nils-herrmann/clean_scirepeval huggingface dataset:
Lower Bounds for Maximal Matchings and Maximal Independent Sets

There are distributed graph algorithms for finding maximal matchings and maximal independent sets in O(Δ + log^* n) communication rounds; here n is the number of nodes and Δ is the maximum degree. The lower bound by Linial (1987, 1992) shows that the dependency on n is optimal: these problems cannot be solved in o(log^* n) rounds even if Δ = 2. However, the dependency on Δ is a long-standing open question, and there is currently an exponential gap between the upper and lower bounds. We prove that the upper bounds are tight. We show that maximal matchings and maximal independent sets cannot be found in o(Δ + log log n / log log log n) rounds with any randomized algorithm in the LOCAL model of distributed computing. As a corollary, it follows that there is no deterministic algorithm for maximal matchings or maximal independent sets that runs in o(Δ + log n / log log n) rounds; this is an improvement over prior lower bounds also as a function of n.
On the nonexistence of F{\o}lner sets

We show that there is $n\in \mathbf N$, a finite system $\Sigma(\vec x,\vec y)$ of equations and inequations having a solution in some group, where $\vec x$ has length $n$, and $\epsilon>0$ such that: for any group $G$ and any $\vec a\in G^n$, if the system $\Sigma(\vec a,\vec y)$ has a solution in $G$, then there is no $(\vec a,\epsilon)$-F{\o} lner set in $G$. The proof uses ideas from model-theoretic forcing together with the observation that no amenable group can be existentially closed. Along the way, we also observe that no existentially closed group can be exact, have the Haagerup property, or have property (T). Finally, we show that, for $n$ large enough and for $\epsilon$ small enough, the existence of $(F,\epsilon)$-F{\o} lner sets, where $F$ has size at most $n$, cannot be expressed in a first-order way uniformly in all groups.
Handling the uncertainties in the Galactic Dark Matter distribution for particle Dark Matter searches

In this work we characterize the distribution of Dark Matter (DM) in the Milky Way (MW), and its uncertainties, adopting the well known “Rotation Curve” method. We perform a full marginalization over the uncertainties of the Galactic Parameters and over the lack of knowledge on the morphology of the baryonic components of the Galaxy. The local DM density ρ0 is constrained to the range 0.3– 0.8 GeV/cm3 at the 2 σ level, and has a strong positive correlation to R0, the local distance from the Galactic Center. The not well-known value of R0 is thus, at the moment, a major limitation in determining ρ0. Similarly, we find that the inner slope of the DM profile, γ, is very weakly constrained, showing no preference for a cored profile (γ ≃ 0) or a cuspy one (γ ≃[1.0,1.4]). Some combination of parameters can be, however, strongly constrained. For example the often used standard ρ0=0.3 GeV/cm3, R0=8.5 kpc is excluded at more than 4 σ. We release the full likelihood of our analysis in a tabular form over a multidimensional grid in the parameters characterizing the DM distribution, namely the scale radius Rs, the scale density ρs, the inner slope of the profile γ, and R0. The likelihood can be used to include the effect of the DM distribution uncertainty on the results of searches for an indirect DM signal in gamma-rays or neutrinos, from the Galactic Center (GC), or the Halo region surrounding it. As one example, we study the case of the GC excess in gamma rays. Further applications of our tabulated uncertainties in the DM distribution involve local DM searches, like direct detection and anti-matter observations, or global fits combining local and GC searches.
Electromagnetism and hidden vector fields in modified gravity theories: Spontaneous and induced vectorization

In general relativity, Maxwell's equations are embedded in curved spacetime through the minimal prescription, but this could change if strong-gravity modifications are present. We show that with a nonminimal coupling between gravity and a massless vector field, nonperturbative effects can arise in compact stars. We find solutions describing stars with nontrivial vector field configurations, some of which are associated to an instability, while others are not. The vector field can be interpreted either as the electromagnetic field, or as a hidden vector field weakly coupled with the standard model.
Bayesian reconstruction of the Milky Way dark matter distribution

We develop a novel Bayesian methodology aimed at reliably and precisely inferring the distribution of dark matter within the Milky Way using rotation curve data. We identify a subset of the available rotation curve tracers that are mutually consistent with each other, thus eliminating data sets that might suffer from systematic bias. We investigate different models for the mass distribution of the luminous (baryonic) component that bracket the range of likely morphologies. We demonstrate the statistical performance of our method on simulated data in terms of coverage, fractional distance, and mean squared error. Applying it to Milky Way data we measure the local dark matter density at the solar circle ρ0 to be ρ0 = 0.43 ± 0.02(stat) ± 0.01(sys) GeV/cm3, with an accuracy ∼ 6%. This result is robust to the assumed baryonic morphology. The scale radius and inner slope of the dark matter profile are degenerate and cannot be individually determined with high accuracy. We show that these results are robust to several possible residual systematic errors in the rotation curve data.
The fate of planetesimal discs in young open clusters: implications for 1I/’Oumuamua, the Kuiper belt, the Oort cloud, and more

