nils-herrmann/clean_scirepeval huggingface dataset:
The Extended Kalman Filter is a Natural Gradient Descent in Trajectory Space

The extended Kalman filter is perhaps the most standard tool to estimate in real time the state of a dynamical system from noisy measurements of some function of the system, with extensive practical applications (such as position tracking via GPS). While the plain Kalman filter for linear systems is well-understood, the extended Kalman filter relies on linearizations which have been debated. 
We recover the exact extended Kalman filter equations from first principles in statistical learning: the extended Kalman filter is equal to Amari's online natural gradient, applied in the space of trajectories of the system. Namely, each possible trajectory of the dynamical system defines a probability law over possible observations. In principle this makes it possible to treat the underlying trajectory as the parameter of a statistical model of the observations. Then the parameter can be learned by gradient ascent on the log-likelihood of observations, as they become available. Using Amari's natural gradient from information geometry (a gradient descent preconditioned with the Fisher matrix, which provides parameterization-invariance) exactly recovers the extended Kalman filter. 
This applies only to a particular choice of process noise in the Kalman filter, namely, taking noise proportional to the posterior covariance - a canonical choice in the absence of specific model information.
Feature reinforcement with word embedding and parsing information in neural TTS

In this paper, we propose a feature reinforcement method under the sequence-to-sequence neural text-to-speech (TTS) synthesis framework. The proposed method utilizes the multiple input encoder to take three levels of text information, i.e., phoneme sequence, pre-trained word embedding, and grammatical structure of sentences from parser as the input feature for the neural TTS system. The added word and sentence level information can be viewed as the feature based pre-training strategy, which clearly enhances the model generalization ability. The proposed method not only improves the system robustness significantly but also improves the synthesized speech to near recording quality in our experiments for out-of-domain text.
Efficient Evolutionary Methods for Game Agent Optimisation: Model-Based is Best

This paper introduces a simple and fast variant of Planet Wars as a test-bed for statistical planning based Game AI agents, and for noisy hyper-parameter optimisation. Planet Wars is a real-time strategy game with simple rules but complex game-play. The variant introduced in this paper is designed for speed to enable efficient experimentation, and also for a fixed action space to enable practical inter-operability with General Video Game AI agents. If we treat the game as a win-loss game (which is standard), then this leads to challenging noisy optimisation problems both in tuning agents to play the game, and in tuning game parameters. Here we focus on the problem of tuning an agent, and report results using the recently developed N-Tuple Bandit Evolutionary Algorithm and a number of other optimisers, including Sequential Model-based Algorithm Configuration (SMAC). Results indicate that the N-Tuple Bandit Evolutionary offers competitive performance as well as insight into the effects of combinations of parameter choices.
Resource-Scalable CNN Synthesis for IoT Applications

State-of-the-art image recognition systems use sophisticated Convolutional Neural Networks (CNNs) that are designed and trained to identify numerous object classes. Such networks are fairly resource intensive to compute, prohibiting their deployment on resource-constrained embedded platforms. On one hand, the ability to classify an exhaustive list of categories is excessive for the demands of most IoT applications. On the other hand, designing a new custom-designed CNN for each new IoT application is impractical, due to the inherent difficulty in developing competitive models and time-to-market pressure. To address this problem, we investigate the question of: "Can one utilize an existing optimized CNN model to automatically build a competitive CNN for an IoT application whose objects of interest are a fraction of categories that the original CNN was designed to classify, such that the resource requirement is proportionally scaled down?" We use the term resource scalability to refer to this concept, and develop a methodology for automated synthesis of resource scalable CNNs from an existing optimized baseline CNN. The synthesized CNN has sufficient learning capacity for handling the given IoT application requirements, and yields competitive accuracy. The proposed approach is fast, and unlike the presently common practice of CNN design, does not require iterative rounds of training trial and error.
Low-Cost Device Prototype for Automatic Medical Diagnosis Using Deep Learning Methods

