nils-herrmann/clean_scirepeval huggingface dataset:
Topological Phases of Polaritons in a Cavity Waveguide.

We study the unconventional topological phases of polaritons inside a cavity waveguide, demonstrating how strong light-matter coupling leads to a breakdown of the bulk-edge correspondence. We observe an ostensibly topologically nontrivial phase, which unexpectedly does not exhibit edge states. Our findings are in direct contrast to topological tight-binding models with electrons, such as the celebrated Su-Schrieffer-Heeger (SSH) model. We present a theory of collective polaritonic excitations in a dimerized chain of oscillating dipoles embedded inside a photonic cavity. The added degree of freedom from the cavity photons upgrades the system from a typical SSH SU(2) model into a largely unexplored SU(3) model. Tuning the light-matter coupling strength by changing the cavity size reveals three critical points in parameter space: when the polariton band gap closes, when the Zak phase changes from being trivial to nontrivial, and when the edge state is lost. These three critical points do not coincide, and thus the Zak phase is no longer an indicator of the presence of edge states. Our discoveries demonstrate some remarkable properties of topological matter when strongly coupled to light, and could be important for the growing field of topological nanophotonics.
Real-time Claim Detection from News Articles and Retrieval of Semantically-Similar Factchecks

Factchecking has always been a part of the journalistic process. However with newsroom budgets shrinking it is coming under increasing pressure just as the amount of false information circulating is on the rise. We therefore propose a method to increase the efficiency of the factchecking process, using the latest developments in Natural Language Processing (NLP). This method allows us to compare incoming claims to an existing corpus and return similar, factchecked, claims in a live system-allowing factcheckers to work simultaneously without duplicating their work.
Combining Q&A Pair Quality and Question Relevance Features on Community-based Question Retrieval

The Q&A community has become an important way for people to access knowledge and information from the Internet. However, existing translation based models do not consider term weights when assigning weights to query terms in question retrieval. We improve the term weighting model based on the traditional topic translation model and further considering the quality characteristics of question and answer pairs, this paper proposes a community-based question retrieval method that combines question and answer on quality and question relevance (T2LM+). We have also proposed a question retrieval method based on convolutional neural networks. The results show that compared with the relatively advanced methods, the two methods proposed in this paper increase MAP by 4.91 % and 6.31%.
Deep neural network-based classification model for Sentiment Analysis

The growing prosperity of social networks has brought great challenges to the sentimental tendency mining of users. As more and more researchers pay attention to the sentimental tendency of online users, rich research results have been obtained based on the sentiment classification of explicit texts. However, research on the implicit sentiment of users is still in its infancy. Aiming at the difficulty of implicit sentiment classification, a research on implicit sentiment classification model based on deep neural network is carried out. Classification models based on DNN, LSTM, Bi-LSTM and CNN were established to judge the tendency of the user's implicit sentiment text. Based on the Bi-LSTM model, the classification model of word-level attention mechanism is studied. The experimental results on the public dataset show that the established LSTM series classification model and CNN classification model can achieve good sentiment classification effect, and the classification effect is significantly better than the DNN model. The Bi-LSTM based attention mechanism classification model obtained the optimal R value in the positive category identification.
Reasoning and Generalization in RL: A Tool Use Perspective

Learning to use tools to solve a variety of tasks is an innate ability of humans and has been observed of animals in the wild. However, the underlying mechanisms that are required to learn to use tools are abstract and widely contested in the literature. In this paper, we study tool use in the context of reinforcement learning and propose a framework for analyzing generalization inspired by a classic study of tool using behavior, the trap-tube task. Recently, it has become common in reinforcement learning to measure generalization performance on a single test set of environments. We instead propose transfers that produce multiple test sets that are used to measure specified types of generalization, inspired by abilities demonstrated by animal and human tool users. The source code to reproduce our experiments is publicly available at this https URL.
Benchmarking Model-Based Reinforcement Learning

