nils-herrmann/clean_scirepeval huggingface dataset:
An Extensible and Personalizable Multi-Modal Trip Planner

Despite a tremendous amount of work in the literature and in the commercial sectors, current approaches to multi-modal trip planning still fail to consistently generate plans that users deem optimal in practice. We believe that this is due to the fact that current planners fail to capture the true preferences of users, e.g., their preferences depend on aspects that are not modeled. An example of this could be a preference not to walk through an unsafe area at night. We present a novel multi-modal trip planner that allows users to upload auxiliary geographic data (e.g., crime rates) and to specify temporal constraints and preferences over these data in combination with typical metrics such as time and cost. Concretely, our planner supports the modes walking, biking, driving, public transit, and taxi, uses linear temporal logic to capture temporal constraints, and preferential cost functions to represent preferences. We show by examples that this allows the expression of very interesting preferences and constraints that, naturally, lead to quite diverse optimal plans.
Confirmation of Planet-mass Objects in Extragalactic Systems

Quasar microlensing serves as a unique probe of discrete objects within galaxies and galaxy clusters. Recent advancement of the technique shows that it can constrain planet-scale objects beyond our native galaxy by studying their induced microlensing signatures, the energy shift of emission lines originating in the vicinity of the black hole of high redshift background quasars. We employ this technique to exert effective constraints on the planet-mass object distribution within two additional lens systems, Q J0158−4325 (zl = 0.317) and SDSS J1004+4112 (zl = 0.68), using Chandra observations of the two gravitationally lensed quasars. The observed variations of the emission line peak energy can be explained as microlensing of the FeKα emission region induced by planet-mass microlenses. To corroborate this, we perform microlensing simulations to determine the probability of a caustic transiting the source region and compare this with the observed line shift rates. Our analysis yields constraints on the substellar population, with masses ranging from Moon (10−8 M⊙) to Jupiter (10−3 M⊙) sized bodies, within these galaxy or cluster scale structures, with total mass fractions of ∼3 × 10−4 and ∼1 × 10−4 with respect to halo mass for Q J0158−4325 and SDSS J1004+4112, respectively. Our analysis suggests that unbound planet-mass objects are universal in galaxies, and we surmise the objects to be either free-floating planets or primordial black holes. We present the first ever constraints on the substellar mass distribution in the intracluster light of a galaxy cluster. Our results provide the most stringent limit on the mass fraction of primordial black holes at the mass range.
Blending of Conceptual Physics and Mathematical Signs

Mathematics is the language of science. Fluent and productive use of mathematics requires one to understand the meaning embodied in mathematical symbols, operators, syntax, etc., which can be a difficult task. For instance, in algebraic symbolization, the negative and positive signs carry multiple meanings depending on contexts. In the context of electromagnetism, we use conceptual blending theory to demonstrate that different physical meanings, such as directionality and location, could associate to the positive and negative signs. With these blends, we analyze the struggles of upper-division students as they work with an introductory level problem where the students must employ multiple signs with different meanings in one mathematical expression. We attribute their struggles to the complexity of choosing blends with an appropriate meaning for each sign, which gives us insight into students' algebraic thinking and reasoning.
Deep Predictive Motion Tracking in Magnetic Resonance Imaging: Application to Fetal Imaging

