nils-herrmann/clean_scirepeval huggingface dataset:
Learning Action-Transferable Policy with Action Embedding

Despite achieving great success on performance in various sequential decision task, deep reinforcement learning is extremely data inefficient. Many approaches have been proposed to improve the data efficiency, e.g. transfer learning which utilizes knowledge learned from related tasks to accelerate training. Previous researches on transfer learning mostly attempt to learn a common feature space of states across related tasks to exploit knowledge as much as possible. However, semantic information of actions may be shared as well, even between tasks with different action space size. In this work, we first propose a method to learn action embedding for discrete actions in RL from generated trajectories without any prior knowledge, and then leverage it to transfer policy across tasks with different state space and/or discrete action space. We validate our method on a set of gridworld navigation tasks, discretized continuous control tasks and fighting tasks in a commercial video game. Our experimental results show that our method can effectively learn informative action embeddings and accelerate learning by policy transfer across tasks.
Efficient Neural Architecture Transformation Searchin Channel-Level for Object Detection

Recently, Neural Architecture Search has achieved great success in large-scale image classification. In contrast, there have been limited works focusing on architecture search for object detection, mainly because the costly ImageNet pretraining is always required for detectors. Training from scratch, as a substitute, demands more epochs to converge and brings no computation saving. To overcome this obstacle, we introduce a practical neural architecture transformation search(NATS) algorithm for object detection in this paper. Instead of searching and constructing an entire network, NATS explores the architecture space on the base of existing network and reusing its weights. We propose a novel neural architecture search strategy in channel-level instead of path-level and devise a search space specially targeting at object detection. With the combination of these two designs, an architecture transformation scheme could be discovered to adapt a network designed for image classification to task of object detection. Since our method is gradient-based and only searches for a transformation scheme, the weights of models pretrained in ImageNet could be utilized in both searching and retraining stage, which makes the whole process very efficient. The transformed network requires no extra parameters and FLOPs, and is friendly to hardware optimization, which is practical to use in real-time application. In experiments, we demonstrate the effectiveness of NATS on networks like {\em ResNet} and {\em ResNeXt}. Our transformed networks, combined with various detection frameworks, achieve significant improvements on the COCO dataset while keeping fast.
Depth Map Estimation for Free-Viewpoint Television and Virtual Navigation

The paper presents a new method of depth estimation, dedicated for free-viewpoint television (FTV) and virtual navigation (VN). In this method, multiple arbitrarily positioned input views are simultaneously used to produce depth maps characterized by high inter-view and temporal consistencies. The estimation is performed for segments and their size is used to control the trade-off between the quality of depth maps and the processing time of depth estimation. Additionally, an original technique is proposed for the improvement of temporal consistency of depth maps. This technique uses the temporal prediction of depth, thus depth is estimated for P-type depth frames. For such depth frames, temporal consistency is high, whereas estimation complexity is relatively low. Similarly, as for video coding, I-type depth frames with no temporal depth prediction are used in order to achieve robustness. Moreover, we propose a novel parallelization technique that significantly reduces the estimation time. The method is implemented in C++ software that is provided together with this paper, so other researchers may use it as a new reference for their future works. In performed experiments, MPEG methodology was used whenever possible. The provided results demonstrate the advantages over the Depth Estimation Reference Software (DERS) developed by MPEG. The fidelity of a depth map, measured by the quality of synthesized views, is higher on average by 2.6 dB. This significant quality improvement is obtained despite a significant reduction of the estimation time, on average 4.5 times. The application of the proposed temporal consistency enhancement method increases this reduction to 29 times. Moreover, the proposed parallelization results in the reduction of the estimation time up to 130 times (using 6 threads). As there is no commonly accepted measure of the consistency of depth maps, the application of compression efficiency of depth is proposed as a measure of depth consistency.
The homunculus for proprioception: Toward learning the representation of a humanoid robot's joint space using self-organizing maps

In primate brains, tactile and proprioceptive inputs are relayed to the somatosensory cortex which is known for somatotopic representations, or, "homunculi". Our research centers on understanding the mechanisms of the formation of these and more higher-level body representations (body schema) by using humanoid robots and neural networks to construct models. We specifically focus on how spatial representation of the body may be learned from somatosensory information in self-touch configurations. In this work, we target the representation of proprioceptive inputs, which we take to be joint angles in the robot. The inputs collected in different body postures serve as inputs to a Self-Organizing Map (SOM) with a 2D lattice on the output. With unrestricted, all-to-all connections, the map is not capable of representing the input space while preserving the topological relationships, because the intrinsic dimensionality of the body posture space is too large. Hence, we use a method we developed previously for tactile inputs (Hoffmann, Straka et al. 2018) called MRF-SOM, where the Maximum Receptive Field of output neurons is restricted so they only learn to represent specific parts of the input space. This is in line with the receptive fields of neurons in somatosensory areas representing proprioception that often respond to combination of few joints (e.g. wrist and elbow).
Beyond integrated information: A taxonomy of information dynamics phenomena