Model-based reinforcement learning (MBRL) is widely seen as having the potential to be significantly more sample efficient than model-free RL. However, research in model-based RL has not been very standardized. It is fairly common for authors to experiment with self-designed environments, and there are several separate lines of research, which are sometimes closed-sourced or not reproducible. Accordingly, it is an open question how these various existing MBRL algorithms perform relative to each other. To facilitate research in MBRL, in this paper we gather a wide collection of MBRL algorithms and propose over 18 benchmarking environments specially designed for MBRL. We benchmark these algorithms with unified problem settings, including noisy environments. Beyond cataloguing performance, we explore and unify the underlying algorithmic differences across MBRL algorithms. We characterize three key research challenges for future MBRL research: the dynamics bottleneck, the planning horizon dilemma, and the early-termination dilemma. Finally, to maximally facilitate future research on MBRL, we open-source our benchmark in this http URL.
Neural Image Captioning

In recent years, the biggest advances in major Computer Vision tasks, such as object recognition, handwritten-digit identification, facial recognition, and many others., have all come through the use of Convolutional Neural Networks (CNNs). Similarly, in the domain of Natural Language Processing, Recurrent Neural Networks (RNNs), and Long Short Term Memory networks (LSTMs) in particular, have been crucial to some of the biggest breakthroughs in performance for tasks such as machine translation, part-of-speech tagging, sentiment analysis, and many others. These individual advances have greatly benefited tasks even at the intersection of NLP and Computer Vision, and inspired by this success, we studied some existing neural image captioning models that have proven to work well. In this work, we study some existing captioning models that provide near state-of-the-art performances, and try to enhance one such model. We also present a simple image captioning model that makes use of a CNN, an LSTM, and the beam search1 algorithm, and study its performance based on various qualitative and quantitative metrics.
Radiation hydrodynamics simulations of the evolution of the diffuse ionized gas in disc galaxies

There is strong evidence that the diffuse ionized gas (DIG) in disc galaxies is photoionized by radiation from UV luminous O and B stars in the galactic disc, both from observations and detailed numerical models. However, it is still not clear what mechanism is responsible for providing the necessary pressure support for a diffuse gas layer at kpc-scale above the disc. In this work, we investigate if the pressure increase caused by photoionization can provide this support. We run self-consistent radiation hydrodynamics (RHD) models of a gaseous disc in an external potential. We find that photoionization feedback can drive low levels of turbulence in the dense galactic disc, and that it provides pressure support for an extended diffuse gas layer. Our results show that there is a natural fine-tuning between the total ionizing radiation budget of the sources in the galaxy and the amount of gas in the different ionization phases of the interstellar medium, and provide the first fully consistent RHD model of the DIG.
Chemical composition of the outer halo globular cluster Palomar 15

Globular clusters (GCs) in the outer Milky Way halo are important tracers of the assembly history of our Galaxy. Only a few of these objects show spreads in heavier elements beyond the canonical light-element variations that have essentially been found throughout the entire Galactic GC system, suggesting a more complex origin and evolution of these objects. Here, we present the first abundance analysis of three red giants in the remote (RGC = 38 kpc) outer halo GC Palomar 15, based on medium-resolution spectra obtained with the Keck/ESI instrument. Our results ascertain a low iron abundance of −1.94 ± 0.06 dex with no evidence of any significant abundance spreads, although this is based on low number statistics. Overall, abundance ratios of 16 species were measured, including carbon, Na, Al, α-peak (Mg,Si,Ca,Ti) and Fe-peak (Sc,V,Cr,Fe,Co,Ni) elements, and the three neutron-capture elements Sr, Ba, and Eu. The majority of abundances are compatible with those of halo field stars and those found in other GCs in the outer and inner halos at similar metallicity. Pal 15 is enhanced to [Mg/Fe] = 0.45 dex, while other α-elements, Ca and Ti, are lower by 0.3 dex. Taking Mg as a representative for [α/Fe], and coupled with the lack of any significant spread in any of the studied elements we conclude that Pal 15 is typical of the outer halo, as is bolstered by its chemical similarity to the benchmark outer halo cluster NGC 7492. One star shows evidence of elevated Na and Al abundances, hinting at the presence of multiple stellar populations in this cluster.
Shape of Higgs Potential at Future Colliders