Fetal magnetic resonance imaging (MRI) is challenged by uncontrollable, large, and irregular fetal movements. It is, therefore, performed through visual monitoring of fetal motion and repeated acquisitions to ensure diagnostic-quality images are acquired. Nevertheless, visual monitoring of fetal motion based on displayed slices, and navigation at the level of stacks-of-slices is inefficient. The current process is highly operator-dependent, increases scanner usage and cost, and significantly increases the length of fetal MRI scans which makes them hard to tolerate for pregnant women. To help build automatic MRI motion tracking and navigation systems to overcome the limitations of the current process and improve fetal imaging, we have developed a new real-time image-based motion tracking method based on deep learning that learns to predict fetal motion directly from acquired images. Our method is based on a recurrent neural network, composed of spatial and temporal encoder-decoders, that infers motion parameters from anatomical features extracted from sequences of acquired slices. We compared our trained network on held-out test sets (including data with different characteristics, e.g. different fetuses scanned at different ages, and motion trajectories recorded from volunteer subjects) with networks designed for estimation as well as methods adopted to make predictions. The results show that our method outperformed alternative techniques, and achieved real-time performance with average errors of 3.5 and 8 degrees for the estimation and prediction tasks, respectively. Our real-time deep predictive motion tracking technique can be used to assess fetal movements, to guide slice acquisitions, and to build navigation systems for fetal MRI.
Considering pricing and uncertainty in designing a reverse logistics network

Companies try to maximize their profits by recovering returned products of highly uncertain quality and quantity. In this paper, a reverse logistics network for an Original Equipment Manufacturer (OEM) is presented. Returned products are selected for remanufacturing or scrapping, based on their quality and proportional prices are offered to customers. A Mixed Integer Non-linear Programming (MINLP) model is proposed to determine the location of collection centers, the optimum price of returned products and the sorting policy. The risk in the objective function is measured using the Conditional Value at Risk (CVaR) metric. CVaR measures the risk of an investment in a conservative way by considering the maximum lost. The results are analyzed for various values of the risk parameters ({\alpha}, and {\lambda}). These parameters indicate that considering risk affects prices, the classification of returned products, the location of collection centers and, consequently, the objective function. The model performs more conservatively when the weight of the CVaR part ({\lambda}) and the value of the confidence level {\alpha} are increased. The results show that better profits are obtained when we take CVaR into account.
Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis

Developing a reliable parametric cost model at the conceptual stage of the project is crucial for project managers and decision makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost prediction. However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples. Artificial intelligence (AI) and machine learning (ML) algorithms include numerous models and algorithms for supervised regression applications. Therefore, a comparative analysis for AI models is required to guide practitioners to the appropriate model. The article focuses on investigating 20 AI techniques which are conducted for conceptual cost modeling, such as fuzzy logic model, artificial neural networks, multiple regression analysis, case-based reasoning, hybrid models, such as genetic fuzzy model, and ensemble methods such as scalable boosting trees (XGBoost) and random forest. Field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the applied ML models. Out of 20 AI techniques, the results show that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on mean absolute percentage error and adjusted R2, respectively. Nonlinear adaptability, handling missing values and outliers, model interpretation, and uncertainty have been discussed for the 20 developed AI models. In addition, this study presents a publicly open dataset for FCIPs to be used for future models’ validation and analysis.
High Fidelity Speech Synthesis with Adversarial Networks

Generative adversarial networks have seen rapid development in recent years and have led to remarkable improvements in generative modelling of images. However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech. To address this paucity, we introduce GAN-TTS, a Generative Adversarial Network for Text-to-Speech. Our architecture is composed of a conditional feed-forward generator producing raw speech audio, and an ensemble of discriminators which operate on random windows of different sizes. The discriminators analyse the audio both in terms of general realism, as well as how well the audio corresponds to the utterance that should be pronounced. To measure the performance of GAN-TTS, we employ both subjective human evaluation (MOS - Mean Opinion Score), as well as novel quantitative metrics (Frechet DeepSpeech Distance and Kernel DeepSpeech Distance), which we find to be well correlated with MOS. We show that GAN-TTS is capable of generating high-fidelity speech with naturalness comparable to the state-of-the-art models, and unlike autoregressive models, it is highly parallelisable thanks to an efficient feed-forward generator. Listen to GAN-TTS reading this abstract at this https URL.
Deep Dynamics Models for Learning Dexterous Manipulation

