nils-herrmann/clean_scirepeval huggingface dataset:
Quantum Computing: Lecture Notes

This is a set of lecture notes suitable for a Master's course on quantum computation and information from the perspective of theoretical computer science. The first version was written in 2011, with many extensions and improvements in subsequent years. The first 10 chapters cover the circuit model and the main quantum algorithms (Deutsch-Jozsa, Simon, Shor, Hidden Subgroup Problem, Grover, quantum walks, Hamiltonian simulation and HHL). They are followed by 2 chapters about complexity, 4 chapters about distributed ("Alice and Bob") settings, and a final chapter about quantum error correction. Appendices A and B give a brief introduction to the required linear algebra and some other mathematical and computer science background. All chapters come with exercises, with some hints provided in
Trusses and Trapezes: Easily-Interpreted Communities in Social Networks

The truss, a relaxation of the clique based on triangles, serves to identify clusters of actors in a way that is easy to interpret and is computationally attractive. This paper introduces the 4-cycle-based relative to the truss, called the trapeze, presents a weighted extension of both the truss and trapeze, and offers the refinements of strong trusses and trapezes and summit trusses and trapezes. Use of trapezes permits the application to bipartite graphs, while the weighted versions permit variation of support due to natural edge weights without increasing computational complexity. Finally, strong and summit versions make for easy determination of communities across graphs of varying density. Each of these structures offers guaranteed computation in polynomial time, is motivated by a natural observation of social cohesion, and is related nicely to other standard structures.
Flat Littlewood polynomials exist

We show that there exist absolute constants $\Delta > \delta > 0$ such that, for all $n \geqslant 2$, there exists a polynomial $P$ of degree $n$, with $\pm 1$ coefficients, such that $$\delta\sqrt{n} \leqslant |P(z)| \leqslant \Delta\sqrt{n}$$ for all $z\in\mathbb{C}$ with $|z|=1$. This confirms a conjecture of Littlewood from 1966.
An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments

In reinforcement learning algorithms, leveraging multiple views of the environment can improve the learning of complicated policies. In multi-view environments, due to the fact that the views may frequently suffer from partial observability, their level of importance are often different. In this paper, we propose a deep reinforcement learning method and an attention mechanism in a multi-view environment. Each view can provide various representative information about the environment. Through our attention mechanism, our method generates a single feature representation of environment given its multiple views. It learns a policy to dynamically attend to each view based on its importance in the decision-making process. Through experiments, we show that our method outperforms its state-of-the-art baselines on TORCS racing car simulator and three other complex 3D environments with obstacles. We also provide experimental results to evaluate the performance of our method on noisy conditions and partial observation settings.
Arena: a toolkit for Multi-Agent Reinforcement Learning

We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research. In MARL, it usually requires customizing observations, rewards and actions for each agent, changing cooperative-competitive agent-interaction, and playing with/against a third-party agent, etc. We provide a novel modular design, called Interface, for manipulating such routines in essentially two ways: 1) Different interfaces can be concatenated and combined, which extends the OpenAI Gym Wrappers concept to MARL scenarios. 2) During MARL training or testing, interfaces can be embedded in either wrapped OpenAI Gym compatible Environments or raw environment compatible Agents. We offer off-the-shelf interfaces for several popular MARL platforms, including StarCraft II, Pommerman, ViZDoom, Soccer, etc. The interfaces effectively support self-play RL and cooperative-competitive hybrid MARL. Also, Arena can be conveniently extended to your own favorite MARL platform.
Characterizing Attacks on Deep Reinforcement Learning

