nils-herrmann/clean_scirepeval huggingface dataset:
Motivic integration over wild Deligne-Mumford stacks

We develop the motivic integration theory over formal DeligneMumford stacks over a power series ring of arbitrary characteristic. This is a generalization of the corresponding theory for tame and smooth DeligneMumford stacks constructed in earlier papers of the author. As an application, we obtain the wild motivic McKay correspondence for linear actions of arbitrary finite groups, which has been known only for cyclic groups of prime order. In particular, this implies the motivic version of Bhargava’s mass formula as a special case, which was earlier proved in a joint work of Tonini and the author by a different method. In fact, we prove a more general result, the invariance of stringy motives of (stacky) log pairs under crepant morphisms.
Neural Document Expansion with User Feedback

This paper presents a neural document expansion approach (NeuDEF) that enriches document representations for neural ranking models. NeuDEF harvests expansion terms from queries which lead to clicks on the document and weights these expansion terms with learned attention. It is plugged into a standard neural ranker and learned end-to-end. Experiments on a commercial search log demonstrate that NeuDEF significantly improves the accuracy of state-of-the-art neural rankers and expansion methods on queries with different frequencies. Further studies show the contribution of click queries and learned expansion weights, as well as the influence of document popularity of NeuDEF's effectiveness.
From stellar haloes to intracluster light: the physics of the Intra-Halo Stellar Component in cosmological hydrodynamical simulations

We study the Intra-Halo Stellar Component (IHSC) of Milky Way-mass systems up to galaxy clusters in the Horizon-AGN cosmological hydrodynamical simulation. We identify the IHSC using an improved phase-space galaxy finder algorithm which provides an adaptive, physically motivated, and shape-independent definition of this stellar component, that can be applied to haloes of arbitrary masses. We explore the IHSC mass fraction – total halo’s stellar mass, $f_{M_{*,\mathrm{IHSC}}} - M_{*}$, relation, and the physical drivers of its scatter. We find that on average, the $f_{M_{*,\mathrm{IHSC}}}$ increases with total stellar mass, with the scatter decreasing strongly with mass from 2 dex at $M_{*,\mathrm{tot}}\simeq 10^{11}\, \mathrm{M}_\odot$ to 0.3 dex at group masses. At high masses, $M_{*,\mathrm{tot}}\gt 10^{11.5}\, \mathrm{M}_\odot$, $f_{M_{*,\mathrm{IHSC}}}$ increases with the number of substructures, and with the mass ratio between the central galaxy and largest satellite, at fixed M*, tot. From mid-size groups and systems below $M_{*,\mathrm{tot}}\lt 10^{12}\, \mathrm{M}_\odot$, we find that the central galaxy’s stellar rotation-to-dispersion velocity ratio, V/σ, displays the strongest (anti)-correlation with $f_{M_{*,\mathrm{IHSC}}}$ at fixed M*, tot of all the galaxy and halo properties explored, transitioning from $f_{M_{*,\mathrm{IHSC}}}\lt 0.1$ per cent for high V/σ, to $f_{M_{*,\mathrm{IHSC}}}\approx 5$ per cent for low V/σ galaxies. By studying the $f_{M_{*,\mathrm{IHSC}}}$ temporal evolution, we find that, in the former, mergers not always take place, but if they did, they happened early (z > 1), while the high $f_{M_{*,\mathrm{IHSC}}}$ population displays a much more active merger history. In the case of massive groups and galaxy clusters, $M_{*,\mathrm{tot}}\gtrsim 10^{12}\, \mathrm{M}_\odot$, a fraction $f_{M_{*,\mathrm{IHSC}}}\approx 10-20$ per cent is reached at z ≈ 1 and then they evolve across lines of constant $f_{M_{*,\mathrm{IHSC}}}$ modulo some small perturbations. Because of the limited simulation’s volume, the latter is only tentative and requires a larger sample of simulated galaxy clusters to confirm.
Graph Node Embeddings using Domain-Aware Biased Random Walks

