Miracle-dz/UniFAR-FTUs huggingface dataset:
The Proper Use of Google Trends in Forecasting Models

It is widely known that Google Trends have become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many papers, from several different fields, concluding that Google Trends improve forecasts' accuracy. However, what seems to be widely unknown, is that each sample of Google search data is different from the other, even if you set the same search term, data and location. This means that it is possible to find arbitrary conclusions merely by chance. This paper aims to show why and when it can become a problem and how to overcome this obstacle.
A transit timing variation observed for the long-period extremely low-density exoplanet HIP 41378 f

HIP 41378 f is a temperate 9.2 ± 0.1R⊕ planet with period of 542.08 days and an extremely low density of 0.09 ± 0.02g cm−3. It transits the bright star HIP 41378 (V=8.93), making it an exciting target for atmospheric characterization including transmission spectroscopy. HIP 41378 was monitored photometrically between the dates of 2019 November 19 and November 28. We detected a transit of HIP 41378 f with NGTS, just the third transit ever detected for this planet, which confirms the orbital period. This is also the first ground-based detection of a transit of HIP 41378 f. Additional ground-based photometry was also obtained and used to constrain the time of the transit. The transit was measured to occur 1.50 hours earlier than predicted. We use an analytic transit timing variation (TTV) model to show the observed TTV can be explained by interactions between HIP 41378 e and HIP 41378 f. Using our TTV model, we predict the epochs of future transits of HIP 41378 f, with derived transit centres of TC,4 = 2459355.087+0.031 −0.022 (May 2021) and TC,5 = 2459897.078 +0.114 −0.060 (Nov 2022).
Exploring Targeted Universal Adversarial Perturbations to End-to-end ASR Models

Although end-to-end automatic speech recognition (e2e ASR) models are widely deployed in many applications, there have been very few studies to understand models' robustness against adversarial perturbations. In this paper, we explore whether a targeted universal perturbation vector exists for e2e ASR models. Our goal is to find perturbations that can mislead the models to predict the given targeted transcript such as"thank you"or empty string on any input utterance. We study two different attacks, namely additive and prepending perturbations, and their performances on the state-of-the-art LAS, CTC and RNN-T models. We find that LAS is the most vulnerable to perturbations among the three models. RNN-T is more robust against additive perturbations, especially on long utterances. And CTC is robust against both additive and prepending perturbations. To attack RNN-T, we find prepending perturbation is more effective than the additive perturbation, and can mislead the models to predict the same short target on utterances of arbitrary length.
The Art of Defense: Letting Networks Fool the Attacker

3D perception of objects is critical for many real-world applications, such as autonomous cars and robots. Among them, most state-of-the-art (SOTA) 3D perception systems are based on deep learning models. Recently, the research community found that 3D object classifiers on point cloud based on deep learning are easily fooled by adversarial point cloud craft by attackers. To overcome this, adversarial defenses are considered the most effective ways to improve the robustness of deep learning models, and most adversarial defenses on point cloud are focused on input transformation. However, all previous defense methods decrease the natural accuracy, and the nature of the point cloud classifiers itself has been overlooked. To this end, in this paper, we propose a novel adversarial defense for 3D point cloud classifiers that makes full use of the nature of the point cloud classifiers. Due to the disorder of point cloud, all point cloud classifiers have the property of permutation invariant to the input point cloud. Based on this nature, we design invariant transformations defense (IT-Defense). We show that, even after accounting for obfuscated gradients, our IT-Defense is a resilient defense against SOTA 3D attacks. Moreover, IT-Defense does not hurt clean accuracy compared to previous SOTA 3D defenses. Our code will be available at: https://github.com/cuge1995/ IT-Defense.
A Parametric Galactic Model toward the Galactic Bulge Based on Gaia and Microlensing Data

We developed a parametric Galactic model toward the Galactic bulge by fitting to spatial distributions of the Gaia DR2 disk velocity, VVV proper motion, BRAVA radial velocity, OGLE-III red clump star count, and OGLE-IV star count and microlens rate, optimized for use in microlensing studies. We include the asymmetric drift of Galactic disk stars and the dependence of velocity dispersion on Galactic location in the kinematic model, which has been ignored in most previous models used for microlensing studies. We show that our model predicts a microlensing parameter distribution significantly different from those typically used in previous studies. We estimate various fundamental model parameters for our Galaxy through our modeling, including the initial mass function (IMF) in the inner Galaxy. Combined constraints from star counts and the microlensing event timescale distribution from the OGLE-IV survey, in addition to a prior on the bulge stellar mass, enable us to successfully measure IMF slopes using a broken power-law form over a broad mass range, αbd=0.22−0.55+0.20 for M < 0.08 M ⊙, αms=1.16−0.15+0.08 for 0.08 M ⊙ ≤ M < M br, and αhm=2.32−0.10+0.14 for M ≥ M br, as well as a break mass at Mbr=0.90−0.14+0.05M⊙ . This is significantly different from the Kroupa IMF for local stars, but similar to the Zoccali IMF measured from a bulge luminosity function. We also estimate the dark matter mass fraction in the bulge region of 28% ± 7% which could be larger than a previous estimate. Because our model is purely parametric, it can be universally applied using the parameters provided in this paper. (A tool for microlensing simulation using our Galactic model has been published, Koshimoto & Ranc 2021, and can be downloaded at https://github.com/nkoshimoto/genulens.)
NeuMIP

