nils-herrmann/clean_scirepeval huggingface dataset:
Kdetrees: Non-parametric Estimation of Phylogenetic Tree Distributions

Although the majority of gene histories found in a clade of organisms are expected to be generated by a common process (e.g. the coalescent process), it is well known that numerous other coexisting processes (e.g. horizontal gene transfers, gene duplication and subsequent neofunctionalization) will cause some genes to exhibit a history distinct from those of the majority of genes. Such 'outlying' gene trees are considered to be biologically interesting, and identifying these genes has become an important problem in phylogenetics.



We propose and implement kdetrees, a non-parametric method for estimating distributions of phylogenetic trees, with the goal of identifying trees that are significantly different from the rest of the trees in the sample. Our method compares favorably with a similar recently published method, featuring an improvement of one polynomial order of computational complexity (to quadratic in the number of trees analyzed), with simulation studies suggesting only a small penalty to classification accuracy. Application of kdetrees to a set of Apicomplexa genes identified several unreliable sequence alignments that had escaped previous detection, as well as a gene independently reported as a possible case of horizontal gene transfer. We also analyze a set of Epichloë genes, fungi symbiotic with grasses, successfully identifying a contrived instance of paralogy.



Our method for estimating tree distributions and identifying outlying trees is implemented as the R package kdetrees and is available for download from CRAN.
Signal amplification in an agent-based herding model

A growing part of the behavioral finance literature has addressed some of the stylized facts of financial time series as macroscopic patterns emerging from herding interactions among groups of agents with heterogeneous trading strategies and a limited rationality. We extend a stochastic herding formalism introduced for the modeling of decision making among financial agents, in order to take also into account an external influence. In particular, we study the amplification of an external signal imposed upon the agents by a mechanism of resonance. This signal can be interpreted as an advertising or a public perception in favor or against one of the two possible trading behaviors, thus periodically breaking the symmetry of the system and acting as a continuously varying exogenous shock. The conditions for the ensemble of agents to more accurately follow the periodicity of the signal are studied, finding a maximum in the response of the system for a given range of values of both the noise and the frequency of the input signal.
Asymptotic Arbitrage in the Heston Model

In this paper, we introduce a new form of asymptotic arbitrage, which we call a partial asymptotic arbitrage, half-way between those of Follmer & Schachermayer (2007) ] and Kabanov & Kramkov (1998) ]. In the context of the Heston model, we establish a precise link between the set of equivalent martingale measures, the ergodicity of the underlying variance process and this partial asymptotic arbitrage. In contrast to Follmer & Schachermayer (2007) ], our result does not assume a suitable condition on the stock price process to allow for (partial) asymptotic arbitrage.
Variational algorithms for marginal MAP

The marginal maximum a posteriori probability (MAP) estimation problem, which calculates the mode of the marginal posterior distribution of a subset of variables with the remaining variables marginalized, is an important inference problem in many models, such as those with hidden variables or uncertain parameters. Unfortunately, marginal MAP can be NP-hard even on trees, and has attracted less attention in the literature compared to the joint MAP (maximization) and marginalization problems. We derive a general dual representation for marginal MAP that naturally integrates the marginalization and maximization operations into a joint variational optimization problem, making it possible to easily extend most or all variational-based algorithms to marginal MAP. In particular, we derive a set of "mixed-product" message passing algorithms for marginal MAP, whose form is a hybrid of max-product, sum-product and a novel "argmax-product" message updates. We also derive a class of convergent algorithms based on proximal point methods, including one that transforms the marginal MAP problem into a sequence of standard marginalization problems. Theoretically, we provide guarantees under which our algorithms give globally or locally optimal solutions, and provide novel upper bounds on the optimal objectives. Empirically, we demonstrate that our algorithms significantly outperform the existing approaches, including a state-of-the-art algorithm based on local search methods.
Naturalness and the Status of Supersymmetry

For decades, the unnaturalness of the weak scale has been the dominant problem motivating new particle physics, and weak-scale supersymmetry has been the dominant proposed solution. This paradigm is now being challenged by a wealth of experimental data. In this review, we begin by recalling the theoretical motivations for weak-scale supersymmetry, including the gauge hierarchy problem, grand unification, and WIMP (weakly interacting massive particle) dark matter, and their implications for superpartner masses. These are set against the leading constraints on supersymmetry from collider searches, the Higgs boson mass, and low-energy constraints on flavor and CP violation. We then critically examine attempts to quantify naturalness in supersymmetry, stressing the many subjective choices that influence the results both quantitatively and qualitatively. Finally, we survey various proposals for natural supersymmetric models, including effective supersymmetry, focus point supersymmetry, compressed supersymmetry...
An Introductory Study on Time Series Modeling and Forecasting