The recent proliferation of publicly available graph-structured data has sparked an interest in machine learning algorithms for graph data. Since most traditional machine learning algorithms assume data to be tabular, embedding algorithms for mapping graph data to real-valued vector spaces has become an active area of research. Existing graph embedding approaches are based purely on structural information and ignore any semantic information from the underlying domain. In this paper, we demonstrate that semantic information can play a useful role in computing graph embeddings. Specifically, we present a framework for devising embedding strategies aware of domain-specific interpretations of graph nodes and edges, and use knowledge of downstream machine learning tasks to identify relevant graph substructures. Using two real-life domains, we show that our framework yields embeddings that are simple to implement and yet achieve equal or greater accuracy in machine learning tasks compared to domain independent approaches.
Progressive Relation Learning for Group Activity Recognition

Group activities usually involve spatio-temporal dynamics among many interactive individuals, while only a few participants at several key frames essentially define the activity. Therefore, effectively modeling the group-relevant and suppressing the irrelevant actions (and interactions) are vital for group activity recognition. In this paper, we propose a novel method based on deep reinforcement learning to progressively refine the low-level features and high-level relations of group activities. Firstly, we construct a semantic relation graph (SRG) to explicitly model the relations among persons. Then, two agents adopting policy according to two Markov decision processes are applied to progressively refine the SRG. Specifically, one feature-distilling (FD) agent in the discrete action space refines the low-level spatio-temporal features by distilling the most informative frames. Another relation-gating (RG) agent in continuous action space adjusts the high-level semantic graph to pay more attention to group-relevant relations. The SRG, FD agent, and RG agent are optimized alternately to mutually boost the performance of each other. Extensive experiments on two widely used benchmarks demonstrate the effectiveness and superiority of the proposed approach.
A VR System for Immersive Teleoperation and Live Exploration with a Mobile Robot

Applications like disaster management and industrial inspection often require experts to enter contaminated places. To circumvent the need for physical presence, it is desirable to generate a fully immersive individual live teleoperation experience. However, standard video-based approaches suffer from a limited degree of immersion and situation awareness due to the restriction to the camera view, which impacts the navigation. In this paper, we present a novel VR-based practical system for immersive robot teleoperation and scene exploration. While being operated through the scene, a robot captures RGB-D data that is streamed to a SLAM-based live multiclient telepresence system. Here, a global 3D model of the already captured scene parts is reconstructed and streamed to the individual remote user clients where the rendering for e.g. head-mounted display devices (HMDs) is performed. We introduce a novel lightweight robot client component which transmits robot-specific data and enables a quick integration into existing robotic systems. This way, in contrast to first- person exploration systems, the operators can explore and navigate in the remote site completely independent of the current position and view of the capturing robot, complementing traditional input devices for teleoperation. We provide a proof-of-concept implementation and demonstrate the capabilities as well as the performance of our system regarding interactive object measurements and bandwidth-efficient data streaming and visualization. Furthermore, we show its benefits over purely video-based teleoperation in a user study revealing a higher degree of situation awareness and a more precise navigation in challenging environments.
Research Leadership Flow Determinants and the Role of Proximity in Research Collaborations Networks

Characterizing the leadership in research is important to revealing the interaction pattern and organizational structure through research collaboration. This research defines the leadership role based on the corresponding author's affiliation, and presents, the first quantitative research on the factors and evolution of five proximity dimensions (geographical, cognitive, institutional, social and economic) of research leadership. The data to capture research leadership consists of a set of multi-institution articles in the life sciences and biomedical during 2013-2017 from Web of Science Core Citation Database. Our sample consists of 484,903 articles from 244 Chinese institutions, which have been the primary affiliation of the corresponding author for at least one paper (with multiple institutions) in each year. A Tobit regression-based gravity model indicates that research leadership mass of both the leading and participating institutions and the geographical, cognitive, institutional, social and economic proximity are important factors of the flow of research leadership among Chinese institutions. In general, the effect of these proximity for research leadership flow has been declining recently. The outcome of this research sheds light on the leadership evolution and flow among Chinese institutions, and thus can provide evidence and support for grant allocation policy to facilitate scientific research and collaborations.
CRIC: A VQA Dataset for Compositional Reasoning on Vision and Commonsense

