nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Learning and Exploration of Reachable Outcome Space

Performing Reinforcement Learning in sparse rewards settings, with very little prior knowledge, is a challenging problem since there is no signal to properly guide the learning process. In such situations, a good search strategy is fundamental. At the same time, not having to adapt the algorithm to every single problem is very desirable. Here we introduce TAXONS, a Task Agnostic eXploration of Outcome spaces through Novelty and Surprise algorithm. Based on a population-based divergent-search approach, it learns a set of diverse policies directly from high-dimensional observations, without any task-specific information. TAXONS builds a repertoire of policies while training an autoencoder on the high-dimensional observation of the final state of the system to build a low-dimensional outcome space. The learned outcome space, combined with the reconstruction error, is used to drive the search for new policies. Results show that TAXONS can find a diverse set of controllers, covering a good part of the ground-truth outcome space, while having no information about such space.
Axion boson stars

We study novel solitonic solutions to Einstein-Klein-Gordon theory in the presence of a periodic scalar potential arising in models of axion-like particles. The potential depends on two parameters: the mass of the scalar field ma and the decay constant fa; the standard case of the QCD axion is recovered when ma ∝ 1/fa. When fa → ∞ the solutions reduce to the standard case of “mini” boson stars supported by a massive free scalar field. As the energy scale fa of the scalar self-interactions decreases we unveil several novel features of the solution: new stability branches emerge at high density, giving rise to very compact, radially stable, boson stars. Some of the most compact configurations acquire a photon sphere. When fa is at the GUT scale, a boson star made of QCD axions can have a mass up to ten solar masses and would be more compact than a neutron star. Gravitational-wave searches for these exotic compact objects might provide indirect evidence for ultralight axion-like particles in a region not excluded by the black-hole superradiant instability.
Construction and classification of point-group symmetry-protected topological phases in two-dimensional interacting fermionic systems

The construction and classification of symmetry-protected topological (SPT) phases in interacting bosonic and fermionic systems have been intensively studied in the past few years. Very recently, a complete classification and construction of space group SPT phases were also proposed for interacting bosonic systems. In this paper, we attempt to generalize this classification and construction scheme systematically into interacting fermion systems. In particular, we construct and classify point group SPT phases for 2D interacting fermion systems via lower-dimensional block-state decorations. We discover several intriguing fermionic SPT states that can only be realized in interacting fermion systems (i.e., not in free-fermion or bosonic SPT systems). Moreover, we also verify the recently conjectured crystalline equivalence principle for 2D interacting fermion systems. Finally, the potential experimental realization of these new classes of point group SPT phases in 2D correlated superconductors is addressed.
Reinforcing Edge Computing with Multipath TCP Enabled Mobile Device Clouds

In recent years, enormous growth has been witnessed in the computational and storage capabilities of mobile devices. However, much of this computational and storage capabilities are not always fully used. On the other hand, popularity of mobile edge computing which aims to replace the traditional centralized powerful cloud with multiple edge servers is rapidly growing. In particular, applications having strict latency requirements can be best served by the mobile edge clouds due to a reduced round-trip delay. In this paper we propose a Multi-Path TCP (MPTCP) enabled mobile device cloud (MDC) as a replacement to the existing TCP based or D2D device cloud techniques, as it effectively makes use of the available bandwidth by providing much higher throughput as well as ensures robust wireless connectivity. We investigate the congestion in mobile-device cloud formation resulting mainly due to the message passing for service providing nodes at the time of discovery, service continuity and formation of cloud composition. We propose a user space agent called congestion handler that enable offloading of packets from one sub-flow to the other under link quality constraints. Further, we discuss the benefits of this design and perform preliminary analysis of the system.
Birth of de Sitter universe from a time crystal universe

We show that a simple sub-class of Horndeski theory can describe a time crystal Universe. The time crystal Universe can be regarded as a baby Universe nucleated from a flat space, which is mediated by an extension of Giddings-Strominger instanton in a 2-form theory dual to the Horndeski theory. Remarkably, when a cosmological constant is included, de Sitter Universe can be created by tunneling from the time crystal Universe. It gives rise to a past completion of an inflationary Universe.
Learning First-Order Symbolic Representations for Planning from the Structure of the State Space

