nils-herrmann/clean_scirepeval huggingface dataset:
New constructions of asymptotically optimal codebooks via character sums over a local ring

In this paper, we present explicit description on the additive characters, multiplicative characters and Gauss sums over a local ring. As an application, based on the additive characters and multiplicative characters satisfying certain conditions, two new constructions of complex codebooks over a local ring are introduced. With these two constructions, we obtain two families of codebooks achieving asymptotically optimal with respect to the Welch bound. It's worth mentioning that the codebooks constructed in this paper have new parameters.
Observation of a first order phase transition to metal hydrogen near 425 GPa

Hydrogen has been the essential element in the development of atomic and molecular physics1). Moving to the properties of dense hydrogen has appeared a good deal more complex than originally thought by Wigner and Hungtinton in their seminal paper predicting metal hydrogen2): the electrons and the protons are strongly coupled to each other and ultimately must be treated equally3)4). The determination of how and when molecular solid hydrogen will transform into a metal is the stepping stone towards a full understanding of the quantum-many body properties of dense hydrogen. The quest for metal hydrogen has pushed major developments of modern experimental high pressure physics, yet the various claims of its observation over the past 30 years have remained controversial5)6)7). Here we show a first order phase transition near 425 GPa from insulator molecular solid hydrogen to metal hydrogen. Pressure in excess of 400 GPa could be achieved by using the recently developed Toroidal Diamond Anvil Cell (T-DAC)8). The structural and electronic properties of dense solid hydrogen at 80 K have been characterized by synchrotron infrared spectroscopy. The continuous vibron frequency shift and the electronic band gap closure down to 0.5 eV, both linearly evolving with pressure, point to the stability of the insulator C2/c-24 phase up to the metallic transition. Upon pressure release, the metallic state transforms back to the C2/c-24 phase with almost no hysteresis, hence suggesting that the metallization proceeds through a structural transformation within the molecular solid, presumably to the Cmca-12 structure. Our results are in good agreement with the scenario recently disclosed by an advanced calculation able to capture many-body electronic correlations9).
Generating Long and Informative Reviews with Aspect-Aware Coarse-to-Fine Decoding

Generating long and informative review text is a challenging natural language generation task. Previous work focuses on word-level generation, neglecting the importance of topical and syntactic characteristics from natural languages. In this paper, we propose a novel review generation model by characterizing an elaborately designed aspect-aware coarse-to-fine generation process. First, we model the aspect transitions to capture the overall content flow. Then, to generate a sentence, an aspect-aware sketch will be predicted using an aspect-aware decoder. Finally, another decoder fills in the semantic slots by generating corresponding words. Our approach is able to jointly utilize aspect semantics, syntactic sketch, and context information. Extensive experiments results have demonstrated the effectiveness of the proposed model.
A Focus on Neural Machine Translation for African Languages

African languages are numerous, complex and low-resourced. The datasets required for machine translation are difficult to discover, and existing research is hard to reproduce. Minimal attention has been given to machine translation for African languages so there is scant research regarding the problems that arise when using machine translation techniques. To begin addressing these problems, we trained models to translate English to five of the official South African languages (Afrikaans, isiZulu, Northern Sotho, Setswana, Xitsonga), making use of modern neural machine translation techniques. The results obtained show the promise of using neural machine translation techniques for African languages. By providing reproducible publicly-available data, code and results, this research aims to provide a starting point for other researchers in African machine translation to compare to and build upon.
Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking

This paper focuses on the end-to-end abstractive summarization of a single product review without supervision. We assume that a review can be described as a discourse tree, in which the summary is the root, and the child sentences explain their parent in detail. By recursively estimating a parent from its children, our model learns the latent discourse tree without an external parser and generates a concise summary. We also introduce an architecture that ranks the importance of each sentence on the tree to support summary generation focusing on the main review point. The experimental results demonstrate that our model is competitive with or outperforms other unsupervised approaches. In particular, for relatively long reviews, it achieves a competitive or better performance than supervised models. The induced tree shows that the child sentences provide additional information about their parent, and the generated summary abstracts the entire review.
Nanosecond pulsed proton beam: upgrade of the accelerator-based neutron facility HiSPANOS at CNA (Spain)

The 3 MV Tandem Pelletron accelerator at the Spanish Accelerator Laboratory (CNA) has been recently upgraded to produce pulsed ion beams for neutron Time-Of-Flight (TOF) measurements. The upgrade has consisted of two actions: a pulsing system installed at the low energy part of the Tandem accelerator and a new line fully equipped. The pulsing system provides approximately one nanosecond pulse width of protons with variable repetition rates from kHz down to MHz. The new line is equipped with conventional devices and a pick-up for timing measurements with high resolution. The properties of the whole system have been tested under various working conditions and they are described in some detail.
The iMaterialist Fashion Attribute Dataset

