nils-herrmann/clean_scirepeval huggingface dataset:
Primordial Black Holes as a Dark Matter Candidate Are Severely Constrained by the Galactic Center 511 keV γ-Ray Line.

We derive the strongest constraint on the fraction of dark matter that can be composed of low mass primordial black holes by using the observation of the Galactic Center 511 keV γ-ray line. Primordial black holes of masses ≲10^{15}  kg will evaporate to produce e^{±} pairs. The positrons will lose energy in the Galactic Center, become nonrelativistic, then annihilate with the ambient electrons. We derive robust and conservative bounds by assuming that the rate of positron injection via primordial black hole evaporation is less than what is required to explain the SPI/INTEGRAL observation of the Galactic Center 511 keV γ-ray line. Depending on the primordial black hole mass function and other astrophysical uncertainties, these constraints are the most stringent in the literature and show that primordial black holes contribute to less than 1% of the dark matter density. Our technique also probes part of the mass range which was completely unconstrained by previous studies.
LIAAD at SemDeep-5 Challenge: Word-in-Context (WiC)

This paper describes the LIAAD system that was ranked second place in the Word-in-Context challenge (WiC) featured in SemDeep-5. Our solution is based on a novel system for Word Sense Disambiguation (WSD) using contextual embeddings and full-inventory sense embeddings. We adapt this WSD system, in a straightforward manner, for the present task of detecting whether the same sense occurs in a pair of sentences. Additionally, we show that our solution is able to achieve competitive performance even without using the provided training or development sets, mitigating potential concerns related to task overfitting
Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace

The development of models and methodology for the analysis of data from multiple heterogeneous networks is of importance both in statistical network theory and across a wide spectrum of application domains. Although single-graph analysis is well-studied, multiple graph inference is largely unexplored, in part because of the challenges inherent in appropriately modeling graph differences and yet retaining sufficient model simplicity to render estimation feasible. This paper addresses exactly this gap, by introducing a new model, the common subspace independent-edge multiple random graph model, which describes a heterogeneous collection of networks with a shared latent structure on the vertices but potentially different connectivity patterns for each graph. The model encompasses many popular network representations, including the stochastic blockmodel. The model is both flexible enough to meaningfully account for important graph differences, and tractable enough to allow for accurate inference in multiple networks. In particular, a joint spectral embedding of adjacency matrices—the multiple adjacency spectral embedding—leads to simultaneous consistent estimation of underlying parameters for each graph. Under mild additional assumptions, the estimates satisfy asymptotic normality and yield improvements for graph eigenvalue estimation. In both simulated and real data, the model and the embedding can be deployed for a number of subsequent network inference tasks, including dimensionality reduction, classification, hypothesis testing, and community detection. Specifically, when the embedding is applied to a data set of connectomes constructed through diffusion magnetic resonance imaging, the result is an accurate classification of brain scans by human subject and a meaningful determination of heterogeneity across scans of different individuals.
Is It Worth the Attention? A Comparative Evaluation of Attention Layers for Argument Unit Segmentation

Attention mechanisms have seen some success for natural language processing downstream tasks in recent years and generated new state-of-the-art results. A thorough evaluation of the attention mechanism for the task of Argumentation Mining is missing. With this paper, we report a comparative evaluation of attention layers in combination with a bidirectional long short-term memory network, which is the current state-of-the-art approach for the unit segmentation task. We also compare sentence-level contextualized word embeddings to pre-generated ones. Our findings suggest that for this task, the additional attention layer does not improve the performance. In most cases, contextualized embeddings do also not show an improvement on the score achieved by pre-defined embeddings.
Analyzing CART.

Decision trees with binary splits are popularly constructed using Classification and Regression Trees (CART) methodology. For regression models, this approach recursively divides the data into two near-homogenous daughter nodes according to a split point that maximizes the reduction in sum of squares error (the impurity) along a particular variable. This paper aims to study the statistical properties of regression trees constructed with CART. In doing so, we find that the training error is governed by Pearson's correlation between the optimal decision stump and response data in each node, which we bound by solving a quadratic program. We leverage this to show that CART with cost-complexity pruning achieves a good bias-variance tradeoff when the depth scales with the logarithm of the sample size. Data dependent quantities, which adapt to the local dimensionality and structure of the regression surface, are seen to govern the rates of convergence of the prediction error.
CMRNet: Camera to LiDAR-Map Registration

