nils-herrmann/clean_scirepeval huggingface dataset:
Hausdorff measures and packing measures of limit sets of CIFSs of generalized complex continued fractions

We consider a certain family of CIFSs of the generalized complex continued fractions with a complex parameter space. We show that for each CIFS of the family, the Hausdorff measure of the limit set of the CIFS with respect to the Hausdorff dimension is zero and the packing measure of the limit set of the CIFS with respect to the Hausdorff dimension is positive (main result). This is a new phenomenon of infinite CIFSs which cannot hold in finite CIFSs. We prove the main result by showing some estimates for the unique conformal measure of each CIFS of the family and by using some geometric observations.
Model-Checking on Ordered Structures

We study the model-checking problem for first- and monadic second-order logic on finite relational structures. The problem of verifying whether a formula of these logics is true on a given structure is considered intractable in general, but it does become tractable on interesting classes of structures, such as on classes whose Gaifman graphs have bounded treewidth. In this article, we continue this line of research and study model-checking for first- and monadic second-order logic in the presence of an ordering on the input structure. We do so in two settings: the general ordered case, where the input structures are equipped with a fixed order or successor relation, and the order-invariant case, where the formulas may resort to an ordering, but their truth must be independent of the particular choice of order. In the first setting we show very strong intractability results for most interesting classes of structures. In contrast, in the order-invariant case we obtain tractability results for order-invariant monadic second-order formulas on the same classes of graphs as in the unordered case. For first-order logic, we obtain tractability of successor-invariant formulas on classes whose Gaifman graphs have bounded expansion. Furthermore, we show that model-checking for order-invariant first-order formulas is tractable on coloured posets of bounded width.
Correlation between the decays h0 → γγ/gg in the MSSM with quark flavor violation

We study the loop-induced decays  see text] and  see text] in the Minimal Supersymmetric Standard Model (MSSM) with quark flavor violation (QFV), identifying  see text] with the Higgs boson with a mass of 125 GeV, where  see text] and  see text] are photon and gluon, respectively. We perform a MSSM parameter scan and a detailed analysis around a fixed reference point respecting theoretical constraints from vacuum stability conditions and experimental constraints, such as those from  see text]-meson data and electroweak precision data, as well as recent limits on Supersymmetric (SUSY) particle masses from LHC experiments. We find that (i) the relative deviation of the decay width  see text] from the Standard Model value,  see text], can be large and negative,  see text], (ii) the analogous deviation of  see text] is strongly correlated,  see text] for  see text], (iii) the relative deviation of the width ratio  see text] from the SM value,  see text], can be large (up to  Such large deviations can be observed at a future  see text] collider like ILC. Observation of the deviation patterns as shown in this study would favor the MSSM with flavor-violating squark mixings and encourage to perform further studies in this model.
A General Centrality Framework-Based on Node Navigability

Centrality metrics are a popular tool in Network Science to identify important nodes within a graph. We introduce the Potential Gain as a centrality measure that unifies many walk-based centrality metrics in graphs and captures the notion of node navigability, interpreted as the property of being reachable from anywhere else (in the graph) through short walks. Two instances of the Potential Gain (called the Geometric and the Exponential Potential Gain) are presented and we describe scalable algorithms for computing them on large graphs. We also give a proof of the relationship between the new measures and established centralities. The geometric potential gain of a node can thus be characterized as the product of its Degree centrality by its Katz centrality scores. At the same time, the exponential potential gain of a node is proved to be the product of Degree centrality by its Communicability index. These formal results connect potential gain to both the “popularity” and “similarity” properties that are captured by the above centralities.
Progressive Data Science: Potential and Challenges

