nils-herrmann/clean_scirepeval huggingface dataset:
Buy-Online-and-Pick-up-in-Store in Omnichannel Retailing

In this paper, we extend the model of Gao and Su (2016) and consider an omnichannel strategy in which inventory can be replenished when a retailer sells only in physical stores. With "buy-online-and-pick-up-in-store" (BOPS) having been introduced, consumers can choose to buy directly online, buy from a retailer using BOPS, or go directly to a store to make purchases without using BOPS. The retailer is able to select the inventory level to maximize the probability of inventory availability at the store. Furthermore, the retailer can incur an additional cost to reduce the BOPS ordering lead time, which results in a lowered hassle cost for consumers who use BOPS. In conclusion, we found that there are two types of equilibrium: that in which all consumers go directly to the store without using BOPS and that in which all consumers use BOPS.
HiCoRe: Visual Hierarchical Context-Reasoning

Reasoning about images/objects and their hierarchical interactions is a key concept for the next generation of computer vision approaches. Here we present a new framework to deal with it through a visual hierarchical context-based reasoning. Current reasoning methods use the fine-grained labels from images' objects and their interactions to predict labels to new objects. Our framework modifies this current information flow. It goes beyond and is independent of the fine-grained labels from the objects to define the image context. It takes into account the hierarchical interactions between different abstraction levels (i.e. taxonomy) of information in the images and their bounding-boxes. Besides these connections, it considers their intrinsic characteristics. To do so, we build and apply graphs to graph convolution networks with convolutional neural networks. We show a strong effectiveness over widely used convolutional neural networks, reaching a gain 3 times greater on well-known image datasets. We evaluate the capability and the behavior of our framework under different scenarios, considering distinct (superclass, subclass and hierarchical) granularity levels. We also explore attention mechanisms through graph attention networks and pre-processing methods considering dimensionality expansion and/or reduction of the features' representations. Further analyses are performed comparing supervised and semi-supervised approaches.
A Bayesian approach to NMR crystal structure determination.

Nuclear Magnetic Resonance (NMR) spectroscopy is particularly well suited to determine the structure of molecules and materials in powdered form. Structure determination usually proceeds by finding the best match between experimentally observed NMR chemical shifts and those of candidate structures. Chemical shifts for the candidate configurations have traditionally been computed by electronic-structure methods, and more recently predicted by machine learning. However, the reliability of the determination depends on the errors in the predicted shifts. Here we propose a Bayesian framework for determining the confidence in the identification of the experimental crystal structure, based on knowledge of the typical errors in the electronic structure methods. We demonstrate the approach on the determination of the structures of six organic molecular crystals. We critically assess the reliability of the structure determinations, facilitated by the introduction of a visualization of the similarity between candidate configurations in terms of their chemical shifts and their structures. We also show that the commonly used values for the errors in calculated 13C shifts are underestimated, and that more accurate, self-consistently determined uncertainties make it possible to use 13C shifts to improve the accuracy of structure determinations. Finally, we extend the recently-developed ShiftML model to render it more efficient, accurate, and, most importantly, to evaluate the uncertainties in its predictions. By quantifying the confidence in structure determinations based on ShiftML predictions we further substantiate that it provides a valid replacement for first-principles calculations in NMR crystallography.
Insights into Hydration Dynamics and Cooperative Interactions in Glycerol-Water Mixtures by Terahertz Dielectric Spectroscopy.

We report relaxation dynamics of glycerol-water mixtures as probed by megahertz-to-terahertz dielectric spectroscopy in a frequency range from 50 MHz to 0.5 THz at room temperature. The dielectric relaxation spectra reveal several polarization processes at the molecular level with different time constants and dielectric strengths, providing an understanding of the hydrogen-bonding network in glycerol-water mixtures. We have determined the structure of hydration shells around glycerol molecules and the dynamics of bound water as a function of glycerol concentration in solutions using the Debye relaxation model. The experimental results show the existence of a critical glycerol concentration of ~7.5 mol %, which is related to the number of water molecules in the hydration layer around a glycerol molecule. At higher glycerol concentrations, water molecules dispersed in a glycerol network become abundant and eventually dominate and four distinct relaxation processes emerge in the mixtures. The relaxation dynamics and hydration structure in glycerol-water mixtures are further probed with molecular dynamics simulations, which confirm the physical picture revealed by the dielectric spectroscopy.
Speed of gravity

