nils-herrmann/clean_scirepeval huggingface dataset:
Many-body perturbation theory calculations using the yambo code

yambo is an open source project aimed at studying excited state properties of condensed matter systems from first principles using many-body methods. As input, yambo requires ground state electronic structure data as computed by density functional theory codes such as Quantum ESPRESSO and Abinit. yambo’s capabilities include the calculation of linear response quantities (both independent-particle and including electron–hole interactions), quasi-particle corrections based on the GW formalism, optical absorption, and other spectroscopic quantities. Here we describe recent developments ranging from the inclusion of important but oft-neglected physical effects such as electron–phonon interactions to the implementation of a real-time propagation scheme for simulating linear and non-linear optical properties. Improvements to numerical algorithms and the user interface are outlined. Particular emphasis is given to the new and efficient parallel structure that makes it possible to exploit modern high performance computing architectures. Finally, we demonstrate the possibility to automate workflows by interfacing with the yambopy and AiiDA software tools.
12 YEARS OF DATA, RESULTS AND EXPERIENCES IN THE EUROPEAN RESEARCHERS’ NIGHT PROJECT

Since twelve years the researchers of the Roman area organize the European Researchers' Night, a project funded by European Commission to discover science and meet researchers through an Europe-wide public event dedicated to fun learning. Since the first edition in 2006, when the National Laboratory of INFN's in Frascati hosted 4000 visitors the project is always grown up to 50,000 attendees and more than 50 scientific partners. In the last edition, The Frascati Scienza association, which was born to coordinate the event, operated in over 30 Italian cities from south to north of the peninsula. In addition, the Made in Science project - European Research Week 2016/17 - has been the one of the largest project funded by the European Commission and it's often referred to as a model for organization and communication to the general public. The 12 yeas data collected and results obtained, as well as some of the most important experiences in public communication of science will be shown.
Reconstructing dynamical networks via feature ranking

Empirical data on real complex systems are becoming increasingly available. Parallel to this is the need for new methods of reconstructing (inferring) the structure of networks from time-resolved observations of their node-dynamics. The methods based on physical insights often rely on strong assumptions about the properties and dynamics of the scrutinized network. Here, we use the insights from machine learning to design a new method of network reconstruction that essentially makes no such assumptions. Specifically, we interpret the available trajectories (data) as "features" and use two independent feature ranking approaches-Random Forest and RReliefF-to rank the importance of each node for predicting the value of each other node, which yields the reconstructed adjacency matrix. We show that our method is fairly robust to coupling strength, system size, trajectory length, and noise. We also find that the reconstruction quality strongly depends on the dynamical regime.
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning

The posteriors over neural network weights are high dimensional and multimodal. Each mode typically characterizes a meaningfully different representation of the data. We develop Cyclical Stochastic Gradient MCMC (SG-MCMC) to automatically explore such distributions. In particular, we propose a cyclical stepsize schedule, where larger steps discover new modes, and smaller steps characterize each mode. We also prove non-asymptotic convergence of our proposed algorithm. Moreover, we provide extensive experimental results, including ImageNet, to demonstrate the scalability and effectiveness of cyclical SG-MCMC in learning complex multimodal distributions, especially for fully Bayesian inference with modern deep neural networks.
Improving Generalization and Stability of Generative Adversarial Networks

Generative Adversarial Networks (GANs) are one of the most popular tools for learning complex high dimensional distributions. However, generalization properties of GANs have not been well understood. In this paper, we analyze the generalization of GANs in practical settings. We show that discriminators trained on discrete datasets with the original GAN loss have poor generalization capability and do not approximate the theoretically optimal discriminator. We propose a zero-centered gradient penalty for improving the generalization of the discriminator by pushing it toward the optimal discriminator. The penalty guarantees the generalization and convergence of GANs. Experiments on synthetic and large scale datasets verify our theoretical analysis.
Fast consensus clustering in complex networks

Algorithms for community detection are usually stochastic, leading to different partitions for different choices of random seeds. Consensus clustering has proven to be an effective technique to derive more stable and accurate partitions than the ones obtained by the direct application of the algorithm. However, the procedure requires the calculation of the consensus matrix, which can be quite dense if (some of) the clusters of the input partitions are large. Consequently, the complexity can get dangerously close to quadratic, which makes the technique inapplicable on large graphs. Here, we present a fast variant of consensus clustering, which calculates the consensus matrix only on the links of the original graph and on a comparable number of additional node pairs, suitably chosen. This brings the complexity down to linear, while the performance remains comparable as the full technique. Therefore, our fast consensus clustering procedure can be applied on networks with millions of nodes and links.
Unifying the Clifford hierarchy via symmetric matrices over rings