Data science requires time-consuming iterative manual activities. In particular, activities such as data selection, preprocessing, transformation, and mining, highly depend on iterative trial-and-error processes that could be sped up significantly by providing quick feedback on the impact of changes. The idea of progressive data science is to compute the results of changes in a progressive manner, returning a first approximation of results quickly and allow iterative refinements until converging to a final result. Enabling the user to interact with the intermediate results allows an early detection of erroneous or suboptimal choices, the guided definition of modifications to the pipeline and their quick assessment. In this paper, we discuss the progressiveness challenges arising in different steps of the data science pipeline. We describe how changes in each step of the pipeline impact the subsequent steps and outline why progressive data science will help to make the process more effective. Computing progressive approximations of outcomes resulting from changes creates numerous research challenges, especially if the changes are made in the early steps of the pipeline. We discuss these challenges and outline first steps towards progressiveness, which, we argue, will ultimately help to significantly speed-up the overall data science process.
Semantic Frame Parsing for Information Extraction : the CALOR corpus

This paper presents a publicly available corpus of French encyclopedic history texts annotated according to the Berkeley FrameNet formalism. The main difference in our approach compared to previous works on semantic parsing with FrameNet is that we are not interested here in full text parsing but rather on partial parsing. The goal is to select from the FrameNet resources the minimal set of frames that are going to be useful for the applicative framework targeted, in our case Information Extraction from encyclopedic documents. Such an approach leverages the manual annotation of larger corpora than those obtained through full text parsing and therefore opens the door to alternative methods for Frame parsing than those used so far on the FrameNet 1.5 benchmark corpus. The approaches compared in this study rely on an integrated sequence labeling model which jointly optimizes frame identification and semantic role segmentation and identification. The models compared are CRFs and multitasks bi-LSTMs.
Analyse automatique FrameNet : une étude sur un corpus français de textes encyclopédiques (FrameNet automatic analysis : a study on a French corpus of encyclopedic texts)

Cet article présente un système d’analyse automatique en cadres sémantiques évalué sur un corpus de textes encyclopédiques d’histoire annotés selon le formalisme FrameNet. L’approche choisie repose sur un modèle intégré d’étiquetage de séquence qui optimise conjointement l’identification des cadres, la segmentation et l’identification des rôles sémantiques associés. Nous cherchons dans cette étude à analyser la complexité de la tâche selon plusieurs dimensions. Une analyse détaillée des performances du système est ainsi proposée, à la fois selon l’angle des paramètres du modèle et de la nature des données.
Inference for heterogeneous effects using low-rank estimations

We study a panel data model with general heterogeneous effects, where slopes are allowed to be varying across both individuals and times. The key assumption for dimension reduction is that the heterogeneous slopes can be expressed as a factor structure so that the high-dimensional slope matrix is of low-rank, so can be estimated using low-rank regularized regression. Our paper makes an important theoretical contribution on the "post-SVD inference". Formally, we show that the post-SVD inference can be conducted via three steps: (1) apply the nuclear-norm penalized estimation; (2) extract eigenvectors from the estimated low-rank matrices, and (3) run least squares to iteratively estimate the individual and time effect components in the slope matrix. To properly control for the effect of the penalized low-rank estimation, we argue that this procedure should be embedded with "partial out the mean structure" and "sample splitting". The resulting estimators are asymptotically normal and admit valid inferences. Empirically, we apply the proposed methods to estimate the county-level minimum wage effects on the employment.
A Standardized Project Gutenberg Corpus for Statistical Analysis of Natural Language and Quantitative Linguistics

The use of Project Gutenberg (PG) as a text corpus has been extremely popular in statistical analysis of language for more than 25 years. However, in contrast to other major linguistic datasets of similar importance,  In fact, most PG studies so far either consider only a small number of manually selected books, leading to potential biased subsets, or employ vastly different pre-processing strategies (often specified in insufficient details), raising concerns regarding the reproducibility of published results. In order to address these shortcomings, here we present the Standardized Project Gutenberg Corpus (SPGC), an open science approach to a curated version of the complete PG data containing more than 50,000 books and more than 3×109 word-tokens. Using different sources of annotated metadata, we not only provide a broad characterization of the content of PG, but also show different examples highlighting the potential of SPGC for investigating language variability across time, subjects, and authors. We publish our methodology in detail, the code to download and process the data, as well as the obtained corpus itself on three different levels of granularity (raw text, timeseries of word tokens, and counts of words). In this way, we provide a reproducible, pre-processed, full-size version of Project Gutenberg as a new scientific resource for corpus linguistics, natural language processing, and information retrieval.
Shallow Cue Guided Deep Visual Tracking via Mixed Models

