nils-herrmann/clean_scirepeval huggingface dataset:
Versal deformations of pairs and Cohen-Macaulay approximation

For a pair (algebra, module) with isolated singularity we establish the existence of a versal henselian deformation. Obstruction theory in terms of an Andr\'e-Quillen cohomology for pairs is a central ingredient in the Artin theory used. Cohen-Macaulay approximation induces maps between versal base spaces for pairs and cohomology conditions ensure properties like smoothness and isomorphism.
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network

With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding and interacting with humans in helpful ways have become ubiquitous. We now expect them to discuss with us the more delicate questions in our world, and they should do so armed with effective arguments. But what makes an argument more persuasive? What will convince you? In this paper, we present a new data set, IBM-EviConv, of pairs of evidence labeled for convincingness, designed to be more challenging than existing alternatives. We also propose a Siamese neural network architecture shown to outperform several baselines on both a prior convincingness data set and our own. Finally, we provide insights into our experimental results and the various kinds of argumentative value our method is capable of detecting.
Coherent showers in decays of colored resonances

We present a new approach to coherent parton showers in the decays of coloured resonances, based on the notion of "resonance-final" (RF) QCD antennae. A full set of mass- and helicity-dependent $2\to 3$ antenna functions are defined, with the additional requirement of positivity over the respective branching phase spaces. Their singularity structure is identical to that of initial-final (IF) antennae in $2\to N$ hard processes (once mass terms associated with the incoming legs are allowed for), but the phase-space factorisations are different. The consequent radiation patterns respect QCD coherence (at leading colour) and reduce to Dokshitzer-Gribov-Lipatov-Altarelli-Parisi and eikonal kernels in the respective collinear and soft limits. The main novelty in the phase-space factorisation is that branchings in RF antennae impart a collective recoil to the other partons within the same decay system. An explicit implementation of these ideas, based on the Sudakov veto algorithm, is provided in the VINCIA antenna-shower plug-in to the PYTHIA 8 Monte Carlo event generator. We apply our formalism, matched to next-to-leading order accuracy using POWHEG, to top quark production at the LHC, and investigate implications for direct measurement of the top quark mass. Finally, we make recommendations for assessing theoretical uncertainties arising from parton showers in this context.
Noise Regularization for Conditional Density Estimation

Modelling statistical relationships beyond the conditional mean is crucial in many settings. Conditional density estimation (CDE) aims to learn the full conditional probability density from data. Though highly expressive, neural network based CDE models can suffer from severe over-fitting when trained with the maximum likelihood objective. Due to the inherent structure of such models, classical regularization approaches in the parameter space are rendered ineffective. To address this issue, we develop a model-agnostic noise regularization method for CDE that adds random perturbations to the data during training. We demonstrate that the proposed approach corresponds to a smoothness regularization and prove its asymptotic consistency. In our experiments, noise regularization significantly and consistently outperforms other regularization methods across seven data sets and three CDE models. The effectiveness of noise regularization makes neural network based CDE the preferable method over previous non- and semi-parametric approaches, even when training data is scarce.
Spectral-based Graph Convolutional Network for Directed Graphs

Graph convolutional networks(GCNs) have become the most popular approaches for graph data in these days because of their powerful ability to extract features from graph. GCNs approaches are divided into two categories, spectral-based and spatial-based. As the earliest convolutional networks for graph data, spectral-based GCNs have achieved impressive results in many graph related analytics tasks. However, spectral-based models cannot directly work on directed graphs. In this paper, we propose an improved spectral-based GCN for the directed graph by leveraging redefined Laplacians to improve its propagation model. Our approach can work directly on directed graph data in semi-supervised nodes classification tasks. Experiments on a number of directed graph datasets demonstrate that our approach outperforms the state-of-the-art methods.
Image Classification with Hierarchical Multigraph Networks

Graph Convolutional Networks (GCNs) are a class of general models that can learn from graph structured data. Despite being general, GCNs are admittedly inferior to convolutional neural networks (CNNs) when applied to vision tasks, mainly due to the lack of domain knowledge that is hardcoded into CNNs, such as spatially oriented translation invariant filters. However, a great advantage of GCNs is the ability to work on irregular inputs, such as superpixels of images. This could significantly reduce the computational cost of image reasoning tasks. Another key advantage inherent to GCNs is the natural ability to model multirelational data. Building upon these two promising properties, in this work, we show best practices for designing GCNs for image classification; in some cases even outperforming CNNs on the MNIST, CIFAR-10 and PASCAL image datasets.
A Comprehensive Analysis of Twitter Trending Topics

