nils-herrmann/clean_scirepeval huggingface dataset:
Lorentz breaking and SU(2)L × U(1)Y gauge invariance for neutrinos

Conceivable Lorentz-violating effects in the neutrino sector remain a research area of great general interest, as they touch upon the very foundations on which the Standard Model and our general understanding of fundamental interactions are laid. Here, we investigate the relation of Lorentz violation in the neutrino sector in light of the fact that neutrinos and the corresponding left-handed charged leptons form  see text] doublets under the electroweak gauge group. Lorentz-violating effects thus cannot be fully separated from questions related to gauge invariance. The model dependence of the effective interaction Lagrangians used in various recent investigations is explored with a special emphasis on neutrino splitting, otherwise known as the neutrino-pair Cerenkov radiation and vacuum-pair emission (electron–positron-pair Cerenkov radiation). We highlight two scenarios in which Lorentz-violating effects do not necessarily also break electroweak gauge invariance. The first of these involves a restricted set of gauge transformations, a subgroup of  see text], while in the second where differential Lorentz violation is exclusively introduced by the mixing of the neutrino flavor and mass eigenstates. Our study culminates in a model which fully preserves  see text] gauge invariance, involves flavor-dependent Lorentz-breaking parameters, and still allows for Cerenkov-type decays to proceed.
Deep Neural Network for Semantic-based Text Recognition in Images

State-of-the-art text spotting systems typically aim to detect isolated words or word-by-word text in images of natural scenes and ignore the semantic coherence within a region of text. However, when interpreted together, seemingly isolated words may be easier to recognize. On this basis, we propose a novel "semantic-based text recognition" (STR) deep learning model that reads text in images with the help of understanding context. STR consists of several modules. We introduce the Text Grouping and Arranging (TGA) algorithm to connect and order isolated text regions. A text-recognition network interprets isolated words. Benefiting from semantic information, a sequenceto-sequence network model efficiently corrects inaccurate and uncertain phrases produced earlier in the STR pipeline. We present experiments on two new distinct datasets that contain scanned catalog images of interior designs and photographs of protesters with hand-written signs, respectively. Our results show that our STR model outperforms a baseline method that uses state-of-the-art single-wordrecognition techniques on both datasets. STR yields a high accuracy rate of 90% on the catalog images and 71% on the more difficult protest images, suggesting its generality in recognizing text.
Automatic Game Design via Mechanic Generation

Game designs often center on the game mechanics - rules governing the logical evolution of the game. We seek to develop an intelligent system that generates computer games. As first steps towards this goal we present a composable and cross-domain representation for game mechanics that draws from AI planning action representations. We use a constraint solver to generate mechanics subject to design requirements on the form of those mechanics - what they do in the game. A planner takes a set of generated mechanics and tests whether those mechanics meet playability requirements - controlling how mechanics function in a game to affect player behavior. We demonstrate our system by modeling and generating mechanics in a role-playing game, platformer game, and combined role-playing-platformer game.
Monte-Carlo Tree Search for Simulation-based Strategy Analysis

Games are often designed to shape player behavior in a desired way; however, it can be unclear how design decisions affect the space of behaviors in a game. Designers usually explore this space through human playtesting, which can be time-consuming and of limited effectiveness in exhausting the space of possible behaviors. In this paper, we propose the use of automated planning agents to simulate humans of varying skill levels to generate game playthroughs. Metrics can then be gathered from these playthroughs to evaluate the current game design and identify its potential flaws. We demonstrate this technique in two games: the popular word game Scrabble and a collectible card game of our own design named Cardonomicon. Using these case studies, we show how using simulated agents to model humans of varying skill levels allows us to extract metrics to describe game balance (in the case of Scrabble) and highlight potential design flaws (in the case of Cardonomicon).
ChemBO: Bayesian Optimization of Small Organic Molecules with Synthesizable Recommendations

In applications such as molecule design or drug discovery, it is desirable to have an algorithm which recommends new candidate molecules based on the results of past tests. These molecules first need to be synthesized and then tested for objective properties. We describe ChemBO, a Bayesian optimization framework for generating and optimizing organic molecules for desired molecular properties. While most existing data-driven methods for this problem do not account for sample efficiency or fail to enforce realistic constraints on synthesizability, our approach explores the synthesis graph in a sample-efficient way and produces synthesizable candidates. We implement ChemBO as a Gaussian process model and explore existing molecular kernels for it. Moreover, we propose a novel optimal-transport based distance and kernel that accounts for graphical information explicitly. In our experiments, we demonstrate the efficacy of the proposed approach on several molecular optimization problems.
Reusability and Transferability of Macro Actions for Reinforcement Learning