In this paper, a robust visual tracking approach via mixed model based convolutional neural networks (SDT) is developed. In order to handle abrupt or fast motion, a prior map is generated to facilitate the localization of region of interest (ROI) before the deep tracker is performed. A top-down saliency model with nineteen shallow cues are employed to construct the prior map with online learnt combination weights. Moreover, apart from a holistic deep learner, four local networks are also trained to learn different components of the target. The generated four local heat maps will facilitate to rectify the holistic map by eliminating the distracters to avoid drifting. Furthermore, to guarantee the instance for online update of high quality, a prioritised update strategy is implemented by casting the problem into a label noise problem. The selection probability is designed by considering both confidence values and bio-inspired memory for temporal information integration. Experiments are conducted qualitatively and quantitatively on a set of challenging image sequences. Comparative study demonstrates that the proposed algorithm outperforms other state-of-the-art methods.
Adam Induces Implicit Weight Sparsity in Rectifier Neural Networks

In recent years, deep neural networks (DNNs) have been applied to various machine leaning tasks, including image recognition, speech recognition, and machine translation. However, large DNN models are needed to achieve state-of-the-art performance, exceeding the capabilities of edge devices. Model reduction is thus needed for practical use. In this paper, we point out that deep learning automatically induces group sparsity of weights, in which all weights connected to an output channel (node) are zero, when training DNNs under the following three conditions: (1) rectified-linear-unit (ReLU) activations, (2) an L2-regularized objective function, and (3) the Adam optimizer. Next, we analyze this behavior both theoretically and experimentally, and propose a simple model reduction method: eliminate the zero weights after training the DNN. In experiments on MNIST and CIFAR-10 datasets, we demonstrate the sparsity with various training setups. Finally, we show that our method can efficiently reduce the model size and performs well relative to methods that use a sparsity-inducing regularizer.
The Holographic Entropy Arrangement

We develop a convenient framework for characterizing multipartite entanglement in composite systems, based on relations between entropies of various subsystems. This continues the program initiated in , of using holography to effectively recast the geometric problem into an algebraic one. We prove that, for an arbitrary number of parties, our procedure identifies a finite set of entropic information quantities that we conveniently represent geometrically in the form of an arrangement of hyperplanes. This leads us to define the holographic entropy arrangement, whose algebraic and combinatorial aspects we explore in detail. Using the framework, we derive three new information quantities for four parties, as well as a new infinite family for any number of parties. A natural construct from the arrangement is the holographic entropy polyhedron which captures holographic entropy inequalities describing the physically allowed region of entropy space. We illustrate how to obtain the polyhedron by winnowing down the arrangement through a sieve to pick out candidate sign‐definite information quantities. Comparing the polyhedron with the holographic entropy cone, we find perfect agreement for 4 parties and corroborating evidence for the conjectured 5‐party entropy cone. We work with explicit configurations in arbitrary (time‐dependent) states leading to both simple derivations and an intuitive picture of the entanglement pattern.
Is There an Analytic Theory of Automorphic Functions for Complex Algebraic Curves?