Most information dynamics and statistical causal analysis frameworks rely on the common intuition that causal interactions are intrinsically pairwise -- every 'cause' variable has an associated 'effect' variable, so that a 'causal arrow' can be drawn between them. However, analyses that depict interdependencies as directed graphs fail to discriminate the rich variety of modes of information flow that can coexist within a system. This, in turn, creates problems with attempts to operationalise the concepts of 'dynamical complexity' or `integrated information.' To address this shortcoming, we combine concepts of partial information decomposition and integrated information, and obtain what we call Integrated Information Decomposition, or $\Phi$ID. We show how $\Phi$ID paves the way for more detailed analyses of interdependencies in multivariate time series, and sheds light on collective modes of information dynamics that have not been reported before. Additionally, $\Phi$ID reveals that what is typically referred to as 'integration' is actually an aggregate of several heterogeneous phenomena. Furthermore, $\Phi$ID can be used to formulate new, tailored measures of integrated information, as well as to understand and alleviate the limitations of existing measures.
Detector with focus: Normalizing gradient in image pyramid

An image pyramid can extend many object detection algorithms to solve detection on multiple scales. However, interpolation during the resampling process of an image pyramid causes gradient variation, which is the difference of the gradients between the original image and the scaled images. Our key insight is that the increased variance of gradients makes the classifiers have difficulty in correctly assigning categories. We prove the existence of the gradient variation by formulating the ratio of gradient expectations between an original image and scaled images, then propose a simple and novel gradient normalization method to eliminate the effect of this variation. The proposed normalization method reduce the variance in an image pyramid and allow the classifier to focus on a smaller coverage. We show the improvement in three different visual recognition problems: pedestrian detection, pose estimation, and object detection. The method is generally applicable to many vision algorithms based on an image pyramid with gradients.
Table-to-Text Generation with Effective Hierarchical Encoder on Three Dimensions (Row, Column and Time)

Although Seq2Seq models for table-to-text generation have achieved remarkable progress, modeling table representation in one dimension is inadequate. This is because (1) the table consists of multiple rows and columns, which means that encoding a table should not depend only on one dimensional sequence or set of records and (2) most of the tables are time series data (e.g. NBA game data, stock market data), which means that the description of the current table may be affected by its historical data. To address aforementioned problems, not only do we model each table cell considering other records in the same row, we also enrich table’s representation by modeling each table cell in context of other cells in the same column or with historical (time dimension) data respectively. In addition, we develop a table cell fusion gate to combine representations from row, column and time dimension into one dense vector according to the saliency of each dimension’s representation. We evaluated our methods on ROTOWIRE, a benchmark dataset of NBA basketball games. Both automatic and human evaluation results demonstrate the effectiveness of our model with improvement of 2.66 in BLEU over the strong baseline and outperformance of state-of-the-art model.
Human-AI Collaboration in Data Science

The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, known as AutoAI or AutoML, aim to automate the work practices of data scientists. AutoAI systems are capable of autonomously ingesting and pre-processing data, engineering new features, and creating and scoring models based on a target objectives (e.g. accuracy or run-time efficiency). Though not yet widely adopted, we are interested in understanding how AutoAI will impact the practice of data science. We conducted interviews with 20 data scientists who work at a large, multinational technology company and practice data science in various business settings. Our goal is to understand their current work practices and how these practices might change with AutoAI. Reactions were mixed: while informants expressed concerns about the trend of automating their jobs, they also strongly felt it was inevitable. Despite these concerns, they remained optimistic about their future job security due to a view that the future of data science work will be a collaboration between humans and AI systems, in which both automation and human expertise are indispensable.
Commonsense Reasoning Using WordNet and SUMO: a Detailed Analysis

We describe a detailed analysis of a sample of large benchmark of commonsense reasoning problems that has been automatically obtained from WordNet, SUMO and their mapping. The objective is to provide a better assessment of the quality of both the benchmark and the involved knowledge resources for advanced commonsense reasoning tasks. By means of this analysis, we are able to detect some knowledge misalignments, mapping errors and lack of knowledge and resources. Our final objective is the extraction of some guidelines towards a better exploitation of this commonsense knowledge framework by the improvement of the included resources.
The Application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Time Series