We perform N-body simulations of the early phases of open cluster evolution including a large population of planetesimals, initially arranged in Kuiper-belt like discs around each star. Using a new, fourth-order, and time-reversible N-body code on Graphics Processing Units (GPUs), we evolve the whole system under the stellar gravity, i.e. treating planetesimals as test particles, and consider two types of initial cluster models, similar to IC348 and the Hyades, respectively. In both cases, planetesimals can be dynamically excited, transferred between stars, or liberated to become free-floating (such as A/2017 U1 or ’Oumuamua) during the early cluster evolution. We find that planetesimals captured from another star are not necessarily dynamically distinct from those native to a star. After an encounter, both native and captured planetesimals can exhibit aligned periastrons, qualitatively similar to that seen in the Solar system and commonly thought to be the signature of Planet 9. We discuss the implications of our results for both our Solar system and exoplanetary systems.
Efficient minimum distance estimation of Pareto exponent from top income shares

We propose an efficient estimation method for the income Pareto exponent when only certain top income shares are observable. Our estimator is based on the asymptotic theory of weighted sums of order statistics and the efficient minimum distance estimator. Simulations show that our estimator has excellent finite sample properties. We apply our estimation method to U.S. top income share data and find that the Pareto exponent has been stable at around 1.5 since 1985, suggesting that the rise in inequality during the last three decades is mainly driven by redistribution between the rich and poor, not among the rich.
A Conjecture Involving Positive Solutions of a Simple Scalar Linear Time-Varying State Equation with Delay

A simple conjecture is presented concerning positive solutions of a scalar, time-varying, linear state equation with delay. Although the equation arises in the context of stock trading, no knowledge of finance is needed in the analysis to follow. Starting from positive initial conditions, the state X(k) is governed by a nonnegative constant parameter alpha and a time-varying parameter v(k) with known bounds but otherwise arbitrarily time-varying. We conjecture that if the state X(k) is positive for k less than or equal to N in response to a "distinguished path" v*(k) which we define, then for all admissible paths v(k), positivity of the state is also guaranteed. The conjecture is supported by theoretical analysis for some special cases and simulations.
An optional decomposition of $\mathscr{Y}^{g,\xi}-submartingales$ and applications to the hedging of American options in incomplete markets

In the recent paper , the notion of Y g,ξ -submartingale processes has been introduced. Within a jump-diﬀusion model, we prove here that a process X which satisﬁes the simultaneous Y Q ,g,ξ submartingale property under a suitable family of equivalent probability measures Q , admits a nonlinear optional decomposition . This is an analogous result to the well known optional decomposition of simultaneous (classical and E g -)supermartingales. We then apply this decomposition to the super-hedging problem of an American option in a jump-diﬀusion model, from the buyer’s point of view. We obtain an inﬁnitesimal characterization of the buyer’s superhedging price, this result being completely new in the literature. Indeed, it is well known that the seller’s superheding price of an American option admits an inﬁnitesimal representation in terms of the minimal supersolution of a constrained reﬂected BSDE . To the best of our knowledge, no analogous result has been established for the buyer of the American option in an incomplete market. Our results ﬁll this gap, and show that the buyer’s super-hedging price admits an inﬁnitesimal characterization in terms of the maximal subsolution of a constrained reﬂected BSDE .
An Application of Manifold Learning in Global Shape Descriptors

With the rapid expansion of applied 3D computational vision, shape descriptors have become increasingly important for a wide variety of applications and objects from molecules to planets. Appropriate shape descriptors are critical for accurate (and efficient) shape retrieval and 3D model classification. Several spectral-based shape descriptors have been introduced by solving various physical equations over a 3D surface model. In this paper, for the first time, we incorporate a specific manifold learning technique, introduced in statistics and machine learning, to develop a global, spectral-based shape descriptor in the computer graphics domain. The proposed descriptor utilizes the Laplacian Eigenmap technique in which the Laplacian eigenvalue problem is discretized using an exponential weighting scheme. As a result, our descriptor eliminates the limitations tied to the existing spectral descriptors, namely dependency on triangular mesh representation and high intra-class quality of 3D models. We also present a straightforward normalization method to obtain a scale-invariant and noise-resistant descriptor. The extensive experiments performed in this study using two standard 3D shape benchmarks—high-resolution TOSCA and McGill datasets—demonstrate that the present contribution provides a highly discriminative and robust shape descriptor under the presence of a high level of noise, random scale variations, and low sampling rate, in addition to the known isometric-invariance property of the Laplace–Beltrami operator. The proposed method significantly outperforms state-of-the-art spectral descriptors in shape retrieval and classification. The proposed descriptor is limited to closed manifolds due to its inherited inability to accurately handle manifolds with boundaries.
Multi-stream CNN based Video Semantic Segmentation for Automated Driving

