nils-herrmann/clean_scirepeval huggingface dataset:
Probability Theory without Bayes' Rule

Within the Kolmogorov theory of probability, Bayes' rule allows one to perform statistical inference by relating conditional probabilities to unconditional probabilities. As we show here, however, there is a continuous set of alternative inference rules that yield the same results, and that may have computational or practical advantages for certain problems. We formulate generalized axioms for probability theory, according to which the reverse conditional probability distribution P(B|A) is not specified by the forward conditional probability distribution P(A|B) and the marginals P(A) and P(B). Thus, in order to perform statistical inference, one must specify an additional "inference axiom," which relates P(B|A) to P(A|B), P(A), and P(B). We show that when Bayes' rule is chosen as the inference axiom, the axioms are equivalent to the classical Kolmogorov axioms. We then derive consistency conditions on the inference axiom, and thereby characterize the set of all possible rules for inference. The set of "first-order" inference axioms, defined as the set of axioms in which P(B|A) depends on the first power of P(A|B), is found to be a 1-simplex, with Bayes' rule at one of the extreme points. The other extreme point, the "inversion rule," is studied in depth.
Predicting clicks in online display advertising with latent features and side-information

We review a method for click-through rate prediction based on the work of Menon et al. , which combines collaborative filtering and matrix factorization with a side-information model and fuses the outputs to proper probabilities in [0,1]. In addition we provide details, both for the modeling as well as the experimental part, that are not found elsewhere. We rigorously test the performance on several test data sets from consecutive days in a click-through rate prediction setup, in a manner which reflects a real-world pipeline. Our results confirm that performance can be increased using latent features, albeit the differences in the measures are small but significant.
Leveraging cloud data to mitigate user experience from ‘breaking bad’

Low latency and high availability of an app or a web service are key, amongst other factors, to the overall user experience (which in turn directly impacts the bottoniline). Exogenic and/or endogenic factors often give rise to breakouts in cloud data which makes maintaining high availability and delivering high performance very challenging. Existing breakout detection techniques are not suitable for cloud data owing to not being robust in the presence of anomalies. To this end, we developed a novel statistical technique to automatically detect breakouts in cloud data. This technique employs Energy Statistics to detect breakouts in both app and system metrics. Further, the technique uses robust statistical metrics, viz., medians, and estimates the statistical significance of a breakout through a permutation test. To the best of our knowledge, this is the first work which addresses breakout detection in the presence of anomalies. We demonstrate the efficacy of the proposed technique using production data and report precision, recall, and f-measure measure. The proposed technique is 3.5× faster than a state-of-the-art technique for breakout detection and is being currently used on a daily basis at Twitter Inc.
Solving Games with Functional Regret Estimation

We propose a novel online learning method for minimizing regret in large extensive-form games. The approach learns a function approximator online to estimate the regret for choosing a particular action. A no-regret algorithm uses these estimates in place of the true regrets to define a sequence of policies. We prove the approach sound by providing a bound relating the quality of the function approximation and regret of the algorithm. A corollary being that the method is guaranteed to converge to a Nash equilibrium in self-play so long as the regrets are ultimately realizable by the function approximator. Our technique can be understood as a principled generalization of existing work onabstraction in large games; in our work, both the abstraction as well as the equilibrium are learned during self-play. We demonstrate empirically the method achieves higher quality strategies than state-of-the-art abstraction techniques given the same resources.
Orientation of galactic bulge planetary nebulae toward the Galactic center

We have used the Wide Field Spectrograph on the Australian National University 2.3-m telescope to perform the integral field spectroscopy for a sample of the Galactic planetary nebulae. The spatially resolved velocity distributions of the Hα emission line were used to determine the kinematic features and nebular orientations. Our findings show that some bulge planetary nebulae toward the Galactic center have a particular orientation.
Misspecified Recovery

Asset prices contain information about the probability distribution of future states and the stochastic discounting of those states as used by investors. To better understand the challenge in distinguishing investors' beliefs from risk-adjusted discounting, we use Perron-Frobenius Theory to isolate a positive martingale component of the stochastic discount factor process. This component recovers a probability measure that absorbs long-term risk adjustments. When the martingale is not degenerate, surmising that this recovered probability captures investors' beliefs distorts inference about risk-return tradeoffs. Stochastic discount factors in many structural models of asset prices have empirically relevant martingale components.
Canonical Logic Programs are Succinctly Incomparable with Propositional Formulas

