nils-herrmann/clean_scirepeval huggingface dataset:
Classifying latent infection states in complex networks

Algorithms for identifying the infection states of nodes in a network are crucial for understanding and containing epidemics and cascades. Often, however, only the infection states of a small set of nodes are known. Moreover, the length of time that each node has been infected is also unknown. This missing data – infection states of most nodes and the infection times of the observed infected nodes – poses a challenge to the study of real-world epidemics/cascades.In this work, we develop techniques to identify the latent infected nodes in the presence of missing infection time-and-state data. Based on likely epidemic paths predicted by a simple susceptible-infected epidemic model, we propose a measure (infection betweenness centrality) for uncovering unknown infection states. We evaluate infection state classifiers based on Naive Bayes, Naive Bayes with kernel density estimation, and decision trees, in combination with features including infection betweenness centrality and different centrality measures. Our experimental results show that among the set of features that include degree and different centrality measures, infection betweenness centrality is the most effective feature for identifying latent infected nodes.
Symmetry-projected wave functions in quantum Monte Carlo calculations

We consider symmetry-projected Hartree-Fock trial wave functions in constrained-path Monte Carlo (CPMC) calculations. Previous CPMC calculations have mostly employed Hartree-Fock (HF) trial wave functions, restricted or unrestricted. The symmetry-projected HF approach results in a hierarchy of wave functions with increasing quality: the more symmetries that are broken and restored in a self-consistent manner, the higher the quality of the trial wave function. This hierarchy is approximately maintained in CPMC calculations: the accuracy in the energy increases and the statistical variance decreases when further symmetries are broken and restored. Significant improvement is achieved in CPMC with the best symmetry-projected trial wave functions over those from simple HF. We analyze and quantify the behavior using the two-dimensional repulsive Hubbard model as an example. In the sign-problem-free region, where CPMC can be made exact but a constraint is deliberately imposed here, spin-projected wave functions remove the constraint bias. Away from half filling, spatial symmetry restoration in addition to that of the spin leads to highly accurate results from CPMC. Since the computational cost of symmetry-projected HF trial wave functions in CPMC can be made to scale algebraically with system size, this provides a potentially general approach for accurate calculations in many-fermion systems.
Neural Variational Inference and Learning in Belief Networks

Highly expressive directed latent variable models, such as sigmoid belief networks, are difficult to train on large datasets because exact inference in them is intractable and none of the approximate inference methods that have been applied to them scale well. We propose a fast non-iterative approximate inference method that uses a feedforward network to implement efficient exact sampling from the variational posterior. The model and this inference network are trained jointly by maximizing a variational lower bound on the log-likelihood. Although the naive estimator of the inference network gradient is too high-variance to be useful, we make it practical by applying several straightforward model-independent variance reduction techniques. Applying our approach to training sigmoid belief networks and deep autoregressive networks, we show that it outperforms the wake-sleep algorithm on MNIST and achieves state-of-the-art results on the Reuters RCV1 document dataset.
Performance of the Survey Propagation-guided decimation algorithm for the random NAE-K-SAT problem

We show that the Survey Propagation-guided decimation algorithm fails to find satisfying assignments on random instances of the "Not-All-Equal-$K$-SAT" problem if the number of message passing iterations is bounded by a constant independent of the size of the instance and the clause-to-variable ratio is above $(1+o_K(1)){2^{K-1}\over K}\log^2 K$ for sufficiently large $K$. Our analysis in fact applies to a broad class of algorithms described as "sequential local algorithms". Such algorithms iteratively set variables based on some local information and then recurse on the reduced instance. Survey Propagation-guided as well as Belief Propagation-guided decimation algorithms - two widely studied message passing based algorithms, fall under this category of algorithms provided the number of message passing iterations is bounded by a constant. Another well-known algorithm falling into this category is the Unit Clause algorithm. Our work constitutes the first rigorous analysis of the performance of the SP-guided decimation algorithm. 
The approach underlying our paper is based on an intricate geometry of the solution space of random NAE-$K$-SAT problem. We show that above the $(1+o_K(1)){2^{K-1}\over K}\log^2 K$ threshold, the overlap structure of $m$-tuples of satisfying assignments exhibit a certain clustering behavior expressed in the form of constraints on distances between the $m$ assignments, for appropriately chosen $m$. We further show that if a sequential local algorithm succeeds in finding a satisfying assignment with probability bounded away from zero, then one can construct an $m$-tuple of solutions violating these constraints, thus leading to a contradiction. Along with (citation), this result is the first work which directly links the clustering property of random constraint satisfaction problems to the computational hardness of finding satisfying assignments.
Study of Coherent Perfect Absorption Using Gaussian Beam in a Composite Medium