We propose NeuMIP, a neural method for representing and rendering a variety of material appearances at different scales. Classical prefiltering (mipmapping) methods work well on simple material properties such as diffuse color, but fail to generalize to normals, self-shadowing, fibers or more complex microstructures and reflectances. In this work, we generalize traditional mipmap pyramids to pyramids of neural textures, combined with a fully connected network. We also introduce neural offsets, a novel method which enables rendering materials with intricate parallax effects without any tessellation. This generalizes classical parallax mapping, but is trained without supervision by any explicit heightfield. Neural materials within our system support a 7-dimensional query, including position, incoming and outgoing direction, and the desired filter kernel size. The materials have small storage (on the order of standard mipmapping except with more texture channels), and can be integrated within common Monte-Carlo path tracing systems. We demonstrate our method on a variety of materials, resulting in complex appearance across levels of detail, with accurate parallax, self-shadowing, and other effects.
Waves in the gas centrifuge: asymptotic theory and similarities with the atmosphere

Abstract We study the stratified gas in a rapidly rotating centrifuge as a model for the Earth's atmosphere. Based on methods of perturbation theory, it is shown that in certain regimes, internal waves in the gas centrifuge have the same dispersion relation to leading order as their atmospheric siblings. Assuming an air filled centrifuge with a radius of around 50 cm, the optimal rotational frequency for realistic atmosphere-like waves is around 10 000 revolutions per minute. Using gases of lower heat capacities at constant pressure, such as xenon, the rotational frequencies can be even halved to obtain the same results. Similar to the atmosphere, it is feasible in the gas centrifuge to generate a clear scale separation of wave frequencies and therefore phase speeds between acoustic waves and internal waves. In addition to the centrifugal force, the Coriolis force acts in the same plane. However, its influence on axially homogeneous internal waves appears only as a higher-order correction. We conclude that the gas centrifuge provides an unprecedented opportunity to investigate atmospheric internal waves experimentally with a compressible working fluid.
Min(d)ing the President: A text analytic approach to measuring tax news

Economic agents react contemporaneously to signals about future tax policy changes. Consequently, estimating their macroeconomic eﬀects requires identiﬁcation of such signals. We propose a novel text analytic approach for transforming textual information into an economically meaningful time series. We apply our method to create a tax news measure from all publicly available post-war communications of U.S. presidents. We ﬁnd that our measure predicts direction and size of (exogenous) future tax changes and contains signals not present in previously considered (narrative) measures of tax changes. We ﬁnd that tax news triggers a signiﬁcant yet delayed response in output.
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network

Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of efforts made on static graphs, among which Graph Convolutional Network (GCN) has emerged as an effective class of models. However, these methods mainly focus on the static graph embedding. In this work, we propose an efficient dynamic graph embedding approach, Dynamic Graph Convolutional Network (DyGCN), which is an extension of GCN-based methods. We naturally generalizes the embedding propagation scheme of GCN to dynamic setting in an efficient manner, which is to propagate the change along the graph to update node embeddings. The most affected nodes are first updated, and then their changes are propagated to the further nodes and leads to their update. Extensive experiments conducted on various dynamic graphs demonstrate that our model can update the node embeddings in a time-saving and performance-preserving way.
SCANimate: Weakly Supervised Learning of Skinned Clothed Avatar Networks

We present SCANimate, an end-to-end trainable framework that takes raw 3D scans of a clothed human and turns them into an animatable avatar. These avatars are driven by pose parameters and have realistic clothing that moves and deforms naturally. SCANimate does not rely on a customized mesh template or surface mesh registration. We observe that fitting a parametric 3D body model, like SMPL, to a clothed human scan is tractable while surface registration of the body topology to the scan is often not, because clothing can deviate significantly from the body shape. We also observe that articulated transformations are invertible, resulting in geometric cycle-consistency in the posed and unposed shapes. These observations lead us to a weakly supervised learning method that aligns scans into a canonical pose by disentangling articulated deformations without template-based surface registration. Furthermore, to complete missing regions in the aligned scans while modeling pose-dependent deformations, we introduce a locally pose-aware implicit function that learns to complete and model geometry with learned pose correctives. In contrast to commonly used global pose embeddings, our local pose conditioning significantly reduces long-range spurious correlations and improves generalization to unseen poses, especially when training data is limited. Our method can be applied to pose-aware appearance modeling to generate a fully textured avatar. We demonstrate our approach on various clothing types with different amounts of training data, outperforming existing solutions and other variants in terms of fidelity and generality in every setting. The code is available at https://scanimate.is. tue.mpg. de.
Hand-Object Contact Consistency Reasoning for Human Grasps Generation