Twitter is among the most used microblogging and online social networking services. In Twitter, a name, phrase, or topic that is mentioned at a greater rate than others is called a "trending topic" or simply "trend". Twitter trends has shown their powerful ability in many public events, elections and market changes. Nevertheless, there has been very few works focusing on understanding the dynamics of these trending topics. In this article, we thoroughly examined the Twitter’s trending topics of 2018. To this end, we accessed Twitter’s trends API for the full year of 2018, and devised six criteria to evaluate our dataset. These six criteria are: lexical analysis, time to reach, trend reoccurrence, trending time, tweets count, and language analysis. In addition to providing general statistics and top trending topics regarding each criterion, we computed several distributions that explain this bulk of data.
ImageNet-trained deep neural networks exhibit illusion-like response to the Scintillating grid

Deep neural network (DNN) models for computer vision are capable of human-level object recognition. Consequently, similarities between DNN and human vision are of interest. Here, we characterize DNN representations of Scintillating grid visual illusion images in which white disks are perceived to be partially black. Specifically, we use VGG-19 and ResNet-101 DNN models that were trained for image classification and consider the representational dissimilarity (L1 distance in the penultimate layer) between pairs of images: one with white Scintillating grid disks and the other with disks of decreasing luminance levels. Results showed a nonmonotonic relation, such that decreasing disk luminance led to an increase and subsequently a decrease in representational dissimilarity. That is, the Scintillating grid image with white disks was closer, in terms of the representation, to images with black disks than images with gray disks. In control nonillusion images, such nonmonotonicity was rare. These results suggest that nonmonotonicity in a deep computational representation is a potential test for illusion-like response geometry in DNN models.
Augmenting a BiLSTM Tagger with a Morphological Lexicon and a Lexical Category Identification Step

Previous work on using BiLSTM models for PoS tagging has primarily focused on small tagsets. We evaluate BiLSTM models for tagging Icelandic, a morphologically rich language, using a relatively large tagset. Our baseline BiLSTM model achieves higher accuracy than any other previously published tagger, when not taking advantage of a morphological lexicon. When we extend the model by incorporating such data, we outperform the earlier state-of-the-art results by a significant margin. We also report on work in progress that attempts to address the problem of data sparsity inherent to morphologically detailed, fine-grained tagsets. We experiment with training a separate model on only the lexical category and using the coarse-grained output tag as an input into to the main model. This method further increases the accuracy and reduces the tagging errors by 21.3% compared to previous state-of-the-art results. Finally, we train and test our tagger on a new gold standard for Icelandic.
Intermediate statistics in thermoelectric properties of solids

We study the thermodynamics of a crystalline solid by applying intermediate statistics manifested by q-deformation. We based part of our study on both Einstein and Debye models, exploring primarily deformed thermal and electrical conductivities as a function of the deformed Debye specific heat. The results revealed that the q-deformation acts in two different ways but not necessarily as independent mechanisms. It acts as a factor of disorder or impurity, modifying the characteristics of a crystalline structure, which are phenomena described by q-bosons, and also as a manifestation of intermediate statistics, the B-anyons (or B-type systems). For the latter case, we have identified the Schottky effect, normally associated with high-Tc superconductors in the presence of rare-earth-ion impurities, and also the increasing of the specific heat of the solids beyond the Dulong-Petit limit at high temperature, usually related to anharmonicity of interatomic interactions. Alternatively, since in the q-bosons the statistics are in principle maintained the effect of the deformation acts more slowly due to a small change in the crystal lattice. On the other hand, B-anyons that belong to modified statistics are more sensitive to the deformation.
Accelerating Experimental Design by Incorporating Experimenter Hunches

Experimental design is a process of obtaining a product with target property via experimentation. Bayesian optimization offers a sample-efficient tool for experimental design when experiments are expensive. Often, expert experimenters have 'hunches' about the behavior of the experimental system, offering potentials to further improve the efficiency. In this paper, we consider per-variable monotonic trend in the underlying property that results in a unimodal trend in those variables for a target value optimization. For example, sweetness of a candy is monotonic to the sugar content. However, to obtain a target sweetness, the utility of the sugar content becomes a unimodal function, which peaks at the value giving the target sweetness and falls off both ways. In this paper, we propose a novel method to solve such problems that achieves two main objectives: a) the monotonicity information is used to the fullest extent possible, whilst ensuring that b) the convergence guarantee remains intact. This is achieved by a two-stage Gaussian process modeling, where the first stage uses the monotonicity trend to model the underlying property, and the second stage uses 'virtual' samples, sampled from the first, to model the target value optimization function. The process is made theoretically consistent by adding appropriate adjustment factor in the posterior computation, necessitated because of using the 'virtual' samples. The proposed method is evaluated through both simulations and real world experimental design problems of a) new short polymer fiber with the target length, and b) designing of a new three dimensional porous scaffolding with a target porosity. In all scenarios our method demonstrates faster convergence than the basic Bayesian optimization approach not using such 'hunches'.
Open Problems in a Logic of Gossips

