nils-herrmann/clean_scirepeval huggingface dataset:
A Machine Learning Method for Prediction of Multipath Channels

In this paper, a machine learning method for predicting the evolution of a mobile communication channel based on a specific type of convolutional neural network is developed and evaluated in a simulated multipath transmission scenario. The simulation and channel estimation are designed to replicate real-world scenarios and common measurements supported by reference signals in modern cellular networks. The capability of the predictor meets the requirements that a deployment of the developed method in a radio resource scheduler of a base station poses. Possible applications of the method are discussed.
A New Luminous Blue Variable in the Outskirts of the Andromeda Galaxy

The hot massive luminous blue variables (LBVs) represent an important evolutionary phase of massive stars. Here, we report the discovery of a new LBV— in the distant outskirt of the Andromeda galaxy. It is located in the southwestern corner (a possible faint spiral arm) of M31 with an unexpectedly large projection distance of ∼22 kpc from the center. The optical light curve shows a 1.2 mag variation in V band and its outburst and quiescence phases both last over several years. The observed spectra indicate an A-type supergiant at an epoch close to the outburst phase and a hot B-type supergiant with weak [Fe ii] emission lines at an epoch of much dimmer brightness. The near-infrared color–color diagram further shows that it follows the distribution of Galactic and M31 LBVs rather than B[e] supergiants. All the existing data strongly show that LAMOST J0037+4016 is an LBV. By spectral energy distribution fitting, we find it has a luminosity (4.42 ± 1.64) × 105 L⊙ and an initial mass ∼30 M⊙, indicating its nature of a less luminosity class of LBV.
Linear Equilibria for Dynamic LQG Games with Asymmetric Information and Dependent Types

We consider a non-zero-sum linear quadratic Gaussian (LQG) dynamic game with asymmetric information. Each player observes privately a noisy version of a (hidden) state of the world V, resulting in dependent private observations. We study perfect Bayesian equilibria (PBE) for this game with equilibrium strategies that are linear in players' private estimates of V. The main difficulty arises from the fact that players need to construct estimates on other players' estimate on V, which in turn would imply that an infinite hierarchy of estimates on estimates needs to be constructed, rendering the problem unsolvable. We show that this is not the case: each player's estimate on other players' estimates on V can be precisely summarized into her own estimate on V and some appropriately defined public information. Based on this finding we characterize the PBE through a backward/forward algorithm akin to dynamic programming for the standard LQG control problem. Unlike the standard LQG problem, however, Kalman filter covariance matrices, as well as some other required quantities, are observation-dependent and thus cannot be evaluated off-line through a forward recursion.
PDA: Progressive Data Augmentation for General Robustness of Deep Neural Networks

Adversarial images are designed to mislead deep neural networks (DNNs), attracting great attention in recent years. Although several defense strategies achieved encouraging robustness against adversarial samples, most of them fail to improve the robustness on common corruptions such as noise, blur, and weather/digital effects (e.g. frost, pixelate). To address this problem, we propose a simple yet effective method, named Progressive Data Augmentation (PDA), which enables general robustness of DNNs by progressively injecting diverse adversarial noises during training. In other words, DNNs trained with PDA are able to obtain more robustness against both adversarial attacks as well as common corruptions than the recent state-of-the-art methods. We also find that PDA is more efficient than prior arts and able to prevent accuracy drop on clean samples without being attacked. Furthermore, we theoretically show that PDA can control the perturbation bound and guarantee better generalization ability than existing work. Extensive experiments on many benchmarks such as CIFAR-10, SVHN, and ImageNet demonstrate that PDA significantly outperforms its counterparts in various experimental setups.
A Deep Learning Approach to Grasping the Invisible