Many Large-scale image databases such as ImageNet have significantly advanced image classification and other visual recognition tasks. However much of these datasets are constructed only for single-label and coarse object-level classification. For real-world applications, multiple labels and fine-grained categories are often needed, yet very few such datasets exist publicly, especially those of large-scale and high quality. In this work, we contribute to the community a new dataset called iMaterialist Fashion Attribute (iFashion-Attribute) to address this problem in the fashion domain. The dataset is constructed from over one million fashion images with a label space that includes 8 groups of 228 fine-grained attributes in total. Each image is annotated by experts with multiple, high-quality fashion attributes. The result is the first known million-scale multi-label and fine-grained image dataset. We conduct extensive experiments and provide baseline results with modern deep Convolutional Neural Networks (CNNs). Additionally, we demonstrate models pre-trained on iFashion-Attribute achieve superior transfer learning performance on fashion related tasks compared with pre-training from ImageNet or other fashion datasets.
The Replica Dataset: A Digital Replica of Indoor Spaces

We introduce Replica, a dataset of 18 highly photo-realistic 3D indoor scene reconstructions at room and building scale. Each scene consists of a dense mesh, high-resolution high-dynamic-range (HDR) textures, per-primitive semantic class and instance information, and planar mirror and glass reflectors. The goal of Replica is to enable machine learning (ML) research that relies on visually, geometrically, and semantically realistic generative models of the world - for instance, egocentric computer vision, semantic segmentation in 2D and 3D, geometric inference, and the development of embodied agents (virtual robots) performing navigation, instruction following, and question answering. Due to the high level of realism of the renderings from Replica, there is hope that ML systems trained on Replica may transfer directly to real world image and video data. Together with the data, we are releasing a minimal C++ SDK as a starting point for working with the Replica dataset. In addition, Replica is `Habitat-compatible', i.e. can be natively used with AI Habitat for training and testing embodied agents.
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models

We identify a new variational inference scheme for dynamical systems whose transition function is modelled by a Gaussian process. Inference in this setting has either employed computationally intensive MCMC methods, or relied on factorisations of the variational posterior. As we demonstrate in our experiments, the factorisation between latent system states and transition function can lead to a miscalibrated posterior and to learning unnecessarily large noise terms. We eliminate this factorisation by explicitly modelling the dependence between state trajectories and the Gaussian process posterior. Samples of the latent states can then be tractably generated by conditioning on this representation. The method we obtain (VCDT: variationally coupled dynamics and trajectories) gives better predictive performance and more calibrated estimates of the transition function, yet maintains the same time and space complexities as mean-field methods. Code is available at: this http URL.
Deep Reinforcement Learning for Industrial Insertion Tasks with Visual Inputs and Natural Rewards

Connector insertion and many other tasks commonly found in modern manufacturing settings involve complex contact dynamics and friction. Since it is difficult to capture related physical effects with first-order modeling, traditional control methods often result in brittle and inaccurate controllers, which have to be manually tuned. Reinforcement learning (RL) methods have been demonstrated to be capable of learning controllers in such environments from autonomous interaction with the environment, but running RL algorithms in the real world poses sample efficiency and safety challenges. Moreover, in practical real-world settings, we cannot assume access to perfect state information or dense reward signals. In this paper, we consider a variety of difficult industrial insertion tasks with visual inputs and different natural reward specifications, namely sparse rewards and goal images. We show that methods that combine RL with prior information, such as classical controllers or demonstrations, can solve these tasks from a reasonable amount of real-world interaction.
Reweighted Expectation Maximization

Training deep generative models with maximum likelihood remains a challenge. The typical workaround is to use variational inference (VI) and maximize a lower bound to the log marginal likelihood of the data. Variational auto-encoders (VAEs) adopt this approach. They further amortize the cost of inference by using a recognition network to parameterize the variational family. Amortized VI scales approximate posterior inference in deep generative models to large datasets. However it introduces an amortization gap and leads to approximate posteriors of reduced expressivity due to the problem known as posterior collapse. In this paper, we consider expectation maximization (EM) as a paradigm for fitting deep generative models. Unlike VI, EM directly maximizes the log marginal likelihood of the data. We rediscover the importance weighted auto-encoder (IWAE) as an instance of EM and propose a new EM-based algorithm for fitting deep generative models called reweighted expectation maximization (REM). REM learns better generative models than the IWAE by decoupling the learning dynamics of the generative model and the recognition network using a separate expressive proposal found by moment matching. We compared REM to the VAE and the IWAE on several density estimation benchmarks and found it leads to significantly better performance as measured by log-likelihood.
Sub-policy Adaptation for Hierarchical Reinforcement Learning