Modeling and forecasting of time series data has fundamental importance in various practical domains. The aim of this book is to present a concise description of some popular time series forecasting models with their salient features. Three important classes of time series models, viz. stochastic, neural networks and support vector machines are studied together with their inherent forecasting strengths and weaknesses. The book also meticulously discusses about several basic issues related to time series analysis, such as stationarity, parsimony, overfitting, etc. Our study is enriched by presenting the empirical forecasting results, conducted on six real-world time series datasets. Five performance measures are used to evaluate the forecasting accuracies of different models as well as to compare the models. For each of the six time series datasets, we further show the obtained forecast diagram which graphically depicts the closeness between the original and predicted observations.
A Scalable Generative Graph Model with Community Structure

Network data is ubiquitous and growing, yet we lack realistic generative network models that can be calibrated to match real-world data. The recently proposed Block Two-Level Erdss-Renyi (BTER) model can be tuned to capture two fundamental properties: degree distribution and clustering coefficients. The latter is particularly important for reproducing graphs with community structure, such as social networks. In this paper, we compare BTER to other scalable models and show that it gives a better fit to real data. We provide a scalable implementation that requires only O(d_max) storage where d_max is the maximum number of neighbors for a single node. The generator is trivially parallelizable, and we show results for a Hadoop MapReduce implementation for a modeling a real-world web graph with over 4.6 billion edges. We propose that the BTER model can be used as a graph generator for benchmarking purposes and provide idealized degree distributions and clustering coefficient profiles that can be tuned for user specifications.
Crowdsourcing for bioinformatics

Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base population and protein structure determination all benefit from human input. In some cases, people are needed in vast quantities, whereas in others, we need just a few with rare abilities. Crowdsourcing encompasses an emerging collection of approaches for harnessing such distributed human intelligence. Recently, the bioinformatics community has begun to apply crowdsourcing in a variety of contexts, yet few resources are available that describe how these human-powered systems work and how to use them effectively in scientific domains.



Here, we provide a framework for understanding and applying several different types of crowdsourcing. The framework considers two broad classes: systems for solving large-volume 'microtasks' and systems for solving high-difficulty 'megatasks'. Within these classes, we discuss system types, including volunteer labor, games with a purpose, microtask markets and open innovation contests. We illustrate each system type with successful examples in bioinformatics and conclude with a guide for matching problems to crowdsourcing solutions that highlights the positives and negatives of different approaches.
Taming the Curse of Dimensionality: Discrete Integration by Hashing and Optimization

Integration is affected by the curse of dimensionality and quickly becomes intractable as the dimensionality of the problem grows. We propose a randomized algorithm that, with high probability, gives a constant-factor approximation of a general discrete integral defined over an exponentially large set. This algorithm relies on solving only a small number of instances of a discrete combinatorial optimization problem subject to randomly generated parity constraints used as a hash function. As an application, we demonstrate that with a small number of MAP queries we can efficiently approximate the partition function of discrete graphical models, which can in turn be used, for instance, for marginal computation or model selection.
Category theory for scientists (Old version)

There are many books designed to introduce category theory to either a mathematical audience or a computer science audience. In this book, our audience is the broader scientific community. We attempt to show that category theory can be applied throughout the sciences as a framework for modeling phenomena and communicating results. In order to target the scientific audience, this book is example-based rather than proof-based. For example, monoids are framed in terms of agents acting on objects, sheaves are introduced with primary examples coming from geography, and colored operads are discussed in terms of their ability to model self-similarity. 
A new version with solutions to exercises will be available through MIT Press.
Evidence of rocky planetesimals orbiting two Hyades stars

The Hyades is the nearest open cluster, relatively young and containing numerous A-type stars; its known age, distance, and metallicity make it an ideal site to study planetary systems around 2–3 M⊙ stars at an epoch similar to the late heavy bombardment. Hubble Space Telescope far-ultraviolet spectroscopy strongly suggests ongoing, external metal pollution in two remnant Hyads. For ongoing accretion in both stars, the polluting material has log [n(Si)/n(C)] > 0.2, is more carbon deficient than chondritic meteorites and is thus rocky. These data are consistent with a picture where rocky planetesimals and small planets have formed in the Hyades around two main-sequence A-type stars, whose white dwarf descendants bear the scars. These detections via metal pollution are shown to be equivalent to infrared excesses of LIR/L* ∼ 10−6 in the terrestrial zone of the stars.
Unravelling obese black holes in the first galaxies