Alternatively inferring on the visual facts and commonsense is fundamental for an advanced VQA system. This ability requires models to go beyond the literal understanding of commonsense. The system should not just treat objects as the entrance to query background knowledge, but fully ground commonsense to the visual world and imagine the possible relationships between objects, e.g., “fork, can lift, food”. To comprehensively evaluate such abilities, we propose a VQA benchmark, CRIC, which introduces new types of questions about Compositional Reasoning on vIsion and Commonsense, and an evaluation metric integrating the correctness of answering and commonsense grounding. To collect such questions and rich additional annotations to support the metric, we also propose an automatic algorithm to generate question samples from the scene graph associated with the images and the relevant knowledge graph. We further analyze several representative types of VQA models on the CRIC dataset. Experimental results show that grounding the commonsense to the image region and joint reasoning on vision and commonsense are still challenging for current approaches. The dataset is available at https://cricvqa.github.io.
Contributed Discussion of "A Bayesian Conjugate Gradient Method"

We would like to congratulate the authors of "A Bayesian Conjugate Gradient Method" on their insightful paper, and welcome this publication which we firmly believe will become a fundamental contribution to the growing field of probabilistic numerical methods and in particular the sub-field of Bayesian numerical methods. In this short piece, which will be published as a comment alongside the main paper, we first initiate a discussion on the choice of priors for solving linear systems, then propose an extension of the Bayesian conjugate gradient (BayesCG) algorithm for solving several related linear systems simultaneously.
Subgroups of an abelian group, related ideals of the group ring, and quotients by those ideals

Let $RG$ be the group ring of an abelian group $G$ over a commutative ring $R$ with identity. An injection $\Phi$ from the subgroups of $G$ to the non-unit ideals of $RG$ is well-known. It is defined by $\Phi(N)=I(R,N)RG$ where $I(R,N)$ is the augmentation ideal of $RN$, and each ideal $\Phi(N)$ has a property : $RG/\Phi(N)$ is $R$-algebra isomorphic to $R(G/N)$. Let $T$ be the set of non-unit ideals of $RG$. While the image of $\Phi$ is rather a small subset of $T$, we give conditions on $R$ and $G$ for the image of $\Phi$ to have some distribution in $T$. In the last section, we give criteria for choosing an element $x$ of $RG$ satisfying $RG/xRG$ is $R$-algebra isomorphic to $R(G/N)$ for a subgroup $N$ of $G$.
Incremental Reinforcement Learning - a New Continuous Reinforcement Learning Frame Based on Stochastic Differential Equation methods

Continuous reinforcement learning such as DDPG and A3C are widely used in robot control and autonomous driving. However, both methods have theoretical weaknesses. While DDPG cannot control noises in the control process, A3C does not satisfy the continuity conditions under the Gaussian policy. To address these concerns, we propose a new continues reinforcement learning method based on stochastic differential equations and we call it Incremental Reinforcement Learning (IRL). This method not only guarantees the continuity of actions within any time interval, but controls the variance of actions in the training process. In addition, our method does not assume Markov control in agents' action control and allows agents to predict scene changes for action selection. With our method, agents no longer passively adapt to the environment. Instead, they positively interact with the environment for maximum rewards.
Study of the decays $\psi(3686)\rightarrow\gamma\chi_{cJ}\rightarrow\gamma\bar{p}K^{*+}\Lambda+c.c.$ and $\psi(3686)\rightarrow\bar{p}K^{*+}\Lambda+c.c.$