Hierarchical reinforcement learning is a promising approach to tackle long-horizon decision-making problems with sparse rewards. Unfortunately, most methods still decouple the lower-level skill acquisition process and the training of a higher level that controls the skills in a new task. Leaving the skills fixed can lead to significant sub-optimality in the transfer setting. In this work, we propose a novel algorithm to discover a set of skills, and continuously adapt them along with the higher level even when training on a new task. Our main contributions are two-fold. First, we derive a new hierarchical policy gradient with an unbiased latent-dependent baseline, and we introduce Hierarchical Proximal Policy Optimization (HiPPO), an on-policy method to efficiently train all levels of the hierarchy jointly. Second, we propose a method for training time-abstractions that improves the robustness of the obtained skills to environment changes. Code and results are available at this http URL
Mergers of globular clusters in the Galactic disc: intermediate mass black hole coalescence and implications for gravitational waves detection

We propose a new formation channel for intermediate mass black hole (IMBH) binaries via globular cluster collisions in the Galactic disc. Using numerical simulations, we show that the IMBHs form a tight binary that enters the gravitational waves (GWs) emission dominated regime driven by stellar interactions, and ultimately merge in $\lesssim 0.5$ Gyr. These events are clearly audible to LISA and can be associated with electromagnetic emission during the last evolutionary stages. During their orbital evolution, the IMBHs produce runaway stars comparable with GAIA and LAMOST observations.
Constraining the Dark-matter Halo Mass of Isolated Low-surface-brightness Galaxies

Recent advancements in the imaging of low-surface-brightness objects revealed numerous ultra-diffuse galaxies in the local universe. These peculiar objects are unusually extended and faint: their effective radii are comparable to the Milky Way, but their surface brightnesses are lower than that of dwarf galaxies. Their ambiguous properties motivate two potential formation scenarios: the “failed” Milky Way, and the dwarf galaxy scenario. In this Letter, for the first time, we employ X-ray observations to test these formation scenarios on a sample of isolated, low-surface-brightness galaxies (LSBGs). Because hot gas X-ray luminosities correlate with the dark-matter halo mass, “failed” Milky-Way–type galaxies, which reside in massive dark-matter halos, are expected to have significantly higher X-ray luminosities than dwarf galaxies, which reside in low-mass dark-matter halos. We perform X-ray photometry on a subset of LSBGs identified in the Hyper Suprime-Cam Subaru survey, utilizing the XMM-Newton XXL North survey. We find that none of the individual galaxies show significant X-ray emission. By co-adding the signal of individual galaxies, the stacked galaxies remain undetected and we set an X-ray luminosity upper limit of for an average isolated LSBG. This upper limit is about 40 times lower than that expected in a galaxy with massive dark-matter halo, implying that the majority of isolated LSBGs reside in dwarf-size dark-matter halos.
Fast Radio Bursts: An Extragalactic Enigma

We summarize our understanding of millisecond radio bursts from an extragalactic population of sources. Fast radio bursts (FRBs) occur at an extraordinary rate, thousands per day over the entire sky with radiation energy densities at the source about ten billion times larger than those from Galactic pulsars. We survey FRB phenomenology, source models and host galaxies, coherent radiation models, and the role of plasma propagation effects in burst detection. The FRB field is guaranteed to be exciting: New telescopes will expand the sample from the current ∼80 unique burst sources (and only a few secure localizations and redshifts) to thousands, with burst localizations that enable host-galaxy redshifts emerging directly from interferometric surveys. ▪ FRBs are now established as an extragalactic phenomenon. ▪ Only a few sources are known to repeat. Despite the failure to redetect other FRBs, they are not inconsistent with all being repeaters. ▪ FRB sources may be new, exotic kinds of objects or known types in extreme circumstances. Many inventive models exist, ranging from alien spacecraft to cosmic strings, but those concerning compact objects and supermassive black holes have gained the most attention. A rapidly rotating magnetar is a promising explanation for FRB 121102 along with the persistent source associated with it, but alternative source models are not ruled out for it or other FRBs. ▪ FRBs are powerful tracers of circumsource environments, “missing baryons” in the intergalactic medium (IGM), and dark matter. ▪ The relative contributions of host galaxies and the IGM to propagation effects have yet to be disentangled, so dispersion measure distances have large uncertainties.
Stochastic PDEs for large portfolios with general mean-reverting volatility processes