We predict the existence and observational signatures of a new class of objects that assembled early, during the first billion years of cosmic time: Obese Black-hole Galaxies (OBGs). OBGs are objects in which the mass of the central black hole initially exceeds that of the stellar component of the host galaxy, and the luminosity from black-hole accretion dominates the starlight. From a cosmological simulation, we demonstrate that there are sites where star formation is initially inhibited and direct-collapse black holes (DCBHs) form due to the photo-dissociating effect of Lyman-Werner radiation on molecular hydrogen. We show that the formation of OBGs is inevitable, because the probability of finding the required extra-galactic environment and the right physical conditions in a halo conducive to DCBH formation is quite high in the early universe. We estimate an OBG number density of 0.009/Mpc^3 at z~8 and 0.03/Mpc^3 at z~6. Extrapolating from our simulation volume, we infer that the most luminous quasars detected at z~6 likely transited through an earlier OBG phase. We find that these primordial galaxies start off with an over-massive BH and acquire their stellar component from subsequent merging as well as in-situ star formation. In doing so, they inevitably go through an OBG phase dominated by the accretion luminosity at the Eddington rate or below, released from the growing BH. The OBG phase is characterised by an ultra-violet (UV) spectrum with slope of beta ~ -2.3 and the absence of a Balmer Break. OBGs should also be spatially unresolved, and are expected to be brighter than the majority of known high-redshift galaxies. OBGs could potentially be revealed via HST follow-up imaging of samples of brighter Lyman-break galaxies provided by wide-area ground-based surveys such as UltraVISTA, and should be easily uncovered and studied with instruments aboard JWST...(abridged)
Maximizing acceptance probability for active friending in online social networks

Friending recommendation has successfully contributed to the explosive growth of online social networks. Most friending recommendation services today aim to support passive friending, where a user passively selects friending targets from the recommended candidates. In this paper, we advocate a recommendation support for active friending, where a user actively specifies a friending target. To the best of our knowledge, a recommendation designed to provide guidance for a user to systematically approach his friending target has not been explored for existing online social networking services. To maximize the probability that the friending target would accept an invitation from the user, we formulate a new optimization problem, namely, Acceptance Probability Maximization (APM), and develop a polynomial time algorithm, called Selective Invitation with Tree and In-Node Aggregation (SITINA), to find the optimal solution. We implement an active friending service with SITINA on Facebook to validate our idea. Our user study and experimental results reveal that SITINA outperforms manual selection and the baseline approach in solution quality efficiently.
Elliptic-flow suppression due to hadron mass spectrum

Hadron resonance gas models provide a good description of the equation of state of quantum chromodynamics determined by lattice QCD calculations at temperatures $T \sim 100-155$ MeV. In this paper we investigate the effects of an exponentially increasing hadron mass spectrum (Hagedorn spectrum) on the azimuthal anisotropy of the rapidly expanding matter formed in ultrarelativistic heavy ion collisions. If the temperature at which the conversion from fluid degrees of freedom to hadrons is sufficiently close to the Hagedorn temperature, the production of Hagedorn resonances suppresses the differential elliptic flow of all hadron species.
Continuous-time Infinite Dynamic Topic Models

Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the collection that resembles the topical theme found in an article at hand. 
The first wave of topic models developed were able to discover the prevailing topics in a big collection of documents spanning a period of time. It was later realized that these time-invariant models were not capable of modeling 1) the time varying number of topics they discover and 2) the time changing structure of these topics. Few models were developed to address this two deficiencies. The online-hierarchical Dirichlet process models the documents with a time varying number of topics. It varies the structure of the topics over time as well. However, it relies on document order, not timestamps to evolve the model over time. The continuous-time dynamic topic model evolves topic structure in continuous-time. However, it uses a fixed number of topics over time. 
In this dissertation, I present a model, the continuous-time infinite dynamic topic model, that combines the advantages of these two models 1) the online-hierarchical Dirichlet process, and 2) the continuous-time dynamic topic model. More specifically, the model I present is a probabilistic topic model that does the following: 1) it changes the number of topics over continuous time, and 2) it changes the topic structure over continuous-time. 
I compared the model I developed with the two other models with different setting values. The results obtained were favorable to my model and showed the need for having a model that has a continuous-time varying number of topics and topic structure.
Fields and Laplacians on Quantum Geometries