This paper introduces a novel low-cost device prototype for the automatic diagnosis of diseases, utilizing inputted symptoms and personal background. The engineering goal is to solve the problem of limited healthcare access with a single device. Diagnosing diseases automatically is an immense challenge, owing to their variable properties and symptoms. On the other hand, Neural Networks have developed into a powerful tool in the field of machine learning, one that is showing to be extremely promising at computing diagnosis even with inconsistent variables.In this research, a cheap device (under $30) was created to allow for straightforward diagnosis and treatment of human diseases. By utilizing Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), outfitted on a Raspberry Pi Zero processor ($5), the device is able to detect up to 1537 different diseases and conditions and utilize a CNN for on-device visual diagnostics. The user can input the symptoms using the buttons on the device and can take pictures using the same mechanism. The algorithm processes inputted symptoms, providing diagnosis and possible treatment options for common conditions. The purpose of this work was to be able to diagnose diseases through an affordable processor with high accuracy, as it is currently achieving an accuracy of 90% (±0.8%) for Top-5 symptom-based diagnoses, and 91% (±0.2%) for visual skin diseases. The NNs achieve performance far above any other tested system (WebMD, MEDoctor, so forth.), and its eﬃciency and ease of use will prove it to be a helpful tool for people around the world. This device could potentially provide low-cost universal access to vital diagnostics and treatment options.
Building a Sybil-Resilient Digital Community Utilizing Trust-Graph Connectivity

Preventing fake or duplicate digital identities (aka sybils) from joining a digital community may be crucial to its survival, especially if it utilizes a consensus protocol among its members or employs democratic governance, where sybils can undermine consensus, tilt decisions, or even take over. Here, we explore the use of a trust-graph of identities, with edges representing trust among identity owners, to allow a community to grow indefinitely without increasing its sybil penetration. Since identities are admitted to the digital community based on their trust by existing digital community members, corrupt identities, which may trust sybils, also pose a threat to the digital community. Sybils and their corrupt perpetrators are together referred to as byzantines, and the overarching aim is to limit their penetration into a digital community. We propose two alternative tools to achieve this goal. One is graph conductance, which works under the assumption that honest people are averse to corrupt ones and tend to distrust them. The second is vertex expansion, which relies on the assumption that there are not too many corrupt identities in the community. Of particular interest is keeping the fraction of byzantines below one third, as it would allow the use of Byzantine Agreement (Lamport et al., 1982) for consensus as well as for sybil-resilient social choice (Shahaf et al., 2019). This paper considers incrementally growing a trust graph and shows that, under its key assumptions and additional requirements, including keeping the conductance or vertex expansion of the community trust graph sufficiently high, a community may grow safely, indefinitely.
Gravitational waves in the presence of viscosity

We analyze gravitational waves propagating in an isotropic cosmic fluid endowed with a bulk viscosity  see text] and a shear viscosity  see text], assuming these coefficients to vary with fluid density  see text] as  see text], with  see text] favored by experimental evidence. We give the general governing equation for the gravitational waves and focus thereafter on two examples. The first concerns waves in the very late universe, close to the Big Rip, where the fate of the cosmic fluid is dependent highly on the values of the parameters. Our second example considers the very early universe, the lepton era, the motivation for this choice being that the microscopical bulk viscosity as calculated from statistical mechanics is then at maximum. We find that the gravitational waves on such an underlying medium are damped, having a decay constant equal to the inverse of the conformal Hubble parameter. Our results turn out to be in good agreement with other viscosity-based approaches.
Personalized explanation in machine learning

Explanation in machine learning and related fields such as artificial intelligence aims at making machine learning models and their decisions understandable to humans. Existing work suggests that personalizing explanations might help to improve understandability. In this work, we derive a conceptualization of personalized explanation by defining and structuring the problem based on prior work on machine learning explanation, personalization (in machine learning) and concepts and techniques from other domains such as privacy and knowledge elicitation. We perform a categorization of explainee information used in the process of personalization as well as describing means to collect this information. We also identify three key explanation properties that are amendable to personalization: complexity, decision information and presentation. We also enhance existing work on explanation by introducing additional desiderata and measures to quantify the quality of personalized explanations.
On the separation distance of minimal Green energy points on compact Riemannian manifolds

In this article we study point configurations minimizing the discrete energy on a compact Riemannian manifold, where the energy kernel is taken to be the Green's function for the Laplacian. We show that every point in a minimizing configuration lies inside an open set called harmonic ball where no other point can enter, and that the minimum distance between any two distinct points has the optimal asymptotic order. We compute explicit bounds for the minimum distance in the case of Compact Rank One Symmetric Spaces.
A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes

Constructing of molecular structural models from CryoElectron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many