textabstractGossip protocols are programs used in a setting in which each agent holds a secret and the aim is to reach a situation in which all agents know all secrets. Such protocols rely on a point-to-point or group communication. Distributed epistemic gossip protocols use epistemic formulas in the component programs for the agents. The advantage of the use of epistemic logic is that the resulting protocols are very concise and amenable for a simple verification. Recently, we introduced a natural modal logic that allows one to express distributed epistemic gossip protocols and to reason about their correctness. We proved that the resulting protocols are implementable and that all aspects of their correctness, including termination, are decidable. To establish these results we showed that both the definition of semantics and of truth of the underlying logic are decidable. We also showed that the analogous results hold for an extension of this logic with the ‘common knowledge’ operator. However, several, often deceptively simple, questions about this logic and the corresponding gossip protocols remain open. The purpose of this paper is to list and elucidate these questions and provide for them an appropriate background information in the form of partial of related results.
A Unified Algebraic Framework for Non-Monotonicity

Tremendous research effort has been dedicated over the years to thoroughly investigate non-monotonic reasoning. With the abundance of non-monotonic logical formalisms, a unified theory that enables comparing the different approaches is much called for. In this paper, we present an algebraic graded logic we refer to as LogAG capable of encompassing a wide variety of non-monotonic formalisms. We build on Lin and Shoham's argument systems first developed to formalize non-monotonic commonsense reasoning. We show how to encode argument systems as LogAG theories, and prove that LogAG captures the notion of belief spaces in argument systems. Since argument systems capture default logic, autoepistemic logic, the principle of negation as failure, and circumscription, our results show that LogAG captures the before-mentioned non-monotonic logical formalisms as well. Previous results show that LogAG subsumes possibilistic logic and any non-monotonic inference relation satisfying Makinson's rationality postulates. In this way, LogAG provides a powerful unified framework for non-monotonicity.
A Conceptually Well-Founded Characterization of Iterated Admissibility Using an "All I Know" Operator

Brandenburger, Friedenberg, and Keisler provide an epistemic characterization of iterated admissibility (IA), also known as iterated deletion of weakly dominated strategies, where uncertainty is represented using LPSs (lexicographic probability sequences). Their characterization holds in a rich structure called a complete structure, where all types are possible. In earlier work, we gave a characterization of iterated admissibility using an "all I know" operator, that captures the intuition that "all the agent knows" is that agents satisfy the appropriate rationality assumptions. That characterization did not need complete structures and used probability structures, not LPSs. However, that characterization did not deal with Samuelson's conceptual concern regarding IA, namely, that at higher levels, players do not consider possible strategies that were used to justify their choice of strategy at lower levels. In this paper, we give a characterization of IA using the all I know operator that does deal with Samuelson's concern. However, it uses LPSs. We then show how to modify the characterization using notions of "approximate belief" and "approximately all I know" so as to deal with Samuelson's concern while still working with probability structures.
Measuring Belief and Risk Attitude

Ramsey (1926) sketches a proposal for measuring the subjective probabilities of an agent by their observable preferences, assuming that the agent is an expected utility maximizer. I show how to extend the spirit of Ramsey's method to a strictly wider class of agents: risk-weighted expected utility maximizers (Buchak 2013). In particular, I show how we can measure the risk attitudes of an agent by their observable preferences, assuming that the agent is a risk-weighted expected utility maximizer. Further, we can leverage this method to measure the subjective probabilities of a risk-weighted expected utility maximizer.
Deterministic Sampling Decoding: Where Sphere Decoding Meets Lattice Gaussian Distribution

In this paper, the paradigm of sphere decoding (SD) based on lattice Gaussian distribution is studied, where the sphere radius $D>0$ in the sense of Euclidean distance is characterized by the initial pruning size $K>1$, the standard deviation $\sigma>0$ and a regularization term $\rho_{\sigma,\mathbf{y}}(\Lambda)>0$ ($\Lambda$ denotes the lattice, $\mathbf{y}$ is the query point). In this way, extra freedom is obtained for analytical diagnosis of both the decoding performance and complexity. Based on it, the equivalent SD (ESD) algorithm is firstly proposed, and we show it is exactly the same with the classic Fincke-Pohst SD but characterizes the sphere radius with $D=\sigma\sqrt{2\ln K}$. By fixing $\sigma$ properly, we show that the complexity of ESD measured by the number of visited nodes is upper bounded by $|S|<nK$, thus resulting in a tractable decoding trade-off solely determined by $K$. In order to further exploit the decoding potential, the regularized SD (RSD) algorithm based on Klein's sampling probability is proposed, which achieves a better decoding trade-off than the equivalent SD by fully utilizing the regularization terms. Moreover, besides the designed criterion of pruning threshold, another decoding criterion named as candidate protection is proposed to solve the decoding problems in the cases of small $K$, which generalizes both the regularized SD and equivalent SD from maximum likelihood (ML) decoding to bounded distance decoding (BDD). Finally, simulation results based on MIMO detection are presented to confirm the tractable decoding trade-off of the proposed lattice Gaussian distribution-based SD algorithms.
DetectFusion: Detecting and Segmenting Both Known and Unknown Dynamic Objects in Real-time SLAM

