nils-herrmann/clean_scirepeval huggingface dataset:
The Precautionary Principle (with Application to the Genetic Modification of Organisms)

We present a non-naive version of the Precautionary (PP) that allows us to avoid paranoia and paralysis by confining precaution to specific domains and problems. PP is intended to deal with uncertainty and risk in cases where the absence of evidence and the incompleteness of scientific knowledge carries profound implications and in the presence of risks of "black swans", unforeseen and unforeseable events of extreme consequence. We formalize PP, placing it within the statistical and probabilistic structure of ruin problems, in which a system is at risk of total failure, and in place of risk we use a formal fragility based approach. We make a central distinction between 1) thin and fat tails, 2) Local and systemic risks and place PP in the joint Fat Tails and systemic cases. We discuss the implications for GMOs (compared to Nuclear energy) and show that GMOs represent a public risk of global harm (while harm from nuclear energy is comparatively limited and better characterized). PP should be used to prescribe severe limits on GMOs.
Generalized Compression Dictionary Distance as Universal Similarity Measure

We present a new similarity measure based on information theoretic measures which is superior than Normalized Compression Distance for clustering problems and inherits the useful properties of conditional Kolmogorov complexity. We show that Normalized Compression Dictionary Size and Normalized Compression Dictionary Entropy are computationally more efficient, as the need to perform the compression itself is eliminated. Also they scale linearly with exponential vector size growth and are content independent. We show that normalized compression dictionary distance is compressor independent, if limited to lossless compressors, which gives space for optimizations and implementation speed improvement for real-time and big data applications. The introduced measure is applicable for machine learning tasks of parameter-free unsupervised clustering, supervised learning such as classification and regression, feature selection, and is applicable for big data problems with order of magnitude speed increase.
Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation

We explore how to improve machine translation systems by adding more translation data in situations where we already have substantial resources. The main challenge is how to buck the trend of diminishing returns that is commonly encountered. We present an active learning-style data solicitation algorithm to meet this challenge. We test it, gathering annotations via Amazon Mechanical Turk, and find that we get an order of magnitude increase in performance rates of improvement.
Mean-Field Networks

The mean field algorithm is a widely used approximate inference algorithm for graphical models whose exact inference is intractable. In each iteration of mean field, the approximate marginals for each variable are updated by getting information from the neighbors. This process can be equivalently converted into a feedforward network, with each layer representing one iteration of mean field and with tied weights on all layers. This conversion enables a few natural extensions, e.g. untying the weights in the network. In this paper, we study these mean field networks (MFNs), and use them as inference tools as well as discriminative models. Preliminary experiment results show that MFNs can learn to do inference very efficiently and perform significantly better than mean field as discriminative models.
Automated placement of stereotactic injections using a laser scan of the skull

Stereotactic targeting is a commonly used technique for performing injections in the brains of mice and other animals. The most common method for targeting stereoscopic injections uses the skull indentations bregma and lambda as reference points and is limited in its precision by factors such as skull curvature and individual variation, as well as an incomplete correspondence between skull landmarks and brain locations. In this software tool, a 3D laser scan of the mouse skull is taken in vitro and registered onto a reference skull using a point cloud matching algorithm, and the parameters of the transformation are used to position a glass pipette to place tracer injections. The software was capable of registering sample skulls with less than 100 micron error, and was able to target an injection in a mouse with error of roughly 500 microns. These results indicate that using skull scan registration has the potential to be widely applicable in automating stereotactic targeting of tracer injections.
Active Regression by Stratification

We propose a new active learning algorithm for parametric linear regression with random design. We provide finite sample convergence guarantees for general distributions in the misspecified model. This is the first active learner for this setting that provably can improve over passive learning. Unlike other learning settings (such as classification), in regression the passive learning rate of O(1/∊) cannot in general be improved upon. Nonetheless, the so-called 'constant' in the rate of convergence, which is characterized by a distribution-dependent risk, can be improved in many cases. For a given distribution, achieving the optimal risk requires prior knowledge of the distribution. Following the stratification technique advocated in Monte-Carlo function integration, our active learner approaches the optimal risk using piecewise constant approximations.
Log-Optimal Portfolio Selection Using the Blackwell Approachability Theorem

