nils-herrmann/clean_scirepeval huggingface dataset:
Networks of Power: Analyzing World Leaders Interactions on Social Media

World leaders have been increasingly using social media platforms as a tool for political communication. However, despite the growing research on governmental accounts on social media, virtually nothing is known about interactions among world leaders. Using a novel, cross-national dataset of Twitter communication for leaders of 193 countries for the period of 2012-2017, we construct retweet and mention networks to explore the patterns of leaders communication. We use social network analysis to conclude that the leaders interactions on social media closely resemble their interactions in the offline world. Besides, consistent with the democratic peace theory that underscores a special connection between democracies, we identify political regime as the main predictor of clustering between countries on Twitter. Finally, we explore the patterns of the leaders centrality to identify features that determine which leaders occupy more central positions in the network. Our findings yield new insights on how social media is used by government actors, and have important implications for our understanding of the impact of new technologies on the new forms of diplomacy.
Adaptive regression with Brownian path covariate

This paper deals with estimation with functional covariates. More precisely, we aim at estimating the regression function $m$ of a continuous outcome $Y$ against a standard Wiener coprocess $W$. Following Cadre and Truquet (2015) and Cadre, Klutchnikoff, and Massiot (2017) the Wiener-It\^o decomposition of $m(W)$ is used to construct a family of estimators. The minimax rate of convergence over specific smoothness classes is obtained. A data-driven selection procedure is defined following the ideas developed by Goldenshluger and Lepski (2011). An oracle-type inequality is obtained which leads to adaptive results.
Quantum plasmons and intraband excitons in doped nanoparticles: Failure of the Tamm-Dancoff approximation and importance of electron-hole attraction

We use excited-state quantum chemistry techniques to investigate the intraband absorption of doped semiconductor nanoparticles as a function of doping density, nanoparticle radius, and material properties. The excess electrons are modeled as interacting particles confined in a sphere. We compare the predictions of various single-excitation theories, including time-dependent Hartree-Fock, the random-phase approximation, and configuration interaction with single excitations. We find that time-dependent Hartree-Fock most accurately describes the character of the excitation, as compared to equation-of-motion coupled-cluster theory with single and double excitations. The excitation evolves from confinement-dominated, to excitonic, to plasmonic with increasing number of electrons at fixed density, and the threshold number of electrons to produce a plasmon increases with density due to quantum confinement. Exchange integrals (attractive electron-hole interactions) are essential to properly describe excitons, and de-excitations (i.e.~avoidance of the Tamm-Dancoff approximation) are essential to properly describe plasmons. We propose a schematic model whose analytic solutions closely reproduce our numerical calculations. Our results are in good agreement with experimental spectra of doped ZnO nanoparticles at a doping density of $1.4\times 10^{20}$ cm$^{-3}$.
Deep Neural Network Symbol Detection for Millimeter Wave Communications

This paper proposes to use a deep neural network (DNN)- based symbol detector for mmWave systems such that channel state information (CSI) acquisition can be bypassed. In particular, we consider a sliding bidirectional recurrent neural network (BRNN) architecture that is suitable for the long memory length of typical mmWave channels. The performance of the DNN detector is evaluated in comparison to that of the Viterbi detector. The results show that the performance of the DNN detector is close to that of the optimal Viterbi detector with perfect CSI, and that it outperforms the Viterbi algorithm with CSI estimation error. Further experiments show that the DNN detector is robust to a wide range of noise levels and varying channel conditions, and that a pretrained detector can be reliably applied to different mmWave channel realizations with minimal overhead.
DEAM: Adaptive Momentum with Discriminative Weight for Stochastic Optimization

Optimization algorithms with momentum, e.g., (ADAM) helps accelerate SGD in parameter updating, which can minify the oscillations of parameters update route. However, the fixed momentum weight (e.g., $\beta_{1}$ in ADAM) will propagate errors in momentum computing. Besides, such a hyperparameter can be extremely hard to tune in applications. In this paper, we introduce a novel optimization algorithm, namely Discriminative wEight on Adaptive Momentum (DEAM). DEAM proposes to compute the momentum weight automatically based on the discriminative angle. The momentum term weight will be assigned with an appropriate value which configures the influence of momentum in the current step. In addition, DEAM also contains a novel backtrack term, which restricts redundant updates when the correction of the last step is needed. The backtrack term can effectively adapt the learning rate and achieve the anticipatory update as well. Extensive experiments demonstrate that DEAM can achieve a faster convergence rate than the existing optimization algorithms in training various models. .
Towards Scalable Gaussian Process Modeling