There is a study of coherent perfect absorption (CPA) in composite medium (CM) which is made of metal-dielectric composition. The CM, an orthorhombic shaped slab, is illuminated by two identical Gaussian beams (GB) incident at equal angle of incidence and focused on the two interfaces between wall of slab and air from opposite side. It has determine the necessary conditions required for CPA and shows that perfect CPA is possible with GBs. Author also shows broader beams exhibit more absorption than comparatively tightly focused beams.
Faster Comparison of Stopping Times by Nested Conditional Monte Carlo

We show that deliberately introducing a nested simulation stage can lead to significant variance reductions when comparing two stopping times by Monte Carlo. We derive the optimal number of nested simulations and prove that the algorithm is remarkably robust to misspecifications of this number. The method is applied to several problems related to Bermudan/American options. In these applications, our method allows us to substantially increase the efficiency of other variance reduction techniques, namely, quasi-control variates and multilevel Monte Carlo.
Bots vs. wikipedians, anons vs. logged-ins

Wikipedia is a global crowdsourced encyclopedia that at time of writing is available in 287 languages. Wikidata is a likewise global crowdsourced knowledge base that provides shared facts to be used by Wikipedias. In the context of this research, we have developed an application and an underlying Application Programming Interface (API) capable of monitoring realtime edit activity of all language versions of Wikipedia and Wikidata. This application allows us to easily analyze edits in order to answer questions such as "Bots vs. Wikipedians, who edits more?", "Which is the most anonymously edited Wikipedia?", or "Who are the bots and what do they edit?". To the best of our knowledge, this is the first time such an analysis could be done in realtime for Wikidata and for really all Wikipedias-large and small. Our application is available publicly online at the URL http://wikipedia-edits.herokuapp.com/, its code has been open-sourced under the Apache 2.0 license.
Efficient Gradient-Based Inference through Transformations between Bayes Nets and Neural Nets

Hierarchical Bayesian networks and neural networks with stochastic hidden units are commonly perceived as two separate types of models. We show that either of these types of models can often be transformed into an instance of the other, by switching between centered and differentiable non-centered parameterizations of the latent variables. The choice of parameterization greatly influences the efficiency of gradient-based posterior inference; we show that they are often complementary to eachother, we clarify when each parameterization is preferred and show how inference can be made robust. In the noncentered form, a simple Monte Carlo estimator of the marginal likelihood can be used for learning the parameters. Theoretical results are supported by experiments.
Planck 2013 and Superconformal Symmetry

We explain why the concept of a spontaneously broken superconformal symmetry is useful to describe inflationary models favored by the Planck. Non-minimal coupling of complex scalars to curvature, N(X, X*) R, is compulsory for superconformal symmetry. Here N is the Kahler potential of the embedding moduli space, including the inflaton and the conformon. It appears that such a non-minimal coupling allows generic chaotic models of inflation to reach an agreement with the observable (n_{s},r) values. We describe here the superconformal versions of the cosmological attractors whose bosonic part was presented in lectures of A. Linde in this volume. A distinguishing feature of this class of models is that they tend to lead to very similar predictions which are not very sensitive with respect to strong modifications of the theory. The (super)conformal symmetry underlying (super)gravity allows a universal description of a large class of models which agree with observations and predict the tensor to scalar ratio 10^{-3} < r < 10^{-1}.
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits

We present a new algorithm for the contextual bandit learning problem, where the learner repeatedly takes one of K actions in response to the observed context, and observes the reward only for that action. Our method assumes access to an oracle for solving fully supervised cost-sensitive classification problems and achieves the statistically optimal regret guarantee with only O(√KT) oracle calls across all T rounds. By doing so, we obtain the most practical contextual bandit learning algorithm amongst approaches that work for general policy classes. We conduct a proof-of-concept experiment which demonstrates the excellent computational and statistical performance of (an online variant of) our algorithm relative to several strong baselines.
Analysis of Watson's Strategies for Playing Jeopardy!

Major advances in Question Answering technology were needed for IBM Watson1 to play Jeopardy! at championship level - the show requires rapid-fire answers to challenging natural language questions, broad general knowledge, high precision, and accurate confidence estimates. In addition, Jeopardy! features four types of decision making carrying great strategic importance: (1) Daily Double wagering; (2) Final Jeopardy wagering; (3) selecting the next square when in control of the board; (4) deciding whether to attempt to answer, i.e., "buzz in." Using sophisticated strategies for these decisions, that properly account for the game state and future event probabilities, can significantly boost a player's overall chances to win, when compared with simple "rule of thumb" strategies. 
 