The geometric Langlands correspondence for complex algebraic curves differs from the original Langlands correspondence for number fields in that it is formulated in terms of sheaves rather than functions (in the intermediate case of curves over finite fields, both formulations are possible). In a recent preprint, Robert Langlands made a proposal for developing an analytic theory of automorphic forms on the moduli space of $G$-bundles on a complex algebraic curve. Langlands envisioned these forms as eigenfunctions of some analogues of Hecke operators. In these notes I show that if $G$ is an abelian group then there are well-defined Hecke operators, and I give a complete description of their eigenfunctions and eigenvalues. For non-abelian $G$, Hecke operators involve integration, which presents some difficulties. However, there is an alternative approach to developing an analytic theory of automorphic forms, based on the existence of a large commutative algebra of global differential operators acting on half-densities on the moduli stack of $G$-bundles. This approach (which implements some ideas of Joerg Teschner) is outlined here, as a preview of a joint work with Pavel Etingof and David Kazhdan.
Testing dark matter and modifications to gravity using local Milky Way observables

Galactic rotation curves are often considered the first robust evidence for the existence of dark matter. However, even in the presence of a dark matter halo, other galactic-scale observations, such as the Baryonic Tully-Fisher Relation and the Radial Acceleration Relation, remain challenging to explain. This has motivated long-distance, infrared modifications to gravity as an alternative to the dark matter hypothesis as well as various DM theories with similar phenomenology. In general, the standard lore has been that any model that reduces to the phenomenology of MOdified Newtonian Dynamics (MOND) on galactic scales explains essentially all galaxy-scale observables. We present a framework to test precisely this statement using local Milky Way observables, including the vertical acceleration field, the rotation curve, the baryonic surface density, and the stellar disk profile. We focus on models that predict scalar amplifications of gravity, i.e., models that increase the magnitude but do not change the direction of the gravitational acceleration. We find that models of this type are disfavored relative to a simple dark matter halo model because the Milky Way data requires a substantial amplification of the radial acceleration with little amplification of the vertical acceleration. We conclude that models which result in a MOND-like force struggle to simultaneously explain both the rotational velocity and vertical motion of nearby stars in the Milky Way.
CI Camelopardalis: The first sgB[e]-high mass X-ray binary twenty years on: A supernova imposter in our own Galaxy?

The Galactic supergiant B[e] star CI Camelopardalis (CI Cam) was the first sgB[e] star detected during an X-ray outburst. The star brightened to ∼2 Crab in the X-ray regime (∼5 × 10−8 ergs cm−2 s−1 in the 2–25 keV range) within hours before decaying to a quiescent level in less than two weeks, clearly indicative of binarity. Since the outburst of CI Cam, several sgB[e] stars have been identified as X-ray overluminous for a single star (i.e. LX >  10−7 Lbol). This small population has recently expanded to include two ultra luminous X-ray sources (ULX), Holmberg II X-1 and NGC 300 ULX-1/supernova imposter SN2010da.
 Since the discovery of X-ray emission from CI Cam, there have been many developments in the field of massive binary evolution. In light of the recent inclusion of two ULXs in the population of X-ray bright sgB[e] stars, we revisit CI Cam to investigate its behaviour over several timescales and shed further light on the nature of the compact object in the system, its X-ray outburst in 1998 and the binary system parameters.
Methods. We analysed archival XMM-Newton EPIC-pn spectra and light curves along with new data from Swift and NuSTAR. We also present high-resolution (R ∼ 85 000) Mercator/HERMES optical spectra, including a spectrum taken 1.02 days after our NuSTAR observation.
 Despite being in quiescence, CI Cam is highly X-ray variable on timescales of days, both in terms of total integrated flux and spectral shape. We interpret these variations by invoking the presence of an accreting compact companion immersed in a dense, highly structured, aspherical circumstellar envelope. The differences in the accretion flux and circumstellar extinction represent either changes in this environment, triggered by variable mass loss from the star, or the local conditions to the accretor due to its orbital motion. We find no evidence for pulsations in the X-ray light curve.
Conclusions. CI Cam has many similarities with SN2010da across mid-IR, optical and X-ray wavelengths suggesting that, subject to distance determination for CI Cam, if CI Cam was located in an external galaxy its 1998 outburst would have led to a classification as a supernova imposter.
Discovering the twin Higgs boson with displaced decays