Numerous engineering problems of interest to the industry are often characterized by expensive black-box objective function evaluations. These objective functions could be physical experiments or computer simulations. Obtaining a comprehensive idea of the problem and/or performing subsequent optimizations generally requires hundreds of thousands of evaluations of the objective function which is most often a practically unachievable task. Gaussian Process (GP) surrogate modeling replaces the expensive function with a cheap-to-evaluate data-driven probabilistic model. While the GP does not assume a functional form of the problem, it is defined by a set of parameters, called hyper-parameters, that need to be learned from the data. The hyperparameters define the characteristics of the objective function, such as smoothness, magnitude, periodicity, etc. Accurately estimating these hyperparameters is a key ingredient in developing a reliable and generalizable surrogate model. Markov chain Monte Carlo (MCMC) is a ubiquitously used Bayesian method to estimate these hyperparameters. At GEs Global Research Center, a customized industry-strength Bayesian hybrid modeling framework utilizing the GP, called GEBHM, has been employed and validated over many years. GEBHM is very effective on problems of small and medium size, typically less than 1000 training points. However, the GP does not scale well in time with a growing dataset and problem dimensionality which can be a major impediment in such problems. For some challenging industry applications, the predictive capability of the GP is required but each second during the training of the GP costs thousands of dollars. In this work, we apply a scalable MCMC-based methodology enabling the modeling of large-scale industry problems. Towards this, we extend and implement in GEBHM an Adaptive Sequential Monte Carlo (ASMC) methodology for training the GP. This implementation saves computational time (especially for large-scale problems) while not sacrificing predictability over the current MCMC implementation. We demonstrate the effectiveness and accuracy of GEBHM with ASMC on four mathematical problems and on two challenging industry applications of varying complexity.
An Open Petri Net Implementation of Gene Regulatory Networks

Gene regulatory network (GRN) plays a central role in system biology and genomics. It provides a promising way to model and study complex biological processes. Several computational methods have been developed for the construction and analysis of GRN. In particular, Petri net and its variants were introduced for GRN years ago. On the other hand, Petri net theory itself has been rapidly advanced recently. Especially noteworthy is the combination or treatment of Petri net with the mathematical framework of category theory (categorization), which endows Petri net with the power of abstraction and composability. Open Petri net is a state-of-art implementation of such "categorized" Petri nets. Applying open Petri net to GRN may potentially facilitate the modeling of large scale GRNs. In this manuscript, we took a shallow step towards that direction.
Graph Informer Networks for Molecules

In machine learning, chemical molecules are often represented by sparse high-dimensional vectorial fingerprints. However, a more natural mathematical object for molecule representation is a graph, which is much more challenging to handle from a machine learning perspective. In recent years, several deep learning architectures have been proposed to directly learn from the graph structure of chemical molecules, including graph convolution (Duvenaud et al., 2015) and graph gating networks (Li et al., 2015). Here, we introduce Graph Informer, a route-based multi-head attention mechanism inspired by transformer networks (Vaswani et al., 2017), which incorporates features for node pairs. We show empirically that the proposed method gives significant improvements over existing approaches in prediction tasks for 13C nuclear magnetic resonance spectra and for drug bioactivity. These results indicate that our method is well suited for both node-level and graph-level prediction tasks.
Probabilistic Approximate Logic and its Implementation in the Logical Imagination Engine

In spite of the rapidly increasing number of applications of machine learning in various domains, a principled and systematic approach to the incorporation of domain knowledge in the engineering process is still lacking and ad hoc solutions that are difficult to validate are still the norm in practice, which is of growing concern not only in mission-critical applications. 
In this note, we introduce Probabilistic Approximate Logic (PALO) as a logic based on the notion of mean approximate probability to overcome conceptual and computational difficulties inherent to strictly probabilistic logics. The logic is approximate in several dimensions. Logical independence assumptions are used to obtain approximate probabilities, but by averaging over many instances of formulas a useful estimate of mean probability with known confidence can usually be obtained. To enable efficient computational inference, the logic has a continuous semantics that reflects only a subset of the structural properties of classical logic, but this imprecision can be partly compensated by richer theories obtained by classical inference or other means. Computational inference, which refers to the construction of models and validation of logical properties, is based on Stochastic Gradient Descent (SGD) and Markov Chain Monte Carlo (MCMC) techniques and hence another dimension where approximations are involved. 
We also present the Logical Imagination Engine (LIME), a prototypical implementation of PALO based on TensorFlow. Albeit not limited to the biological domain, we illustrate its operation in a quite substantial bioinformatics machine learning application concerned with network synthesis and analysis in a recent DARPA project.
Image Enhancement by Recurrently-trained Super-resolution Network