This article presents our approach to developing Watson's game-playing strategies, comprising development of a faithful simulation model, and then using learning and Monte-Carlo methods within the simulator to optimize Watson's strategic decision-making. After giving a detailed description of each of our game-strategy algorithms, we then focus in particular on validating the accuracy of the simulator's predictions, and documenting performance improvements using our methods. Quantitative performance benefits are shown with respect to both simple heuristic strategies, and actual human contestant performance in historical episodes. We further extend our analysis of human play to derive a number of valuable and counterintuitive examples illustrating how human contestants may improve their performance on the show.
The evolution of carrying capacity in constrained and expanding tumour cell populations

Cancer cells are known to modify their micro-environment such that it can sustain a larger population, or, in ecological terms, they construct a niche which increases the carrying capacity of the population. It has however been argued that niche construction, which benefits all cells in the tumour, would be selected against since cheaters could reap the benefits without paying the cost. We have investigated the impact of niche specificity on tumour evolution using an individual based model of breast tumour growth, in which the carrying capacity of each cell consists of two components: an intrinsic, subclone-specific part and a contribution from all neighbouring cells. Analysis of the model shows that the ability of a mutant to invade a resident population depends strongly on the specificity. When specificity is low selection is mostly on growth rate, while high specificity shifts selection towards increased carrying capacity. Further, we show that the long-term evolution of the system can be predicted using adaptive dynamics. By comparing the results from a spatially structured versus well-mixed population we show that spatial structure restores selection for carrying capacity even at zero specificity, which poses a solution to the niche construction dilemma. Lastly, we show that an expanding population exhibits spatially variable selection pressure, where cells at the leading edge exhibit higher growth rate and lower carrying capacity than those at the centre of the tumour.
New equidistribution estimates of Zhang type

In May 2013, Y. Zhang [52] proved the existence of infinitely many pairs of primes with bounded gaps. In particular, he showed that there exists at least one h ě 2 such that the set tp prime | p` h is primeu is infinite. (In fact, he showed this for some even h between 2 and 7ˆ 10, although the precise value of h could not be extracted from his method.) Zhang’s work started from the method of Goldston, Pintz and Yıldırım [23], who had earlier proved the bounded gap property, conditionally on distribution estimates concerning primes in arithmetic progressions to large moduli, i.e., beyond the reach of the Bombieri–Vinogradov theorem. Based on work of Fouvry and Iwaniec [11, 12, 13, 14] and Bombieri, Friedlander and Iwaniec [3, 4, 5], distribution estimates going beyond the Bombieri–Vinogradov range for arithmetic functions such as the von Mangoldt function were already known. However, they involved restrictions concerning the residue classes which were incompatible with the method of Goldston, Pintz and Yıldırım. Zhang’s resolution of this difficulty proceeded in two stages. First, he isolated a weaker distribution estimate that sufficed to obtain the bounded gap property (still
The rise of SN2014J in the nearby galaxy M82

We report on the discovery of SN 2014J in the nearby galaxy M 82. Given its proximity, it oﬀers the best opportunity to date to study a thermonuclear supernova over a wide range of the electromagnetic spectrum. Optical, near-IR and mid-IR observations on the rising lightcurve, orchestrated by the intermediate Palomar Transient Factory (iPTF), show that SN 2014J is a spectroscopically normal Type Ia supernova, albeit exhibiting high-velocity features in its spectrum and heavily reddened by dust in the host galaxy. Our earliest detections start just hours after the ﬁtted time of explosion. We use high-resolution optical spectroscopy to analyze the dense intervening material and do not detect any evolution in the resolved absorption features during the lightcurve rise. Similarly to other highly reddened Type Ia supernovae, a low value of total-to-selective extinction, R V < ∼ 2, provides the best match to our observations. We also study pre-explosion optical and near-IR images from HST with special emphasis on the sources nearest to the SN location.
Disordered horizons: holography of randomly disordered fixed points.

We deform conformal field theories with classical gravity duals by marginally relevant random disorder. We show that the disorder generates a flow to IR fixed points with a finite amount of disorder. The randomly disordered fixed points are characterized by a dynamical critical exponent z > 1 that we obtain both analytically (via resummed perturbation theory) and numerically (via a full simulation of the disorder). The IR dynamical critical exponent increases with the magnitude of disorder, probably tending to z → ∞ in the limit of infinite disorder.
Quantum quenches and competing orders

We study the non-equlibrium dynamics of an electronic model of competing bond density wave order and $d$-wave superconductivity. In a time-dependent Hartree-Fock+BCS approximation, the dynamics reduces to the equations of motion of operators realizing the generators of SU(4) at each pair of momenta, $(\boldsymbol{k}, -\boldsymbol{k})$, in the Brillouin zone. We compare the results of numerical studies of our model with recent picosecond optical experiments.
Energy carries Information