In this paper we present CMRNet, a realtime approach based on a Convolutional Neural Network (CNN) to localize an RGB image of a scene in a map built from LiDAR data. Our network is not trained in the working area, i. e., CMRNet does not learn the map. Instead it learns to match an image to the map. We validate our approach on the KITTI dataset, processing each frame independently without any tracking procedure. CMRNet achieves 0.27m and 1.07° median localization accuracy on the sequence 00 of the odometry dataset, starting from a rough pose estimate displaced up to 3.5m and 17°. To the best of our knowledge this is the first CNN-based approach that learns to match images from a monocular camera to a given, preexisting 3D LiDAR-map.
Assembly line balancing with task division

In a commonly-used version of the Simple Assembly Line Balancing Problem (SALBP-1) tasks are assigned to stations along an assembly line with a fixed cycle time in order to minimize the required number of stations. It has traditionally been assumed that the total work needed for each product unit has been partitioned into economically indivisible tasks. However, in practice, it is sometimes possible to divide particular tasks in limited ways at additional time penalty cost. Despite the penalties, task division where possible, now and then leads to a reduction in the minimum number of stations. Deciding which allowable tasks to divide creates a new assembly line balancing problem, TDALBP (Task Division Assembly Line Balancing Problem). We propose a mathematical model of the TDALBP, an exact solution procedure for it and present promising computational results for the adaptation of some classical SALBP instances from the research literature. The results demonstrate that the TDALBP sometimes has the potential to significantly improve assembly line performance.
Hybrid symbiotic organisms search feedforward neural network model for stock price prediction

The prediction of stock prices is an important task in economics, investment and financial decision-making. It has for several decades, spurred the interest of many researchers to design stock price predictive models. In this paper, the symbiotic organisms search algorithm, a new metaheuristic algorithm is employed as an efficient method for training feedforward neural networks (FFNN). The training process is used to build a better stock price predictive model. The Straits Times Index, Nikkei 225, NASDAQ Composite, S&P 500, and Dow Jones Industrial Average indices were utilized as time series data sets for training and testing proposed predic-tive model. Three evaluation methods namely, Root Mean Squared Error, Mean Absolute Percentage Error and Mean Absolution Deviation are used to compare the results of the implemented model. The computational results obtained revealed that the hybrid Symbiotic Organisms Search Algorithm exhibited outstanding predictive performance when compared to the hybrid Particle Swarm Optimization, Genetic Algorithm, and ARIMA based models. The new model is a promising predictive technique for solving high dimensional nonlinear time series data that are difficult to capture by traditional models.
On Multi-Agent Learning in Team Sports Games

In recent years, reinforcement learning has been successful in solving video games from Atari to Star Craft II. However, the end-to-end model-free reinforcement learning (RL) is not sample efficient and requires a significant amount of computational resources to achieve superhuman level performance. Model-free RL is also unlikely to produce human-like agents for playtesting and gameplaying AI in the development cycle of complex video games. In this paper, we present a hierarchical approach to training agents with the goal of achieving human-like style and high skill level in team sports games. While this is still work in progress, our preliminary results show that the presented approach holds promise for solving the posed multi-agent learning problem.
Discovery of an equal-mass ‘twin’ binary population reaching 1000 + au separations

We use a homogeneous catalogue of 42 000 main-sequence wide binaries identified by Gaia to measure the mass ratio distribution, p(q), of binaries with primary masses 0.1 < M1/M⊙ < 2.5, mass ratios 0.1 ≲ q < 1, and separations $50 \lt s/{\rm au} \lt 50\, 000$. A well-understood selection function allows us to constrain p(q) in 35 independent bins of primary mass and separation, with hundreds to thousands of binaries in each bin. Our investigation reveals a sharp excess of equal-mass ‘twin’ binaries that is statistically significant out to separations of 1000–10 000 au, depending on primary mass. The excess is narrow: a steep increase in p(q) at 0.95 ≲ q < 1, with no significant excess at q ≲ 0.95. A range of tests confirm the signal is real, not a data artefact or selection effect. Combining the Gaia constraints with those from close binaries, we show that the twin excess decreases with increasing separation, but its width (q ≳ 0.95) is constant over $0.01 \lt a/{\rm au} \lt 10\, 000$. The wide twin population would be difficult to explain if the components of all wide binaries formed via core fragmentation, which is not expected to produce strongly correlated component masses. We conjecture that wide twins formed at closer separations (a ≲ 100 au), likely via accretion from circumbinary discs, and were subsequently widened by dynamical interactions in their birth environments. The separation-dependence of the twin excess then constrains the efficiency of dynamical widening and disruption of binaries in young clusters. We also constrain p(q) across 0.1 ≲ q < 1. Besides changes in the twin fraction, p(q) is independent of separation at fixed primary mass over $100 \lesssim s/{\rm au} \lt 50\, 000$. It is flatter than expected for random pairings from the initial mass function but more bottom-heavy for wide binaries than for binaries with a ≲100 au.
Evidence for Low Radiative Efficiency or Highly Obscured Growth of z > 7 Quasars