While predicting robot grasps with parallel jaw grippers have been well studied and widely applied in robot manipulation tasks, the study on natural human grasp generation with a multi-finger hand remains a very challenging problem. In this paper, we propose to generate human grasps given a 3D object in the world. Our key observation is that it is crucial to model the consistency between the hand contact points and object contact regions. That is, we encourage the prior hand contact points to be close to the object surface and the object common contact regions to be touched by the hand at the same time. Based on the hand-object contact consistency, we design novel objectives in training the human grasp generation model and also a new self-supervised task which allows the grasp generation network to be adjusted even during test time. Our experiments show significant improvement in human grasp generation over state-of-the-art approaches by a large margin. More interestingly, by optimizing the model during test time with the self-supervised task, it helps achieve larger gain on unseen and out-of-domain objects. Project page: https://hwjiang1510.github. io/GraspTTA/.
Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection

This paper reports the first successful application of a differentiable architecture search (DARTS) approach to the deepfake and spoofing detection problems. An example of neural architecture search, DARTS operates upon a continuous, differentiable search space which enables both the architecture and parameters to be optimised via gradient descent. Solutions based on partially-connected DARTS use random channel masking in the search space to reduce GPU time and automatically learn and optimise complex neural architectures composed of convolutional operations and residual blocks. Despite being learned quickly with little human effort, the resulting networks are competitive with the best performing systems reported in the literature. Some are also far less complex, containing 85% fewer parameters than a Res2Net competitor.
Dynamic Principal Subspaces with Sparsity in High Dimensions

Principal component analysis (PCA) is a versatile tool to reduce the dimensionality which has wide applications in statistics and machine learning community. It is particularly useful to model data in high-dimensional scenarios where the number of variables p is comparable to, or much larger than the sample size n . Despite extensive literature on this topic, researches have focused on modeling static principal eigenvectors or subspaces, which is unsuitable for stochastic processes that are dynamic in nature. To characterize the change in the whole course of high-dimensional data collection, we propose a uniﬁed framework to estimate dynamic principal subspaces spanned by leading eigenvectors of covariance matrices. In the proposed framework, we formulate an optimization problem by combining the kernel smoothing and regularization penalty together with the orthogonality constraint, which can be eﬀectively solved by the proximal gradient method for manifold optimization. We show that our method is suitable for high-dimensional data observed under both common and irregular designs. In addition, theoretical properties of the estimators are investigated under l q (0 ≤ q ≤ 1) sparsity. Extensive experiments demonstrate the eﬀectiveness of the proposed method in both simulated and real data examples.
[RE] CNN-generated images are surprisingly easy to spot...for now

This work evaluates the reproducibility of the paper"CNN-generated images are surprisingly easy to spot. for now"by Wang et al. published at CVPR 2020. The paper addresses the challenge of detecting CNN-generated imagery, which has reached the potential to even fool humans. The authors propose two methods which help an image classifier to generalize from being trained on one specific CNN to detecting imagery produced by unseen architectures, training methods, or data sets. The paper proposes two methods to help a classifier generalize: (i) utilizing different kinds of data augmentations and (ii) using a diverse data set. This report focuses on assessing if these techniques indeed help the generalization process. Furthermore, we perform additional experiments to study the limitations of the proposed techniques.
Thermophysical Model for Realistic Surface Layers on Airless Small Bodies: Applied to Study the Spin Orientation and Surface Dust Properties of (24) Themis from WISE/NEOWISE Multiepoch Thermal Light Curves

This work proposes a thermophysical model for realistic surface layers on airless small bodies (RSTPM) for the use of interpreting their multiepoch thermal light curves (e.g., WISE/NEOWISE). RSTPM considers the real orbital cycle, rotation cycle, rough surface, temperature-dependent thermal parameters, as well as contributions of sunlight reflection to observations. It is thus able to produce a precise temperature distribution and thermal emission of airless small bodies regarding the variations on orbital timescales. Details of the physics, mathematics, and numerical algorithms of RSTPM are presented. When used to interpret multiepoch thermal light curves by WISE/NEOWISE, RSTPM can give constraints on the spin orientation and surface physical properties, such as the mean thermal inertia or the mean size of dust grains, the roughness fraction, and the albedo via a radiometric procedure. As an application example, we apply this model to the main-belt object (24) Themis, the largest object of the Themis family, which is believed to be the source region of many main-belt comets. We find multiepoch (2010, 2014–2018) observations of Themis by WISE/NEOWISE, yielding 18 thermal light curves. By fitting these data with RSTPM, the best-fit spin orientation of Themis is derived to be (λ = 137°, β = 59°) in ecliptic coordinates, and the mean radius of dust grains on the surface is estimated to be b˜=140−114+500(6∼640) μm, indicating that the surface thermal inertia varies from ∼3 Jm−2 s−0.5 K−1 to ∼60 Jm−2 s −0.5 K−1 due to seasonal temperature variation. A more detailed analysis found that the thermal light curves of Themis show a weak feature that depends on the rotation phase, which is indicative of heterogeneous thermal properties or imperfections of the light-curve inversion shape model.
Characterization and Modeling of Native MOSFETs Down to 4.2 K