Although the Higgs boson has been discovered, its self couplings are poorly constrained. It leaves the nature of the Higgs boson undetermined. Motivated by different Higgs potential scenarios other than the Landau-Ginzburg type in the standard model, we systematically organize various new physics scenarios: elementary Higgs, Nambu-Goldstone Higgs, Coleman-Weinberg Higgs, and tadpole-induced Higgs, etc. We find that double-Higgs production at the 27 TeV high energy LHC can be used to discriminate different Higgs potential scenarios, while it is necessary to use triple-Higgs production at the 100 TeV collider to fully determine the shape of the Higgs potential.
Screening maps of the local Universe I – Methodology

We introduce the LOCal Universe Screening Test Suite (locusts) project, an effort to create ‘screening maps’ in the nearby Universe to identify regions in our neighbourhood which are screened, i.e. regions where deviations from General Relativity (GR) are suppressed, in various modified gravity (MG) models. In these models, deviations from the GR force law are often stronger for smaller astrophysical objects, making them ideal test beds of gravity in the local Universe. However, the actual behaviour of the modified gravity force also depends on the environment of the objects, and to make accurate predictions one has to take the latter into account. This can be done approximately using luminous objects in the local Universe as tracers of the underlying dark matter field. Here, we propose a new approach that takes advantage of state-of-the-art Bayesian reconstruction of the mass distribution in the Universe, which allows us to solve the modified gravity equations and predict the screening effect more accurately. This is the first of a series of works, in which we present our methodology and some qualitative results of screening for a specific MG model, f(R) gravity. Applications to test models using observations and extensions to other classes of models will be studied in future works.
The prevalence of pseudo-bulges in the Auriga simulations

We study the galactic bulges in the Auriga simulations, a suite of 30 cosmological magneto-hydrodynamical zoom-in simulations of late-type galaxies in Milky Way sized dark matter haloes performed with the moving-mesh code arepo. We aim to characterize bulge formation mechanisms in this large suite of galaxies simulated at high resolution in a fully cosmological context. The bulges of the Auriga galaxies show a large variety in their shapes, sizes, and formation histories. According to observational classification criteria, such as Sérsic index and degree of ordered rotation, the majority of the Auriga bulges can be classified as pseudo-bulges, while some of them can be seen as composite bulges with a classical component; however, none can be classified as a classical bulge. Auriga bulges show mostly an in situ origin, $21{{\ \rm per\ cent}}$ of them with a negligible accreted fraction (facc < 0.01). In general, their in situ component was centrally formed, with ${\sim}75{{\ \rm per\ cent}}$ of the bulges forming most of their stars inside the bulge region at z = 0. Part of their in situ mass growth is rapid and is associated with the effects of mergers, while another part is more secular in origin. In $90{{\ \rm per\ cent}}$ of the Auriga bulges, the accreted bulge component originates from less than four satellites. We investigate the relation between the accreted stellar haloes and the bulges of the Auriga simulations. The total bulge mass shows no correlation with the accreted stellar halo mass, as in observations. However, the accreted mass of bulges tends to correlate with their respective accreted stellar halo mass.
The bridge: a transient phenomenon of forming stellar multiples