One of the main obstacles for developing flexible AI systems is the split between data-based learners and model-based solvers. Solvers such as classical planners are very flexible and can deal with a variety of problem instances and goals but require first-order symbolic models. Data-based learners, on the other hand, are robust but do not produce such representations. In this work we address this split by showing how the first-order symbolic representations that are used by planners can be learned from non-symbolic inputs that encode the structure of the state space. The representation learning problem is formulated as the problem of inferring planning instances over a common but unknown first-order domain that account for the structure of the observed state space. This means to infer a complete first-order representation (i.e. general action schemas, relational symbols, and objects) that explains the observed state space structures. The inference problem is cast as a two-level combinatorial search where the outer level searches for values of a small set of hyperparameters and the inner level, solved via SAT, searches for a first-order symbolic model. The framework is shown to produce general and correct first-order representations for standard problems like Gripper, Blocksworld, and Hanoi from input graphs that encode the flat state-space structure of a single instance.
CUNI System for the Building Educational Applications 2019 Shared Task: Grammatical Error Correction

Our submitted models are NMT systems based on the Transformer model, which we improve by incorporating several enhancements: applying dropout to whole source and target words, weighting target subwords, averaging model checkpoints, and using the trained model iteratively for correcting the intermediate translations. The system in the Restricted Track is trained on the provided corpora with oversampled “cleaner” sentences and reaches 59.39 F0.5 score on the test set. The system in the Low-Resource Track is trained from Wikipedia revision histories and reaches 44.13 F0.5 score. Finally, we finetune the system from the Low-Resource Track on restricted data and achieve 64.55 F0.5 score.
Current and Future Applications of Reverberation-Mapped Quasars in Cosmology

Reverberation mapping technique is an important milestone in AGN demographics, kinematics and the structure of the Broad Line Region (BLR) based on the time-delay response between the continuum and emission line. The time delay is directly related to the size of BLR which is related to the continuum luminosity of the source, producing the well-known Radius-Luminosity (RL) relation. The majority of sources have been monitored for their H$\beta$ emission line in low redshift sources ($z<0.1$), while there are some attempts using the MgII line for higher redshifts. We present a recent MgII monitoring for the quasar CTS C30.10 ($z=0.90$) observed with the 10-meter SALT, for which RL scaling based on MgII holds within measurement and time-delay uncertainties. One of the most important advantages of reverberation mapping technique is the independent determination to the distant source, and considering the large range of redshifts and luminosities found in AGNs, their application to cosmology is promising. However, recently it has been found that highly accreting sources show the time delays shorter than expected from RL relation. We propose correction for this effect using a sample of 117 H$\beta$ reverberating-mapped AGNs with $0.02<z<0.9$, recovering the low scatter along with the relation. We are able to determine the cosmological constants, $\Omega_m$ and $\Omega_\Lambda$ within 2$\sigma$ confidence level. We present the first steps in modelling of light curves for H$\beta$ and MgII and discuss the quasar selection in the context of photometric reverberation mapping with Large Synoptic Survey Telescope (LSST). With the onset of the LSST era, we expect a huge rise in the overall quasar counts and redshift range covered ($z\lesssim 7.0$), which will provide a better constraint of AGN properties with cosmological purposes.
Human-Machine Collaborative Design for Accelerated Design of Compact Deep Neural Networks for Autonomous Driving

An effective deep learning development process is critical for widespread industrial adoption, particularly in the automotive sector. A typical industrial deep learning development cycle involves customizing and re-designing an off-the-shelf network architecture to meet the operational requirements of the target application, leading to considerable trial and error work by a machine learning practitioner. This approach greatly impedes development with a long turnaround time and the unsatisfactory quality of the created models. As a result, a development platform that can aid engineers in greatly accelerating the design and production of compact, optimized deep neural networks is highly desirable. In this joint industrial case study, we study the efficacy of the GenSynth AI-assisted AI design platform for accelerating the design of custom, optimized deep neural networks for autonomous driving through human-machine collaborative design. We perform a quantitative examination by evaluating 10 different compact deep neural networks produced by GenSynth for the purpose of object detection via a NASNet-based user network prototype design, targeted at a low-cost GPU-based accelerated embedded system. Furthermore, we quantitatively assess the talent hours and GPU processing hours used by the GenSynth process and three other approaches based on the typical industrial development process. In addition, we quantify the annual cloud cost savings for comprehensive testing using networks produced by GenSynth. Finally, we assess the usability and merits of the GenSynth process through user feedback. The findings of this case study showed that GenSynth is easy to use and can be effective at accelerating the design and production of compact, customized deep neural network.
Inflation, dark energy, and dark matter in supergravity