In fundamentally discrete approaches to quantum gravity such as loop quantum gravity, spin-foam models, group field theories or Regge calculus observables are functions on discrete geometries. We present a bra-ket formalism of function spaces and discrete calculus on abstract simplicial complexes equipped with geometry and apply it to the mentioned theories of quantum gravity. In particular we focus on the quantum geometric Laplacian and discuss as an example the expectation value of the heat kernel trace from which the spectral dimension follows.
Seiberg-Witten Instability of Various Topological Black Holes

We review the Seiberg-Witten instability of topological black holes in Anti-de Sitter space due to nucleation of brane-anti-brane pairs. We start with black holes in general relativity, and then proceed to discuss the peculiar property of topological black holes in Ho\v{r}ava-Lifshitz gravity -- they have instabilities that occur at only finite range of distance away from the horizon. This behavior is not unique to black holes in Ho\v{r}ava-Lifshitz theory, as it is also found in the relatively simple systems of charged black hole with dilaton hair that arise in low energy limit of string theory.
Weak and Strong No-Arbitrage Conditions for Continuous Financial Markets

We propose a unified analysis of a whole spectrum of no-arbitrage conditions for financial market models based on continuous semimartingales. In particular, we focus on no-arbitrage conditions weaker than the classical notions of No Arbitrage opportunity (NA) and No Free Lunch with Vanishing Risk (NFLVR). We provide a complete characterization of the considered no-arbitrage conditions, linking their validity to the characteristics of the discounted asset price process and to the existence and the properties of (weak) martingale deflators, and review classical as well as recent results.
Testing the Pauli Exclusion Principle for Electrons

One of the fundamental rules of nature and a pillar in the foundation of quantum theory and thus of modern physics is represented by the Pauli Exclusion Principle. We know that this principle is extremely well fulfilled due to many observations. Numerous experiments were performed to search for tiny violation of this rule in various systems. The experiment VIP at the Gran Sasso underground laboratory is searching for possible small violations of the Pauli Exclusion Principle for electrons leading to forbidden X-ray transitions in copper atoms. VIP is aiming at a test of the Pauli Exclusion Principle for electrons with high accuracy, down to the level of 10−29 – 10−30, thus improving the previous limit by 3–4 orders of magnitude. The experimental method, results obtained so far and new developments within VIP2 (follow-up experiment at Gran Sasso, in preparation) to further increase the precision by 2 orders of magnitude will be presented.
Progress towards understanding baryon resonances

The composite nature of baryons manifests itself in the existence of a rich spectrum of excited states, in particular in the important mass region 1–2 GeV for the light-flavoured baryons. The properties of these resonances can be identified by systematic investigations using electromagnetic and strong probes, primarily with beams of electrons, photons, and pions. After decades of research, the fundamental degrees of freedom underlying the baryon excitation spectrum are still poorly understood. The search for hitherto undiscovered but predicted resonances continues at many laboratories around the world. Recent results from photo- and electroproduction experiments provide intriguing indications for new states and shed light on the structure of some of the known nucleon excitations. The continuing study of available data sets with consideration of new observables and improved analysis tools have also called into question some of the earlier findings in baryon spectroscopy. Other breakthrough measurements have been performed in the heavy-baryon sector, which has seen a fruitful period in recent years, in particular at the B factories and the Tevatron. First results from the large hadron collider indicate rapid progress in the field of bottom baryons. In this review, we discuss the recent experimental progress and give an overview of theoretical approaches.
Sparse Shape Reconstruction

This paper introduces a new shape-based image reconstruction technique applicable to a large class of imaging problems formulated in a variational sense. Given a collection of shape priors (a shape dictionary), we define our problem as choosing the right elements and geometrically composing them through basic set operations to characterize desired regions in the image. This combinatorial problem can be relaxed and then solved using classical descent methods. The main component of this relaxation is forming certain compactly supported functions which we call "knolls", and reformulating the shape representation as a basis expansion in terms of such functions. To select suitable elements of the dictionary, our problem ultimately reduces to solving a nonlinear program with sparsity constraints. We provide a new sparse nonlinear reconstruction technique to approach this problem. The performance of proposed technique is demonstrated with some standard imaging problems including image segmentation, X-ray tomography and diffusive tomography.