Automated systems for detecting deformation in satellite interferometric synthetic aperture radar (InSAR) imagery could be used to develop a global monitoring system for volcanic and urban environments. Here, we explore the limits of a convolutional neural networks for detecting slow, sustained deformations in wrapped interferograms. Using synthetic data, we estimate a detection threshold of 3.9 cm for deformation signals alone and 6.3 cm when atmospheric artifacts are considered. Overwrapping reduces this to 1.8 and 5.2 cm, respectively, as more fringes are generated without altering signal to noise ratio. We test the approach on time series of cumulative deformation from Campi Flegrei and Dallol, where overwrapping improves classification performance by up to 15%. We propose a mean‐filtering method for combining results of different wrap parameters to flag deformation. At Campi Flegrei, deformation of 8.5 cm/year was detected after 60 days and at Dallol, deformation of 3.5 cm/year was detected after 310 days. This corresponds to cumulative displacements of 3 and 4 cm consistent with estimates based on synthetic data.
Convolution morphisms and Kottwitz conjecture

We introduce convolution morphisms, duality morphisms and twist morphisms between moduli spaces of mixed characteristic local shtukas. Using these morphisms, we relate the etale cohomology of different moduli spaces of mixed characteristic local shtukas. As an application, we show the Kottwitz conjecture in many new cases including the cases for all inner forms of GL(3) and minuscule cocharacters. We study also some non-minuscule cases and show that the Kottwitz conjecture does not hold as it is in non-minuscule cases if the Langlands parameter is not cuspidal.
Occ-Traj120: Occupancy Maps with Associated Trajectories

Trajectory modelling had been the principal research area for understanding and anticipating human behaviour. Predicting the dynamic path by observing the agent and its surrounding environment are essential for applications such as autonomous driving and indoor navigation suggestions. However, despite the numerous researches that had been presented, most available dataset does not contains any information on environmental factors---such as the occupancy representation of the map---which arguably plays a significant role on how an agent chooses its trajectory. 
We present a trajectory dataset with the corresponding occupancy representations of different local-maps. The dataset contains more than 120 locally-structured maps with occupancy representation and more than 110K trajectories in total. Each map has few hundred corresponding simulated trajectories that navigate from a spatial location of a room to another point. The dataset is freely available online.
A complex network approach to political analysis: Application to the Brazilian Chamber of Deputies

In this paper, we introduce a network-based methodology to study how political entities evolve over time. We constructed networks of voting data from the Brazilian Chamber of Deputies, where deputies are nodes and edges are represented by voting similarity among deputies. The Brazilian Chamber of deputies is characterized by a multi-party political system. Thus, we would expect a broad spectrum of ideas to be represented. Our results, however, revealed that plurality of ideas is not present at all: the effective number of communities representing ideas based on agreement/disagreement in propositions is about 3 over the entire studied time span. The obtained results also revealed different patterns of coalitions between distinct parties. Finally, we also found signs of early party isolation before presidential impeachment proceedings effectively started. We believe that the proposed framework could be used to complement the study of political dynamics and even applied in similar social networks where individuals are organized in a complex manner.
Rewarding Coreference Resolvers for Being Consistent with World Knowledge

Unresolved coreference is a bottleneck for relation extraction, and high-quality coreference resolvers may produce an output that makes it a lot easier to extract knowledge triples. We show how to improve coreference resolvers by forwarding their input to a relation extraction system and reward the resolvers for producing triples that are found in knowledge bases. Since relation extraction systems can rely on different forms of supervision and be biased in different ways, we obtain the best performance, improving over the state of the art, using multi-task reinforcement learning.
Developing peer instruction questions for quantitative problems for an upper-division astronomy course

Decades of research show that students learn more in classes that utilize active learning than they do in traditional, lecture-only classes. Active learning also reduces the achievement gaps that are often present between various demographic groups. Given these well-established results, instructors of upper-division astronomy courses may decide to search the astronomy education research literature in hopes of finding some guidance on common student difficulties, as well as research-validated and research-based active learning curricula. But their search will be in vain. The current literature on upper-division astronomy is essentially non-existent. This is a shame,since many upper-division astronomy students will experience conceptual and problem-solving difficulties with the quantitative problems they encounter. These difficulties may exist even if students have a strong background in mathematics. In this paper, I examine one quantitative problem that is representative of those that upper-division astronomy students are expected to solve. I list many of the subtle pieces of information that students need to understand in order to advance toward a solution and I describe how such a list can be used to generate Peer Instruction (PI) questions. I also provide guidelines for instructors who wish to develop and implement their own PI questions. These PI questions can be used to increase the amount of active learning that occurs in an upper-division astronomy course. They help develop students' understandings of symbolic, mathematical representations and they help improve students' problem-solving skills.
Polaritonic coupled-cluster theory