The Clifford hierarchy is a foundational concept for universal quantum computation (UQC). It was introduced to show that UQC can be realized via quantum teleportation, given access to certain standard resources. While the full structure of the hierarchy is still not understood, Cui et al.  recently described the structure of diagonal unitaries in the hierarchy. They considered diagonal gates whose action on a computational basis qudit state is described by a $2^k$-th root of unity raised to a polynomial function of the state, and they established the level of such unitaries in the hierarchy. For qubit systems, we consider $k$-th level diagonal gates that can be described just by quadratic forms of the state over the ring $\mathbb{Z}_{2^k}$ of integers mod $2^k$. These involve symmetric matrices over $\mathbb{Z}_{2^k}$ that can be used to efficiently describe all $2$-local and certain higher locality diagonal gates in the hierarchy. We also provide explicit algebraic descriptions of their action on Pauli matrices, which establishes a natural recursion to diagonal gates from lower levels. This involves symplectic matrices over $\mathbb{Z}_{2^k}$ and hence our perspective unifies these gates with the binary symplectic framework for Clifford gates. We augment our description with simple examples for certain standard gates. In addition to demonstrating structure, these formulas might prove useful in applications such as (i) classical simulation of quantum circuits, especially via the stabilizer rank approach, (ii) synthesis of logical non-Clifford unitaries, specifically alternatives to magic state distillation, and (iii) decomposition of arbitrary unitaries beyond the Clifford+$T$ set of gates, perhaps leading to shorter depth circuits. Our results suggest that some non-diagonal gates might be understood by generalizing other binary symplectic matrices to integer rings.
Search for dark matter with a 231-day exposure of liquid argon using DEAP-3600 at SNOLAB

DEAP-3600 is a single-phase liquid argon (LAr) direct-detection dark matter experiment, operating 2 km underground at SNOLAB (Sudbury, Canada). The detector consists of 3279 kg of LAr contained in a spherical acrylic vessel. This paper reports on the analysis of a 758  tonne⋅day exposure taken over a period of 231 live-days during the first year of operation. No candidate signal events are observed in the WIMP-search region of interest, which results in the leading limit on the WIMP-nucleon spin-independent cross section on a LAr target of 3.9×10−45  cm2 (1.5×10−44  cm2) for a 100  GeV/c2 (1  TeV/c2) WIMP mass at 90% C.L. In addition to a detailed background model, this analysis demonstrates the best pulse-shape discrimination in LAr at threshold, employs a Bayesian photoelectron-counting technique to improve the energy resolution and discrimination efficiency, and utilizes two position reconstruction algorithms based on the charge and photon detection time distributions observed in each photomultiplier tube.
Iteratively Reweighted Penalty Alternating Minimization Methods with Continuation for Image Deblurring

In this paper, we consider a class of nonconvex problems with linear constraints appearing frequently in the area of image processing. We solve this problem by the penalty method and propose the iteratively reweighted alternating minimization algorithm. To speed up the algorithm, we also apply the continuation strategy to the penalty parameter. A convergence result is proved for the algorithm. Compared with the nonconvex ADMM, the proposed algorithm enjoys both theoretical and computational advantages like weaker convergence requirements and faster speed. Numerical results demonstrate the efficiency of the proposed algorithm.
Adversarial Generation of Time-Frequency Features with application in audio synthesis

Time-frequency (TF) representations provide powerful and intuitive features for the analysis of time series such as audio. But still, generative modeling of audio in the TF domain is a subtle matter. Consequently, neural audio synthesis widely relies on directly modeling the waveform and previous attempts at unconditionally synthesizing audio from neurally generated invertible TF features still struggle to produce audio at satisfying quality. In this article, focusing on the short-time Fourier transform, we discuss the challenges that arise in audio synthesis based on generated invertible TF features and how to overcome them. We demonstrate the potential of deliberate generative TF modeling by training a generative adversarial network (GAN) on short-time Fourier features. We show that by applying our guidelines, our TF-based network was able to outperform a state-of-the-art GAN generating waveforms directly, despite the similar architecture in the two networks.
BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model

We show that BERT (Devlin et al., 2018) is a Markov random field language model. This formulation gives way to a natural procedure to sample sentences from BERT. We generate from BERT and find that it can produce high quality, fluent generations. Compared to the generations of a traditional left-to-right language model, BERT generates sentences that are more diverse but of slightly worse quality.
Psi-Net: Shape and boundary aware joint multi-task deep network for medical image segmentation