Deep reinforcement learning (DRL) has achieved great success in various applications. However, recent studies show that machine learning models are vulnerable to adversarial attacks. DRL models have been attacked by adding perturbations to observations. While such observation based attack is only one aspect of potential attacks on DRL, other forms of attacks which are more practical require further analysis, such as manipulating environment dynamics. Therefore, we propose to understand the vulnerabilities of DRL from various perspectives and provide a thorough taxonomy of potential attacks. We conduct the first set of experiments on the unexplored parts within the taxonomy. In addition to current observation based attacks against DRL, we propose the first targeted attacks based on action space and environment dynamics. We also introduce the online sequential attacks based on temporal consistency information among frames. To better estimate gradient in black-box setting, we propose a sampling strategy and theoretically prove its efficiency and estimation error bound. We conduct extensive experiments to compare the effectiveness of different attacks with several baselines in various environments, including game playing, robotics control, and autonomous driving.
Model Adaptation via Model Interpolation and Boosting for Web Search Ranking

This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The results show that model interpolation, though simple, achieves the best results on all the open test sets where the test data is very different from the training data. The tree-based boosting algorithm achieves the best performance on most of the closed test sets where the test data and the training data are similar, but its performance drops significantly on the open test sets due to the instability of trees. Several methods are explored to improve the robustness of the algorithm, with limited success.
Design of 1-year mortality forecast at hospital admission: A machine learning approach

Palliative care is referred to a set of programs for patients that suffer life-limiting illnesses. These programs aim to maximize the quality of life (QoL) for the last stage of life. They are currently based on clinical evaluation of the risk of 1-year mortality. The main aim of this work is to develop and validate machine-learning-based models to predict the exitus of a patient within the next year using data gathered at hospital admission. Five machine-learning techniques were applied using a retrospective dataset. The evaluation was performed with five metrics computed by a resampling strategy: Accuracy, the area under the ROC curve, Specificity, Sensitivity, and the Balanced Error Rate. All models reported an AUC ROC from 0.857 to 0.91. Specifically, Gradient Boosting Classifier was the best model, producing an AUC ROC of 0.91, a sensitivity of 0.858, a specificity of 0.808, and a BER of 0.1687. Information from standard procedures at hospital admission combined with machine learning techniques produced models with competitive discriminative power. Our models reach the best results reported in the state of the art. These results demonstrate that they can be used as an accurate data-driven palliative care criteria inclusion.
Deep Reinforcement Learning for Clinical Decision Support: A Brief Survey

Owe to the recent advancements in Artificial Intelligence especially deep learning, many data-driven decision support systems have been implemented to facilitate medical doctors in delivering personalized care. We focus on the deep reinforcement learning (DRL) models in this paper. DRL models have demonstrated human-level or even superior performance in the tasks of computer vision and game playings, such as Go and Atari game. However, the adoption of deep reinforcement learning techniques in clinical decision optimization is still rare. We present the first survey that summarizes reinforcement learning algorithms with Deep Neural Networks (DNN) on clinical decision support. We also discuss some case studies, where different DRL algorithms were applied to address various clinical challenges. We further compare and contrast the advantages and limitations of various DRL algorithms and present a preliminary guide on how to choose the appropriate DRL algorithm for particular clinical applications.
The evolution of CNO isotopes: the impact of massive stellar rotators

Chemical abundances and abundance ratios measured in galaxies provide precious information about the mechanisms, modes, and time-scales of the assembly of cosmic structures. Yet, the nucleogenesis and chemical evolution of elements heavier than helium are dictated mostly by the physics of the stars and the shape of the stellar mass spectrum. In particular, estimates of CNO isotopic abundances in the hot, dusty media of high-redshift starburst galaxies offer a unique glimpse into the shape of the stellar initial mass function (IMF) in extreme environments that cannot be accessed with direct observations (star counts). Underlying uncertainties in stellar evolution and nucleosynthesis theory, however, may hurt our chances of getting a firm grasp of the IMF in these galaxies. In this work, we adopt new yields for massive stars, covering different initial rotational velocities. First, we implement the new yield set in a well-tested chemical evolution model for the Milky Way. The calibrated model is then adapted to the specific case of a prototype submillimetre galaxy (SMG). We show that, if the formation of fast-rotating stars is favoured in the turbulent medium of violently star-forming galaxies irrespective of metallicity, the IMF needs to be skewed towards high-mass stars in order to explain the CNO isotopic ratios observed in SMGs. If, instead, stellar rotation becomes negligible beyond a given metallicity threshold, as is the case for our own Galaxy, there is no need to invoke a top-heavy IMF in starbursts.
Context-Aware Convolutional Neural Network for Grading of Colorectal Cancer Histology Images