In this article we consider a large structural market model of defaultable assets, where the asset value processes are modelled by using stochastic volatility models with default upon hitting a lower boundary. The volatility processes are picked from a class of general mean-reverting diffusions satisfying certain regularity assumptions. The value processes and the volatility processes are all correlated through systemic Brownian motions. We prove that our system converges as the portfolio becomes large, and the limit of the empirical measure process has a density which is the unique solution to an SPDE in the two-dimensional half-space with a Dirichlet boundary condition. We use Malliavin calculus to establish the existence of a regular density for the volatility component of the density satisfying our stochastic initial-boundary value problem, and we improve an existing kernel smoothing technique to obtain higher regularity and uniqueness results.
Meaning to Form: Measuring Systematicity as Information

A longstanding debate in semiotics centers on the relationship between linguistic signs and their corresponding semantics: is there an arbitrary relationship between a word form and its meaning, or does some systematic phenomenon pervade? For instance, does the character bigram ‘gl’ have any systematic relationship to the meaning of words like ‘glisten’, ‘gleam’ and ‘glow’? In this work, we offer a holistic quantification of the systematicity of the sign using mutual information and recurrent neural networks. We employ these in a data-driven and massively multilingual approach to the question, examining 106 languages. We find a statistically significant reduction in entropy when modeling a word form conditioned on its semantic representation. Encouragingly, we also recover well-attested English examples of systematic affixes. We conclude with the meta-point: Our approximate effect size (measured in bits) is quite small—despite some amount of systematicity between form and meaning, an arbitrary relationship and its resulting benefits dominate human language.
Hallucinating Bag-of-Words and Fisher Vector IDT terms for CNN-based Action Recognition

In this paper, we revive the use of old-fashioned handcrafted video representations and put new life into these techniques via a CNN-based hallucination step. Specifically, we address the problem of action classification in videos via an I3D network pre-trained on the large scale Kinetics-400 dataset. Despite of the use of RGB and optical flow frames, the I3D model (amongst others) thrives on combining its output with the Improved Dense Trajectory (IDT) and extracted with it low-level video descriptors encoded via Bag-of-Words (BoW) and Fisher Vectors (FV). Such a fusion of CNNs and hand crafted representations is time-consuming due to various pre-processing steps, descriptor extraction, encoding and fine-tuning of the model. In this paper, we propose an end-to-end trainable network with streams which learn the IDT-based BoW/FV representations at the training stage and are simple to integrate with the I3D model. Specifically, each stream takes I3D feature maps ahead of the last 1D conv. layer and learns to `translate' these maps to BoW/FV representations. Thus, our enhanced I3D model can hallucinate and use such synthesized BoW/FV representations at the testing stage. We demonstrate simplicity/usefulness of our model on three publicly available datasets and we show state-of-the-art results.
A Variational Autoencoder for Probabilistic Non-Negative Matrix Factorisation

We introduce and demonstrate the variational autoencoder (VAE) for probabilistic non-negative matrix factorisation (PAE-NMF). We design a network which can perform non-negative matrix factorisation (NMF) and add in aspects of a VAE to make the coefficients of the latent space probabilistic. By restricting the weights in the final layer of the network to be non-negative and using the non-negative Weibull distribution we produce a probabilistic form of NMF which allows us to generate new data and find a probability distribution that effectively links the latent and input variables. We demonstrate the effectiveness of PAE-NMF on three heterogeneous datasets: images, financial time series and genomic.
Solving Large-Scale 0-1 Knapsack Problems and its Application to Point Cloud Resampling

0-1 knapsack is of fundamental importance in computer science, business, operations research, etc. In this paper, we present a deep learning technique-based method to solve large-scale 0-1 knapsack problems where the number of products (items) is large and/or the values of products are not necessarily predetermined but decided by an external value assignment function during the optimization process. Our solution is greatly inspired by the method of Lagrange multiplier and some recent adoptions of game theory to deep learning. After formally defining our proposed method based on them, we develop an adaptive gradient ascent method to stabilize its optimization process. In our experiments, the presented method solves all the large-scale benchmark KP instances in a minute whereas existing methods show fluctuating runtime. We also show that our method can be used for other applications, including but not limited to the point cloud resampling.
Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors

Network Embedding (NE) methods, which map network nodes to low-dimensional feature vectors, have wide applications in network analysis and bioinformatics. Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e.g., text describing the nodes. Recent attempts to combine the two sources of information only consider local network structure. We extend NODE2VEC, a well-known NE method that considers broader network structure, to also consider textual node descriptors using recurrent neural encoders. Our method is evaluated on link prediction in two networks derived from UMLS. Experimental results demonstrate the effectiveness of the proposed approach compared to previous work.