The twin Higgs mechanism keeps the scalar sector of the Standard Model (SM) natural while remaining consistent with the nonobservation of new colored particles at the Large Hadron Collider (LHC). In this construction the heavy twin Higgs boson provides a portal between the SM particles and the twin sector, but is quite challenging to discover at colliders. In the fraternal twin Higgs setup, where light twin quarks are absent, we study a novel discovery channel for the heavy twin Higgs boson by considering its decay to a pair of light Higgs bosons, one of which subsequently decays to glueball states in the twin sector, leading to displaced vertex signatures. We estimate the sensitivity of existing LHC searches in this channel, and assess the discovery potential of the high luminosity (HL) LHC. We show that the glueballs probed by these searches are outside the sensitivity of existing searches for exotic decays of the light Higgs boson. In addition, we demonstrate that the displaced signals we consider probe a region of heavy Higgs masses beyond the reach of prompt signals. We also comment on the possibility of probing the input parameters of the microscopic physics and providing a way to test the twin Higgs mechanism with this channel.
Cosmic neutrino background search experiments as decaying dark matter detectors

We investigate the possibility that particles that are long-lived on cosmological scales, making up part or all of the dark matter, decay to neutrinos that have present day energies around an eV. The neutrinos from these decays can potentially be visible at experiments that hope to directly observe the cosmic neutrino background through neutrino capture on tritium, such as PTOLEMY. In the context of a simple model that can realize such decays, we discuss the allowed signatures at a PTOLEMY-like experiment given current cosmological constraints.
Symmetry breaking and reheating after inflation in no-scale flipped SU(5)

No-scale supergravity and the flipped SU(5)×U(1) gauge group provide an ambitious prototype string-inspired scenario for physics below the string scale, which can accommodate the Starobinsky-like inflation favoured by observation when the inflaton is associated with one of the singlet fields associated with neutrino mass generation. During inflation, the vacuum remains in the unbroken GUT phase, and GUT symmetry breaking occurs later when a field with a flat direction (the flaton) acquires a vacuum expectation value. Inflaton decay and the reheating process depend crucially on GUT symmetry breaking, as decay channels open and close, depending on the value of the flaton vacuum expectation value. Here, we consider the simultaneous cosmological evolution of both the inflaton and flaton fields after inflation. We distinguish weak, moderate and strong reheating regimes, and calculate in each case the entropy produced as all fields settle to their global minima. These three reheating scenarios differ in the value of a Yukawa coupling that introduces mass mixing between the singlets and the 10s of SU(5). The dynamics of the GUT transition has an important impact on the production of gravitinos, and we also discuss the pattern of neutrino masses we expect in each of the three cases. Finally, we use recent CMB limits on neutrino masses to constrain the reheating models, finding that neutrino masses and the cosmological baryon asymmetry can both be explained if the reheating is strong.
Composite gravity from a metric-independent theory of fermions

We present a metric-independent, diffeomorphism-invariant model with interacting fermions that contains a massless composite graviton in its spectrum. The model is motivated by the supersymmetric D-brane action, modulated by a fermion potential. The gravitational coupling is related to new physics at the cutoff scale that regularizes UV divergences. We also speculate on possible extensions of the model.
Operator growth in random quantum circuits with symmetry

We study random quantum circuits with symmetry, where the local 2-site unitaries are drawn from a quotient or subgroup of the full unitary group $U(d)$. Random quantum circuits are minimal models of local quantum chaotic dynamics and can be used to study operator growth and the emergence of diffusive hydrodynamics. We derive the transition probabilities for the stochastic process governing the growth of operators in four classes of symmetric random circuits. We then compute the butterfly velocities and diffusion constants for a spreading operator by solving a simple random walk in each class of circuits.