Based on the data sample of $448.1\times10^{6}$ $\psi(3686)$ events collected with the BESIII detector at BEPCII, we present a study of the decays $\psi(3686)\rightarrow\gamma\chi_{cJ}\rightarrow\gamma\bar{p}K^{*+}\Lambda+c.c.$ and $\psi(3686)\rightarrow\bar{p}K^{*+}\Lambda+c.c.$. The branching fractions of $\chi_{cJ}\rightarrow\bar{p}K^{*+}\Lambda+c.c.$ ($J$=0, 1, 2) are measured to be $(4.8\pm0.7\pm0.5)\times10^{-4}$, $(5.0\pm0.5\pm0.4)\times10^{-4}$, and $(8.2\pm0.9\pm0.7)\times10^{-4}$, respectively, where the first uncertainties are statistical and the second systematic. The branching fraction of $\psi(3686)\rightarrow\bar{p}K^{*+}\Lambda+c.c.$ is measured to be $(6.3\pm0.5\pm0.5)\times10^{-5}$. All these decay modes are observed for the first time.
Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning

Semi-supervised learning, i.e. jointly learning from labeled and unlabeled samples, is an active research topic due to its key role on relaxing human supervision. In the context of image classification, recent advances to learn from unlabeled samples are mainly focused on consistency regularization methods that encourage invariant predictions for different perturbations of unlabeled samples. We, conversely, propose to learn from unlabeled data by generating soft pseudo-labels using the network predictions. We show that a naive pseudo-labeling overfits to incorrect pseudo-labels due to the so-called confirmation bias and demonstrate that mixup augmentation and setting a minimum number of labeled samples per mini-batch are effective regularization techniques for reducing it. The proposed approach achieves state-of-the-art results in CIFAR-10/100, SVHN, and Mini-ImageNet despite being much simpler than other methods. These results demonstrate that pseudo-labeling alone can outperform consistency regularization methods, while the opposite was supposed in previous work.
Fast Point R-CNN

We present a unified, efficient and effective framework for point-cloud based 3D object detection. Our two-stage approach utilizes both voxel representation and raw point cloud data to exploit respective advantages. The first stage network, with voxel representation as input, only consists of light convolutional operations, producing a small number of high-quality initial predictions. Coordinate and indexed convolutional feature of each point in initial prediction are effectively fused with the attention mechanism, preserving both accurate localization and context information. The second stage works on interior points with their fused feature for further refining the prediction. Our method is evaluated on KITTI dataset, in terms of both 3D and Bird's Eye View (BEV) detection, and achieves state-of-the-arts with a 15FPS detection rate.
Manifold Modeling in Embedded Space: A Perspective for Interpreting "Deep Image Prior"

Deep image prior (DIP), which utilizes a deep convolutional network (ConvNet) structure itself as an image prior, has attractive attentions in computer vision community. It empirically showed that the effectiveness of ConvNet structure in various image restoration applications. However, why the DIP works so well is still in black box, and why ConvNet is essential for images is not very clear. 
In this study, we tackle this question by considering the convolution divided into "embedding" and "transformation", and proposing a simple, but essential, modeling approach of images/tensors related with dynamical system or self-similarity. The proposed approach named as manifold modeling in embedded space (MMES) can be implemented by using a denoising-auto-encoder in combination with multiway delay-embedding transform. In spite of its simplicity, the image/tensor completion and super-resolution results of MMES were very similar even competitive with DIP in our experiments, and these results would help us for reinterpreting/characterizing the DIP from a perspective of "smooth patch-manifold prior".
Local Differential Privacy for Deep Learning