Observations with modern instruments such as Herschel reveal that stars form clustered inside filamentary arms of ~1 pc length embedded in giant molecular clouds (GMCs). On smaller scales of ~1000 au, observations of IRAS 16293–2422, for example, show signs of filamentary “bridge” structures connecting young protostars to their birth environment.
Aims. We aim to find the origin of bridges associated with deeply embedded protostars by characterizing their connection to the filamentary structure present on GMC scales and to the formation of protostellar multiples.
Methods. Using the magnetohydrodynamical code RAMSES, we carried out zoom-in simulations of low-mass star formation starting from GMC scales. We analyzed the morphology and dynamics involved in the formation process of a triple system.
Results. Colliding flows of gas in the filamentary arms induce the formation of two protostellar companions at distances of ~1000 au from the primary. After their birth, the stellar companions quickly approach, at Δt ~ 10 kyr, and orbit the primary on eccentric orbits with separations of ~100 au. The colliding flows induce transient structures lasting for up to a few 10 kyr that connect two forming protostellar objects that are kinematically quiescent along the line-of-sight.
 Colliding flows compress gas and trigger the formation of stellar companions via turbulent fragmentation. Our results suggest that protostellar companions initially form with a wide separation of ~1000 au. Smaller separations of a ≲ 100 au are a consequence of subsequent migration and capturing. Associated with the formation phase of the companion, the turbulent environment induces the formation of arc- and bridge-like structures. These bridges can become kinematically quiescent when the velocity components of the colliding flows eliminate each other. However, the gas in bridges still contributes to stellar accretion later. Our results demonstrate that bridge-like structures are a transient phenomenon of stellar multiple formation.
Three- and two-point spatial correlations of intergalactic medium at z ∼ 2 using projected quasar triplets

We present analysis of two- and three-point correlation functions of Lyα forest (at 2 ≤ z ≤ 2.5) using X-Shooter spectra of three background quasar triplets probing transverse separations of 0.5–1.6 pMpc. We present statistics based on transmitted flux and clouds identified using Voigt profile fitting. We show that the observed two-, three-point correlation functions and reduced three-point correlation (i.e. Q) are well reproduced by our simulations. We assign probabilities for realizing all the observed correlation properties simultaneously using our simulations. Our simulations suggest an increase in correlation amplitudes and Q with increasing NH i. We roughly see this trend in the observations too. We identify a concurrent gap of 17Å (i.e. 14.2 h−1 cMpc, one of the longest reported) wide along one of the triplets. Such gap is realized only in 14.2 per cent of our simulated sightlines and most of the time belongs to a void in the matter distribution. In the second triplet, we detect Damped Lyα systems (DLAs) along all three sightlines (with spatial separations 0.64 to 1.6 pMpc) within a narrow redshift interval (i.e. Δz = 0.088). Detection of a foreground quasar (∼1 pMpc from the triplet sightlines) and excess partial Lyman Limit systems (with NH i in the range $10^{16-17.2} \, \mathrm{cm}^{-2}$) around these DLAs suggest that we may be probing a large overdense region. We also report positive C iv–C iv correlations up to ∼500 km s−1 only in the longitudinal direction. Additionally, we conclude a positive C iv–Lyα correlations for higher NH i thresholds (${\gt}10^{15}\, \mathrm{cm}^{-2}$) up to a scale of ∼1000 km s−1 both in transverse and longitudinal directions.
Exploring a new definition of the green valley and its implications

The distribution of galaxies on a colour–magnitude diagram reveals a bimodality, featuring a passively evolving red sequence and a star-forming blue cloud. The region between these two, the green valley (GV), represents a fundamental transition where quenching processes operate. We exploit an alternative definition of the GV using the 4000 Å break strength, an indicator that is more resilient than colour to dust attenuation. We compare and contrast our GV definition with the traditional one, based on dust-corrected colour, making use of data from the Sloan Digital Sky Survey. Our GV selection – that does not need a dust correction and thus does not carry the inherent systematics – reveals very similar trends regarding nebular activity (star formation, AGN, quiescence) to the standard dust-corrected 0.1(g − r). By use of high-SNR stacked spectra of the quiescent GV subsample, we derive the simple stellar population (SSP) age difference across the GV, a rough proxy of the quenching time-scale (Δt). We obtain an increasing trend with velocity dispersion (σ), from Δt ∼ 1.5 Gyr at σ = 100 km s−1, up to 3.5 Gyr at σ = 200 km s−1, followed by a rapid decrease in the most massive GV galaxies (Δt ∼ 1 Gyr at σ = 250 km s−1), suggesting two different modes of quenching, or the presence of an additional channel (rejuvenation).
A comprehensive evaluation of full-reference image quality assessment algorithms on KADID-10k