We develop coupled-cluster theory for systems of electrons strongly coupled to photons, providing a promising theoretical tool in polaritonic chemistry with a perspective of application to all types of fermion-boson coupled systems. We show benchmark results for model molecular Hamiltonians coupled to cavity photons. By comparing to full configuration interaction results for various ground-state properties and optical spectra, we demonstrate that our method captures all key features present in the exact reference, including Rabi splittings and multi-photon processes. Further, a path on how to incorporate our bosonic extension of coupled-cluster theory into existing quantum chemistry programs is given.
Riemannian batch normalization for SPD neural networks

Covariance matrices have attracted attention for machine learning applications due to their capacity to capture interesting structure in the data. The main challenge is that one needs to take into account the particular geometry of the Riemannian manifold of symmetric positive definite (SPD) matrices they belong to. In the con- text of deep networks, several architectures for these matrices have recently been proposed. In our article, we introduce a Riemannian batch normalization (batch- norm) algorithm, which generalizes the one used in Euclidean nets. This novel layer makes use of geometric operations on the manifold, notably the Riemannian barycenter, parallel transport and non-linear structured matrix transformations. We derive a new manifold-constrained gradient descent algorithm working in the space of SPD matrices, allowing to learn the batchnorm layer. We validate our proposed approach with experiments in three different contexts on diverse data types: a drone recognition dataset from radar observations, and on emotion and action recognition datasets from video and motion capture data. Experiments show that the Riemannian batchnorm systematically gives better classification performance compared with leading methods and a remarkable robustness to lack of data.
Energy asymptotics of a Dirichlet to Neumann problem related to water waves

We consider a Dirichlet to Neumann operator La arising in a model for water waves, with a nonlocal parameter a ∈ (−1, 1). We deduce the expression of the operator in terms of the Fourier transform, highlighting a local behaviour for small frequencies and a nonlocal behaviour for large frequencies. We further investigate the Γ-convergence of the energy associated to the equation La(u)=W′(u), where W is a double-well potential. When a ∈ (−1, 0] the energy Γ-converges to the classical perimeter, while for a ∈ (0, 1) the Γ-limit is a new nonlocal operator, that in dimension n = 1 interpolates the classical and the nonlocal perimeter.
Furstenberg sets in finite fields: Explaining and improving the Ellenberg-Erman proof

A subset $S \subset \mathbb{F}_q^n$, where $\mathbb{F}_q$ is a finite field, is called $(k,m)$-Furstenberg if it has $m$ common points with a $k$-flat in each direction. That is, any $k$-dimensional subspace of $\mathbb{F}_q^n$ can be translated so that it intersects $S$ in at least $m$ points. Using sophisticated scheme-theoretic machinery, Ellenberg and Erman proved that $(k,m)$-Furstenberg sets must have size at least $C_{n,k}m^{n/k}$ with a constant $C_{n,k}$ depending only $n$ and $k$. In this work we follow the overall proof strategy of Ellenberg-Erman, replacing the scheme-theoretic language with more elementary machinery. In addition to presenting the proof in a self-contained and accessible form, we are also able to improve the constant $C_{n,k}$ by modifying certain key parts of the argument.
Best Practices for Scientific Research on Neural Architecture Search

Finding a well-performing architecture is often tedious for both DL practitioners and researchers, leading to tremendous interest in the automation of this task by means of neural architecture search (NAS). Although the community has made major strides in developing better NAS methods, the quality of scientific empirical evaluations in the young field of NAS is still lacking behind that of other areas of machine learning. To address this issue, we describe a set of possible issues and ways to avoid them, leading to the NAS best practices checklist available at this http URL
Hadronic contributions to the muon anomalous moment

We discuss the hadronic contributions to the muon anomalous magnetic moment. They are dominated by light quark contributions which are constrained by the mechanism of chiral symmetry breaking. Using the leading order result based on $e^+ e^-$ scattering data, we show that the next-to-leading order contributions in the fine structure constant $\alpha$ can be reliably calculated. Extending this idea to the hadronic four-point function we give a prediction for the light-by-light contribution.