Within the standard effective field theory of General Relativity, we show that the speed of gravitational waves deviates, ever so slightly, from luminality on cosmological and other spontaneously Lorentz-breaking backgrounds. This effect results from loop contributions from massive fields of any spin, including Standard Model fields, or from tree level effects from massive higher spins $s \ge  We show that for the choice of interaction signs implied by S-matrix and spectral density positivity bounds suggested by analyticity and causality, the speed of gravitational waves is in general superluminal at low-energies on NEC preserving backgrounds, meaning gravitational waves travel faster than allowed by the metric to which photons and Standard Model fields are minimally coupled. We show that departure of the speed from unity increases in the IR and argue that the speed inevitably returns to luminal at high energies as required by Lorentz invariance. Performing a special tuning of the EFT so that renormalization sensitive curvature-squared terms are set to zero, we find that finite loop corrections from Standard Model fields still lead to an epoch dependent modification of the speed of gravitational waves which is determined by the precise field content of the lightest particles with masses larger than the Hubble parameter today. Depending on interpretation, such considerations could potentially have far-reaching implications on light scalar models, such as axionic or fuzzy cold dark matter.
Transfer Fine-Tuning: A BERT Case Study

A semantic equivalence assessment is defined as a task that assesses semantic equivalence in a sentence pair by binary judgment (i.e., paraphrase identification) or grading (i.e., semantic textual similarity measurement). It constitutes a set of tasks crucial for research on natural language understanding. Recently, BERT realized a breakthrough in sentence representation learning (Devlin et al., 2019), which is broadly transferable to various NLP tasks. While BERT’s performance improves by increasing its model size, the required computational power is an obstacle preventing practical applications from adopting the technology. Herein, we propose to inject phrasal paraphrase relations into BERT in order to generate suitable representations for semantic equivalence assessment instead of increasing the model size. Experiments on standard natural language understanding tasks confirm that our method effectively improves a smaller BERT model while maintaining the model size. The generated model exhibits superior performance compared to a larger BERT model on semantic equivalence assessment tasks. Furthermore, it achieves larger performance gains on tasks with limited training datasets for fine-tuning, which is a property desirable for transfer learning.
"Can you say more about the location?" The Development of a Pedagogical Reference Resolution Agent

In an increasingly globalized world, geographic literacy is crucial. In this paper, we present a collaborative two-player game to improve people's ability to locate countries on the world map. We discuss two implementations of the game: First, we created a web-based version which can be played with the remote-controlled agent Nellie. With the knowledge we gained from a large online data collection, we re-implemented the game so it can be played face-to-face with the Furhat robot Neil. Our analysis shows that participants found the game not just engaging to play, they also believe they gained lasting knowledge about the world map.
Algorithms and System Architecture for Immediate Personalized News Recommendations

Personalization plays an important role in many services, just as news does. Many studies have examined news personalization algorithms, but few have considered practical environments. This paper provides algorithms and system architecture for generating immediate personalized news in a practical environment. Immediacy means changes in news trends and user interests are reflected in recommended news lists quickly. Since news trends and user interests rapidly change, immediacy is critical in news personalization applications. We develop algorithms and system architecture to realize immediacy. Our algorithms are based on collaborative filtering of user clusters and evaluate news articles using click-through rate and decay scores based on the time elapsed since the user’s last access. Existing studies have not fully discussed system architecture, so a major contribution of this paper is that we demonstrate a system architecture and realize our algorithms and a configuration example implemented on top of Amazon Web Services. We evaluate the proposed method both offline and online. The offline experiments are conducted through a real-world dataset from a commercial news delivery service, and online experiments are conducted via A/B testing on production environments. We confirm the effectiveness of our proposed method and also that our system architecture can operate in large-scale production environments.CCS CONCEPTS• Information systems → Personalization; Web service; • Software and its engineering → Real-time systems software.
Fog Computing Architectures: A Reference for Practitioners

