrathin metasurfaces, comprising gold meta‐atoms with four‐fold rotational symmetry, can be controlled by manipulating the spin of the excitation beams. The mutual orientation of the meta‐atoms in the metasurface influences the intensity of four‐wave mixing via the geometric phase effects. These findings provide novel solutions for designing metasurfaces for spin‐controlled nonlinear optical processes with inbuilt all‐optical switching.
Joint Network Topology Inference via Structural Fusion Regularization

Joint network topology inference represents a canonical problem of jointly learning multiple graph Laplacian matrices from heterogeneous graph signals. In such a problem, a widely employed assumption is that of a simple common component shared among multiple graphs. However, in practice, a more intricate topological pattern, comprising simultaneously of homogeneous and heterogeneous components, would exhibit in multiple graphs. In this paper, we propose a general graph estimator based on a novel structural fusion regularization that enables us to jointly learn multiple graphs with such complex topological patterns, and enjoys rigorous theoretical guarantees. Specifically, in the proposed regularization term, the structural similarity among graphs is characterized by a Gram matrix, which enables us to flexibly model different types of network structural similarities through different Gram matrix choices. Algorithmically, the regularization term, coupling the parameters together, makes the formulated optimization problem intractable, and thus, we develop an implementable algorithm based on the alternating direction method of multipliers (ADMM) to solve it. Theoretically, non-asymptotic statistical analysis is provided, which precisely characterizes the minimum sample size required for the consistency of the graph estimator. This analysis also provides high-probability bounds on the estimation error as a function of graph structural similarities and other key problem parameters. Finally, the superior performance of the proposed method is demonstrated through simulated and real data examples.
On the Quantification of Imbalance and Inertia–Gravity Waves Generated in Numerical Simulations of Moist Baroclinic Waves Using the WRF Model

AbstractQuantification of inertia–gravity waves (IGWs) generated by upper-level jet–surface front systems and their parameterization in global models of the atmosphere relies on suitable methods to estimate the strength of IGWs. A harmonic divergence analysis (HDA) that has been previously employed for quantification of IGWs combines wave properties from linear dynamics with a sophisticated statistical analysis to provide such estimates. A question of fundamental importance that arises is how the measures of IGW activity provided by the HDA are related to the measures coming from the wave–vortex decomposition (WVD) methods. The question is addressed by employing the nonlinear balance relations of the first-order δ–γ, the Bolin–Charney, and the first- to third-order Rossby number expansion to carry out WVD. The global kinetic energy of IGWs given by the HDA and WVD are compared in numerical simulations of moist baroclinic waves by the Weather Research and Forecasting (WRF) Model in a channel on the f plane.
Numerical approximations of McKean anticipative backward stochastic differential equations arising in initial margin requirements

We introduce a new class of anticipative backward stochastic differential equations with a dependence of McKean type on the law of the solution, that we name MKABSDE. We provide existence and uniqueness results in a general framework with relatively general regularity assumptions on the coefficients. We show how such stochastic equations arise within the modern paradigm of derivative pricing where a central counterparty (CCP) requires the members to deposit variation and initial margins to cover their exposure. In the case when the initial margin is proportional to the Conditional Value-at-Risk (CVaR) of the contract price, we apply our general result to define the price as a solution of a MKABSDE. We provide several linear and non-linear simpler approximations, which we solve using different numerical (deterministic and Monte-Carlo) methods.
Practical Regulation of Nonholonomic Systems Using Virtual Trajectories and LaSalle Invariance Principle

This paper investigates the regulation problem for a class of nonholonomic systems that includes power form systems and an approximated system of the rolling sphere as special cases. The basic idea is first to introduce a virtual periodic moving trajectory that satisfies certain persistent excitation condition (PE) and has the zero value at some time instants. Based on LaSalle invariance principle, the associated tracking problem is then solved under a necessary condition for stabilization and particularly true for the power form systems and the rolling sphere. With the help of virtual trajectory, the achieved tracking result is applied to the regulation problem and used to guarantee practical stability. The proposed controllers have a simple and explicit form, and hence are easily implemented. Simultaneously, fast convergence is guaranteed, thanks to the $\boldsymbol {\mathcal {K}}$ -exponential convergence. More interestingly, the used approach is adding sufficiently exciting signals to the systems by considering virtual tracking signals so that the attractivity of the origin can be guaranteed based on LaSalle invariance principle. Thus, it is possible to extend the proposed results to more general systems. To verify the effectiveness of the proposed scheme, interesting simulation results are presented.
SMGEA: A New Ensemble Adversarial Attack Powered by Long-Term Gradient Memories