Image segmentation is a primary task in many medical applications. Recently, many deep networks derived from U-Net has been extensively used in various medical image segmentation tasks. However, in most of the cases, networks similar to U-net produce coarse and non-smooth segmentations with lots of discontinuities. To improve and refine the performance of U-Net like networks, we propose the use of parallel decoders which along with performing the mask predictions also perform contour prediction and distance map estimation. The contour and distance map aid in ensuring smoothness in the segmentation predictions. To facilitate joint training of three tasks, we propose a novel architecture called Psi-Net with a single encoder and three parallel decoders (thus having a shape of Ψ), one decoder to learn the segmentation mask prediction and other two decoders to learn the auxiliary tasks of contour detection and distance map estimation. The learning of these auxiliary tasks helps in capturing the shape and the boundary information. We also propose a new joint loss function for the proposed architecture. The loss function consists of a weighted combination of Negative Log Likelihood and Mean Square Error loss. We have used two publicly available datasets: 1) Origa dataset for the task of optic cup and disc segmentation and 2) Endovis segment dataset for the task of polyp segmentation to evaluate our model. We have conducted extensive experiments using our network to show our model gives better results in terms of segmentation, boundary and shape metrics.
Bag of Freebies for Training Object Detection Neural Networks

Training heuristics greatly improve various image classification model accuracies~\cite{he2018bag}. Object detection models, however, have more complex neural network structures and optimization targets. The training strategies and pipelines dramatically vary among different models. In this works, we explore training tweaks that apply to various models including Faster R-CNN and YOLOv3. These tweaks do not change the model architectures, therefore, the inference costs remain the same. Our empirical results demonstrate that, however, these freebies can improve up to 5% absolute precision compared to state-of-the-art baselines.
Statistical Model Checking for Probabilistic Hyperproperties

In this paper, we propose the temporal logic HyperPCTL$^*$ that extends PCTL$^*$ and HyperPCTL to reason about probabilistic hyperproperties. It allows expressing probabilistic hyperproperties with nested temporal and probability operators. We show that HyperPCTL can express important probabilistic information-flow security policies. Furthermore, for the first time, we investigate statistical model checking (SMC) algorithms for HyperPCTL$^*$ specifications in discrete-time Markov chains (DTMC). To this end, we first study SMC for HyperPCTL$^*$ specifications with non-nested probability operators for a desired confidence or significance level. Unlike existing SMC algorithms which are based on sequential probability ratio tests (SPRT), we use the Clopper-Pearson confidence interval to avoid the need of a priori knowledge on the indifference margin. Then, we extend the proposed SMC algorithms to HyperPCTL$^*$ specifications with multiple probability operators that are nested in different ways. Finally, we evaluate the proposed algorithms on two examples, dining cryptographers and probabilistic causation.
On the Convergence of Network Systems

The apparent disconnection between the microscopic and the macroscopic is a major issue in the understanding of complex systems. To this extend, we study the convergence of repeatedly applying local rules on a network, and touch on the expressive power of this model. We look at network systems and study their behavior when different types of local rules are applied on them. For a very general class of local rules, we prove convergence and provide a certain member of this class that, when applied on a graph, efficiently computes its k-core and its (k-1)-crust giving hints on the expressive power of such a model. Furthermore, we provide guarantees on the speed of convergence for an important subclass of the aforementioned class. We also study more general rules, and show that they do not converge. Our counterexamples resolve an open question of (Zhang, Wang, Wang, Zhou, KDD- 2009) as well, concerning whether a certain process converges. Finally, we show the universality of our network system, by providing a local rule under which it is Turing-Complete.
Degeneracy loci, virtual cycles and nested Hilbert schemes, I

We express nested Hilbert schemes of points and curves on a smooth projective surface as "virtual resolutions" of degeneracy loci of maps of vector bundles on smooth ambient spaces. 
We show how to modify the resulting obstruction theories to produce the virtual cycles of Vafa-Witten theory and other sheaf-counting problems. The result is an effective way of calculating invariants (VW, SW, local PT and local DT) via Thom-Porteous-like Chern class formulae.
Max-C and Min-D Projection Autoassociative Fuzzy Morphological Memories: Theory and Applications for Face Recognition

C and min-D projection autoassociative fuzzy morphological memories (max-C and min-D PAFMMs) are two layer feedforward fuzzy morphological neural networks able to implement an associative memory designed for the storage and retrieval of finite fuzzy sets or vectors on a hypercube. In this paper we address the main features of these autoassociative memories, which include unlimited absolute storage capacity, fast retrieval of stored items, few spurious memories, and an excellent tolerance to either dilative noise or erosive noise. Particular attention is given to the so-called PAFMM of Zadeh which, besides performing no floating-point operations, exhibit the largest noise tolerance among max-C and min-D PAFMMs. Computational experiments reveal that Zadeh's max-C PFAMM, combined with a noise masking strategy, yields a fast and robust classifier with strong potential for face recognition.
Evaluation of Transfer Learning for Classification of: (1) Diabetic Retinopathy by Digital Fundus Photography and (2) Diabetic Macular Edema, Choroidal Neovascularization and Drusen by Optical Coherence Tomography