Majority of semantic segmentation algorithms operate on a single frame even in the case of videos. In this work, the goal is to exploit temporal information within the algorithm model for leveraging motion cues and temporal consistency. We propose two simple high-level architectures based on Recurrent FCN (RFCN) and Multi-Stream FCN (MSFCN) networks. In case of RFCN, a recurrent network namely LSTM is inserted between the encoder and decoder. MSFCN combines the encoders of different frames into a fused encoder via 1x1 channel-wise convolution. We use a ResNet50 network as the baseline encoder and construct three networks namely MSFCN of order 2 & 3 and RFCN of order 2. MSFCN-3 produces the best results with an accuracy improvement of 9% and 15% for Highway and New York-like city scenarios in the SYNTHIA-CVPR'16 dataset using mean IoU metric. MSFCN-3 also produced 11% and 6% for SegTrack V2 and DAVIS datasets over the baseline FCN network. We also designed an efficient version of MSFCN-2 and RFCN-2 using weight sharing among the two encoders. The efficient MSFCN-2 provided an improvement of 11% and 5% for KITTI and SYNTHIA with negligible increase in computational complexity compared to the baseline version.
Autoencoders and Generative Adversarial Networks for Anomaly Detection for Sequences

We introduce synthetic oversampling in anomaly detection for multi-feature sequence datasets based on autoencoders and generative adversarial networks. The first approach considers the use of an autoencoder in conjunction with standard oversampling methods to generate synthetic data that captures the sequential nature of the data. A different model uses generative adversarial networks to generate structure preserving synthetic data for the minority class. We also use generative adversarial networks on the majority class as an outlier detection method for novelty detection. We show that the use of generative adversarial network based synthetic data improves classification model performance on a variety of sequence data sets.
Lattice Identification and Separation: Theory and Algorithm

Motivated by lattice mixture identification and grain boundary detection, we present a framework for lattice pattern representation and comparison, and propose an efficient algorithm for lattice separation. We define new scale and shape descriptors, which helps to considerably reduce the size of equivalence classes of lattice bases. These finitely many equivalence relations are fully characterized by modular group theory. We construct the lattice space $\mathscr{L}$ based on the equivalent descriptors and define a metric $d_{\mathscr{L}}$ to accurately quantify the visual similarities and differences between lattices. Furthermore, we introduce the Lattice Identification and Separation Algorithm (LISA), which identifies each lattice patterns from superposed lattices. LISA finds lattice candidates from the high responses in the image spectrum, then sequentially extracts different layers of lattice patterns one by one. Analyzing the frequency components, we reveal the intricate dependency of LISA's performances on particle radius, lattice density, and relative translations. Various numerical experiments are designed to show LISA's robustness against a large number of lattice layers, moir\'{e} patterns and missing particles.
On the Possibilities and Limitations of Multi-hop Reasoning Under Linguistic Imperfections

Systems for language understanding have become remarkably strong at overcoming linguistic imperfections in tasks involving phrase matching or simple reasoning. Yet, their accuracy drops dramatically as the number of reasoning steps increases. We present the first formal framework to study such empirical observations. It allows one to quantify the amount and effect of ambiguity, redundancy, incompleteness, and inaccuracy that the use of language introduces when representing a hidden conceptual space. The idea is to consider two interrelated spaces: a conceptual meaning space that is unambiguous and complete but hidden, and a linguistic space that captures a noisy grounding of the meaning space in the words of a language---the level at which all systems, whether neural or symbolic, operate. Applying this framework to a special class of multi-hop reasoning, namely the connectivity problem in graphs of relationships between concepts, we derive rigorous intuitions and impossibility results even under this simplified setting. For instance, if a query requires a moderately large (logarithmic) number of hops in the meaning graph, no reasoning system operating over a noisy graph grounded in language is likely to correctly answer it. This highlights a fundamental barrier that extends to a broader class of reasoning problems and systems, and suggests an alternative path forward: focusing on aligning the two spaces via richer representations, before investing in reasoning with many hops.
Viewpoint Invariant Change Captioning

The ability to detect that something has changed in an environment is valuable, but often only if it can be accurately conveyed to a human operator. We introduce Viewpoint Invariant Change Captioning, and develop models which can both localize and describe via natural language complex changes in an environment. Moreover, we distinguish between a change in a viewpoint and an actual scene change (e.g. a change of objects' attributes). To study this new problem, we collect a Viewpoint Invariant Change Captioning Dataset (VICC), building it off the CLEVR dataset and engine. We introduce 5 types of scene changes, including changes in attributes, positions, etc. To tackle this problem, we propose an approach that distinguishes a viewpoint change from an important scene change, localizes the change between "before" and "after" images, and dynamically attends to the relevant visual features when describing the change. We benchmark a number of baselines on our new dataset, and systematically study the different change types. We show the superiority of our proposed approach in terms of change captioning and localization. Finally, we also show that our approach is general and can be applied to real images and language on the recent Spot-the-diff dataset.
Fast 3D Line Segment Detection From Unorganized Point Cloud