We study an emerging problem named “grasping the invisible” in robotic manipulation, in which a robot is tasked to grasp an initially invisible target object via a sequence of pushing and grasping actions. In this problem, pushes are needed to search for the target and rearrange cluttered objects around it to enable effective grasps. We propose to solve the problem by formulating a deep learning approach in a critic-policy format. The target-oriented motion critic, which maps both visual observations and target information to the expected future rewards of pushing and grasping motion primitives, is learned via deep Q-learning. We divide the problem into two subtasks, and two policies are proposed to tackle each of them, by combining the critic predictions and relevant domain knowledge. A Bayesian-based policy accounting for past action experience performs pushing to search for the target; once the target is found, a classifier-based policy coordinates target-oriented pushing and grasping to grasp the target in clutter. The motion critic and the classifier are trained in a self-supervised manner through robot-environment interactions. Our system achieves a 93% and 87% task success rate on each of the two subtasks in simulation and an 85% task success rate in real robot experiments on the whole problem, which outperforms several baselines by large margins.
A Discrete Hard EM Approach for Weakly Supervised Question Answering

Many question answering (QA) tasks only provide weak supervision for how the answer should be computed. For example, TriviaQA answers are entities that can be mentioned multiple times in supporting documents, while DROP answers can be computed by deriving many different equations from numbers in the reference text. In this paper, we show it is possible to convert such tasks into discrete latent variable learning problems with a precomputed, task-specific set of possible solutions (e.g. different mentions or equations) that contains one correct option. We then develop a hard EM learning scheme that computes gradients relative to the most likely solution at each update. Despite its simplicity, we show that this approach significantly outperforms previous methods on six QA tasks, including absolute gains of 2–10%, and achieves the state-of-the-art on five of them. Using hard updates instead of maximizing marginal likelihood is key to these results as it encourages the model to find the one correct answer, which we show through detailed qualitative analysis.
Bayesian inference on volatility in the presence of infinite jump activity and microstructure noise

Volatility estimation based on high-frequency data is key to accurately measure and control the risk of financial assets. A L\'{e}vy process with infinite jump activity and microstructure noise is considered one of the simplest, yet accurate enough, models for financial data at high-frequency. Utilizing this model, we propose a "purposely misspecified" posterior of the volatility obtained by ignoring the jump-component of the process. The misspecified posterior is further corrected by a simple estimate of the location shift and re-scaling of the log likelihood. Our main result establishes a Bernstein-von Mises (BvM) theorem, which states that the proposed adjusted posterior is asymptotically Gaussian, centered at a consistent estimator, and with variance equal to the inverse of the Fisher information. In the absence of microstructure noise, our approach can be extended to inferences of the integrated variance of a general It\^o semimartingale. Simulations are provided to demonstrate the accuracy of the resulting credible intervals, and the frequentist properties of the approximate Bayesian inference based on the adjusted posterior.
Regularity and counting lemmas for multidimensional matrices

In the present paper we propose generalizations of the regularity and counting lemmas for multidimensional matrices under a finite alphabet. Firstly, we prove a variant of a multidimensional regularity lemma with the help of a translation of $\varepsilon$-regularity from graphs to matrices. Next, we state that this $\varepsilon$-regularity is sufficient for obtaining a matrix analogue of the counting lemma for $2$-dimensional matrices but not for higher-dimensional cases. Finally, we introduce $\varepsilon$-regular patterns that allow us to deduce a multidimensional counting lemma.
Re. I. Understanding galaxy sizes, associated luminosity densities, and the artificial division of the early-type galaxy population