Digital histology images are amenable to the application of convolutional neural networks (CNNs) for analysis due to the sheer size of pixel data present in them. CNNs are generally used for representation learning from small image patches (e.g. <inline-formula> <tex-math notation="LaTeX">$224\times 224$ </tex-math></inline-formula>) extracted from digital histology images due to computational and memory constraints. However, this approach does not incorporate high-resolution contextual information in histology images. We propose a novel way to incorporate a larger context by a context-aware neural network based on images with a dimension of <inline-formula> <tex-math notation="LaTeX">$1792\times 1792$ </tex-math></inline-formula> pixels. The proposed framework first encodes the local representation of a histology image into high dimensional features then aggregates the features by considering their spatial organization to make a final prediction. We evaluated the proposed method on two colorectal cancer datasets for the task of cancer grading. Our method outperformed the traditional patch-based approaches, problem-specific methods, and existing context-based methods. We also presented a comprehensive analysis of different variants of the proposed method.
Cyanoacetylene in the outflow/hot molecular core G331.512−0.103

Using APEX-1 and APEX-2 observations, we have detected and studied the rotational lines of the HC3N molecule (cyanoacetylene) in the powerful outflow/hot molecular core G331.512−0.103. We identified 31 rotational lines at J levels between 24 and 39; 17 of them in the ground vibrational state v = 0 (9 lines corresponding to the main C isotopologue and 8 lines corresponding to the 13C isotopologues), and 14 in the lowest vibrationally excited state v7 = 1. Using local thermodynamic equilibrium (LTE)-based population diagrams for the beam-diluted v = 0 transitions, we determined Texc = 85 ± 4 K and N(HC3N) = (6.9 ± 0.8) × 1014 cm−2, while for the beam-diluted v7 = 1 transitions we obtained Texc = 89 ± 10 K and N(HC3N) = (2 ± 1) × 1015 cm−2. Non-LTE calculations using H2 collision rates indicate that the HC3N emission is in good agreement with LTE-based results. From the non-LTE method, we estimated Tkin ≃90 K, n(H2) ≃ 2 × 107 cm−3 for a central core of 6 arcsec in size. A vibrational temperature in the range from 130 to 145 K was also determined, values which are very likely lower limits. Our results suggest that rotational transitions are thermalized, while infrared radiative pumping processes are probably more efficient than collisions in exciting the molecule to the vibrationally excited state v7 = 1. Abundance ratios derived under LTE conditions for the 13C isotopologues suggest that the main formation pathway of HC3N is C2H2 + CN → HC3N + H.
EXOFASTv2: A public, generalized, publication-quality exoplanet modeling code

We present the next generation public exoplanet fitting software, EXOFASTv2. It is capable of fitting an arbitrary number of planets, radial velocity data sets, astrometric data sets, and/or transits observed with any combination of wavelengths. We model the star simultaneously in the fit and provide several state-of-the-art ways to constrain its properties, including taking advantage of the now-ubiquitous all-sky catalog photometry and Gaia parallaxes. EXOFASTv2 can model the star by itself, too. Multi-planet systems are modeled self-consistently with the same underlying stellar mass that defines their semi-major axes through Kepler's law and the planetary period. Transit timing, duration, and depth variations can be modeled with a simple command line option. 
We explain our methodology and rationale as well as provide an improved version of the core transit model that is both 25\% faster and more accurate. We highlight several potential pitfalls in exoplanet modeling, including the handling of eccentricity in transit-only fits, that the standard exoplanet convention for $\omega$ uses a left-handed coordinate system, contrary to most modern textbooks, how to avoid an important degeneracy when allowing negative companion masses, and a widely unappreciated, potential 10-minute ambiguity in the reported transit times. 
EXOFASTv2 is available at this https URL . The code is written in IDL, and includes an executable that can be run freely and legally without an IDL license or any knowledge of the language. Extensive documentation and tutorials are included in the distribution for a variety of example fits. Advanced amateurs and undergrads have successfully performed sophisticated global fits of complex planetary systems with EXOFASTv2. It is therefore a powerful tool for education and outreach as well as the broader professional community.
The southern stellar stream spectroscopic survey (S5): Overview, target selection, data reduction, validation, and early science