We show that for every qubit of quantum information, there is a well-defined notion of "the amount of energy that carries it," because it is a conserved quantity. This generalizes to larger systems and any conserved quantities: the eigenvalue spectrum of conserved charges has to be preserved while transferring quantum information. It is possible to "apparently" violate these conservations by losing a small fraction of information, but that must invoke a specific process which requires a large scale coherence. We discuss its implication regarding the black hole information paradox.
Stochastic Quantum Gravity, Gravitational Collapse and Grey Holes

Quantum gravity is treated as a stochastic quantization of the spacetime metric of general relativity. It is found that owing to the stochastic fluctuating behavior of the geometry, the singularity in gravitational collapse of a star has a zero probability density of occurring. Moreover, as a star collapses the probability density of a distant observer seeing an infinite red shift at the Schwarzschild radius of the star is zero. Therefore, there is a vanishing probability density of a black hole event horizon forming during gravitational collapse. A grey apparent event horizon forms during the collapse that allows information to escape.
A policy switching approach to consolidating load shedding and islanding protection schemes

In recent years there have been many improvements in the reliability of critical infrastructure systems. Despite these improvements, the power systems industry has seen relatively small advances in this regard. For instance, power quality deficiencies, a high number of localized contingencies, and large cascading outages are still too widespread. Though progress has been made in improving generation, transmission, and distribution infrastructure, remedial action schemes (RAS) remain non-standardized and are often not uniformly implemented across different utilities, independent system operators (ISOs), and regional transmission organizations (RTOs). Traditionally, load shedding and islanding have been successful protection measures in restraining propagation of contingencies and large cascading outages. This paper proposes a novel, algorithmic approach to selecting RAS policies to optimize the operation of the power network during and after a contingency. Specifically, we use policy-switching to consolidate traditional load shedding and islanding schemes. In order to model and simulate the functionality of the proposed power systems protection algorithm, we conduct Monte-Carlo, time-domain simulations using Siemens PSS/E. The algorithm is tested via experiments on the IEEE-39 topology to demonstrate that the proposed approach achieves optimal power system performance during emergency situations, given a specific set of RAS policies.
Discovering Latent Network Structure in Point Process Data

Networks play a central role in modern data analysis, enabling us to reason about systems by studying the relationships between their parts. Most often in network analysis, the edges are given. However, in many systems it is difficult or impossible to measure the network directly. Examples of latent networks include economic interactions linking financial instruments and patterns of reciprocity in gang violence. In these cases, we are limited to noisy observations of events associated with each node. To enable analysis of these implicit networks, we develop a probabilistic model that combines mutually-exciting point processes with random graph models. We show how the Poisson superposition principle enables an elegant auxiliary variable formulation and a fully-Bayesian, parallel inference algorithm. We evaluate this new model empirically on several datasets.
Learning Ordered Representations with Nested Dropout

In this paper, we present results on ordered representations of data in which different dimensions have different degrees of importance. To learn these representations we introduce nested dropout, a procedure for stochastically removing coherent nested sets of hidden units in a neural network. We first present a sequence of theoretical results for the special case of a semilinear autoencoder. We rigorously show that the application of nested dropout enforces identifiability of the units, which leads to an exact equivalence with PCA. We then extend the algorithm to deep models and demonstrate the relevance of ordered representations to a number of applications. Specifically, we use the ordered property of the learned codes to construct hash-based data structures that permit very fast retrieval, achieving retrieval in time logarithmic in the database size and independent of the dimensionality of the representation. This allows codes that are hundreds of times longer than currently feasible for retrieval. We therefore avoid the diminished quality associated with short codes, while still performing retrieval that is competitive in speed with existing methods. We also show that ordered representations are a promising way to learn adaptive compression for efficient online data reconstruction.
Input Warping for Bayesian Optimization of Non-Stationary Functions

Bayesian optimization has proven to be a highly effective methodology for the global optimization of unknown, expensive and multimodal functions. The ability to accurately model distributions over functions is critical to the effectiveness of Bayesian optimization. Although Gaussian processes provide a flexible prior over functions, there are various classes of functions that remain difficult to model. One of the most frequently occurring of these is the class of non-stationary functions. The optimization of the hyperparameters of machine learning algorithms is a problem domain in which parameters are often manually transformed a priori, for example by optimizing in "log-space," to mitigate the effects of spatially-varying length scale. We develop a methodology for automatically learning a wide family of bijective transformations or warpings of the input space using the Beta cumulative distribution function. We further extend the warping framework to multi-task Bayesian optimization so that multiple tasks can be warped into a jointly stationary space. On a set of challenging benchmark optimization tasks, we observe that the inclusion of warping greatly improves on the state-of-the-art, producing better results faster and more reliably.