We present a method for constructing the log-optimal portfolio using the well-calibrated forecasts of market values. Dawid's notion of calibration and the Blackwell approachability theorem are used for computing well-calibrated forecasts. We select a portfolio using this "artificial" probability distribution of market values. Our portfolio performs asymptotically at least as well as any stationary portfolio that redistributes the investment at each round using a continuous function of side information. Unlike in classical mathematical finance theory, no stochastic assumptions are made about market values.
Bitcoin over Tor isn't a Good Idea

Bit coin is a decentralized P2P digital currency in which coins are generated by a distributed set of miners and transactions are broadcasted via a peer-to-peer network. While Bit coin provides some level of anonymity (or rather pseudonymity) by encouraging the users to have any number of random-looking Bit coin addresses, recent research shows that this level of anonymity is rather low. This encourages users to connect to the Bit coin network through anonymizers like Tor and motivates development of default Tor functionality for popular mobile SPV clients. In this paper we show that combining Tor and Bit coin creates a new attack vector. A low-resource attacker can gain full control of information flows between all users who chose to use Bit coin over Tor. In particular the attacker can link together user's transactions regardless of pseudonyms used, control which Bit coin blocks and transactions are relayed to user and can delay or discard user's transactions and blocks. Moreover, we show how an attacker can fingerprint users and then recognize them and learn their IP addresses when they decide to connect to the Bit coin network directly.
Online Energy Price Matrix Factorization for Power Grid Topology Tracking

Grid security and open markets are two major smart grid goals. Transparency of market data facilitates a competitive and efficient energy environment. But it may also reveal critical physical system information. Recovering the grid topology based solely on publicly available market data is explored here. Real-time energy prices are typically calculated as the Lagrange multipliers of network-constrained economic dispatch; that is, via a linear program (LP) typically solved every 5 min. Since the grid Laplacian matrix is a parameter of this LP, someone apart from the system operator could try inferring this topology-related matrix upon observing successive LP dual outcomes. It is first shown that the matrix of spatio-temporal prices can be factored as the product of the inverse Laplacian times a sparse matrix. Leveraging results from sparse matrix decompositions, topology recovery schemes with complementary strengths are subsequently formulated. Solvers scalable to high-dimensional and streaming market data are devised. Numerical validation using synthetic and real-load data on the IEEE 30-bus grid provide useful input for current and future market designs.
Non-unitarity or hidden observables?

A free hermitian conformal field theory is considered in Minkowski, de Sitter and anti-de Sitter spacetimes. The first part of the paper studies spacetime inversion and conformal inversion, wherein their role in the field quantization is elucidated in those spaces. The second part of the paper is concerned with the non-unitary evolution of detector's state. Several examples of such processes are provided with a clarification of how the unitarity is preserved with still having well-known thermal effects.
Exceptional AGN-driven turbulence inhibits star formation in the 3C 326N radio-galaxy

We detect bright [CII]158$\mu$m line emission from the radio galaxy 3C 326N at z=0.09, which shows weak star formation ($SFR<0.07$M$_{\odot}$~yr$^{-1}$) despite having strong H$_2$ line emission and $2\times 10^9$M$_{\odot}$ of molecular gas. The [CII] line is twice as strong as the 0-0S(1) 17$\mu$m H$_2$ line, and both lines are much in excess what is expected from UV heating. We combine infrared Spitzer and Herschel data with gas and dust modeling to infer the gas physical conditions. The [CII] line traces 30 to 50% of the molecular gas mass, which is warm (70<T<100K) and at moderate densities $700<n_{H}<3000$cm$^{-3}$. The [CII] line is broad with a blue-shifted wing, and likely to be shaped by a combination of rotation, outflowing gas, and turbulence. It matches the near-infrared H$_2$ and the Na D optical absorption lines. If the wing is interpreted as an outflow, the mass loss rate would be larger than 20M$_{\odot}$/yr, and the depletion timescale shorter than the orbital timescale ($10^8$yr). These outflow rates may be over-estimated because the stochastic injection of turbulence on galactic scales can contribute to the skewness of the line profile and mimic outflowing gas. We argue that the dissipation of turbulence is the main heating process of this gas. Cosmic rays can also contribute to the heating but they require an average gas density larger than the observational constraints. We show that strong turbulent support maintains a high gas vertical scale height (0.3-4kpc) in the disk and can inhibit the formation of gravitationally-bound structures at all scales, offering a natural explanation for the weakness of star formation in 3C 326N. To conclude, the bright [CII] line indicates that strong AGN jet-driven turbulence may play a key role in enhancing the amount of molecular gas (positive feedback) but yet can prevent star formation on galactic scales (negative feedback).
UV structure of quantum unimodular gravity