Dexterous multi-fingered hands can provide robots with the ability to flexibly perform a wide range of manipulation skills. However, many of the more complex behaviors are also notoriously difficult to control: Performing in-hand object manipulation, executing finger gaits to move objects, and exhibiting precise fine motor skills such as writing, all require finely balancing contact forces, breaking and reestablishing contacts repeatedly, and maintaining control of unactuated objects. Learning-based techniques provide the appealing possibility of acquiring these skills directly from data, but current learning approaches either require large amounts of data and produce task-specific policies, or they have not yet been shown to scale up to more complex and realistic tasks requiring fine motor skills. In this work, we demonstrate that our method of online planning with deep dynamics models (PDDM) addresses both of these limitations; we show that improvements in learned dynamics models, together with improvements in online model-predictive control, can indeed enable efficient and effective learning of flexible contact-rich dexterous manipulation skills -- and that too, on a 24-DoF anthropomorphic hand in the real world, using just 4 hours of purely real-world data to learn to simultaneously coordinate multiple free-floating objects. Videos can be found at this https URL
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space

Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistry is the design of tailor-made organic materials and molecules, which requires efficient methods to explore the chemical space. We present a genetic algorithm (GA) that is enhanced with a neural network (DNN) based discriminator model to improve the diversity of generated molecules and at the same time steer the GA. We show that our algorithm outperforms other generative models in optimization tasks. We furthermore present a way to increase interpretability of genetic algorithms, which helped us to derive design principles.
Explicitly disentangling image content from translation and rotation with spatial-VAE

Given an image dataset, we are often interested in finding data generative factors that encode semantic content independently from pose variables such as rotation and translation. However, current disentanglement approaches do not impose any specific structure on the learned latent representations. We propose a method for explicitly disentangling image rotation and translation from other unstructured latent factors in a variational autoencoder (VAE) framework. By formulating the generative model as a function of the spatial coordinate, we make the reconstruction error differentiable with respect to latent translation and rotation parameters. This formulation allows us to train a neural network to perform approximate inference on these latent variables while explicitly constraining them to only represent rotation and translation. We demonstrate that this framework, termed spatial-VAE, effectively learns latent representations that disentangle image rotation and translation from content and improves reconstruction over standard VAEs on several benchmark datasets, including applications to modeling continuous 2-D views of proteins from single particle electron microscopy and galaxies in astronomical images.
Large-misalignment mechanism for the formation of compact axion structures: Signatures from the QCD axion to fuzzy dark matter

Axions are some of the best motivated particles beyond the Standard Model. We show how the attractive self-interactions of dark matter (DM) axions over a broad range of masses, from $10^{-22}$ eV to $10^7$ GeV, can lead to nongravitational growth of density fluctuations and the formation of bound objects. This structure formation enhancement is driven by parametric resonance when the initial field misalignment is large, and it affects axion density perturbations on length scales of order the Hubble horizon when the axion field starts oscillating, deep inside the radiation-dominated era. This effect can turn an otherwise nearly scale-invariant spectrum of adiabatic perturbations into one that has a spike at the aforementioned scales, producing objects ranging from dense DM halos to scalar-field configurations such as solitons and oscillons. We call this class of cosmological scenarios for axion DM production "the large-misalignment mechanism." 
We explore observational consequences of this mechanism for axions with masses up to $10$ eV. For axions heavier than $10^{-5}$ eV, the compact axion halos are numerous enough to significantly impact Earth-bound direct detection experiments, yielding intermittent but coherent signals with repetition rates exceeding one per decade and crossing times less than a day. These episodic increases in the axion density and kinematic coherence suggest new approaches for axion DM searches, including for the QCD axion. Dense structures made up of axions from $10^{-22}$ eV to $10^{-5}$ eV are detectable through gravitational lensing searches, and their gravitational interactions can also perturb baryonic structures and alter star formation. At very high misalignment amplitudes, the axion field can undergo self-interaction-induced implosions long before matter-radiation equality, producing potentially-detectable low-frequency stochastic gravitational waves.
The Physical Origins of the Identified and Still Missing Components of the Warm–Hot Intergalactic Medium: Insights from Deep Surveys in the Field of Blazar 1ES1553+113