Significant progress has been made in the past decade for full-reference image quality assessment (FR-IQA). However, new large scale image quality databases have been released for evaluating image quality assessment algorithms. In this study, our goal is to give a comprehensive evaluation of state-of-the-art FR-IQA metrics using the recently published KADID-10k database which is largest available one at the moment. Our evaluation results and the associated discussions is very helpful to obtain a clear understanding about the status of state-of-the-art FR-IQA metrics.
Machine learning and behavioral economics for personalized choice architecture

Behavioral economics changed the way we think about market participants and revolutionized policy-making by introducing the concept of choice architecture. However, even though effective on the level of a population, interventions from behavioral economics, nudges, are often characterized by weak generalisation as they struggle on the level of individuals. Recent developments in data science, artificial intelligence (AI) and machine learning (ML) have shown ability to alleviate some of the problems of weak generalisation by providing tools and methods that result in models with stronger predictive power. This paper aims to describe how ML and AI can work with behavioral economics to support and augment decision-making and inform policy decisions by designing personalized interventions, assuming that enough personalized traits and psychological variables can be sampled.
The Informativeness of Estimation Moments

This paper introduces measures for how each moment contributes to the precision of parameter estimates in GMM settings. For example, one of the measures asks what would happen to the variance of the parameter estimates if a particular moment was dropped from the estimation. The measures are all easy to compute. We illustrate the usefulness of the measures through two simple examples as well as an application to a model of joint retirement planning of couples. We estimate the model using the UK-BHPS, and we find evidence of complementarities in leisure. Our sensitivity measures illustrate that the estimate of the complementarity is primarily informed by the distribution of differences in planned retirement dates. The estimated econometric model can be interpreted as a bivariate ordered choice model that allows for simultaneity. This makes the model potentially useful in other applications.
A Unified Approach to Mixed-Integer Optimization Problems With Logical Constraints

We propose a unified framework to address a family of classical mixed-integer optimization problems with logically constrained decision variables, including network design, facility location, unit commitment, sparse portfolio selection, binary quadratic optimization, sparse principal component analysis, and sparse learning problems. These problems exhibit logical relationships between continuous and discrete variables, which are usually reformulated linearly using a big-M formulation. In this work, we challenge this longstanding modeling practice and express the logical constraints in a non-linear way. By imposing a regularization condition, we reformulate these problems as convex binary optimization problems, which are solvable using an outer-approximation procedure. In numerical experiments, we establish that a general-purpose numerical strategy, which combines cutting-plane, first-order, and local search methods, solves these problems faster and at a larger scale than state-of-the-art mixed-integer linear or second-order cone methods. Our approach successfully solves network design problems with 100s of nodes and provides solutions up to 40% better than the state-of-the-art; sparse portfolio selection problems with up to 3, 200 securities compared with 400 securities for previous attempts; and sparse regression problems with up to 100, 000 covariates.
DeepMRSeg: A convolutional deep neural network for anatomy and abnormality segmentation on MR images

Segmentation has been a major task in neuroimaging. A large number of automated methods have been developed for segmenting healthy and diseased brain tissues. In recent years, deep learning techniques have attracted a lot of attention as a result of their high accuracy in different segmentation problems. We present a new deep learning based segmentation method, DeepMRSeg, that can be applied in a generic way to a variety of segmentation tasks. The proposed architecture combines recent advances in the field of biomedical image segmentation and computer vision. We use a modified UNet architecture that takes advantage of multiple convolution filter sizes to achieve multi-scale feature extraction adaptive to the desired segmentation task. Importantly, our method operates on minimally processed raw MRI scan. We validated our method on a wide range of segmentation tasks, including white matter lesion segmentation, segmentation of deep brain structures and hippocampus segmentation. We provide code and pre-trained models to allow researchers apply our method on their own datasets.