The supermassive black holes (SMBHs) observed at the centers of all massive galaxies are believed to have grown via luminous accretion during quasar phases in the distant past. The fraction of inflowing rest mass energy emitted as light, the radiative efficiency, has been inferred to be 10%, in agreement with expectations from thin disk accretion models. But the existence of billion solar-mass SMBHs powering quasars at z > 7 challenges this picture: provided they respect the Eddington limit, there is not enough time to grow z > 7 SMBHs from stellar remnant seeds unless the radiative efficiency is below 10%. Here we show that one can constrain the radiative efficiencies of the most distant quasars known using foreground neutral intergalactic gas as a cosmological-scale ionizing photon counter. From the Lyα absorption profiles of ULAS J1120+0641 (z = 7.09) and ULAS J1342+0928 (z = 7.54), we determine posterior median radiative efficiencies of 0.08% and 0.1%, respectively, and the combination of the two measurements rules out the canonical 10% efficiency at 99.8% credibility after marginalizing over the unknown obscured fraction. This low radiative efficiency implies rapid mass accretion for the earliest SMBHs, greatly easing the tension between the age of the universe and the SMBH masses. However, our measured efficiency may instead reflect nearly complete obscuration by dusty gas in the quasar host galaxies over the vast majority of their SMBH growth. Assuming 10% efficiency during unobscured phases, we find that the obscured fraction would be >82% at 95% credibility, and imply a times larger obscured than unobscured luminous quasar population at z > 7.
CMB-HD: An Ultra-Deep, High-Resolution Millimeter-Wave Survey Over Half the Sky

A millimeter-wave survey over half the sky, that spans frequencies in the range of 30 to 350 GHz, and that is both an order of magnitude deeper and of higher-resolution than currently funded surveys would yield an enormous gain in understanding of both fundamental physics and astrophysics. By providing such a deep, high-resolution millimeter-wave survey (about 0.5 uK-arcmin noise and 15 arcsecond resolution at 150 GHz), CMB-HD will enable major advances. It will allow 1) the use of gravitational lensing of the primordial microwave background to map the distribution of matter on small scales (k~10/hMpc), which probes dark matter particle properties. It will also allow 2) measurements of the thermal and kinetic Sunyaev-Zel'dovich effects on small scales to map the gas density and gas pressure profiles of halos over a wide field, which probes galaxy evolution and cluster astrophysics. In addition, CMB-HD would allow us to cross critical thresholds in fundamental physics: 3) ruling out or detecting any new, light (< 0.1eV), thermal particles, which could potentially be the dark matter, and 4) testing a wide class of multi-field models that could explain an epoch of inflation in the early Universe. Such a survey would also 5) monitor the transient sky by mapping the full observing region every few days, which opens a new window on gamma-ray bursts, novae, fast radio bursts, and variable active galactic nuclei. Moreover, CMB-HD would 6) provide a census of planets, dwarf planets, and asteroids in the outer Solar System, and 7) enable the detection of exo-Oort clouds around other solar systems, shedding light on planet formation. CMB-HD will deliver this survey in 5 years of observing half the sky, using two new 30-meter-class off-axis cross-Dragone telescopes to be located at Cerro Toco in the Atacama Desert. The telescopes will field about 2.4 million detectors (600,000 pixels) in total.
The Iκεα model of feedback-regulated galaxy formation

We present the Iκεα model of galaxy formation, in which a galaxy’s star formation rate is set by the balance between energy injected by feedback from massive stars and energy lost by the deepening of the potential of its host dark matter halo due to cosmological accretion. Such a balance is secularly stable provided that the star formation rate increases with the pressure in the star forming gas. The Iκεα model has four parameters that together control the feedback from star formation and the cosmological accretion rate onto a halo. Iκεα reproduces accurately the star formation rate as a function of halo mass and redshift in the eagle hydrodynamical simulation, even when all four parameters are held constant. It predicts the emergence of a star forming main sequence along which the specific star formation rate depends weakly on stellar mass with an amplitude that increases rapidly with redshift. We briefly discuss the emerging mass-metallicity relation, the evolution of the galaxy stellar mass function, and an extension of the model that includes feedback from active galactic nuclei (AGN). These self-regulation results are independent of the star formation law and the galaxy’s gas content. Instead, star forming galaxies are shaped by the balance between stellar feedback and cosmological accretion, with accurately accounting for energy losses associated with feedback a crucial ingredient.
Dark matter interactions with muons in neutron stars