We introduce the southern stellar stream spectroscopy survey (S5), an on-going program to map the kinematics and chemistry of stellar streams in the southern hemisphere. The initial focus of S5 has been spectroscopic observations of recently identified streams within the footprint of the dark energy survey (DES), with the eventual goal of surveying streams across the entire southern sky. Stellar streams are composed of material that has been tidally striped from dwarf galaxies and globular clusters and hence are excellent dynamical probes of the gravitational potential of the Milky Way, as well as providing a detailed snapshot of its accretion history. Observing with the 3.9 m Anglo-Australian Telescope’s 2-degree-Field fibre positioner and AAOmega spectrograph, and combining the precise photometry of DES DR1 with the superb proper motions from Gaia DR2, allows us to conduct an efficient spectroscopic survey to map these stellar streams. So far S5 has mapped nine DES streams and three streams outside of DES; the former are the first spectroscopic observations of these recently discovered streams. In addition to the stream survey, we use spare fibres to undertake a Milky Way halo survey and a low-redshift galaxy survey. This paper presents an overview of the S5 program, describing the scientific motivation for the survey, target selection, observation strategy, data reduction, and survey validation. Finally, we describe early science results on stellar streams and Milky Way halo stars drawn from the survey. Updates on S5, including future public data releases, can be found at http://s5collab.github.io.
Pseudo-evolution of galaxies in Λ CDM cosmology

Our knowledge about galaxy evolution comes from transforming observed galaxy properties at different redshifts to comoving physical scales. This transformation depends on using a cosmological model. Here, the effects of unintentional mixing of two different cosmological models on the size evolution of galaxies is studied. As a gedanken experiment, a galaxy of fixed proper size and luminosity is moved across different redshifts. The apparent size of this galaxy is then interpreted with a cosmological model presumed by the observer, which is different compared to the cosmology exhibited by the Universe. In such a case, a spurious size evolution of the galaxy is observed. A galaxy behaving according to the Rh = ct and Neumann’s cosmology, when interpreted with the Λ cold dark matter (ΛCDM) cosmological model, shows an increase in size by a factor of 1.1 and 1.3 from $z$ = 7.5 to ≈ 0, respectively. The apparent size of a galaxy in a static Euclidean cosmology, when interpreted in the ΛCDM model, shows a factor of 23.8 increase in size between $z$ = 7.5 to ≈ 0. This is in close agreement with the observational data with a size increase of a factor of 6.8 between $z$ = 3.2 to ≈ 0. Furthermore, using the apparent size data, it is shown that the difference between the derived proper sizes in Rh = ct, Neumann’s and ΛCDM cosmological models are minimal.
Free-form Grale reconstruction of Abell 2744: robustness of uncertainties against changes in lensing data