It is a well known result that any formulation of unimodular gravity is classically equivalent to General Relativity (GR), however a debate exists in the literature about this equivalence at the quantum level. In this work, we investigate the UV quantum structure of a diffeomorphism invariant formulation of unimodular gravity using functional renormalisation group methods in a Wilsonian context. We show that the effective action of the unimodular theory acquires essentially the same form with that of GR in the UV, as well as that both theories share similar UV completions within the framework of the asymptotic safety scenario for quantum gravity. Furthermore, we find that in this context the unimodular theory can appear to be non--predictive due to an increasing number of relevant couplings at high energies, and explain how this unwanted feature is in the end avoided.
Two-photon absorption in gapped bilayer graphene with a tunable chemical potential

Despite the now vast body of two-dimensional materials under study, bilayer graphene remains unique in two ways: it hosts a simultaneously tunable band gap and electron density; and stems from simple fabrication methods. These two advantages underscore why bilayer graphene is critical as a material for optoelectronic applications. In the work that follows, we calculate the one- and two-photon absorption coefficients for degenerate interband absorption in a graphene bilayer hosting an asymmetry gap and adjustable chemical potential—all at finite temperature. Our analysis is comprehensive, characterizing one- and two-photon absorptive behavior over wide ranges of photon energy, gap, chemical potential, and thermal broadening. The two-photon absorption coefficient for bilayer graphene displays a rich structure as a function of photon energy and band gap due to the existence of multiple absorption pathways and the nontrivial dispersion of the low energy bands. This systematic work will prove integral to the design of bilayer-graphene-based nonlinear optical devices.
Discovery of a new galactic center excess consistent with upscattered starlight

We present a new extended gamma ray excess detected with the Fermi Satellite Large Area Telescope toward the Galactic Center that traces the morphology of infrared starlight emission. Combined with its measured spectrum, this new extended source is approximately consistent with inverse Compton emission from a high-energy electron-positron population with energies up to about 10 GeV . Previously detected emissions tracing the 20 cm radio, interpreted as bremsstrahlung radiation, and the Galactic Center Extended emission tracing a spherical distribution and peaking at 2 GeV, are also detected. We show that the inverse Compton and bremsstrahlung emissions are likely due to the same source of electrons and positrons. All three extended emissions may be explained within the framework of a model where the dark matter annihilates to leptons or a model with unresolved millisecond pulsars in the Galactic Center.
Signal inference with unknown response: calibration uncertainty renormalized estimator

The calibration of a measurement device is crucial for every scientific experiment, where a signal has to be inferred from data. We present CURE, the calibration-uncertainty renormalized estimator, to reconstruct a signal and simultaneously the instrument's calibration from the same data without knowing the exact calibration, but its covariance structure. The idea of the CURE method, developed in the framework of information field theory, is to start with an assumed calibration to successively include more and more portions of calibration uncertainty into the signal inference equations and to absorb the resulting corrections into renormalized signal (and calibration) solutions. Thereby, the signal inference and calibration problem turns into a problem of solving a single system of ordinary differential equations and can be identified with common resummation techniques used in field theories. We verify the CURE method by applying it to a simplistic toy example and compare it against existent self-calibration schemes, Wiener filter solutions, and Markov chain Monte Carlo sampling. We conclude that the method is able to keep up in accuracy with the best self-calibration methods and serves as a noniterative alternative to them.
Attribute Efficient Linear Regression with Data-Dependent Sampling

In this paper we analyze a budgeted learning setting, in which the learner can only choose and observe a small subset of the attributes of each training example. We develop efficient algorithms for ridge and lasso linear regression, which utilize the geometry of the data by a novel data-dependent sampling scheme. When the learner has prior knowledge on the second moments of the attributes, the optimal sampling probabilities can be calculated precisely, and result in data-dependent improvements factors for the excess risk over the state-of-the-art that may be as large as $O(\sqrt{d})$, where $d$ is the problem's dimension. Moreover, under reasonable assumptions our algorithms can use less attributes than full-information algorithms, which is the main concern in budgeted learning settings. To the best of our knowledge, these are the first algorithms able to do so in our setting. Where no such prior knowledge is available, we develop a simple estimation technique that given a sufficient amount of training examples, achieves similar improvements. We complement our theoretical analysis with experiments on several data sets which support our claims.
Markov Chain Monte Carlo and Variational Inference: Bridging the Gap