Conventional reinforcement learning (RL) typically determines an appropriate primitive action at each timestep. However, by using a proper macro action, defined as a sequence of primitive actions, an RL agent is able to bypass intermediate states to a farther state and facilitate its learning procedure. The problem we would like to investigate is what associated beneficial properties that macro actions may possess. In this article, we unveil the properties of reusability and transferability of macro actions. The first property, reusability, means that a macro action derived along with one RL method can be reused by another RL method for training, while the second one, transferability, indicates that a macro action can be utilized for training agents in similar environments with different reward settings. In our experiments, we first derive macro actions along with RL methods. We then provide a set of analyses to reveal the properties of reusability and transferability of the derived macro actions.
Walking with MIND: Mental Imagery eNhanceD Embodied QA

The EmbodiedQA is a task of training an embodied agent by intelligently navigating in a simulated environment and gathering visual information to answer questions. Existing approaches fail to explicitly model the mental imagery function of the agent, while the mental imagery is crucial to embodied cognition, and has a close relation to many high-level meta-skills such as generalization and interpretation. In this paper, we propose a novel Mental Imagery eNhanceD (MIND) module for the embodied agent, as well as a relevant deep reinforcement framework for training. The MIND module can not only model the dynamics of the environment (e.g. 'what might happen if the agent passes through a door') but also help the agent to create a better understanding of the environment (e.g. 'The refrigerator is usually in the kitchen'). Such knowledge makes the agent a faster and better learner in locating a feasible policy with only a few trails. Furthermore, the MIND module can generate mental images that are treated as short-term subgoals by our proposed deep reinforcement framework. These mental images facilitate policy learning since short-term subgoals are easy to achieve and reusable. This yields better planning efficiency than other algorithms that learn a policy directly from primitive actions. Finally, the mental images visualize the agent's intentions in a way that human can understand, and this endows our agent's actions with more interpretability. The experimental results and further analysis prove that the agent with the MIND module is superior to its counterparts not only in EQA performance but in many other aspects such as route planning, behavioral interpretation, and the ability to generalize from a few examples.
On the Relationship Between Coupling and Refactoring: An Empirical Viewpoint

Refactoring has matured over the past twenty years to become part of a developer’s toolkit. However, many fundamental research questions still remain largely unexplored.  The goal of this paper is to investigate the highest and lowest quartile of refactoring-based data using two coupling metrics – the Coupling between Objects metric and the more recent Conceptual Coupling between Classes metric to answer this question. Can refactoring trends and patterns be identified based on the level of class coupling?  In this paper, we analyze over six thousand refactoring operations drawn from releases of three open-source systems to address one such question.  Results showed no meaningful difference in the types of refactoring applied across either lower or upper quartile of coupling for both metrics; refactorings usually associated with coupling removal were actually more numerous in the lower quartile in some cases. A lack of inheritance-related refactorings across all systems was also noted.  The emerging message (and a perplexing one) is that developers seem to be largely indifferent to classes with high coupling when it comes to refactoring types – they treat classes with relatively low coupling in almost the same way.
A Fast Content-Based Image Retrieval Method Using Deep Visual Features

Fast and scalable Content-Based Image Retrieval using visual features is required for document analysis, Medical image analysis, etc. in the present age. Convolutional Neural Network (CNN) activations as features achieved their outstanding performance in this area. Deep Convolutional representations using the softmax function in the output layer are also ones among visual features. However, almost all the image retrieval systems hold their index of visual features on main memory in order to high responsiveness, limiting their applicability for big data applications. In this paper, we propose a fast calculation method of cosine similarity with L2 norm indexed in advance on Elasticsearch. We evaluate our approach with ImageNet Dataset and VGG-16 pre-trained model. The evaluation results show the effectiveness and efficiency of our proposed method.
Large angular scale fluctuations of near-infrared extragalactic background light based on the IRTS observations

We measure the spatial fluctuations of the Near-Infrared Extragalactic Background Light (NIREBL) from 2° to 20° in angular scale at the 1.6 and $2.2\, \mu \mathrm{m}$ using data obtained with Near-Infrared Spectrometer (NIRS) on board the Infrared Telescope in Space (IRTS). The brightness of the NIREBL is estimated by subtracting foreground components such as zodiacal light, diffuse Galactic light, and integrated star light from the observed sky. The foreground components are estimated using well-established models and archive data. The NIREBL fluctuations for the 1.6 and $2.2\, \mu \mathrm{m}$ connect well toward the sub-degree scale measurements from previous studies. Overall, the fluctuations show a wide bump with a center at around 1° and the power decreases toward larger angular scales with nearly a single power-law spectrum (i.e., ${F[\sqrt{l(l+1)C_l/2\pi }]} \sim \theta ^{-1}]$, indicating that the large-scale power is dominated by the random spatial distribution of the sources. After examining several known sources, contributors such as normal galaxies, high-redshift objects, intra-halo light, and far-IR cosmic background, we conclude that the excess fluctuation at around the 1° scale cannot be explained by any of them.
3D Reconstruction of Deformable Revolving Object under Heavy Hand Interaction