The Dark Side of the Universe, which includes the cosmological inflation in the early Universe, the current dark energy and dark matter, can be theoretically described by supergravity, though it is non-trivial. We recall the arguments pro and contra supersymmetry and supergravity, and define the viable supergravity models describing the Dark Side of the Universe in agreement with all current observations. Our approach to inflation is based on the Starobinsky model, the dark energy is identified with the positive cosmological constant (de Sitter vacuum), and the dark matter particle is given by the lightest supersymmetric particle identified with the supermassive gravitino. The key role is played by spontaneous supersymmetry breaking.
The Emergence of Innovation Complexity at Different Geographical and Technological Scales

We define a novel quantitative strategy inspired by the ecological notion of nestedness to single out the scale at which innovation complexity emerges from the aggregation of specialized building blocks. Our analysis not only suggests that the innovation space can be interpreted as a natural system in which advantageous capabilities are selected by evolutionary pressure, but also that the emerging structure of capabilities is not independent of the scale of observation at which they are observed. Expanding on this insight allows us to understand whether the capabilities characterizing the innovation space at a given scale are compatible with a complex evolutionary dynamics or, rather, a set of essentially independent activities allowing to reduce the system at that scale to a set of disjoint non interacting sub-systems. This yields a measure of the innovation complexity of the system, i.e. of the degree of interdependence between the sets of capabilities underlying the system's building blocks.
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System

We present ABSApp, a portable system for weakly-supervised aspect-based sentiment ex- traction. The system is interpretable and user friendly and does not require labeled training data, hence can be rapidly and cost-effectively used across different domains in applied setups. The system flow includes three stages: First, it generates domain-specific aspect and opinion lexicons based on an unlabeled dataset; second, it enables the user to view and edit those lexicons (weak supervision); and finally, it enables the user to select an unlabeled target dataset from the same domain, classify it, and generate an aspect-based sentiment report. ABSApp has been successfully used in a number of real-life use cases, among them movie review analysis and convention impact analysis.
DeepBbox: Accelerating Precise Ground Truth Generation for Autonomous Driving Datasets

Autonomous driving requires various computer vision algorithms, such as object detection and tracking. Precisely-labeled datasets (i.e., objects are fully contained in bounding boxes with only a few extra pixels) are preferred for training such algorithms, so that the algorithms can detect exact locations of the objects. However, it is very time-consuming and hence expensive to generate precise labels for image sequences at scale. In this paper, we propose DeepBbox, an algorithm that "corrects" loose object labels into right bounding boxes to reduce human annotation efforts. We use Cityscapes  dataset to show annotation efficiency and accuracy improvement using DeepBbox. Experimental results show that, with DeepBbox, we can increase the number of object edges that are labeled automatically (within 1% error) by 50% to reduce manual annotation time.
Reinforcing Medical Image Classifier to Improve Generalization on Small Datasets

With the advents of deep learning, improved image classification with complex discriminative models has been made possible. However, such deep models with increased complexity require a huge set of labeled samples to generalize the training. Such classification models can easily overfit when applied for medical images because of limited training data, which is a common problem in the field of medical image analysis. This paper proposes and investigates a reinforced classifier for improving the generalization under a few available training data. Partially following the idea of reinforcement learning, the proposed classifier uses a generalization-feedback from a subset of the training data to update its parameter instead of only using the conventional cross-entropy loss about the training data. We evaluate the improvement of the proposed classifier by applying it on three different classification problems against the standard deep classifiers equipped with existing overfitting-prevention techniques. Besides an overall improvement in classification performance, the proposed classifier showed remarkable characteristics of generalized learning, which can have great potential in medical classification tasks.
Harmonic Forms, Price Inequalities, and Benjamini-Schramm Convergence

We study Betti numbers of sequences of Riemannian manifolds which Benjamini-Schramm converge to their universal covers. Using the Price inequalities we developed elsewhere, we derive two distinct convergence results. First, under a negative Ricci curvature assumption and no assumption on sign of the sectional curvature, we have a convergence result for weakly uniform discrete sequences of closed Riemannian manifolds. In the negative sectional curvature case, we are able to remove the weakly uniform discreteness assumption. This is achieved by combining a refined Thick-Thin decomposition together with a Moser iteration argument for harmonic forms on manifolds with boundary.
Faster and Accurate Classification for JPEG2000 Compressed Images in Networked Applications

JPEG2000 (j2k) is a highly popular format for image and video compression.With the rapidly growing applications of cloud based image classification, most existing j2k-compatible schemes would stream compressed color images from the source before reconstruction at the processing center as inputs to deep CNNs. We propose to remove the computationally costly reconstruction step by training a deep CNN image classifier using the CDF 9/7 Discrete Wavelet Transformed (DWT) coefficients directly extracted from j2k-compressed images. We demonstrate additional computation savings by utilizing shallower CNN to achieve classification of good accuracy in the DWT domain. Furthermore, we show that traditional augmentation transforms such as flipping/shifting are ineffective in the DWT domain and present different augmentation transformations to achieve more accurate classification without any additional cost. This way, faster and more accurate classification is possible for j2k encoded images without image reconstruction. Through experiments on CIFAR-10 and Tiny ImageNet data sets, we show that the performance of the proposed solution is consistent for image transmission over limited channel bandwidth.
Illuminated Decision Trees with Lucid