This paper presents a very simple but efficient algorithm for 3D line segment detection from large scale unorganized point cloud. Unlike traditional methods which usually extract 3D edge points first and then link them to fit for 3D line segments, we propose a very simple 3D line segment detection algorithm based on point cloud segmentation and 2D line detection. Given the input unorganized point cloud, three steps are performed to detect 3D line segments. Firstly, the point cloud is segmented into 3D planes via region growing and region merging. Secondly, for each 3D plane, all the points belonging to it are projected onto the plane itself to form a 2D image, which is followed by 2D contour extraction and Least Square Fitting to get the 2D line segments. Those 2D line segments are then re-projected onto the 3D plane to get the corresponding 3D line segments. Finally, a post-processing procedure is proposed to eliminate outliers and merge adjacent 3D line segments. Experiments on several public datasets demonstrate the efficiency and robustness of our method. More results and the C++ source code of the proposed algorithm are publicly available at this https URL.
Supervised Transfer Learning for Product Information Question Answering

Popular e-commerce websites such as Amazon offer community question answering systems for users to pose product-related questions and experienced customers may provide answers voluntarily. In this paper, we show that the large volume of existing community question answering data can be beneficial when building a system for answering questions related to product facts and specifications. Our experimental results demonstrate that the performance of a model for answering questions related to products listed in the Home Depot website can be improved by a large margin via a simple transfer learning technique from an existing large-scale Amazon community question answering dataset. Transfer learning can result in an increase of about 10% in accuracy in the experimental setting where we restrict the size of the data of the target task used for training. As an application of this work, we integrate the best performing model trained in this work into a mobile-based shopping assistant and show its usefulness.
J-PAS: forecasts on interacting dark energy from baryon acoustic oscillations and redshift-space distortions

We estimate the constraining power of Javalambre-Physics of the Accelerated Universe Astrophysical Survey (J-PAS) for parameters of an interacting dark energy (DE) cosmology. The survey is expected to map several millions of luminous red galaxies, emission line galaxies, and quasars in an area of thousands of square degrees in the northern sky with precise photometric redshift measurements. Forecasts for the DESI and Euclid surveys are also evaluated and compared to J-PAS. Using the Fisher matrix approach, we find that J-PAS can place constraints on the interaction parameter comparable to those from DESI, with an absolute uncertainty of about 0.02, when the interaction term is proportional to the dark matter energy density, and almost as good, of about 0.01, when the interaction is proportional to the DE density. For the equation of state of DE, the constraints from J-PAS are slightly better in the two cases (uncertainties 0.04–0.05 against 0.05–0.07 around the fiducial value −1). Both surveys stay behind Euclid but follow it closely, imposing comparable constraints in all specific cases considered.
Asteroids detection tehnique: Classic “blink”: An automated approch

Asteroids detection is a very important research field that received increased attention in the last couple of decades. Some major surveys have their own dedicated people, equipment and detection applications, so they are discovering Near Earth Asteroids (NEAs) daily. The interest in asteroids is not limited to those major surveys, it is shared by amateurs and mini-surveys too. Some of them are using the few existing software solutions, most of which are developed by amateurs. The rest obtain their results in a visual manner: they “blink” a sequence of reduced images of the same field, taken at a specific time interval, and they try to detect a real moving object in the resulting animation. Such a technique becomes harder with the increase in size of the CCD cameras. Aiming to replace manual detection, we propose an automated “blink” technique for asteroids detection.
Second RIT binary black hole simulations catalog and its application to gravitational waves parameter estimation

The RIT numerical relativity group is releasing the second public catalog of black-hole-binary waveforms \url{this http URL}. This release consists of 320 accurate simulations that include 46 precessing and 274 nonprecessing binary systems with mass ratios $q=m_1/m_2$ in the range $1/6\leq q\leq1$ and individual spins up to $s/m^2=0.95$. The new catalog contains search and ordering tools for the waveforms based on initial parameters of the binary, trajectory information, peak radiation, and final remnant black hole properties. The final black hole remnant properties provided here can be used to model the merger of black-hole binaries from its initial configurations. The waveforms are extrapolated to infinite observer location and can be used to independently interpret gravitational wave signals from laser interferometric detectors. As an application of this waveform catalog we reanalyze the signal of GW150914 implementing parameter estimation techniques that make use of only numerical waveforms without any reference to information from phenomenological models.