Learning hydrologic models for accurate riverine flood prediction at scale is a challenge of great importance. One of the key difficulties is the need to rely on in-situ river discharge measurements, which can be quite scarce and unreliable, particularly in regions where floods cause the most damage every year. Accordingly, in this work we tackle the problem of river discharge estimation at different river locations. A core characteristic of the data at hand (e.g. satellite measurements) is that we have few measurements for many locations, all sharing the same physics that underlie the water discharge. We capture this scenario in a simple but powerful common mechanism regression (CMR) model with a local component as well as a shared one which captures the global discharge mechanism. The resulting learning objective is non-convex, but we show that we can find its global optimum by leveraging the power of joining local measurements across sites. In particular, using a spectral initialization with provable near-optimal accuracy, we can find the optimum using standard descent methods. We demonstrate the efficacy of our approach for the problem of discharge estimation using simulations.
The Impact of Country of Origin in Mobile Phone Choice of Generation Y and Z

Mobile phones play a very important role in our life. Mobile phone sales have been soaring over the last decade due to the growing acceptance of technological innovations, especially by Generations Y and Z. Understanding the change in customers' requirement is the key to success in the smartphone business. New, strong mobile phone models will emerge if the voice of the customer can be heard. Although it has been widely known that country of origin has serious impact on the attitudes and purchase decisions of mobile phone consumers, there lack substantial studies that investigate the mobile phone preference of young adults aged 18-25, members of late Generation Y and early Generation Z. In order to investigate the role of country of origin in mobile phone choice of Generations Y and Z, an online survey with 228 respondents was conducted in Hungary in 2016. Besides the descriptive statistical methods, crosstabs, ANOVA and Pearson correlation are used to analyze the collected data and find out significant relationships. Factor analysis (Principal Component Analysis) is used for data reduction to create new factor components. The findings of this exploratory study support the idea that country of origin plays a significant role in many respects related to young adults' mobile phone choice. Mobile phone owners with different countries of origin attribute crucial importance to the various product features including technical parameters, price, design, brand name, operating system, and memory size. Country of origin has a moderating effect on the price sensitivity of consumers with varied net income levels. It is also found that frequent buyers of mobile phones, especially US brand products, spend the most significant amount of money for their consumption in this aspect.
Modelling Italian mortality rates with a geometric-type fractional Ornstein-Uhlenbeck process

We propose to model mortality hazard rates for human population using the exponential of the solution of a stochastic differential equation (SDE). The noise in the SDE is a fractional Brownian motion. We will use the well-known fractional Ornstein-Uhlenbeck process. Using the Hurst parameter we showed that mortality rates exhibit long-term memory. The proposed model is a generalization of the model introduced by , where they used an SDE driven with a Brownian motion. We tested our model with the Italian population between the years 1950 to 2004.
Observation data pre-processing and scientific data products generation of POLAR

POLAR is a compact space-borne detector initially designed to measure the polarization of hard X-rays emitted from Gamma-Ray Bursts in the energy range 50–500 keV. This instrument was launched successfully onboard the Chinese space laboratory Tiangong-2 (TG-2) on 2016 September 15. After being switched on a few days later, tens of gigabytes of raw detection data were produced in-orbit by POLAR and transferred to the ground every day. Before the launch date, a full pipeline and related software were designed and developed for the purpose of quickly pre-processing all the raw data from POLAR, which include both science data and engineering data, then to generate the high level scientific data products that are suitable for later science analysis. This pipeline has been successfully applied for use by the POLAR Science Data Center in the Institute of High Energy Physics (IHEP) after POLAR was launched and switched on. A detailed introduction to the pipeline and some of the core relevant algorithms are presented in this paper.
Novel couplings between nonmetricity and matter

We present a novel theory of gravity, namely, an extension of symmetric teleparallel gravity. This is done by introducing a new class of theories where the nonmetricity $Q$ is coupled nonminimally to the matter Lagrangian. This nonminimal coupling entails the nonconservation of the energy-momentum tensor, and consequently the appearance of an extra force. We also present several cosmological applications.
Weak second Bianchi identity for static, spherically symmetric spacetimes with timelike singularities

The (twice-contracted) second Bianchi identity is a differential curvature identity that holds on any smooth manifold with a metric. In the case when such a metric is Lorentzian and solves Einstein’s equations with an (in this case inevitably smooth) energy–momentum–stress tensor of a ‘matter field’ as the source of spacetime curvature, this identity implies the physical laws of energy and momentum conservation for the ‘matter field’. The present work inquires into whether such a Bianchi identity can still hold in a weak sense for spacetimes with curvature singularities associated with timelike singularities in the ‘matter field’. Sufficient conditions that establish a distributional version of the twice-contracted second Bianchi identity are found. In our main theorem, a large class of spherically symmetric static Lorentzian metrics with timelike one-dimensional singularities is identified, for which this identity holds. As an important first application we show that the well-known Reissner–Weyl–Nordström spacetime of a point charge does not belong to this class, but that Hoffmann’s spacetime of a point charge with negative bare mass in the Born–Infeld electromagnetic vacuum does.
Elementary Microeconomics of the Talmudic Rule