Deep neural networks are vulnerable to adversarial attacks. More importantly, some adversarial examples crafted against an ensemble of source models transfer to other target models and, thus, pose a security threat to black-box applications (when attackers have no access to the target models). Current transfer-based ensemble attacks, however, only consider a limited number of source models to craft an adversarial example and, thus, obtain poor transferability. Besides, recent query-based black-box attacks, which require numerous queries to the target model, not only come under suspicion by the target model but also cause expensive query cost. In this article, we propose a novel transfer-based black-box attack, dubbed serial-minigroup-ensemble-attack (SMGEA). Concretely, SMGEA first divides a large number of pretrained white-box source models into several “minigroups. ” For each minigroup, we design three new ensemble strategies to improve the intragroup transferability. Moreover, we propose a new algorithm that recursively accumulates the “long-term” gradient memories of the previous minigroup to the subsequent minigroup. This way, the learned adversarial information can be preserved, and the intergroup transferability can be improved. Experiments indicate that SMGEA not only achieves state-of-the-art black-box attack ability over several data sets but also deceives two online black-box saliency prediction systems in real world, i.e., DeepGaze-II (https://deepgaze.bethgelab.org/) and SALICON (http://salicon.net/demo/). Finally, we contribute a new code repository to promote research on adversarial attack and defense over ubiquitous pixel-to-pixel computer vision tasks. We share our code together with the pretrained substitute model zoo at https://github.com/CZHQuality/AAA-Pix2pix.
Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.
Avoiding Dynamic Small Obstacles With Onboard Sensing and Computation on Aerial Robots

In practical applications, autonomous quadrotors are still facing significant challenges, such as the detection and avoidance of very small and even dynamic obstacles (e.g., tree branches, power lines). In this paper, we propose a compact, integrated, and fully autonomous quadrotor system, which can fly safely in cluttered environments while avoiding dynamic small obstacles. Our quadrotor platform is equipped with a forward-looking three-dimensional (3D) light detection and ranging (lidar) sensor to perceive the environment and an onboard embedded computer to perform all the estimation, mapping, and planning tasks. Specifically, the computer estimates the current pose of the UAV, maintains a local map (time-accumulated point clouds KD-Trees), and computes a safe trajectory using kinodynamic A* search to the goal point. The whole perception and planning system can run onboard at 50 Hz. Various indoor and outdoor experiments show that the system can avoid dynamic small obstacles (down to 9 mm diameter bar) while flying at 2 m/s in cluttered environments. High-speed experiments are also carried out, with a maximum speed of 5.5 m/s. Our codes are open-sourced on Github1.
SUPPLEMENTARY FILE: On the Value of Inﬁnite Gradients in Variational Autoencoder Models

As mentioned in Section 2.2 of the main text, the ability of the VAE to produce optimal sparse representations can potentially be leveraged to detect outliers. To test this possibility, we employ the basic experimental paradigm from [Nalisnick et al., 2019], training models on a given inlier set and then comparing evaluation metrics applied to both inlier train/test samples and distinct outlier samples. Consistent with [Nalisnick et al., 2019] and convention elsewhere, we use the bits-per-dimension (BPD) metric for expressing aggregate results in a convenient range for tables. This metric correlates with the negative log-likelihood, with lower values indicating a higher likelihood. Please see [Theis et al., 2016] for further information regarding this metric and how it is computed. In general, a successful model should produce higher BPD scores for the outlier samples than the inlier test samples.
Mathematical Modeling and Error Analysis of 3D Matching Based on Variance Minimization Principle