We reconstruct 3D deformable object through time, in the context of a live pottery making process where the crafter molds the object. Because the object suffers from heavy hand interaction, and is being deformed, classical techniques cannot be applied. We use particle energy optimization to estimate the object profile and benefit of the object radial symmetry to increase the robustness of the reconstruction to both occlusion and noise. Our method works with an unconstrained scalable setup with one or more depth sensors. We evaluate on our database (released upon publication) on a per-frame and temporal basis and shows it significantly outperforms state-of-the-art achieving 7.60mm average object reconstruction error. Further ablation studies demonstrate the effectiveness of our method.
Discriminating Spatial and Temporal Relevance in Deep Taylor Decompositions for Explainable Activity Recognition

Current techniques for explainable AI have been applied with some success to image processing. The recent rise of research in video processing has called for similar work n deconstructing and explaining spatio-temporal models. While many techniques are designed for 2D convolutional models, others are inherently applicable to any input domain. One such body of work, deep Taylor decomposition, propagates relevance from the model output distributively onto its input and thus is not restricted to image processing models. However, by exploiting a simple technique that removes motion information, we show that it is not the case that this technique is effective as-is for representing relevance in non-image tasks. We instead propose a discriminative method that produces a naive representation of both the spatial and temporal relevance of a frame as two separate objects. This new discriminative relevance model exposes relevance in the frame attributed to motion, that was previously ambiguous in the original explanation. We observe the effectiveness of this technique on a range of samples from the UCF-101 action recognition dataset, two of which are demonstrated in this paper.
ESA's Voyage 2050 Long-term Plan for Education and Public Engagement: White Paper

This white paper responds to the Voyage 2050 Call for White Papers from the Science Programme of the European Space Agency (ESA) and argues that education, communication and public engagement (hereafter EPE) should have priority in the Voyage 2050 planning cycle. The ESA Science's Voyage 2050 missions promise insights into the big existential questions of our era: the prevalence of life in the Universe; the nature of space and time; and the intertwined nature of matter, energy and gravity. It is likely that innovations in the acquisition, handling and processing of vast data sets will drive these themes to scientific maturity in the next decades. They offer us a timely opportunity to underline the relevance of space sciences to everyday life and thinking. More generally, space science is maturing to the point where it contributes to every major aspect of our cultural discourse. Citizens need information, resources and opportunities to actively participate in that discourse, and ESA Science can provide these. This white paper is a modest attempt to support ESA Science improve its engagement with society. It focuses on issues and topics to improve ESA Science's Education and Public Engagement activities. It does not dwell on the topics that ESA already excels at; hence this White Paper provides a critical review of what should and could be improved. We believe ESA's Voyage 2050 programme teams have a responsibility to represent Europe's social and cultural diversity, and our suggestions are conceived in that spirit: to support ESA Science's complex task of engaging a hugely diverse audience in the complex issues of planning, building and operating fascinating space missions.
Detection of White Dwarf companions to Blue Straggler Stars from UVIT observations of M67

We investigate the old open cluster M67 using ultraviolet photometric data of Ultra-Violet Imaging Telescope in multi-filter far-UV bands. M67, well known for the presence of several blue straggler stars (BSS), has been put to detailed tests to understand their formation pathways. Currently, there are three accepted formation channels: mass transfer due to Roche-lobe overflow in binary systems, stellar mergers either due to dynamical collisions or through coalescence of close binaries. So far, there had not been any confirmed detection of a white dwarf (WD) companion to any of the BSSs in this cluster. Here, we present the detection of WD companions to 5 bright BSSs in M67. The multiwavelength spectral energy distributions covering 0.12 -11.5 μm range, were found to require binary spectral fits for 5 BSSs, consisting of a cool (BSS) and a hot companion. The parameters (Luminosity, Temperature, Radius and Mass) of the hot companions suggest them to be WDs with mass in the range 0.2 - 0.35 M⊙ with Teff ~11000 –24000 K.
Music of the Spheres: Teaching Quantum Field Theory at the Introductory Level

Quantum field theory has formed the conceptual framework of most of physics for more than sixty years. It incorporates a complete revision of our conception of the nature of matter and existence itself. Yet it is rarely taught, or even mentioned, in introductory physics---from high school, college, and university survey courses through upper-division "modern physics" courses. This omission is not necessary: This paper describes an approach through which the fundamental concepts and consequent insights of quantum field theory can be grasped, building upon familiar notions from classical and quantum mechanics.
Knowledge Consistency between Neural Networks and Beyond