Soon after realizing that cloud computing could indeed help several industries overcome classical product-centric approaches in favor of more affordable service-oriented business models, we are witnessing the rise of a new disruptive computing paradigm, namely fog computing. Essentially, fog computing can be considered as an evolution of cloud computing, in the sense that the former extends the latter to the edge of the network (i.e., where the connected devices -- the things -- are) without discontinuity, realizing the so-called "cloud-to-thing continuum." Since its infancy, fog computing has been considered as a necessity within several Internet of Things (IoT) domains (one for all: Industrial IoT) and, more generally, wherever embedded artificial intelligence and/or more advanced distributed capabilities were required. Fog computing cannot be considered only a fancy buzzword: according to separate, authoritative analyses, its global market will reach $18 billion by 2022, while nearly 45 percent of the world's data will be moved to the network edge by 2025. In this article, we take stock of the situation, summarizing the most modern and mature fog computing initiatives from the standardization, commercial, and open source communities' perspectives.
MANAS: Multi-Agent Neural Architecture Search

The Neural Architecture Search (NAS) problem is typically formulated as a graph search problem where the goal is to learn the optimal operations over edges in order to maximise a graph-level global objective. Due to the large architecture parameter space, efficiency is a key bottleneck preventing NAS from its practical use. In this paper, we address the issue by framing NAS as a multi-agent problem where agents control a subset of the network and coordinate to reach optimal architectures. We provide two distinct lightweight implementations, with reduced memory requirements (1/8th of state-of-the-art), and performances above those of much more computationally expensive methods. Theoretically, we demonstrate vanishing regrets of the form O(sqrt(T)), with T being the total number of rounds. Finally, aware that random search is an, often ignored, effective baseline we perform additional experiments on 3 alternative datasets and 2 network configurations, and achieve favourable results in comparison.
Language Models as Knowledge Bases?

Recent progress in pretraining language models on large textual corpora led to a surge of improvements for downstream NLP tasks. Whilst learning linguistic knowledge, these models may also be storing relational knowledge present in the training data, and may be able to answer queries structured as “fill-in-the-blank” cloze statements. Language models have many advantages over structured knowledge bases: they require no schema engineering, allow practitioners to query about an open class of relations, are easy to extend to more data, and require no human supervision to train. We present an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models. We find that (i) without fine-tuning, BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge, (ii) BERT also does remarkably well on open-domain question answering against a supervised baseline, and (iii) certain types of factual knowledge are learned much more readily than others by standard language model pretraining approaches. The surprisingly strong ability of these models to recall factual knowledge without any fine-tuning demonstrates their potential as unsupervised open-domain QA systems. The code to reproduce our analysis is available at https://github.com/facebookresearch/LAMA.
Oxygen yields as a constraint on feedback processes in galaxies

We study the interplay between several properties determined from optical and a combination of optical/radio measurements, such as the effective oxygen yield (yeff), the star formation efficiency, gas metallicity, depletion time, gas fraction, and baryonic mass (Mbar), among others. We use spectroscopic data from the SDSS survey, and H i information from the ALFALFA survey to build a statistically significant sample of more than 5000 galaxies. Furthermore, we complement our analysis with data from the GASS and COLD GASS surveys, and with a sample of star-forming galaxies from the Virgo cluster. Additionally, we have compared our results with predictions from the EAGLE simulations, finding a very good agreement when using the high-resolution run. We explore in detail the Mbar–yeff relation, finding a bimodal trend that can be separated when the stellar age of galaxies is considered. On one hand, yeff increases with Mbar for young galaxies [log(tr) < 9.2 yr], while yeff shows an anticorrelation with Mbar for older galaxies [log(tr) > 9.4 yr]. While a correlation between Mbar and yeff has been observed and studied before, mainly for samples of dwarfs and irregular galaxies, their anticorrelated counterpart for massive galaxies has not been previously reported. The EAGLE simulations indicate that AGN feedback must have played an important role in their history by quenching their star formation rate, whereas low-mass galaxies would have been affected by a combination of outflows and infall of gas.
Active learning reduces academic risk of students with nonformal reasoning skills: Evidence from an introductory physics massive course in a Chilean public university

We present the findings of a pilot plan of active learning implemented in introductory physics in a Chilean public university. The model is research based as it considered a literature review for adequate selection and design of activities, consistent with the levels of students' reasoning skills. The level of scientific reasoning is positively correlated to student success. By contrast to a control group of students following traditional lectures, we observed a significant reduction in failure rate for students that do not yet posses formal scientific reasoning. This profile of student being the majority, we conclude that implementing active learning is particularly suited to first year of higher education in the context of a developing country. It fits the particularities of student profile and typical classroom size, leading to learning improvement and reduction of academic risk as well as being financially sound.
Cosmological Solutions to the Lithium Problem