The extremely low threshold voltage (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {TH}}$ </tex-math></inline-formula>) of native MOSFETs (<inline-formula> <tex-math notation="LaTeX">${V}_{\text {TH}}~\approx $ </tex-math></inline-formula> 0 V@300 K) is conducive to the design of cryogenic circuits. Previous research on cryogenic MOSFETs mainly focused on the standard threshold voltage (SVT) and low threshold voltage (LVT) MOSFETs. In this article, we characterize native MOSFETs within the temperature range from 300 to 4.2 K. The cryogenic <inline-formula> <tex-math notation="LaTeX">${V}_{\text {TH}}$ </tex-math></inline-formula> increases up to ~0.25 V (W/L = 10/<inline-formula> <tex-math notation="LaTeX">$10~\mu \text{m}$ </tex-math></inline-formula>) and the improved subthreshold swing (SS) ≈ 14.30 mV/dec@4.2 K. The OFF-state current (<inline-formula> <tex-math notation="LaTeX">${I}_{ \mathrm{\scriptscriptstyle OFF}}$ </tex-math></inline-formula>) and the gate-induced drain leakage (GIDL) effect are ameliorated greatly. The step-up effect caused by the substrate charge and the transconductance peak effect caused by the energy quantization in different sub-bands are also discussed. Based on the EKV model, we modified the mobility calculation equations and proposed a compact model of large-size native MOSFETs suitable for the range of 300-4.2 K. The mobility-related parameters are extracted via a machine learning (ML) approach, and the temperature dependences of the scattering mechanisms are analyzed. This work is beneficial to both the research on cryogenic MOSFETs’ modeling and the design of cryogenic CMOS circuits for quantum chips.
Adjustable Robust Two-Stage Polynomial Optimization with Application to AC Optimal Power Flow

In this work, we consider two-stage polynomial optimization problems under uncertainty. In the first stage, one needs to decide upon the values of a subset of optimization variables (control variables). In the second stage, the uncertainty is revealed and the rest of optimization variables (state variables) are set up as a solution to a known system of possibly non-linear equations. This type of problem occurs, for instance, in optimization for dynamical systems, such as electric power systems. We combine tools from polynomial and robust optimization to provide a framework for general adjustable robust polynomial optimization problems. In particular, we propose an iterative algorithm to build a sequence of (approximately) robustly feasible solutions with an improving objective value and verify robust feasibility or infeasibility of the resulting solution under a semialgebraic uncertainty set. At each iteration, the algorithm optimizes over a subset of the feasible set and uses affine approximations of the second-stage equations while preserving the non-linearity of other constraints. The algorithm allows for additional simplifications in case of possibly non-convex quadratic problems under ellipsoidal uncertainty. We implement our approach for AC Optimal Power Flow and demonstrate the performance of our proposed method on Matpower instances.
The Value of Excess Supply in Spatial Matching Markets

We study dynamic matching in a spatial setting. Drivers are distributed at random on some interval. Riders arrive in some (possibly adversarial) order at randomly drawn points. The platform observes the location of the drivers and can match newly arrived riders immediately or can wait for more riders to arrive. Unmatched riders incur a waiting cost of c per period. Furthermore, the platform can match riders and drivers irrevocably, and the cost of matching a driver to a rider is equal to the distance between them. Our goal is to investigate the value of 'slack supply'. For this purpose, we ask two questions: (1) What is the performance of a naive algorithm with extra drivers? and (2) How does this compare to the performance of a smart algorithm, but without the extra drivers? We prove that a naive algorithm with extra drivers performs better than the ex-post optimal algorithm in a balanced market. With n riders and drivers in the system, even the ex-post optimal matching does not have a cost less than Θ(√n). However, when there are (1+ε) drivers per rider (for any ε > 0$), the cost of matching by the greedy algorithm, which pairs each arriving rider to the closest available driver, is O(log(n)). In particular, if in total there are O(n5/6) excess drivers, then the greedy algorithm has a cost of o(√n) and hence beats the optimal algorithm. Our results shed light on the critical role of (small) excess supply in spatial matching markets.