Canonical (logic) programs (CP) refer to the class of normal programs (LP) augmented with connective not not, and are equally expressive as propositional formulas (PF). In this paper we address the question of whether CP and PF are succinctly incomparable. Our main result shows that the PARITY problem only has exponential CP representations, while it can be polynomially represented in PF. In other words, PARITY separates PF from CP. Simply speaking, this means that exponential size blowup is generally inevitable when translating a set of PF formulas into a (logically) equivalent CP program (without introducing new variables). Furthermore, since it has been shown by Lifschitz and Razborov that there is also a problem which separates CP from PF (assuming P ⊈ NC1/poly), it follows that the two formalisms are indeed succinctly incomparable.
A real-time fast radio burst: polarization detection and multiwavelength follow-up

Fast radio bursts (FRBs) are one of the most tantalizing mysteries of the radio sky; their progenitors and origins remain unknown and until now no rapid multiwavelength follow-up of an FRB has been possible. New instrumentation has decreased the time between observation and discovery from years to seconds, and enables polarimetry to be performed on FRBs for thefirst time. We have discovered an FRB (FRB 140514) in real-time on 2014 May 14 at 17:14:11.06 UTCattheParkesradiotelescopeandtriggeredfollow-upatotherwavelengthswithinhoursof theevent.FRB140514wasfoundwithadispersionmeasure(DM)of562.7(6)cm −3 pc,giving an upper limit on source redshift of z 0.5. FRB 140514 was found to be 21 ± 7 per cent (3σ) circularly polarized on the leading edge with a 1σ upper limit on linear polarization <10 per cent. We conclude that this polarization is intrinsic to the FRB. If there was any intrinsic linear polarization, as might be expected from coherent emission, then it may have been depolarized by Faraday rotation caused by passing through strong magnetic fields and/or high-density environments. FRB 140514 was discovered during a campaign to re-observe known FRB fields, and lies close to a previous discovery, FRB 110220; based on the difference in DMs of these bursts and time-on-sky arguments, we attribute the proximity to sampling bias and conclude that they are distinct objects. Follow-up conducted by 12 telescopes observing from X-ray to radio wavelengths was unable to identify a variable multiwavelength counterpart, allowing us to rule out models in which FRBs originate from nearby ( z< 0.3) supernovae and long duration gamma-ray bursts.
BigDataViewer: Interactive Visualization and Image Processing for Terabyte Data Sets

The increasingly popular light sheet microscopy techniques generate very large 3D time-lapse recordings of living biological specimen. The necessity to make large volumetric datasets available for interactive visualization and analysis has been widely recognized. However, existing solutions build on dedicated servers to generate virtual slices that are transferred to the client applications, practically leading to insufficient frame rates (less than 10 frames per second) for truly interactive experience. An easily accessible open source solution for interactive arbitrary virtual re-slicing of very large volumes and time series of volumes has yet been missing. We fill this gap with BigDataViewer, a Fiji plugin to interactively navigate and visualize large image sequences from both local and remote data sources.
Game-theoretical control with continuous action sets

Motivated by the recent applications of game-theoretical learning techniques to the design of distributed control systems, we study a class of control problems that can be formulated as potential games with continuous action sets, and we propose an actor-critic reinforcement learning algorithm that provably converges to equilibrium in this class of problems. The method employed is to analyse the learning process under study through a mean-field dynamical system that evolves in an infinite-dimensional function space (the space of probability distributions over the players' continuous controls). To do so, we extend the theory of finite-dimensional two-timescale stochastic approximation to an infinite-dimensional, Banach space setting, and we prove that the continuous dynamics of the process converge to equilibrium in the case of potential games. These results combine to give a provably-convergent learning algorithm in which players do not need to keep track of the controls selected by the other agents.
Burstiness and aging in social temporal networks

The presence of burstiness in temporal social networks, revealed by a power-law form of the waiting time distribution of consecutive interactions, is expected to produce aging effects in the corresponding time-integrated network. Here, we propose an analytically tractable model, in which interactions among the agents are ruled by a renewal process, that is able to reproduce this aging behavior. We develop an analytic solution for the topological properties of the integrated network produced by the model, finding that the time translation invariance of the degree distribution is broken. We validate our predictions against numerical simulations, and we check for the presence of aging effects in a empirical temporal network, ruled by bursty social interactions.
How to monitor and mitigate stair-casing in L1 trend filtering

In this paper we study the estimation of changing trends in time-series using ℓ1 trend filtering. This method generalizes 1D Total Variation (TV) denoising for detection of step changes in means to detecting changes in trends, and it relies on a convex optimization problem for which there are very efficient numerical algorithms. It is known that TV denoising suffers from the so-called stair-case effect, which leads to detecting false change points. The objective of this paper is to show that ℓ1 trend filtering also suffers from a certain stair-case problem. The analysis is based on an interpretation of the dual variables of the optimization problem in the method as integrated random walk. We discuss consistency conditions for ℓ1 trend filtering, how to monitor their fulfillment, and how to modify the algorithm to avoid the stair-case false detection problem.
Algebraic geometrization of the Kuramoto model: Equilibria and stability analysis.