Deep learning has been successfully applied to a variety of image classification tasks. There has been keen interest to apply deep learning in the medical domain, particularly specialties that heavily utilize imaging, such as ophthalmology. One issue that may hinder application of deep learning to the medical domain is the vast amount of data necessary to train deep neural networks (DNNs). Because of regulatory and privacy issues associated with medicine, and the generally proprietary nature of data in medical domains, obtaining large datasets to train DNNs is a challenge, particularly in the ophthalmology domain. 
Transfer learning is a technique developed to address the issue of applying DNNs for domains with limited data. Prior reports on transfer learning have examined custom networks to fully train or used a particular DNN for transfer learning. However, to the best of my knowledge, no work has systematically examined a suite of DNNs for transfer learning for classification of diabetic retinopathy, diabetic macular edema, and two key features of age-related macular degeneration. This work attempts to investigate transfer learning for classification of these ophthalmic conditions. Part I gives a condensed overview of neural networks and the DNNs under evaluation. Part II gives the reader the necessary background concerning diabetic retinopathy and prior work on classification using retinal fundus photographs. The methodology and results of transfer learning for diabetic retinopathy classification are presented, showing that transfer learning towards this domain is feasible, with promising accuracy. Part III gives an overview of diabetic macular edema, choroidal neovascularization and drusen (features associated with age-related macular degeneration), and presents results for transfer learning evaluation using optical coherence tomography to classify these entities.
Stochastic Reinforcement Learning

In reinforcement learning episodes, the rewards and punishments are often non-deterministic, and there are invariably stochastic elements governing the underlying situation. Such stochastic elements are often numerous and cannot be known in advance, and they have a tendency to obscure the underlying rewards and punishments patterns. Indeed, if stochastic elements were absent, the same outcome would occur every time and the learning problems involved could be greatly simplified. In addition, in most practical situations, the cost of an observation to receive either a reward or punishment can be significant, and one would wish to arrive at the correct learning conclusion by incurring minimum cost. In this paper, we present a stochastic approach to reinforcement learning which explicitly models the variability present in the learning environment and the cost of observation. Criteria and rules for learning success are quantitatively analyzed, and probabilities of exceeding the observation cost bounds are also obtained.
Performance Dynamics and Termination Errors in Reinforcement Learning – A Unifying Perspective

In reinforcement learning, a decision needs to be made at some point as to whether it is worthwhile to carry on with the learning process or to terminate it. In many such situations, stochastic elements are often present which govern the occurrence of rewards, with the sequential occurrences of positive rewards randomly interleaved with negative rewards. For most practical learners, the learning is considered useful if the number of positive rewards always exceeds the negative ones. A situation that often calls for learning termination is when the number of negative rewards exceeds the number of positive rewards. However, while this seems reasonable, the error of premature termination, whereby termination is enacted along with the conclusion of learning failure despite the positive rewards eventually far outnumber the negative ones, can be significant. In this paper, using combinatorial analysis we study the error probability in wrongly terminating a reinforcement learning activity which undermines the effectiveness of an optimal policy, and we show that the resultant error can be quite high. Whilst we demonstrate mathematically that such errors can never be eliminated, we propose some practical mechanisms that can effectively reduce such errors. Simulation experiments have been carried out, the results of which are in close agreement with our theoretical findings.
Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifolds

Dictionary leaning (DL) and dimensionality reduction (DR) are powerful tools to analyze high-dimensional noisy signals. This paper presents a proposal of a novel Riemannian joint dimensionality reduction and dictionary learning (R-JDRDL) on symmetric positive definite (SPD) manifolds for classification tasks. The joint learning considers the interaction between dimensionality reduction and dictionary learning procedures by connecting them into a unified framework. We exploit a Riemannian optimization framework for solving DL and DR problems jointly. Finally, we demonstrate that the proposed R-JDRDL outperforms existing state-of-the-arts algorithms when used for image classification tasks.
Test of the Einstein Equivalence Principle near the Galactic Center Supermassive Black Hole.

During its orbit around the four million solar mass black hole Sagittarius A* the star S2 experiences significant changes in gravitational potential. We use this change of potential to test one part of the Einstein equivalence principle: the local position invariance (LPI). We study the dependency of different atomic transitions on the gravitational potential to give an upper limit on violations of the LPI. This is done by separately measuring the redshift from hydrogen and helium absorption lines in the stellar spectrum during its closest approach to the black hole. For this measurement we use radial velocity data from 2015 to 2018 and combine it with the gravitational potential at the position of S2, which is calculated from the precisely known orbit of S2 around the black hole. This results in a limit on a violation of the LPI of |β_{He}-β_{H}|=(2.4±5.1)×10^{-2}. The variation in potential that we probe with this measurement is six magnitudes larger than possible for measurements on Earth, and a factor of 10 larger than in experiments using white dwarfs. We are therefore testing the LPI in a regime where it has not been tested before.