For decades, the deceptive simplicity of the radius 
$R_{\rm e}$
 , enclosing an arbitrary 50% of a galaxy’s light, has hamstrung the understanding of early-type galaxies (ETGs). Half a century ago, using these ‘effective half-light’ radii from de Vaucouleurs’ 
$R^{1/4}$
 model, Sérsic reported that bright ETGs follow the relation 
$\mathfrak{M}_B\propto2.5\log R_{\rm e}$
 ; and consequently, one has that 
$\langle\mu\rangle_{\rm e}\propto2.5\log R_{\rm e}$
 and 
$\mu_{\rm e}\propto2.5\log R_{\rm e}$
 , where 
$\mu_{\rm e}$
 and 
$\langle\mu\rangle_{\rm e}$
 are the effective surface brightness at 
$R_{\rm e}$
 and the mean effective surface brightness within 
$R_{\rm e}$
 , respectively. Sérsic additionally observed an apparent transition which led him to advocate for a division between what he called dwarf and giant ETGs; a belief frequently restated to occur at 
$\mathfrak{M}_B \approx -18$
 mag or 
$n\approx 2.5$
 . Here, the location of this false dichotomy in diagrams using ‘effective’ parameters is shown to change by more than 3 mag simply depending on the arbitrary percentage of light used to quantify a galaxy’s size. A range of alternative radii are explored, including where the projected intensity has dropped by a fixed percentage plus a battery of internal radii, further revealing that the transition at 
$\mathfrak{M}_B
\approx -18$
 mag is artificial and does not demark a boundary between different physical processes operating on the ETG population. The above understanding surrounding the effective radius 
$R_{\rm e}$
 is of further importance because quantities such as dynamical mass 
$\sigma^2R/G$
 , gravitational-binding energy 
$GM^2/R$
 , acceleration 
$GM/R^2$
 , and the ‘Fundamental Plane’ also depend on the arbitrary percentage of light used to define R, with implications for dark matter estimates, galaxy formation theories, compact massive galaxies, studies of peculiar velocity flows, and more. Finally, some of the vast literature which has advocated for segregating the ETG population at 
$\mathfrak{M}_B \approx -18$
 mag ( 
$M\approx1$
 – 
$2\times10^{10}\,{\rm M}_{\odot}$
 ) is addressed, and it is revealed how this pervasive mindset has spilled over to influence both the classical bulge versus pseudobulge debate and recently also correlations involving supermassive black hole masses.
Algebraic Property Graphs

In this paper, we use algebraic data types to define a formal basis for the property graph data models supported by popular open source and commercial graph databases. Developed as a kind of inter-lingua for enterprise data integration, algebraic property graphs encode the binary edges and key-value pairs typical of property graphs, and also provide a well-defined notion of schema and support straightforward mappings to and from non-graph datasets, including relational, streaming, and microservice data commonly encountered in enterprise environments. We propose algebraic property graphs as a simple but mathematically rigorous bridge between graph and non-graph data models, broadening the scope of graph computing by removing obstacles to the construction of virtual graphs.
Semi-supervised Vector-valued Learning: From Theory to Algorithm

Vector-valued learning, where the output space admits a vector-valued structure, is an important problem that covers a broad family of important domains, e.g. multi-label learning and multi-class classification. Using local Rademacher complexity and unlabeled data, we derive novel data-dependent excess risk bounds for learning vector-valued functions in both the kernel space and linear space. The derived bounds are much sharper than existing ones, where convergence rates are improved from O(1/ √ n) to O(1/ √ n+ u), and O(1/n) in special cases. Motivated by our theoretical analysis, we propose a unified framework for learning vector-valued functions, incorporating both local Rademacher complexity and Laplacian regularization. Empirical results on a wide number of benchmark datasets show that the proposed algorithm significantly outperforms baseline methods, which coincides with our theoretical findings. Index Terms Vector-valued Functions, Semi-supervised Learning, Generalization Analysis, Local Rademacher Complexity.
Estimating the volatility of Bitcoin using GARCH models