Finding equilibria of the finite size Kuramoto model amounts to solving a nonlinear system of equations, which is an important yet challenging problem. We translate this into an algebraic geometry problem and use numerical methods to find all of the equilibria for various choices of coupling constants K, natural frequencies, and on different graphs. We note that for even modest sizes (N ~ 10-20), the number of equilibria is already more than 100 000. We analyze the stability of each computed equilibrium as well as the configuration of angles. Our exploration of the equilibrium landscape leads to unexpected and possibly surprising results including non-monotonicity in the number of equilibria, a predictable pattern in the indices of equilibria, counter-examples to conjectures, multi-stable equilibrium landscapes, scenarios with only unstable equilibria, and multiple distinct extrema in the stable equilibrium distribution as a function of the number of cycles in the graph.
RoboBrain: Large-Scale Knowledge Engine for Robots

In this paper we introduce a knowledge engine, which learns and shares knowledge representations, for robots to carry out a variety of tasks. Building such an engine brings with it the challenge of dealing with multiple data modalities including symbols, natural language, haptic senses, robot trajectories, visual features and many others. The \textit{knowledge} stored in the engine comes from multiple sources including physical interactions that robots have while performing tasks (perception, planning and control), knowledge bases from the Internet and learned representations from several robotics research groups. 
We discuss various technical aspects and associated challenges such as modeling the correctness of knowledge, inferring latent information and formulating different robotic tasks as queries to the knowledge engine. We describe the system architecture and how it supports different mechanisms for users and robots to interact with the engine. Finally, we demonstrate its use in three important research areas: grounding natural language, perception, and planning, which are the key building blocks for many robotic tasks. This knowledge engine is a collaborative effort and we call it RoboBrain.
Streaming Variational Inference for Bayesian Nonparametric Mixture Models

In theory, Bayesian nonparametric (BNP) models are well suited to streaming data scenarios due to their ability to adapt model complexity with the observed data. Unfortunately, such benefits have not been fully realized in practice; existing inference algorithms are either not applicable to streaming applications or not extensible to BNP models. For the special case of Dirichlet processes, streaming inference has been considered. However, there is growing interest in more flexible BNP models building on the class of normalized random measures (NRMs). We work within this general framework and present a streaming variational inference algorithm for NRM mixture models. Our algorithm is based on assumed density filtering (ADF), leading straightforwardly to expectation propagation (EP) for large-scale batch inference as well. We demonstrate the efficacy of the algorithm on clustering documents in large, streaming text corpora.
Basis functions for electronic structure calculations on spheres.

We introduce a new basis function (the spherical Gaussian) for electronic structure calculations on spheres of any dimension D. We find general expressions for the one- and two-electron integrals and propose an efficient computational algorithm incorporating the Cauchy-Schwarz bound. Using numerical calculations for the D = 2 case, we show that spherical Gaussians are more efficient than spherical harmonics when the electrons are strongly localized.
Hierarchical Network Structure Promotes Dynamical Robustness

The relationship between network topology and system dynamics has significant implications for unifying our understanding of the interplay among metabolic, gene-regulatory, and ecosystem network architecures. Here we analyze the stability and robustness of a large class of dynamics on such networks. We determine the probability distribution of robustness as a function of network topology and show that robustness is classified by the number of links between modules of the network. We also demonstrate that permutation of these modules is a fundamental symmetry of dynamical robustness. Analysis of these findings leads to the conclusion that the most robust systems have the most hierarchical structure. This relationship provides a means by which evolutionary selection for a purely dynamical phenomenon may shape network architectures across scales of the biological hierarchy.
C3D: Generic Features for Video Analysis

Videos have become ubiquitous due to the ease of capturing and sharing via social platforms like Youtube, Facebook, Instagram, and others. The computer vision community has tried to tackle various video analysis problems independently. As a consequence, even though some really good hand-crafted features have been proposed there is a lack of generic features for video analysis. On the other hand, the image domain has progressed rapidly by using features from deep convolutional networks. These deep features are proving to be generic and perform well on variety of image tasks. In this work we propose Convolution 3D (C3D) feature, a generic spatio-temporal feature obtained by training a deep 3-dimensional convolutional network on a large annotated video dataset comprising objects, scenes, actions, and other frequently occurring concepts. We show that by using spatio-temporal convolutions the trained features encapsulate appearance and motion cues and perform well on different video classification tasks. C3D has three main advantages. First, it is generic: achieving state-ofthe-art results on object recognition, scene classification, sport classification, and action similarity labeling in videos. Second, it is compact: obtaining better accuracy than best hand-crafted features and best deep image features with a lower dimensional feature descriptor. Third, it is efficient to compute: 91 times faster than current hand-crafted features, and two orders of magnitude faster than current deeplearning based video classification methods.
Beyond Einstein's General Relativity