The relationship between galaxies and the state/chemical enrichment of the warm–hot intergalactic medium (WHIM) expected to dominate the baryon budget at low-z provides sensitive constraints on structure formation and galaxy evolution models. We present a deep redshift survey in the field of 1ES1553+113, a blazar with a unique combination of ultraviolet (UV)+X-ray spectra for surveys of the circumgalactic/intergalactic medium (CGM/IGM). Nicastro et al. reported the detection of two O vii WHIM absorbers at z = 0.4339 and 0.3551 in its spectrum, suggesting that the WHIM is metal rich and sufficient to close the missing baryons problem. Our survey indicates that the blazar is a member of a z = 0.433 group and that the higher-z O vii candidate arises from its intragroup medium. The resulting bias precludes its use in baryon censuses. The z = 0.3551 candidate occurs in an isolated environment 630 kpc from the nearest galaxy (with stellar mass ), which we show is unexpected for the WHIM. Finally, we characterize the galactic environments of broad H i Lyα absorbers (Doppler widths of b = 40–80 km s−1; T ≲ 4 × 105 K) that provide metallicity-independent WHIM probes. On average, broad Lyα absorbers are ≈2× closer to the nearest luminous (L > 0.25L*) galaxy (700 kpc) than narrow (b < 30 km s−1; T ≲ 4 × 105 K) ones (1300 kpc) but ≈2× further than O vi absorbers (350 kpc). These observations suggest that gravitational collapse heats portions of the IGM to form the WHIM, but with feedback that does not enrich the IGM far beyond galaxy/group halos to levels currently observable in UV/X-ray metal lines.
Probing the Higgs portal at the Fermilab short-baseline neutrino experiments

The Fermilab Short-Baseline Neutrino (SBN) experiments, MicroBooNE, ICARUS, and SBND, are expected to have significant sensitivity to light weakly coupled hidden sector particles. Here we study the capability of the SBN experiments to probe dark scalars interacting through the Higgs portal. We investigate production of dark scalars using both the Fermilab Booster 8 GeV and NuMI 120 GeV proton beams, simulating kaons decaying to dark scalars and taking into account the beamline geometry. We also investigate strategies to mitigate backgrounds from beam-related neutrino scattering events. We find that SBND, with its comparatively short ${\cal O}(100\ {\rm m})$ baseline, will have the best sensitivity to scalars produced with Booster, while ICARUS, with its large detector volume, will provide the best limits on off-axis dark scalar production from NuMI. The SBN experiments can provide leading tests of dark scalars with masses in the 50 - 350 MeV range in the near term. Our results motivate dedicated experimental searches for dark scalars and other long-lived hidden sector states at these experiments.
Data Valuation using Reinforcement Learning

Quantifying the value of data is a fundamental problem in machine learning. Data valuation has multiple important use cases: (1) building insights about the learning task, (2) domain adaptation, (3) corrupted sample discovery, and (4) robust learning. To adaptively learn data values jointly with the target task predictor model, we propose a meta learning framework which we name Data Valuation using Reinforcement Learning (DVRL). We employ a data value estimator (modeled by a deep neural network) to learn how likely each datum is used in training of the predictor model. We train the data value estimator using a reinforcement signal of the reward obtained on a small validation set that reflects performance on the target task. We demonstrate that DVRL yields superior data value estimates compared to alternative methods across different types of datasets and in a diverse set of application scenarios. The corrupted sample discovery performance of DVRL is close to optimal in many regimes (i.e. as if the noisy samples were known apriori), and for domain adaptation and robust learning DVRL significantly outperforms state-of-the-art by 14.6% and 10.8%, respectively.
The X-ray and radio activity of typical and luminous Ly α emitters from z ∼ 2 to z ∼ 6: evidence for a diverse, evolving population