In this paper, an application of three GARCH-type models (sGARCH, iGARCH, and tGARCH) with Student t-distribution, Generalized Error distribution (GED), and Normal Inverse Gaussian (NIG) distribution are examined. The new development allows for the modeling of volatility clustering effects, the leptokurtic and the skewed distributions in the return series of Bitcoin. Comparative to the two distributions, the normal inverse Gaussian distribution captured adequately the fat tails and skewness in all the GARCH type models. The tGARCH model was the best model as it described the asymmetric occurrence of shocks in the Bitcoin market. That is, the response of investors to the same amount of good and bad news are distinct. From the empirical results, it can be concluded that tGARCH-NIG was the best model to estimate the volatility in the return series of Bitcoin. Generally, it would be optimal to use the NIG distribution in GARCH type models since time series of most cryptocurrency are leptokurtic.
Flag versions of quiver Grassmannians for Dynkin quivers have no odd cohomology over $\mathbb{Z}$

We prove that flag versions of quiver Grassmannians (also knows as Lusztig's fibers) for Dynkin quivers (types $A$, $D$, $E$) have no odd cohomology over $\mathbb{Z}$. Moreover, for types $A$ and $D$ we prove that these varieties have $\alpha$-partitions into affine spaces. We also show that to prove the same statement for type $E$, it is enough to check this for indecomposable representations. 
We also give a flag version of the result of Irelli-Esposito-Franzen-Reineke on rigid representations: we prove that flag versions of quiver Grassmannians for rigid representations have a diagonal decomposition. In particular, they have no odd cohomology over $\mathbb{Z}$.
Distortion-Adaptive Salient Object Detection in 360$^\circ$ Omnidirectional Images

Image-based salient object detection (SOD) has been extensively explored in the past decades. However, SOD on 360<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> omnidirectional images is less studied owing to the lack of datasets with pixel-level annotations. Toward this end, this paper proposes a 360<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> image-based SOD dataset that contains 500 high-resolution equirectangular images. We collect the representative equirectangular images from five mainstream 360<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> video datasets and manually annotate all objects and regions over these images with precise masks with a free-viewpoint way. To the best of our knowledge, it is the first public available dataset for salient object detection on 360<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> scenes. By observing this dataset, we find that distortion from projection, large-scale complex scene and small salient objects are the most prominent characteristics. Inspired by the founding, this paper proposes a baseline model for SOD on equirectangular images. In the proposed approach, we construct a distortion-adaptive module to deal with the distortion caused by the equirectangular projection. In addition, a multi-scale contextual integration block is introduced to perceive and distinguish the rich scenes and objects in omnidirectional scenes. The whole network is organized in a progressively manner with deep supervision. Experimental results show the proposed baseline approach outperforms the top-performanced state-of-the-art methods on 360<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> SOD dataset. Moreover, benchmarking results of the proposed baseline approach and other methods on 360<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> SOD dataset show the proposed dataset is very challenging, which also validate the usefulness of the proposed dataset and approach to boost the development of SOD on 360<inline-formula><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> omnidirectional scenes.
On weighted uncertainty sampling in active learning

This note explores probabilistic sampling weighted by uncertainty in active learning. This method has been previously used and authors have tangentially remarked on its efficacy. The scheme has several benefits: (1) it is computationally cheap, (2) it can be implemented in a single-pass streaming fashion which is a benefit when deployed in real-world systems where different subsystems perform the suggestion scoring and extraction of user feedback, and (3) it is easily parameterizable. In this paper, we show on publicly available datasets that using probabilistic weighting is often beneficial and strikes a good compromise between exploration and representation especially when the starting set of labelled points is biased.
Multi-year vector dynamic time warping-based crop mapping

Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. Classification accuracies of these methods degrade considerably in cross-year mapping. Cross-year crop mapping is more useful as it allows the prediction of the following years’ crop maps using previously labeled data. We propose vector dynamic time warping (VDTW), an innovative multi-year classification approach based on warping of angular distances between phenological vectors. The results prove that the proposed VDTW method is robust to temporal and spectral variations compensating for different farming practices, climate and atmospheric effects, and measurement errors between years. We also describe a method for determining the most discriminative time window that allows high classification accuracies with limited data. We carried out tests with Landsat 8 time-series imagery from years 2013 to 2015 for the classification of corn and cotton in the Harran Plain of southeastern Turkey. In addition, we tested VDTW corn and soybean in Kansas, the US for 2017 and 2018 with the Harmonized Landsat Sentinel data. The VDTW method improved cross-year overall accuracies by 3% with fewer training samples compared to other state-of-the-art approaches.
Multi-Sensor 3D Object Box Refinement for Autonomous Driving