We introduce a new learning strategy for image enhancement by recurrently training the same simple superresolution (SR) network multiple times. After initially training an SR network by using pairs of a corrupted low resolution (LR) image and an original image, the proposed method makes use of the trained SR network to generate new high resolution (HR) images with a doubled resolution from the original uncorrupted images. Then, the new HR images are downscaled to the original resolution, which work as target images for the SR network in the next stage. The newly generated HR images by the repeatedly trained SR network show better image quality and this strategy of training LR to mimic new HR can lead to a more efficient SR network. Up to a certain point, by repeating this process multiple times, better and better images are obtained. This recurrent leaning strategy for SR can be a good solution for downsizing convolution networks and making a more efficient SR network. To measure the enhanced image quality, for the first time in this area of super-resolution and image enhancement, we use VIQET MOS score which reflects human visual quality more accurately than the conventional MSE measure.
Camera Distance-Aware Top-Down Approach for 3D Multi-Person Pose Estimation From a Single RGB Image

Although significant improvement has been achieved recently in 3D human pose estimation, most of the previous methods only treat a single-person case. In this work, we firstly propose a fully learning-based, camera distance-aware top-down approach for 3D multi-person pose estimation from a single RGB image. The pipeline of the proposed system consists of human detection, absolute 3D human root localization, and root-relative 3D single-person pose estimation modules. Our system achieves comparable results with the state-of-the-art 3D single-person pose estimation models without any groundtruth information and significantly outperforms previous 3D multi-person pose estimation methods on publicly available datasets. The code is available in \footnote{\url{https://github.com/mks0601/3DMPPE_ROOTNET_RELEASE}}\textsuperscript{,}\footnote{\url{https://github.com/mks0601/3DMPPE_POSENET_RELEASE}}.
Doubly-Robust Lasso Bandit

Contextual multi-armed bandit algorithms are widely used in sequential decision tasks such as news article recommendation systems, web page ad placement algorithms, and mobile health. Most of the existing algorithms have regret proportional to a polynomial function of the context dimension, $d$. In many applications however, it is often the case that contexts are high-dimensional with only a sparse subset of size $s_0 (\ll d)$ being correlated with the reward. We consider the stochastic linear contextual bandit problem and propose a novel algorithm, namely the Doubly-Robust Lasso Bandit algorithm, which exploits the sparse structure of the regression parameter as in Lasso, while blending the doubly-robust technique used in missing data literature. The high-probability upper bound of the regret incurred by the proposed algorithm does not depend on the number of arms and scales with $\mathrm{log}(d)$ instead of a polynomial function of $d$. The proposed algorithm shows good performance when contexts of different arms are correlated and requires less tuning parameters than existing methods.
Adaptive Compressed Sensing MRI with Unsupervised Learning

In compressed sensing MRI, k-space measurements are under-sampled to achieve accelerated scan times. There are two fundamental problems in compressed sensing MRI: (1) where to sample and (2) how to reconstruct. In this paper, we tackle both problems simultaneously, using a novel unsupervised, end-to-end learning framework, called LOUPE. Our method trains a neural network model on a set of full-resolution MRI scans, which are retrospectively under-sampled and forwarded to an anti-aliasing model that computes a reconstruction, which is in turn compared with the input. In our experiments, we demonstrate that LOUPE-optimized under-sampling masks are data-dependent, varying significantly with the imaged anatomy, and perform well with different reconstruction methods. We present empirical results obtained with a large-scale, publicly available knee MRI dataset, where LOUPE offered the most superior reconstruction quality across different conditions. Even with an aggressive 8-fold acceleration rate, LOUPE's reconstructions contained much of the anatomical detail that was missed by alternative masks and reconstruction methods. Our experiments also show how LOUPE yielded optimal under-sampling patterns that were significantly different for brain vs knee MRI scans. Our code is made freely available at this https URL.
Time-Inconsistency with Rough Volatility

Motivated by recent advances in rough volatility, this paper investigates the impact of roughness on equilibrium feedback strategies for time-inconsistent objectives. Under a general framework embracing non-Markovian and non-semimartingale models, we develop an extended path-dependent Hamilton-Jacobi-Bellman (PHJB) equation system. A verification theorem is provided. By deriving explicit solutions to three problems, including mean-variance portfolio problem (MVP) with constant risk aversion, MVP for log-returns, and an investment/consumption problem with non-exponential discounting, we present that volatility roughness adjusts the equilibrium strategies considerably, up to 40% in certain settings. Since rough volatility models capture the near-term downside risk by fitting the volatility skews, we interpret the adjustments as a hedge for this risk.
Interaction mechanisms quantified from dynamical features of frog choruses