The Internet of Things (IoT) is transforming major industries, including but not limited to healthcare, agriculture, finance, energy, and transportation. IoT platforms are continually improving with innovations, such as the amalgamation of software-defined networks (SDNs) and network function virtualization (NFV) in the edge-cloud interplay. Deep learning (DL) is becoming popular due to its remarkable accuracy when trained with a massive amount of data such as generated by IoT. However, DL algorithms tend to leak privacy when trained on highly sensitive crowd-sourced data such as medical data. The existing privacy-preserving DL algorithms rely on the traditional server-centric approaches requiring high processing powers. We propose a new local differentially private (LDP) algorithm named LATENT that redesigns the training process. LATENT enables a data owner to add a randomization layer before data leave the data owners’ devices and reach a potentially untrusted machine learning service. This feature is achieved by splitting the architecture of a convolutional neural network (CNN) into three layers: 1) convolutional module (CNM); 2) randomization module; and 3) fully connected module. Hence, the randomization module can operate as an NFV privacy preservation service in an SDN-controlled NFV, making LATENT more practical for IoT-driven cloud-based environments compared to existing approaches. The randomization module employs a newly proposed LDP protocol named utility enhancing randomization, which allows LATENT to maintain high utility compared to existing LDP protocols. Our experimental evaluation of LATENT on convolutional deep neural networks demonstrates excellent accuracy (e.g., 91%–96%) with high model quality even under low privacy budgets (e.g., $\varepsilon =0.5$ ).
Learning Vision-Based Flight in Drone Swarms by Imitation

Decentralized drone swarms deployed today either rely on sharing of positions among agents or detecting swarm members with the help of visual markers. This letter proposes an entirely visual approach to coordinate markerless drone swarms based on imitation learning. Each agent is controlled by a small and efficient convolutional neural network that takes raw omnidirectional images as inputs and predicts 3D velocity commands that match those computed by a flocking algorithm. We start training in simulation and propose a simple yet effective unsupervised domain adaptation approach to transfer the learned controller to the real world. We further train the controller with data collected in our motion capture hall. We show that the convolutional neural network trained on the visual inputs of the drone can learn not only robust inter-agent collision avoidance but also cohesion of the swarm in a sample-efficient manner. The neural controller effectively learns to localize other agents in the visual input, which we show by visualizing the regions with the most influence on the motion of an agent. We remove the dependence on sharing positions among swarm members by taking only local visual information into account for control. Our work can therefore be seen as the first step towards a fully decentralized, vision-based swarm without the need for communication or visual markers.
Feature selection of neural networks is skewed towards the less abstract cue

Artificial neural networks (ANNs) have become an important tool for image classification with many applications in research and industry. However, it remains largely unknown how relevant image features are selected and how data properties affect this process. In particular, we are interested whether the abstraction level of image cues correlating with class membership influences feature selection. We perform experiments with binary images that contain a combination of cues, representing two different levels of abstractions: one is a pattern drawn from a random distribution where class membership correlates with the statistics of the pattern, the other a combination of symbol-like entities, where the symbolic code correlates with class membership. When the network is trained with data in which both cues are equally significant, we observe that the cues at the lower abstraction level, i.e., the pattern, is learned, while the symbolic information is largely ignored, even in networks with many layers. Symbol-like entities are only learned if the importance of low-level cues is reduced compared to the high-level ones. These findings raise important questions about the relevance of features that are learned by deep ANNs and how learning could be shifted towards symbolic features.
Dimer models and Hochschild cohomology

Dimer models provide a method of constructing noncommutative crepant resolutions of affine toric Gorenstein threefolds. In homological mirror symmetry, they can also be used to describe noncommutative Landau--Ginzburg models dual to punctured Riemann surfaces. For a zigzag consistent dimer embedded in a torus, we explicitly compute the Hochschild cohomology of its Jacobi algebra in terms of dimer combinatorics. This includes a full characterization of the Batalin--Vilkovisky structure induced by the Calabi--Yau structure of the Jacobi algebra. We then compute the compactly supported Hochschild cohomology of the category of matrix factorizations for the Jacobi algebra with its canonical potential.