This paper takes a look at the Talmudic rule aka the 1/N rule aka the uniform investment strategy from the viewpoint of elementary microeconomics. Specifically, we derive the cardinal utility function for a Talmud-obeying agent which happens to have the Cobb-Douglas form. Further, we investigate individual supply and demand due to rebalancing and compare them to market depth of an exchange. Finally, we discuss how operating as a liquidity provider can benefit the Talmud-obeying agent with every exchange transaction in terms of the identified utility function.
The market nanostructure origin of asset price time reversal asymmetry

We introduce a method to infer lead-lag networks between the states of elements of complex systems, determined at different timescales. As such networks encode a causal structure of a system, inferring lead-lag networks for many pairs of timescales provides a global picture of the mutual influence between timescales. We apply our method to two trader-resolved FX data sets and document a strong and complex asymmetric influence of timescales on the structure of lead-lag networks. This asymmetry extends to the propagation of trader activity between timescales. For both retail and institutional traders, we find that historical activity over longer timescales has a greater correlation with future activity over shorter timescales (Zumbach effect), for sufficiently large timescales both in the past and future (about one hour for retail traders and two hours for institutional traders); remarkably the effect is opposite for smaller timescales, and much weaker for retail traders.
On Finding Local Nash Equilibria (and Only Local Nash Equilibria) in Zero-Sum Games

We propose local symplectic surgery, a two-timescale procedure for finding local Nash equilibria in two-player zero-sum games. We first show that previous gradient-based algorithms cannot guarantee convergence to local Nash equilibria due to the existence of non-Nash stationary points. By taking advantage of the differential structure of the game, we construct an algorithm for which the local Nash equilibria are the only attracting fixed points. We also show that the algorithm exhibits no oscillatory behaviors in neighborhoods of equilibria and show that it has the same per-iteration complexity as other recently proposed algorithms. We conclude by validating the algorithm on two numerical examples: a toy example with multiple Nash equilibria and a non-Nash equilibrium, and the training of a small generative adversarial network (GAN).
Variational Quantum Generators: Generative Adversarial Quantum Machine Learning for Continuous Distributions

A hybrid quantum–classical approach to model continuous classical probability distributions using a variational quantum circuit is proposed. The architecture of this quantum generator consists of a quantum circuit that encodes a classical random variable into a quantum state and a parameterized quantum circuit trained to mimic the target distribution. The model allows for easy interfacing with a classical function, such as a neural network, and is trained using an adversarial learning approach. It is shown that the quantum generator is able to learn using either a classical neural network or a variational quantum circuit as the discriminator model. This implementation takes advantage of automatic differentiation tools to perform the optimization of the variational circuits employed. The framework presented here for the design and implementation of the variational quantum generators can serve as a blueprint for designing hybrid quantum–classical models for other machine learning tasks.
CLEVR-Ref+: Diagnosing Visual Reasoning With Referring Expressions

Referring object detection and referring image segmentation are important tasks that require joint understanding of visual information and natural language. Yet there has been evidence that current benchmark datasets suffer from bias, and current state-of-the-art models cannot be easily evaluated on their intermediate reasoning process. To address these issues and complement similar efforts in visual question answering, we build CLEVR-Ref+, a synthetic diagnostic dataset for referring expression comprehension. The precise locations and attributes of the objects are readily available, and the referring expressions are automatically associated with functional programs. The synthetic nature allows control over dataset bias (through sampling strategy), and the modular programs enable intermediate reasoning ground truth without human annotators. In addition to evaluating several state-of-the-art models on CLEVR-Ref+, we also propose IEP-Ref, a module network approach that significantly outperforms other models on our dataset. In particular, we present two interesting and important findings using IEP-Ref: (1) the module trained to transform feature maps into segmentation masks can be attached to any intermediate module to reveal the entire reasoning process step-by-step; (2) even if all training data has at least one object referred, IEP-Ref can correctly predict no-foreground when presented with false-premise referring expressions. To the best of our knowledge, this is the first direct and quantitative proof that neural modules behave in the way they are intended. We will release data and code for CLEVR-Ref+.