We propose a 3D object detection system with multi-sensor refinement in the context of autonomous driving. In our framework, the monocular camera serves as the fundamental sensor for 2D object proposal and initial 3D bounding box prediction. While the stereo cameras and LiDAR are treated as adaptive plug-in sensors to refine the 3D box localization performance. For each observed element in the raw measurement domain (e.g., pixels for stereo, 3D points for LiDAR), we model the local geometry as an instance vector representation, which indicates the 3D coordinate of each element respecting to the object frame. Using this unified geometric representation, the 3D object location can be unified refined by the stereo photometric alignment or point cloud alignment. We demonstrate superior 3D detection and localization performance compared to state-of-the-art monocular, stereo methods and competitive performance compared with the baseline LiDAR method on the KITTI object benchmark.
BERTgrid: Contextualized Embedding for 2D Document Representation and Understanding

For understanding generic documents, information like font sizes, column layout, and generally the positioning of words may carry semantic information that is crucial for solving a downstream document intelligence task. Our novel BERTgrid, which is based on Chargrid by Katti et al. (2018), represents a document as a grid of contextualized word piece embedding vectors, thereby making its spatial structure and semantics accessible to the processing neural network. The contextualized embedding vectors are retrieved from a BERT language model. We use BERTgrid in combination with a fully convolutional network on a semantic instance segmentation task for extracting fields from invoices. We demonstrate its performance on tabulated line item and document header field extraction.
Emotional Distraction for Children Anxiety Reduction During Vaccination

Social assistive robots are starting to be widely used in pediatric health-care environments with the aim of distracting and entertaining children, and so of reducing a possible state of anxiety. In this paper, we present some initial results of a study (N=69) conducted in a Health-Vaccines Center, where the distraction role of a social robot, which interacts with a child showing an emotional behavior, is compared with the same not showing any emotional social cue. Outcome criteria for the evaluation of the intervention included the parents reported level of anxiety before, during and after the procedure.
Teaching algebraic curves for gifted learners at age 11 by using LEGO linkages and GeoGebra

A summary of an experimental course on algebraic curves is given that was held for young learners at age 11. The course was a part of Epsilon camp, a program designed for very gifted students who have already demonstrated high interest in studying mathematics. Prerequisites for the course were mastery of Algebra I and at least one preliminary year in a prior Epsilon camp. The summary gives an overview of the flow of teaching, the achieved results and some evaluation of the given feedback.
Computer-Aided Automated Detection of Gene-Controlled Social Actions of Drosophila

Gene expression of social actions in Drosophilae has been attracting wide interest from biologists, medical scientists and psychologists. Gene-edited Drosophilae have been used as a test platform for experimental investigation. For example, Parkinson’s genes can be embedded into a group of newly bred Drosophilae for research purpose. However, human observation of numerous tiny Drosophilae for a long term is an arduous work, and the dependence on human’s acute perception is highly unreliable. As a result, an automated system of social action detection using machine learning has been highly demanded. In this study, we propose to automate the detection and classification of two innate aggressive actions demonstrated by Drosophilae. Robust keypoint detection is achieved using selective spatio-temporal interest points (sSTIP) which are then described using the 3D Scale Invariant Feature Transform (3D-SIFT) descriptors. Dimensionality reduction is performed using Spectral Regression Kernel Discriminant Analysis (SR-KDA) and classification is done using the nearest centre rule. The classification accuracy shown demonstrates the feasibility of the proposed system.