3D matching is the basis for machining allowance distribution and profile error evaluation of complex surface, but existing methods are hard to guarantee stable matching accuracy, due to inherent measurement defects such as uneven point distribution, uneven density and local point missing. This paper proposes a novel 3D matching method of mathematical modeling and parameter calculation based on the variance minimization principle. It can linearly calculate the motion variables during each iteration, and appear quadratic convergence. The equivalent condition between the variance minimization matching and the TDM matching is derived, and the quantitative representations of both matching parameter error and point position error are analyzed. In addition, the effectiveness of the proposed method is demonstrated by the experiments of visual localization in robot grinding, allowance distribution in propeller machining and profile analysis in crankshaft inspection. It shows that the variance minimization has the advantage of overcoming the wrong squared distance minimization with respect to the high-density points or noisy points.
SUPPLEMENTARY FILE : The Usual Suspects ? Reassessing Blame for VAE Posterior Collaps

Three different kinds of network structures were used in the experiments: fully connected networks, convolution networks, and residual networks. For all these structures, we set the dimension of the latent variable z to 64. We now describe the network details accordingly. Fully Connected Network: This experiment is only applied on the simple FashionMNIST dataset, which contains 60000 28 × 28 black-and-while images. These images are first flattened to a 784 dimensional vector. Both the encoder and decoder have multiple number of 512-dimensional hidden layers, each followed by ReLU activations. Convolution Network: The original images are either 32× 32× 3 (Cifar10, Cifar100 and SVHN) or 64 × 64 × 3 (CelebA and ImageNet). In the encoder, we use a multiple number (denoted as t) of 3 × 3 convolution layers for each spatial scale. Each convolution layer is followed by a ReLU activation. Then we use a 2×2 max pooling to downsample the feature map to a smaller spatial scale. The number of channels is doubled when the spatial scale is halved. We use 64 channels when the spatial scale is 32×32. When the spatial scale reaches 4× 4 (there should be 512 channels in this feature map), we use an average pooling to transform the feature map to a vector, which is then transformed into the latent variable using a fully connected layer. In the decoder, the latent variable is first transformed to a 4096-dimensional vector using a fully connected layer and then reshaped to 2 × 2 × 1024. Again in each spatial scale, we use 1 transpose convolution layer to upscale the feature map and halve the number of channels followed by t − 1 convolution layers. Each convolution and transpose convolution layer is followed by a ReLU activation layer. When the spatial scale reaches that of the original image, we use a convolution layer to transofrm the feature map to 3 channels. Residual Network: The network structure of the residual network is similar to that of a convolution network described above. We simply replace the convolution layer with a residual block. Inside the residual block, we use different numbers of convolution numbers. (The typical number of convolution layers inside a residual block is 2 or 3. In our experiments, we try 2, 3, 4 and 5.)
Controlled Lagrangians and Stabilization of Discrete Mechanical Systems I

Controlled Lagrangian and matching techniques are developed for the stabilization of relative equilibria and equilibria of discrete mechanical systems with symmetry as well as broken symmetry. Interesting new phenomena arise in the controlled Lagrangian approach in the discrete context that are not present in the continuous theory. In particular, to make the discrete theory effective, one can make an appropriate selection of momentum levels or, alternatively, introduce a new parameter into the controlled Lagrangian to complete the kinetic matching procedure. Specifically, new terms in the controlled shape equation that are necessary for potential matching in the discrete setting are introduced. The theory is illustrated with the problem of stabilization of the cart-pendulum system on an incline. The paper also discusses digital and model predictive controllers.
Resolving 3D Human Pose Ambiguities with 3D Scene Constraints

Our method enforces Proximal Relationships with Object eXclusion and is called PROX . The ﬁgures below show representative examples where the human body pose is estimated with (gray color) and without (yellow color) our environmental terms. From the viewpoint of the camera, both solutions look good and match the 2D image features but, when placed in a scan of the 3D scene, the results without environment constraints can be grossly inaccurate. Adding our constraints to the optimization reduces inter-penetration and encourages appropriate contact