Abell 2744, a massive Hubble Frontier Fields merging galaxy cluster with many multiple images in the core has been the subject of many lens inversions using different methods. While most existing studies compare various inversion methods, we focus on a comparison of reconstructions that use different input lensing data. Since the quantity and quality of lensing data is constantly improving, it makes sense to ask if the estimated uncertainties are robust against changes in the data. We address this question using free-form Grale, which takes only image information as input, and nothing pertaining to cluster galaxies. We reconstruct Abell 2744 using two sets of strong lensing data from the Hubble Frontier Fields community. Our first and second reconstructions use 55 and 91 images, respectively, and only 10 of the 91 images have the same positions and redshifts as in the first reconstruction. Comparison of the two mass maps shows that Grale uncertainties are robust against these changes, as well as small modifications in the inversion routine. Additionally, applying the methods used in Sebesta et al. (2016) for MACS J0416, we conclude that, in a statistical sense, light follows mass in Abell 2744, with brighter galaxies clustering stronger with the recovered mass than the fainter ones. We also show that the faintest galaxies are anti-correlated with mass, which is likely the result of light contamination from bright galaxies, and lensing magnification bias acting on galaxies background to the cluster.
IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification

Deep learning models have achieved huge success in numerous fields, such as computer vision and natural language processing. However, unlike such fields, it is hard to apply traditional deep learning models on the graph data due to the 'node-orderless' property. Normally, adjacency matrices will cast an artificial and random node-order on the graphs, which renders the performance of deep models on graph classification tasks extremely erratic, and the representations learned by such models lack clear interpretability. To eliminate the unnecessary node-order constraint, we propose a novel model named Isomorphic Neural Network (IsoNN), which learns the graph representation by extracting its isomorphic features via the graph matching between input graph and templates. IsoNN has two main components: graph isomorphic feature extraction component and classification component. The graph isomorphic feature extraction component utilizes a set of subgraph templates as the kernel variables to learn the possible subgraph patterns existing in the input graph and then computes the isomorphic features. A set of permutation matrices is used in the component to break the node-order brought by the matrix representation. Three fully-connected layers are used as the classification component in IsoNN. Extensive experiments are conducted on benchmark datasets, the experimental results can demonstrate the effectiveness of ISONN, especially compared with both classic and state-of-the-art graph classification methods.
The bahamas project: effects of a running scalar spectral index on large-scale structure

Recent analyses of the cosmic microwave background (CMB) and the Lyman α forest indicate a mild preference for a deviation from a power-law primordial matter power spectrum (a so-called running). We introduce an extension to the bahamas suite of simulations to explore the effects that a running scalar spectral index has on large-scale structure (LSS), using Planck CMB constraints to initialize the simulations. We focus on five key statistics: (i) the non-linear matter power spectrum (ii) the halo mass function; (iii) the halo two-point autocorrelation function; (iv) total mass halo density profiles; and (v) the halo concentration–mass relation. We find that the matter power spectrum in a Planck-constrained running cosmology is affected on all k-scales examined in this study. These effects on the matter power spectrum should be detectable with upcoming surveys such as LSST and Euclid. A positive running cosmology leads to an increase in the mass of galaxy groups and clusters, with the favoured negative running leading to a decrease in mass of lower mass ($M \lesssim 10^{13} \, \textrm{M}_{\odot }$) haloes, but an increase for the most massive ($M \gtrsim 10^{13} \, \textrm{M}_{\odot }$) haloes. Changes in the mass are generally confined to $5\rm {-}10{{\ \rm per\ cent}}$ which, while not insignificant, cannot by itself reconcile the claimed tension between the primary CMB and cluster number counts. We also demonstrate that the observed effects on LSS due to a running scalar spectral index are separable from those of baryonic effects to typically a few per cent precision.
Pure Lovelock black holes in dimensions 
d=3N+1
 are stable

In this paper we show that pure Lovelock static Schwarzschild's analogue black hole in dimensions $d>3N+1$, where $N$ is the degree of Lovelock polynomial action, is stable even though pure Gauss-Bonnet $N=2$ black hole is unstable in dimension $d<7$. We also discuss and compare quasinormal modes for pure Lovelock and the corresponding Einstein black hole in the same dimension. We find that perturbations decay with characteristic time which is weakly dimensional dependent as it depends only on the gravitational potential of the background solution, while frequency of oscillations however depend on the dimension. Also we show that spectrum of perturbations is not isospectral except in $d=4$.