We present DetectFusion, an RGB-D SLAM system that runs in real-time and can robustly handle semantically known and unknown objects that can move dynamically in the scene. Our system detects, segments and assigns semantic class labels to known objects in the scene, while tracking and reconstructing them even when they move independently in front of the monocular camera. In contrast to related work, we achieve real-time computational performance on semantic instance segmentation with a novel method combining 2D object detection and 3D geometric segmentation. In addition, we propose a method for detecting and segmenting the motion of semantically unknown objects, thus further improving the accuracy of camera tracking and map reconstruction. We show that our method performs on par or better than previous work in terms of localization and object reconstruction accuracy, while achieving about 20 FPS even if the objects are segmented in each frame.
Feature Graph Learning for 3D Point Cloud Denoising

Identifying an appropriate underlying graph kernel that reflects pairwise similarities is critical in many recent graph spectral signal restoration schemes, including image denoising, dequantization, and contrast enhancement. Existing graph learning algorithms compute the most likely entries of a properly defined graph Laplacian matrix <inline-formula><tex-math notation="LaTeX">$\mathbf {L}$</tex-math></inline-formula>, but require a large number of signal observations <inline-formula><tex-math notation="LaTeX">$\mathbf {z}$</tex-math></inline-formula>'s for a stable estimate. In this work, we assume instead the availability of a relevant feature vector <inline-formula><tex-math notation="LaTeX">$\mathbf {f}_i$</tex-math></inline-formula> per node <inline-formula><tex-math notation="LaTeX">$i$</tex-math></inline-formula>, from which we compute an optimal feature graph via optimization of a feature metric. Specifically, we alternately optimize the diagonal and off-diagonal entries of a Mahalanobis distance matrix <inline-formula><tex-math notation="LaTeX">$\mathbf {M}$</tex-math></inline-formula> by minimizing the graph Laplacian regularizer (GLR) <inline-formula><tex-math notation="LaTeX">$\mathbf {z}^{\top } \mathbf {L} \mathbf {z}$</tex-math></inline-formula>, where edge weight is <inline-formula><tex-math notation="LaTeX">$w_{i,j} = \exp \lbrace - (\mathbf {f}_i - \mathbf {f}_j)^{\top } \mathbf {M} (\mathbf {f}_i - \mathbf {f}_j) \rbrace$</tex-math></inline-formula>, given a single observation <inline-formula><tex-math notation="LaTeX">$\mathbf {z}$</tex-math></inline-formula>. We optimize diagonal entries via proximal gradient (PG), where we constrain <inline-formula><tex-math notation="LaTeX">$\mathbf {M}$</tex-math></inline-formula> to be positive definite (PD) via linear inequalities derived from the Gershgorin circle theorem. To optimize off-diagonal entries, we design a block descent algorithm that iteratively optimizes one row and column of <inline-formula><tex-math notation="LaTeX">$\mathbf {M}$</tex-math></inline-formula>. To keep <inline-formula><tex-math notation="LaTeX">$\mathbf {M}$</tex-math></inline-formula> PD, we constrain the Schur complement of sub-matrix <inline-formula><tex-math notation="LaTeX">$\mathbf {M}_{2,2}$</tex-math></inline-formula> of <inline-formula><tex-math notation="LaTeX">$\mathbf {M}$</tex-math></inline-formula> to be PD when optimizing via PG. Our algorithm mitigates full eigen-decomposition of <inline-formula><tex-math notation="LaTeX">$\mathbf {M}$</tex-math></inline-formula>, thus ensuring fast computation speed even when feature vector <inline-formula><tex-math notation="LaTeX">$\mathbf {f}_i$</tex-math></inline-formula> has high dimension. To validate its usefulness, we apply our feature graph learning algorithm to the problem of 3D point cloud denoising, resulting in state-of-the-art performance compared to competing schemes in extensive experiments.
CONSISTENT UPPER PRICE BOUNDS FOR EXOTIC OPTIONS

We consider the problem of finding a consistent upper price bound for exotic options whose payoff depends on the stock price at two different predetermined time points (e.g. Asian option), given a finite number of observed call prices for these maturities. A model-free approach is used, only taking into account that the (discounted) stock price process is a martingale under the no-arbitrage condition. In case the payoff is directionally convex we obtain the worst case marginal pricing measures. The speed of convergence of the upper price bound is determined when the number of observed stock prices increases. We illustrate our findings with some numerical computations.