Despite recent progress in understanding Ly α emitters (LAEs), relatively little is known regarding their typical black hole activity across cosmic time. Here, we study the X-ray and radio properties of ∼4000 LAEs at 2.2 < $z$ < 6 from the SC4K survey in the COSMOS field. We detect 254 ($6.8{\rm{ per\ cent}} \pm 0.4{\rm{ per\ cent}}$) LAEs individually in the X-rays (S/N > 3) with an average luminosity of $\rm 10^{44.31\pm 0.01}\, erg\, s^{-1}$ and average black hole accretion rate (BHAR) of $\rm 0.72 \pm 0.01$ M⊙ yr−1, consistent with moderate to high accreting active galactic neuclei (AGNs). We detect 120 sources in deep radio data (radio AGN fraction of $3.2{\rm{ per\ cent}} \pm 0.3{\rm{ per\ cent}}$). The global AGN fraction ($\rm 8.6{\rm{ per\ cent}} \pm 0.4{\rm{ per\ cent}}$) rises with Ly α luminosity and declines with increasing redshift. For X-ray-detected LAEs, Ly α luminosities correlate with the BHARs, suggesting that Ly α luminosity becomes a BHAR indicator. Most LAEs ($93.1{\rm{ per\ cent}} \pm 0.6{\rm{ per\ cent}}$) at 2 < $z$ < 6 have no detectable X-ray emission (BHARs < 0.017 M⊙ yr−1). The median star formation rate (SFR) of star-forming LAEs from Ly α and radio luminosities is $7.6^{+6.6}_{-2.8}$ M⊙ yr−1. The black hole to galaxy growth ratio (BHAR/SFR) for LAEs is <0.0022, consistent with typical star-forming galaxies and the local BHAR/SFR relation. We conclude that LAEs at 2 < $z$ < 6 include two different populations: an AGN population, where Ly α luminosity traces BHAR, and another with low SFRs which remain undetected in even the deepest X-ray stacks but is detected in the radio stacks.
Mapping the Magnetic Interstellar Medium in Three Dimensions over the Full Sky with Neutral Hydrogen

Recent analyses of 21 cm neutral hydrogen (H i) emission have demonstrated that H i gas is organized into linear filamentary structures that are preferentially aligned with the local magnetic field, and that the coherence of these structures in velocity space traces line-of-sight magnetic field tangling. On this basis, we introduce a paradigm for modeling the properties of magnetized, dusty regions of the interstellar medium (ISM), using the orientation of H i structure at different velocities to map “magnetically coherent” regions of space. We construct three-dimensional (position–position–velocity) Stokes parameter maps using H i4PI full-sky spectroscopic H i data. We compare these maps, integrated over the velocity dimension, to Planck maps of the polarized dust emission at 353 GHz. Without any free parameters governing the relation between H i intensity and dust emission, we find that our Q and U maps are highly correlated (r > 0.75) with the 353 GHz Q and U maps of polarized dust emission observed by Planck and reproduce many of its large-scale features. The E/B ratio of the dust emission maps agrees well with the H i-derived maps at large angular scales (ℓ ≲ 120), supporting the interpretation that this asymmetry arises from the coupling of linear density structures to the Galactic magnetic field. We demonstrate that our 3D Stokes parameter maps constrain the 3D structure of the Galactic ISM and the orientation of the interstellar magnetic field.
Bounds on triangle anomalies in 
(3+1)D

How many charged degrees of freedom are necessary to accommodate a certain amount of 't Hooft anomaly? Using the conformal bootstrap for the four-point function of flavor current multiplets, we show that in all (3+1)d superconformal field theories the 't Hooft anomaly of a continuous flavor symmetry is bounded from above by the 3/2 power of the current two-point function coefficient, which measures the amount of charged degrees of freedom. We check our bounds against free fields and SQCD in the conformal window.