We employ a mathematical model (a phase oscillator model) to describe the deterministic and stochastic features of frog choruses in which male frogs attempt to avoid call overlaps. The mathematical model with a general interaction term is identified using a Bayesian approach, and it qualitatively reproduces the stationary and dynamical features of the empirical data. In addition, we quantify the magnitude of attention paid among the male frogs from the identified model, and then analyse the relationship between attention and behavioural parameters using a statistical approach. Our analysis demonstrates a negative correlation between attention and inter-frog distance, and also suggests a behavioural strategy in which male frogs selectively attend to a less attractive male frog (i.e. a male producing calls at longer intervals) in order to more effectively advertise their superior relative attractiveness to females.
Dual Invertible Polynomials with Permutation Symmetries and the Orbifold Euler Characteristic

P. Berglund, T. Hubsch, and M. Henningson proposed a method to construct mirror symmetric Calabi-Yau manifolds. They considered a pair consisting of an invertible polynomial and of a finite (abelian) group of its diagonal symmetries together with a dual pair. A. Takahashi suggested a method to generalize this construction to symmetry groups generated by some diagonal symmetries and some permutations of variables. In a previous paper, we explained that this construction should work only under a special condition on the permutation group called parity condition (PC). Here we prove that, if the permutation group is cyclic and satisfies PC, then the reduced orbifold Euler characteristics of the Milnor fibres of dual pairs coincide up to sign.
Convergence of optimal expected utility for a sequence of discrete‐time markets

We examine Kreps' conjecture that optimal expected utility in the classic Black–Scholes–Merton (BSM) economy is the limit of optimal expected utility for a sequence of discrete‐time economies that “approach” the BSM economy in a natural sense: The nth discrete‐time economy is generated by a scaled n‐step random walk, based on an unscaled random variable ζ with mean 0, variance 1, and bounded support. We confirm Kreps' conjecture if the consumer's utility function U has asymptotic elasticity strictly less than one, and we provide a counterexample to the conjecture for a utility function U with asymptotic elasticity equal to 1, for ζ such that E[ζ3]>0 .
Semantic Deep Intermodal Feature Transfer: Transferring Feature Descriptors Between Imaging Modalities

Under difficult environmental conditions, the view of RGB cameras may be restricted by fog, dust or difficult lighting situations. Because thermal cameras visualize thermal radiation, they are not subject to the same limitations as RGB cameras. However, because RGB and thermal imaging differ significantly in appearance, common, state-of-the-art feature descriptors are unsuitable for intermodal feature matching between these imaging modalities. As a consequence, visual maps created with an RGB camera can currently not be used for localization using a thermal camera. In this paper, we introduce the Semantic Deep Intermodal Feature Transfer (Se-DIFT), an approach for transferring image feature descriptors from the visual to the thermal spectrum and vice versa. For this purpose, we predict potential feature appearance in varying imaging modalities using a deep convolutional encoder-decoder architecture in combination with a global feature vector. Since the representation of a thermal image is not only affected by features which can be extracted from an RGB image, we introduce the global feature vector which augments the auto encoder's coding. The global feature vector contains additional information about the thermal history of a scene which is automatically extracted from external data sources. By augmenting the encoder's coding, we decrease the L1 error of the prediction by more than 7% compared to the prediction of a traditional U-Net architecture. To evaluate our approach, we match image feature descriptors detected in RGB and thermal images using Se-DIFT. Subsequently, we make a competitive comparison on the intermodal transferability of SIFT, SURF, and ORB features using our approach.
An Information-theoretic On-line Learning Principle for Specialization in Hierarchical Decision-Making Systems

Information-theoretic bounded rationality describes utility-optimizing decision-makers whose limited information-processing capabilities are formalized by information constraints. One of the consequences of bounded rationality is that resource-limited decision-makers can join together to solve decision-making problems that are beyond the capabilities of each individual. Here, we study an information-theoretic principle that drives division of labor and specialization when decision-makers with information constraints are joined together. We devise an on-line learning rule of this principle that learns a partitioning of the problem space such that it can be solved by specialized linear policies. We demonstrate the approach for decision-making problems whose complexity exceeds the capabilities of individual decision-makers, but can be solved by combining the decision-makers optimally. The strength of the model is that it is abstract and principled, yet has direct applications in classification, regression, reinforcement learning and adaptive control.