The abundance of primordial lithium is derived from the observed spectroscopy of metal-poor stars in the galactic halo. However, the observationally inferred abundance remains at about a factor of three below the abundance predicted by standard big bang nucleosynthesis (BBN). The resolution of this dilemma can be either astrophysical (stars destroy lithium after BBN), nuclear (reactions destroy lithium during BBN), or cosmological, i.e. new physics beyond the standard BBN is responsible for destroying lithium. Here, we overview a variety of possible cosmological solutions, and their shortcomings. On the one hand, we examine the possibility of physical processes that modify the velocity distribution of particles from the usually assumed Maxwell-Boltzmann statistics. A physical justification for this is an inhomogeneous spatial distribution of domains of primordial magnetic field strength as a means to reduce the primordial lithium abundance. Another possibility is that scattering with the mildly relativistic electrons in the background plasma alters the baryon distribution to one resembling a Fermi-Dirac distribution. We show that neither of these possibilities can adequately resolve the lithium problem. A number of alternate hybrid models are discussed including a mix of neutrino degeneracy, unified dark matter, axion cooling, and the presence of decaying and/or charged supersymmetric particles.
Neural Attentive Bag-of-Entities Model for Text Classification

This study proposes a Neural Attentive Bag-of-Entities model, which is a neural network model that performs text classification using entities in a knowledge base. Entities provide unambiguous and relevant semantic signals that are beneficial for text classification. We combine simple high-recall entity detection based on a dictionary, to detect entities in a document, with a novel neural attention mechanism that enables the model to focus on a small number of unambiguous and relevant entities. We tested the effectiveness of our model using two standard text classification datasets (i.e., the 20 Newsgroups and R8 datasets) and a popular factoid question answering dataset based on a trivia quiz game. As a result, our model achieved state-of-the-art results on all datasets. The source code of the proposed model is available online at https://github.com/wikipedia2vec/wikipedia2vec.
Deciphering the AMS cosmic-ray positron flux.

The flux of cosmic-ray high-energy positrons has recently been measured by AMS with unprecedented precision. This flux is well above the expectation from secondary positrons made by the observed fluxes of nuclear cosmic rays impinging on the interstellar medium. Various authors have pointed out that the positron excess may originate at the primary cosmic-ray source itself, rather than in the more local ISM, thus avoiding the temptation to invoke a dark-matter decay or annihilation origin, or nearby pulsars. We investigate the possibility that the source is the one of a comprehensive model of gamma-ray bursts and cosmic rays, proposed two decades ago. The result, based on the original unmodified priors of the model --and with no fitting of parameters-- very closely reproduces the shape and magnitude of the AMS observations.
Learning without feedback: Direct random target projection as a feedback-alignment algorithm with layerwise feedforward training

While the backpropagation of error algorithm allowed for a rapid rise in the development and deployment of artificial neural networks, two key issues currently preclude biological plausibility: (i) symmetry is required between forward and backward weights, which is known as the weight transport problem, and (ii) updates are locked before both the forward and backward passes have been completed. The feedback alignment (FA) algorithm uses fixed random feedback weights to release the weight transport problem. The direct feedback alignment (DFA) variation directly propagates the output error to each hidden layer through fixed random connectivity matrices. In this work, we show that using only the error sign is sufficient to maintain feedback alignment and to provide learning in the hidden layers. As in classification problems the error sign information is already contained in the target vector, using the latter as a proxy for the error brings three advantages: (i) it solves the weight transport problem by eliminating the requirement for an explicit feedback pathway, which also reduces the computational workload, (ii) it reduces memory requirements by removing update locking, allowing for weight updates to be computed in each layer independently without requiring a full forward pass, and (iii) it leads to a purely feedforward and low-cost algorithm that only requires a label-dependent random vector selection to estimate the layerwise loss gradients. Therefore, in this work, we propose the direct random target projection (DRTP) algorithm and demonstrate on the MNIST and CIFAR-10 datasets that, despite the absence of an explicit error feedback, DRTP performance can still lie close to the one of BP, FA and DFA. The low memory and computational cost of DRTP and its reliance only on layerwise feedforward computation make it suitable for deployment in adaptive edge computing devices.