Modern astrophysical and cosmological models are plagued with two severe theoretical difficulties, namely, the dark energy and the dark matter problems. Relative to the former, high-precision observational data have confirmed with startling evidence that the Universe is undergoing a phase of accelerated expansion. This phase, one of the most important and challenging current problems in cosmology, represents a new imbalance in the governing gravitational equations. Several candidates, responsible for this expansion, have been proposed in the literature, in particular, dark energy models and modified gravity, amongst others. Outstanding questions are related to the nature of this so-called “dark energy” that is driving the acceleration of the universe, and whether it is due to the vacuum energy or a dynamical field. On the other hand, the late-time cosmic acceleration may be due to modifications of General Relativity, which introduce new degrees of freedom to the gravitational sector itself. We analyze some of the modified theories of gravity that address these intriguing and exciting problems facing modern physics, and explore the foundations of gravitation theory, essential for the construction of modified theories of gravity.
Watsonsim: Overview of a Question Answering Engine

The objective of the project is to design and run a system similar to Watson, designed to answer Jeopardy questions. In the course of a semester, we developed an open source question answering system using the Indri, Lucene, Bing and Google search engines, Apache UIMA, Open- and CoreNLP, and Weka among additional modules. By the end of the semester, we achieved 18% accuracy on Jeopardy questions, and work has not stopped since then.
Taxonomic Provenance: Two Influential Primate Classifications Logically Aligned

Classification standards such as the Mammal Species of the World (MSW) aim to unify name usages at the global scale, but may nevertheless experience significant levels of taxonomic change from one edition to the next. This circumstance challenges the biodiversity and phylogenetic data communities to develop more granular identifiers to track taxonomic congruence and incongruence in ways that both humans and machines can process, i.e., to logically represent taxonomic provenance across multiple classification hierarchies. Here we show that reasoning over taxonomic provenance is feasible for two classifications of primates corresponding to the second and third MSW editions. Our approach entails three main components: (1) individuation of name usages as taxonomic concepts, (2) articulation of concepts via human-asserted Region Connection Calculus (RCC-5) relationships, and (3) the use of an Answer Set Programming toolkit to infer and visualize logically consistent alignments of these taxonomic input constraints. Our use case entails the Primates sec. Groves (1993; MSW2 - 317 taxonomic concepts; 233 at the species level) and Primates sec. Groves (2005; MSW3 - 483 taxonomic concepts; 376 at the species level). Using 402 concept-to-concept input articulations, the reasoning process yields a single, consistent alignment, and infers 153,111 Maximally Informative Relations that constitute a comprehensive provenance resolution map for every concept pair in the Primates sec. MSW2/MSW3. The entire alignment and various partitions facilitate quantitative analyses of name/meaning dissociation, revealing that approximately one in three paired name usages across treatments is not reliable - in the sense of the same name identifying congruent taxonomic meanings. We conclude with an optimistic outlook for logic-based provenance tools in next-generation biodiversity and phylogeny data platforms.
The escaping set of transcendental self-maps of the punctured plane

We study the different rates of escape of points under iteration by holomorphic self-maps of $\mathbb{C}^{\ast }=\mathbb{C}\setminus \{0\}$ for which both zero and infinity are essential singularities. Using annular covering lemmas we construct different types of orbits, including fast escaping and arbitrarily slowly escaping orbits to either zero, infinity or both. We also prove several properties about the set of fast escaping points for this class of functions. In particular, we show that there is an uncountable collection of disjoint sets of fast escaping points, each of which has $J(f)$ as its boundary.
A CSP implementation of the bigraph embedding problem

A crucial problem for many results and tools about bigraphs and bigraphical reactive systems is bigraph embedding. An embedding is more informative than a bigraph matching, since it keeps track of the correspondence between the various components of the redex (guest) within the agent (host). In this paper, we present an algorithm for computing embeddings based on a reduction to a constraint satisfaction problem. This algorithm, that we prove to be sound and complete, has been successfully implemented in LibBig, a library for manipulating bigraphical reactive systems. This library can be used for implementing a wide range of tools, and it can be adapted to various extensions of bigraphs.
Problem Theory

We define a problem theory from first principles. We investigate the objects of this theory: problems, resolutions, and solutions. We relate problem theory with set theory and with computing theory. We find taxonomies for resolutions and for problems. We build a hierarchy of resolvers: mechanism, adaptor, internalizer, learner, and subject. We show that the problem theory is complete, that is, that there are just three ways to resolve any problem: routine, trial, and analogy. Finally, we propose a thesis: We are Turing complete subjects because we are the result of an evolution of resolvers of the survival problem.