Recent advances in stochastic gradient variational inference have made it possible to perform variational Bayesian inference with posterior approximations containing auxiliary random variables. This enables us to explore a new synthesis of variational inference and Monte Carlo methods where we incorporate one or more steps of MCMC into our variational approximation. By doing so we obtain a rich class of inference algorithms bridging the gap between variational methods and MCMC, and offering the best of both worlds: fast posterior approximation through the maximization of an explicit objective, with the option of trading off additional computation for additional accuracy. We describe the theoretical foundations that make this possible and show some promising first results.
Model Selection for Topic Models via Spectral Decomposition

Topic models have achieved significant successes in analyzing large-scale text corpus. In practical applications, we are always confronted with the challenge of model selection, i.e., how to appropriately set the number of topics. Following recent advances in topic model inference via tensor decomposition, we make a first attempt to provide theoretical analysis on model selection in latent Dirichlet allocation. Under mild conditions, we derive the upper bound and lower bound on the number of topics given a text collection of finite size. Experimental results demonstrate that our bounds are accurate and tight. Furthermore, using Gaussian mixture model as an example, we show that our methodology can be easily generalized to model selection analysis for other latent models.
Gauge-Invariant Average of Einstein Equations for finite Volumes

For the study of cosmological backreacktion an avaragng procedure is required. In this work a covariant and gauge invariant averaging formalism for finite volumes will be developed. This averaging will be applied to the scalar parts of Einstein's equations. For this purpose dust as a physical laboratory will be coupled to the gravitating system. The goal is to study the deviation from the homogeneous universe and the impact of this deviation on the dynamics of our universe. Fields of physical observers are included in the studied system and used to construct a reference frame to perform the averaging without a formal gauge fixing. The derived equations resolve the question whether backreaction is gauge dependent.
The Barolo Palace: medieval astronomy in the streets of Buenos Aires

Cultural heritage relating to the sky in the form of sundials, old observatories and the like, are commonly found in many cities in the Old World, but rarely in the New. This paper examines astronomical heritage embodied in the Barolo Palace in Buenos Aires. While references to Dante Alighieri and his poetry are scattered in streets, buildings and monuments around the Western world, in the city of Buenos Aires, the only street carrying Dante's name is less than three blocks long and, appropriately, is a continuation of Virgilio street. A couple of Italian immigrants -a wealthy businessman, Luis Barolo, and an imaginative architect, Mario Palanti- foresaw this situation nearly a century ago, and did not save any efforts or money with the aim of getting Dante and his cosmology an appropriate monumental recognition, in reinforced concrete. The Barolo Palace is a unique combination of both astronomy and the worldview displayed in the Divine Comedy, Dante's poetic masterpiece. It is known that the Palace's design was inspired by the great poet, but the details are not recorded; this paper relies on Dante's text to consider whether it may add to our understanding of the building. Although the links of the Palace's main architectural structure with the three realms of the Comedy have been studied in the past, its unique astronomical flavor has not been sufficiently emphasized. The word of God, as interpreted by the Fathers of the Church in Sacred Scripture, Aristotle's physics and Ptolemy's astronomy, all beautifully converge in Dante's verses, and the Barolo Palace reflects this.
Detection of core–periphery structure in networks using spectral methods and geodesic paths

We introduce several novel and computationally efficient methods for detecting “core–periphery structure” in networks. Core–periphery structure is a type of mesoscale structure that consists of densely connected core vertices and sparsely connected peripheral vertices. Core vertices tend to be well-connected both among themselves and to peripheral vertices, which tend not to be well-connected to other vertices. Our first method, which is based on transportation in networks, aggregates information from many geodesic paths in a network and yields a score for each vertex that reflects the likelihood that that vertex is a core vertex. Our second method is based on a low-rank approximation of a network's adjacency matrix, which we express as a perturbation of a tensor-product matrix. Our third approach uses the bottom eigenvector of the random-walk Laplacian to infer a coreness score and a classification into core and peripheral vertices. We also design an objective function to (1) help classify vertices into core or peripheral vertices and (2) provide a goodness-of-fit criterion for classifications into core versus peripheral vertices. To examine the performance of our methods, we apply our algorithms to both synthetically generated networks and a variety of networks constructed from real-world data sets.