This paper aims to analyze knowledge consistency between pre-trained deep neural networks. We propose a generic definition for knowledge consistency between neural networks at different fuzziness levels. A task-agnostic method is designed to disentangle feature components, which represent the consistent knowledge, from raw intermediate-layer features of each neural network. As a generic tool, our method can be broadly used for different applications. In preliminary experiments, we have used knowledge consistency as a tool to diagnose knowledge representations of neural networks. Knowledge consistency provides new insights to explain the success of existing deep-learning techniques, such as knowledge distillation and network compression. More crucially, knowledge consistency can also be used to refine pre-trained networks and boost performance.
Motivic interpretation of Albanese varieties of smooth varieties.

For any noetherian scheme $X$ smooth and separated over an algebraically closed field $k$, we describe the Albanese variety ${\rm Alb}\,X$ of $X$ in the world of ${{\rm DM}^{e\! f\! f}(k)}$. As an application, we explain the structure of the Picard functor ${\rm Pic}_{X/k}^0$.
A sceptical analysis of quantized inertia

We perform an analysis of the derivation of quantized inertia (QI) theory, formerly known with the acronym MiHsC, as presented by McCulloch. Two major flaws were found in the original derivation. We derive a discrete blackbody radiation spectrum, deriving a different formulation for F(a) than the one presented in the original theory. We present a numerical result of the new solution which is compared against the original prediction.
Solving high-dimensional optimal stopping problems using deep learning

Nowadays many financial derivatives, such as American or Bermudan options, are of early exercise type. Often the pricing of early exercise options gives rise to high-dimensional optimal stopping problems, since the dimension corresponds to the number of underlying assets. High-dimensional optimal stopping problems are, however, notoriously difficult to solve due to the well-known curse of dimensionality. In this work, we propose an algorithm for solving such problems, which is based on deep learning and computes, in the context of early exercise option pricing, both approximations of an optimal exercise strategy and the price of the considered option. The proposed algorithm can also be applied to optimal stopping problems that arise in other areas where the underlying stochastic process can be efficiently simulated. We present numerical results for a large number of example problems, which include the pricing of many high-dimensional American and Bermudan options, such as Bermudan max-call options in up to 5000 dimensions. Most of the obtained results are compared to reference values computed by exploiting the specific problem design or, where available, to reference values from the literature. These numerical results suggest that the proposed algorithm is highly effective in the case of many underlyings, in terms of both accuracy and speed.
Learned Clause Minimization in Parallel SAT Solvers

Learned clauses minimization (LCM) let to performance improvements of modern SAT solvers especially in solving hard SAT instances. Despite the success of LCM approaches in sequential solvers, they are not widely incorporated in parallel SAT solvers. In this paper we explore the potential of LCM for parallel SAT solvers by defining multiple LCM approaches based on clause vivification, comparing their runtime in different SAT solvers and discussing reasons for performance gains and losses. Results show that LCM only boosts performance of parallel SAT solvers on a fraction of SAT instances. More commonly applying LCM decreases performance. Only certain LCM approaches are able to improve the overall performance of parallel SAT solvers.
A young galaxy cluster in the old Universe

Galaxies evolve from a blue star-forming phase into a red quiescent one by quenching their star formation activity. In high-density environments, this galaxy evolution proceeds earlier and more efficiently. Therefore, local galaxy clusters are dominated by well-evolved red elliptical galaxies. The fraction of blue galaxies in clusters monotonically declines with decreasing redshift, i.e. the Butcher–Oemler effect. In the local Universe, observed blue fractions of massive clusters are as small as ≲0.2. Here we report a discovery of a ‘blue cluster’ that is a local galaxy cluster with an unprecedentedly high fraction of blue star-forming galaxies yet hosted by a massive dark matter halo. The blue fraction is 0.57, which is 4.0σ higher than those of the other comparison clusters under the same selection and identification criteria. The velocity dispersion of the member galaxies is 510 km s−1, which corresponds to a dark matter halo mass of 2.0$^{+1.9}_{-1.0}\times 10^{14}$ M⊙. The blue fraction of the cluster is more than 4.7σ beyond the standard theoretical predictions including semi-analytic models of galaxy formation. The probability to find such a high blue fraction in an individual cluster is only 0.003 per cent, which challenges the current standard frameworks of the galaxy formation and evolution in the ΛCDM universe. The spatial distribution of galaxies around the blue cluster suggests that filamentary cold gas streams can exist in massive haloes even in the local Universe. However these cold streams have already disappeared in the theoretically simulated local universes.