The Lucid methods described by Olah et al. (2018) provide a way to inspect the inner workings of neural networks trained on image classification tasks using feature visualization. Such methods have generally been applied to networks trained on visually rich, large-scale image datasets like ImageNet, which enables them to produce enticing feature visualizations. To investigate these methods further, we applied them to classifiers trained to perform the much simpler (in terms of dataset size and visual richness), yet challenging task of distinguishing between different kinds of white blood cell from microscope images. Such a task makes generating useful feature visualizations difficult, as the discriminative features are inherently hard to identify and interpret. We address this by presenting the "Illuminated Decision Tree" approach, in which we use a neural network trained on the task as a feature extractor, then learn a decision tree based on these features, and provide Lucid visualizations for each node in the tree. We demonstrate our approach with several examples, showing how this approach could be useful both in model development and debugging, and when explaining model outputs to non-experts.
A Camera That CNNs: Towards Embedded Neural Networks on Pixel Processor Arrays

We present a convolutional neural network implementation for pixel processor array (PPA) sensors. PPA hardware consists of a fine-grained array of general-purpose processing elements, each capable of light capture, data storage, program execution, and communication with neighboring elements. This allows images to be stored and manipulated directly at the point of light capture, rather than having to transfer images to external processing hardware. Our CNN approach divides this array up into 4x4 blocks of processing elements, essentially trading-off image resolution for increased local memory capacity per 4x4 ”pixel”. We implement parallel operations for image addition, subtraction and bit-shifting images in this 4x4 block format. Using these components we formulate how to perform ternary weight convolutions upon these images, compactly store results of such convolutions, perform max-pooling, and transfer the resulting sub-sampled data to an attached micro-controller. We train ternary weight filter CNNs for digit recognition and a simple tracking task, and demonstrate inference of these networks upon the SCAMP5 PPA system. This work represents a first step towards embedding neural network processing capability directly onto the focal plane of a sensor.
UER: An Open-Source Toolkit for Pre-training Models

Existing works, including ELMO and BERT, have revealed the importance of pre-training for NLP tasks. While there does not exist a single pre-training model that works best in all cases, it is of necessity to develop a framework that is able to deploy various pre-training models efficiently. For this purpose, we propose an assemble-on-demand pre-training toolkit, namely Universal Encoder Representations (UER). UER is loosely coupled, and encapsulated with rich modules. By assembling modules on demand, users can either reproduce a state-of-the-art pre-training model or develop a pre-training model that remains unexplored. With UER, we have built a model zoo, which contains pre-trained models based on different corpora, encoders, and targets (objectives). With proper pre-trained models, we could achieve new state-of-the-art results on a range of downstream datasets.
Spectral decimation of the magnetic Laplacian on the Sierpinski gasket: Hofstadter's butterfly, determinants, and loop soup entropy

The magnetic Laplacian (also called the line bundle Laplacian) on a connected weighted graph is a self-adjoint operator wherein the real-valued adjacency weights are replaced by complex-valued weights. When properly interpreted, these complex weights give rise to magnetic fluxes through cycles in the graph. 
In this paper we establish the spectrum of the magnetic Laplacian on the Sierpinski gasket graph ($SG$) where the magnetic fluxes equal $\alpha$ through the smallest upright triangles, and $\beta$ through the smallest downright triangles. This is achieved upon showing the spectral self-similarity of the magnetic Laplacian via a 3-parameter map involving non-rational functions, which takes into account $\alpha$, $\beta$, and the spectral parameter $\lambda$. When $\alpha,\beta\in \{0,\frac{1}{2}\}$, we can list the eigenvalues according to their multiplicities. When not both $\alpha$ and $\beta$ are in $\{0,\frac{1}{2}\}$, we show that the spectrum is essentially determined by the Julia set of the 3-parameter map. 
We provide two applications of our results: 1) demonstrate the true Hofstadter's butterfly appearing in the energy spectrum on $SG$ under uniform magnetic field; and 2) compute the magnetic Laplacian determinant which corresponds to the partition function for random cycle-rooted spanning forests on $SG$, derive the corresponding asymptotic complexity, and give its probabilistic interpretation in terms of a "loop soup entropy."