Neutron stars contain a significant number of stable muons due to the large chemical potential and degenerate electrons. This makes them the unique vessel to capture muonphilic dark matter, which does not interact with other astrophysical objects, including Earth and its direct-detection experiments. The infalling dark matter can heat up the neutron star both kinetically and via annihilations, which is potentially observable with future infrared telescopes. New physics models for muonphilic dark matter can easily be motivated by, and connected to, existing anomalies in the muon sector, e.g. the anomalous magnetic moment or LHCb's recent hints for lepton-flavor non-universality in $B\to K\mu^+\mu^-$ decays. We study the implications for a model with dark matter charged under a local $U(1)_{L_\mu-L_\tau}$.
Three Red Suns in the Sky: A Transiting, Terrestrial Planet in a Triple M-dwarf System at 6.9 pc

We present the discovery from Transiting Exoplanet Survey Satellite (TESS) data of LTT 1445Ab. At a distance of 6.9 pc, it is the second nearest transiting exoplanet system found to date, and the closest one known for which the primary is an M dwarf. The host stellar system consists of three mid-to-late M dwarfs in a hierarchical configuration, which are blended in one TESS pixel. We use MEarth data and results from the Science Processing Operations Center data validation report to determine that the planet transits the primary star in the system. The planet has a radius of , an orbital period of days, and an equilibrium temperature of K. With radial velocities from the High Accuracy Radial Velocity Planet Searcher, we place a 3σ upper mass limit of 8.4 on the planet. LTT 1445Ab provides one of the best opportunities to date for the spectroscopic study of the atmosphere of a terrestrial world. We also present a detailed characterization of the host stellar system. We use high-resolution spectroscopy and imaging to rule out the presence of any other close stellar or brown dwarf companions. Nineteen years of photometric monitoring of A and BC indicate a moderate amount of variability, in agreement with that observed in the TESS light-curve data. We derive a preliminary astrometric orbit for the BC pair that reveals an edge-on and eccentric configuration. The presence of a transiting planet in this system hints that the entire system may be co-planar, implying that the system may have formed from the early fragmentation of an individual protostellar core.
Machine Learning Phase Transitions with a Quantum Processor

Machine learning has emerged as a promising approach to unveil properties of many-body systems. Recently proposed as a tool to classify phases of matter, the approach relies on classical simulation methods---such as Monte Carlo---which are known to experience an exponential slowdown when simulating certain quantum systems. To overcome this slowdown while still leveraging machine learning, we propose a variational quantum algorithm which merges quantum simulation and quantum machine learning to classify phases of matter. Our classifier is directly fed labeled states recovered by the variational quantum eigensolver algorithm, thereby avoiding the data-reading slowdown experienced in many applications of quantum enhanced machine learning. We propose families of variational ansatz states that are inspired directly by tensor networks. This allows us to use tools from tensor network theory to explain properties of the phase diagrams the presented quantum algorithm recovers. Finally, we propose a majority vote quantum classifier built from a nearest-neighbor (checkerboard) quantum neural network. This classifier is successfully trained to recognize phases of matter with 99% accuracy for the transverse field Ising model and 94% accuracy for the $XXZ$ model. These findings suggest that a merger between quantum simulation and quantum enhanced machine learning offers a fertile ground to develop computational insights into quantum systems.
Sample-constrained partial identification with application to selection bias

Many partial identification problems can be characterized by the optimal value of a function over a set where both the function and set need to be estimated by empirical data. Despite some progress for convex problems, statistical inference in this general setting remains to be developed. To address this, we derive an asymptotically valid confidence interval for the optimal value through an appropriate relaxation of the estimated set. We then apply this general result to the problem of selection bias in population-based cohort studies. We show that existing sensitivity analyses, which are often conservative and difficult to implement, can be formulated in our framework and made significantly more informative via auxiliary information on the population. We conduct a simulation study to evaluate the finite sample performance of our inference procedure and conclude with a substantive motivating example on the causal effect of education on income in the highly-selected UK Biobank cohort. We demonstrate that our method can produce informative bounds using plausible population-level auxiliary constraints. We implement this method in the R package selectioninterval.
Training an Interactive Helper

Developing agents that can quickly adapt their behavior to new tasks remains a challenge. Meta-learning has been applied to this problem, but previous methods require either specifying a reward function which can be tedious or providing demonstrations which can be inefficient. In this paper, we investigate if, and how, a "helper" agent can be trained to interactively adapt their behavior to maximize the reward of another agent, whom we call the "prime" agent, without observing their reward or receiving explicit demonstrations. To this end, we propose to meta-learn a helper agent along with a prime agent, who, during training, observes the reward function and serves as a surrogate for a human prime. We introduce a distribution of multi-agent cooperative foraging tasks, in which only the prime agent knows the objects that should be collected. We demonstrate that, from the emerged physical communication, the trained helper rapidly infers and collects the correct objects.